ALBERT

Description: Eddy covariance sites can experience data losses as high as 30 to 45% on an annual basis. Artificial neural networks (ANNs) have been identified as powerful tools for gap filling, but their performance depends on the representativeness of data used to train the model. In this paper, we develop a normalization method, which has similar performance compared to conventional training approaches, but exhibits differences in the timing of fluxes, indicating different and previously unused information in the data record. Specifically, the differences between half-hourly model fluxes, especially during summer months, indicate that the structure of the information content in the data changes seasonally, diurnally and with the rate of data loss. This variation between gap-filling models complicates the application of their output as consistent data sets for land surface modeling, and points to the need for improved data and models to address flux behavior at critical times. We advise several approaches to address these concerns, including use of separate models for day and nighttime processes and the use of multiple data streams at dawn, when eddy covariance may be particularly ineffective due to the timing of the onset of turbulent mixing.

Description: Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved retrievals of surface soil moisture variations at global scales. Here we propose a technique to take advantage of retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates over sparse-to-moderately vegetated areas to obtain an improved soil moisture product. To do this, absolute soil moisture values from AMSR-E and relative soil moisture derived from ASCAT are rescaled against a reference land surface model date set using a cumulative distribution function (CDF) matching approach. While this technique imposes the bias of the reference to the rescaled satellite products, it adjusts both satellite products to the same range and almost preserves the correlation between satellite products and in situ measurements. Comparisons with in situ data demonstrated that over the regions where the correlation coefficient between rescaled AMSR-E and ASCAT is above 0.65 (hereafter referred to as transitional regions), merging the different satellite products together increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT are respectively used in the merged product. Thus the merged product carries the advantages of better spatial coverage overall and increased number of observations particularly for the transitional regions. The combination approach developed in this study has the potential to be applied to existing microwave satellites as well as to new microwave missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

Description: Hydrological modelling of floods relies on precipitation data with a high resolution in space and time. A reliable spatial representation of short time step rainfall is often difficult to achieve due to a low network density. In this study hourly precipitation was spatially interpolated with the multivariate geostatistical method kriging with external drift (KED) using additional information from topography, rainfall data from the denser daily networks and weather radar data. Investigations were carried out for several flood events in the time period between 2000 and 2005 caused by different meteorological conditions. The 125 km radius around the radar station Ummendorf in northern Germany covered the overall study region. One objective was to assess the effect of different approaches for estimation of semivariograms on the interpolation performance of short time step rainfall. Another objective was the refined application of the method kriging with external drift. Special attention was not only given to find the most relevant additional information, but also to combine the additional information in the best possible way. A multi-step interpolation procedure was applied to better consider sub-regions without rainfall. The impact of different semivariogram types on the interpolation performance was low. While it varied over the events, an averaged semivariogram was sufficient overall. Weather radar data were the most valuable additional information for KED for convective summer events. For interpolation of stratiform winter events using daily rainfall as additional information was sufficient. The application of the multi-step procedure significantly helped to improve the representation of fractional precipitation coverage.

Description: The targeting of sediment management strategies is a key requirement in developing countries including Iran because of the limited resources available. These targeting is, however hampered by the lack of reliable information on catchment sediment sources. This paper reports the results of using a quantitative composite fingerprinting technique to estimate the relative importance of the primary potential sources within the Amrovan and Royan catchments in Semnan Province, Iran. Fifteen tracers were first selected for tracing and samples were analyzed in the laboratory for these parameters. Statistical methods were applied to the data including nonparametric Kruskal-Wallis test and Differentiation Function Analysis (DFA). For Amrovan catchment three parameters (N, Cr and Co) were found to be not significant in making the discrimination. The optimum fingerprint, comprising Oc, PH, Kaolinite and K was able to distinguish correctly 100% of the source material samples. For the Royan catchment, all of the 15 properties were able to distinguish between the six source types and the optimum fingerprint provided by stepwise DFA (Cholorite, XFD, N and C) correctly classifies 92.9% of the source material samples. The mean contributions from each sediment source obtained by multivariate mixing model varied at two catchments. For Amrovan catchment Upper Red formation is the main sediment sources as this sediment source approximately supplies 36% of the reservoir sediment whereas the dominant sediment source for the Royan catchment is from Karaj formation that supplies 33% of the reservoir sediments. Results indicate that the source fingerprinting approach appears to work well in the study catchments and to generate reliable results.

Description: Routine information on regional evapotranspiration (ET) and dryness index is essential for agricultural water management, drought monitoring, and studies of water cycle and climate. However, this information is not currently available for the East Africa highlands. The main purpose of this study is to develop (1) a new methodology that produces spatially gridded daily ET estimates on a (near) real-time basis exclusively from satellite data, and (2) a new dryness index that depends only on satellite data and weather forecast data. The methodology that calculates daily actual ET involves combining data from two sensors (MODIS and SEVIRI) onboard two kinds of platforms (Terra/Aqua – polar orbit satellite and MSG – geostationary orbit satellite). The methodology is applied to the East African highlands, and results are compared to eddy covariance measurements at one site. Results show that the methodology produces ET estimates that have high skills in reproducing the daily fluctuation in ET but tends to underestimate ET on the average. It is concluded that the synergistic use of the polar-orbiting MODIS data and the geostationary-orbiting SEVIRI data has potential to produce reliable daily ET, but further research is needed to improve the accuracy of the results. This study also proposes an operational new dryness index that can be calculated from the satellite-based actual daily ET estimates and reference daily ET estimates based on SEVIRI data and weather forecast air temperature. Comparison of this index against ground measurements of actual daily ET at one site indicates that the new dryness index is operational for drought monitoring.

Description: The interactions between shallow groundwater and land surface processes, mediated by capillary rise processes from groundwater, may play an important role in the ecohydrology of riparian zones in both humid and semi-arid ecosystems. Some recent land surface models (LSM) incorporate the contribution of groundwater to land surface processes with varying levels of complexity. In this paper, we examine the sensitivity of evapotranspiration at the land surface to the depth of groundwater using three models with different levels of complexity, two widely used representative soil hydraulic parameter sets, and four soil textures. The selected models are Hydrus-1D, which solves the Richards equation, the Integrated Biosphere Simulator (IBIS), which uses a multi-bucket approach with interactions between buckets, and a single-bucket model coupled with a classic simple capillary rise flux approximation. These models are first corroborated with field observations of soil moisture and groundwater elevation data from a site located in south-central Nebraska, USA. We then examine the sensitivity of the Richards equation to node spacing, as well as the relationship between groundwater depth and the ratio of actual to potential evapotranspiration (ET) for various soil textures and water table depths. The results show that selecting one representative soil parameter set over another may result in up to a 70% difference in actual ET (relative to the potential ET) when the depth to water table is in 0–5 m depending on the soil type. Moreover, solution type of the Richards equation and node spacing have also effect on surface ET up to 50% and 30% respectively depending on the depth-to-groundwater and node spacing. Therefore, further studies are needed to understand the sensitivities of land surface and atmospheric models to the existence of saturated layers, including studies with more field validation in regions with different climates and land cover types.

Description: The objective of this study is to get a better understanding of radar signal over irrigated wheat fields and to assess the potentialities of radar observations for the monitoring of soil moisture. Emphasis is put on the use of high spatial and temporal resolution satellite data (ENVISAT/ASAR and FORMOSAT-2). Time series of images were collected over the Yaqui irrigated area (Mexico) throughout one agricultural season from December 2007 to May 2008, together with measurements of soil and vegetation characteristics and agricultural practices. The comprehensive analysis of these data indicates that the sensitivity of the radar signal to vegetation is masked by the variability of soil conditions. On-going irrigated areas can be detected all over the wheat growing season. The empirical algorithm developed for the retrieval of topsoil moisture from ENVISAT/ASAR images takes advantage of the unique capabilities of the FORMOSAT-2 instrument to monitor the seasonality of wheat canopies. Topsoil moisture estimates are scattered at the timing of plant emergence and during plant senescence. Estimates are much more accurate from tillering to grain filling stages with an absolute error about 9% (0.09 m3 m−3, 35% in relative value). This result is attractive since topsoil moisture is estimated at a high spatial resolution (i.e. over subfields of about 5 ha) for a large range of biomass water content (from 5 and 65 t ha−1) independently from the viewing angle of ASAR acquisition (incidence angles IS1 to IS6).

Description: In September 2009, tropical storm Ketsana (local name: TS Ondoy) hit Metro Manila and brought an anomalous volume of rain that exceeded the Philippines' forty-year meteorological record. The storm caused exceptionally high and extensive flooding. Part of this study was a survey conducted along the stretch of the Marikina River, one of the major rivers that flooded. Hydraulic and hydrologic modeling was carried out to understand the mechanism that brought the flood. The study revealed that while there were anthropogenic factors that exacerbated flooding in Marikina, the observed flood heights can be simulated in the models generated. Peak floods occurred at different hours along the river resulting from the transmission of water from the main watershed to the downstream areas and the contribution of smaller tributaries entering the main river. Prediction of flood heights and the use of the known time lag between the peak rainfall and the peak runoff could be utilized to issue timely flood forecasts to allow people to prepare for future flooding.

Description: This paper investigates whether the use of remotely sensed latent heat fluxes improves the accuracy of spatially-distributed soil moisture predictions by a hydrological model. By using real data we aim to show the potential and limitations in practice. We use (i) satellite data of both ASTER and MODIS for the same two days in the summer of 2006 that, in association with the Surface Energy Balance Algorithm for Land (SEBAL), provides us the spatial distribution of daily ETact and (ii) an operational physically based distributed (25 m×25 m) hydrological model of a small catchment (70 km2) in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Firstly, model outcomes of ETact are compared to the processed satellite data. Secondly, we perform data assimilation that updates the modelled soil moisture. We show that remotely sensed ETact is useful in hydrological modelling for two reasons. Firstly, in the procedure of model calibration: comparison of modeled and remotely sensed ETact together with the outcomes of our data assimilation procedure points out potential model errors (both conceptual and flux-related). Secondly, assimilation of remotely sensed ETact results in a realistic spatial adjustment of soil moisture, except for the area with forest and deep groundwater levels. As both ASTER and MODIS images were available for the same days, this study provides also an excellent opportunity to compare the worth of these two satellite sources. It is shown that, although ASTER provides much better insight in the spatial distribution of ETact due to its higher spatial resolution than MODIS, they appeared in this study just as useful.

Description: Entomological studies to determine the effect of the physical characteristics of larval breeding water bodies and reservoir water level changes on the occurrence of Anopheles mosquito larvae and on the spatial and temporal formation of larval breeding habitats were conducted in two villages at Koka reservoir between August and December 2007. Of the two study villages, Ejersa is in close proximity to the reservoir, and Kuma is 5 km away from it. Data on the type, number and physical characteristics of Anopheles larval breeding habitat, species composition and densities of anopheles mosquitoes in and around the study villages were investigated and recorded. Meteorological and reservoir water level data were compared with availability of Anopheles larval breeding sites and densities. Entomological data from the weekly larval collections showed that Anopheles pharoensis Theobald, Anopheles gambiae s.l. Giles, Anopheles coustani Laveran and Anopheles squamosus Theobald were breeding in the study area. The mean larval density of A. gambiae s.l. in this study was higher in slightly turbid and shallow aquatic habitats than in turbid and relatively deep aquatic habitats (F=16.97, p

Description: The Mekong River Basin comprises a key regional resource in Southeast Asia for sectors that include agriculture, fisheries and electricity production. Here we explore the potential impacts of climate change on freshwater resources within the river basin. We quantify uncertainty in these projections associated with GCM structure and climate sensitivity, as well as from hydrological model parameter specification. This is achieved by running pattern-scaled GCM output through a semi-distributed hydrological model (SLURP) of the basin. These pattern-scaled GCM outputs allow investigation of specific thresholds of global climate change including the postulated 2 ºC threshold of "dangerous" climate change as simulated using outputs from seven different GCMs. Detailed analysis of results based on HadCM3 climate scenarios reveals a relatively small but non-linear response of annual river discharge to increasing global mean temperature, ranging from a 5.4% decrease to 4.5% increase. Intra-annual (monthly) changes in river discharge are greater (from −16% to +55%, with greatest decreases in July and August, greatest increases in May and June) and result from complex and contrasting intra-basin changes in precipitation, evaporation and snow storage/melt. Whilst overall results are highly GCM dependent (in both direction and magnitude), this uncertainty is primarily driven by differences in GCM projections of future precipitation. In contrast, there is strong consistency between GCMs in terms of both increased potential evapotranspiration and a shift to an earlier and less substantial snowmelt season. Indeed, in the upper Mekong (Lancang sub-basin), the temperature-related signal in discharge is strong enough to overwhelm the precipitation-related uncertainty in the direction of change in discharge, with scenarios from all GCMs leading to increased river flow from April–June, and decreased flow from July–August.

Description: The increasing pressure on water systems in the Mediterranean enhances existing water conflicts and threatens water supply for agriculture. In this context, one of the main priorities for agricultural research and public policy is the adaptation of crop yields to water pressures. This paper focuses on the evaluation of hydrological risk and water policy implications for food production. Our methodological approach includes four steps. For the first step, we estimate the impacts of rainfall and irrigation water on crop yields. However, this study is not limited to general crop production functions since it also considers the linkages between those economic and biophysical aspects which may have an important effect on crop productivity. We use statistical models of yield response to address how hydrological variables affect the yield of the main Mediterranean crops in the Ebro River Basin. In the second step, this study takes into consideration the effects of those interactions and analyzes gross value added sensitivity to crop production changes. We then use Montecarlo simulations to characterize crop yield risk to water variability. Finally we evaluate some policy scenarios with irrigated area adjustments that could cope in a context of increased water scarcity. A substantial decrease in irrigated land, of up to 30% of total, results in only moderate losses of crop productivity. The response is crop and region specific and may serve to prioritise adaptation strategies.

Description: Modeling of sediment dynamics for developing best management practices of reducing soil erosion and of sediment control has become essential for sustainable management of watersheds. Precise estimation of sediment dynamics is very important since soils are a major component of enormous environmental processes and sediment transport controls lake and river pollution extensively. Different hydrological processes govern sediment dynamics in a river basin, which are highly variable in spatial and temporal scales. This paper presents a process-based distributed modeling approach for analysis of sediment dynamics at river basin scale by integrating sediment processes (soil erosion, sediment transport and deposition) with an existing process-based distributed hydrological model. In this modeling approach, the watershed is divided into an array of homogeneous grids to capture the catchment spatial heterogeneity. Hillslope and river sediment dynamic processes have been modeled separately and linked to each other consistently. Water flow and sediment transport at different surface grids and river nodes are modeled using one-dimensional kinematic wave approximation of Saint-Venant equations. The mechanics of sediment dynamics are integrated into the model using representative physical equations after a comprehensive review. The model has been tested on river basins in two different hydro climatic areas, the Abukuma River Basin, Japan and Latrobe River Basin, Australia. Sediment transport and deposition are modeled using Govers transport capacity equation. All spatial datasets, such as, Digital Elevation Model (DEM), land use and soil classification data, etc., have been prepared using raster "Geographic Information System (GIS)" tools. The results of relevant statistical checks (Nash-Sutcliffe efficiency and R-squared value) indicate that the model simulates basin hydrology and its associated sediment dynamics reasonably well. This paper presents the model including descriptions of the various components and the results of its application on case study areas.

Description: The research has been carried out in Biebrza Ramsar Convention test site situated in the N-E part of Poland. Data from optical and microwave satellite images have been analysed and compared to the detailed soil-vegetation ground truth measurements conducted during the satellite overpasses. Satellite data applied for the study include: ENVISAT.ASAR, ENVISAT.MERIS, ALOS.PALSAR, ALOS.AVNIR-2, ALOS.PRISM, TERRA.ASTER, and NOAA.AVHRR. Optical images have been used for classification of wetlands vegetation habitats and vegetation surface roughness expressed by LAI. Also, heat fluxes have been calculated using NOAA.AVHRR data and meteorological data. Microwave images have been used for the assessment of soil moisture. For each of the classified wetlands vegetation habitats the relationship between soil moisture and backscattering coefficient has been examined, and the best combination of microwave variables (wave length, incidence angle, polarization) has been used for mapping and monitoring of soil moisture. The results of this study give possibility to improve models of water cycle over wetlands ecosystems by adding information about soil moisture and surface heat fluxes derived from satellite images. Such information is very essential for better protection of the European sensitive wetland ecosystems. ENVISAT and ALOS images have been obtained from ESA for AO ID 122 and AOALO.3742 projects.

Description: With the flow of the Mara River becoming increasingly erratic especially in the upper reaches, attention has been directed to land use change as the major cause of this problem. The semi-distributed hydrological model Soil and Water Assessment Tool (SWAT) and Landsat imagery were utilized in the upper Mara River Basin in order to 1) map existing field scale land use practices in order to determine their impact 2) determine the impacts of land use change on water flux; and 3) determine the impacts of rainfall (0%, ±10% and ±20%) and air temperature variations (0% and +5%) based on the Intergovernmental Panel on Climate Change projections on the water flux of the upper Mara River. This study found that the different scenarios impacted on the water balance components differently. Land use changes resulted in a slightly more erratic discharge while rainfall and air temperature changes had a more predictable impact on the discharge and water balance components. These findings demonstrate that the model results show the flow was more sensitive to the rainfall changes than land use changes. It was also shown that land use changes can reduce dry season flow which is the most important problem in the basin. The model shows also deforestation in the Mau Forest increased the peak flows which can also lead to high sediment loading in the Mara River. The effect of the land use and climate change scenarios on the sediment and water quality of the river needs a thorough understanding of the sediment transport processes in addition to observed sediment and water quality data for validation of modeling results.

Description: We present a comparative analysis of projected impacts of climate change on river runoff from two types of distributed hydrological model, a global hydrological model (GHM) and catchment-scale hydrological models (CHM). Analyses are conducted for six catchments that are global in coverage and feature strong contrasts in spatial scale as well as climatic and developmental conditions. These include the Liard (Canada), Mekong (SE Asia), Okavango (SW Africa), Rio Grande (Brazil), Xiangxi (China) and Harper's Brook (UK). A single GHM (Mac-PDM.09) is applied to all catchments whilst different CHMs are applied for each catchment. The CHMs include SLURP v. 12.2 (Liard), SLURP v. 12.7 (Mekong), Pitman (Okavango), MGB-IPH (Rio Grande), AV-SWAT-X 2005 (Xiangxi) and Cat-PDM (Harper's Brook). Simulations of mean annual runoff, mean monthly runoff and high (Q5) and low (Q95) monthly runoff under baseline (1961–1990) and climate change scenarios are presented. We compare the simulated runoff response of each hydrological model to (1) prescribed increases in global-mean air temperature of 1.0, 2.0, 3.0, 4.0, 5.0 and 6.0 °C relative to baseline from the UKMO HadCM3 Global Climate Model (GCM) to explore response to different amounts of climate forcing, and (2) a prescribed increase in global-mean air temperature of 2.0 °C relative to baseline for seven GCMs to explore response to climate model structural uncertainty. We find that the differences in projected changes of mean annual runoff between the two types of hydrological model can be substantial for a given GCM, and they are generally larger for indicators of high and low monthly runoff. However, they are relatively small in comparison to the range of projections across the seven GCMs. Hence, for the six catchments and seven GCMs we considered, climate model structural uncertainty is greater than the uncertainty associated with the type of hydrological model applied. Moreover, shifts in the seasonal cycle of runoff with climate change are represented similarly by both hydrological models, although for some catchments the monthly timing of high and low flows differs. This implies that for studies that seek to quantify and assess the role of climate model uncertainty on catchment-scale runoff, it may be equally as feasible to apply a GHM as it is to apply a CHM, especially when climate modelling uncertainty across the range of available GCMs is as large as it currently is. Whilst the GHM is able to represent the broad climate change signal that is represented by the CHMs, we find however, that for some catchments there are differences between GHMs and CHMs in mean annual runoff due to differences in potential evapotranspiration estimation methods, in the representation of the seasonality of runoff, and in the magnitude of changes in extreme (Q5, Q95) monthly runoff, all of which have implications for future water management issues.

Description: Effective management of water resources requires that all elements of the water balance be estimated. Groundwater recharge measurements are difficult, time consuming and expensive. In some cases a field study cannot be justified and simple empirical relationships are used to estimate recharge, and often the value chosen is simply a percentage of rainfall. This paper aims to use a data-base of 4386 field based estimates of recharge from 172 studies in Australia to produce simple empirical relationships that relate recharge to nationally available datasets and hence can be used to estimate recharge in data-poor areas in a scientifically defensible way. It was found that the vegetation and soil type were critical determinants in forming relationships between average annual rainfall and average annual recharge. Climate zones and surface geology were not found to be significant determinants in the relationship between rainfall and recharge. The method used to estimate recharge had an impact upon the magnitude of the recharge estimates due to the spatial and temporal scales over which the different methods estimate recharge. Relationships have been developed here between average annual rainfall and average annual recharge for combinations of soil and vegetation type that can be used with only nationally available datasets to provide a recharge estimate. The 95 percent confidence limits about the recharge predicted using these relationships is generally greater than an order of magnitude either side of the relationship developed. This means that if these relationships are used to help determine water allocations then the precautionary principle should limit allocations to less than about 5% of the estimated recharge, if allocations are greater than this a more detailed site specific study is warranted.

Description: This paper assesses the hydrological response to scenarios of climate change in the Okavango River catchment in Southern Africa. Climate scenarios are constructed representing different changes in global mean temperature from an ensemble of 7 climate models assessed in the IPCC AR4. The results show a substantial change in mean flow associated with a global warming of 2 °C. However, there is considerable uncertainty in the sign and magnitude of the projected changes between different climate models, implying that the ensemble mean is not an appropriate generalised indicator of impact. The uncertainty in response between different climate model patterns is considerably greater than the range due to uncertainty in hydrological model parameterisation. There is also a clear need to evaluate the physical mechanisms associated with the model projected changes in this region. The implications for water resource management policy are considered.

Description: This paper proposes the estimation of high return period quantiles using upper bounded distribution functions with Systematic and additional Non-Systematic information. The aim of the developed methodology is to reduce the estimation uncertainty of these quantiles, assuming the upper bound parameter of these distribution functions as a statistical estimator of the Probable Maximum Flood (PMF). Three upper bounded distribution functions, firstly used in Hydrology in the 90's (referred to in this work as TDF, LN4 and EV4), were applied at the Jucar River in Spain. Different methods to estimate the upper limit of these distribution functions have been merged with the Maximum Likelihood (ML) method. Results show that it is possible to obtain a statistical estimate of the PMF value and to establish its associated uncertainty. The behaviour for high return period quantiles is different for the three evaluated distributions and, for the case study, the EV4 gave better descriptive results. With enough information, the associated estimation uncertainty is considered acceptable, even for the PMF estimate. From the robustness analysis, EV4 distribution function appears to be more robust than the GEV and TCEV unbounded distribution functions in a typical Mediterranean river and Non-Systematic information availability scenario.

Description: In regional flood frequency estimation, a homogeneous pooling group of sites leads to a reduction in the error of quantile estimators which is the main aim of a regional flood frequency analysis. Examination of the homogeneity of regions/pooling groups is usually based on a statistic that relates to the formulation of a frequency distribution model, e.g. the coefficient of variation (Wiltshire, 1986; Fill and Stedinger, 1995) and/or skew coefficient, their L-moment equivalents (Chowdhury et al., 1991; Hosking and Wallis, 1997) or of dimensionless quantiles such as the 10-yr event (Dalrymple, 1960; Lu and Stedinger, 1992). Hosking andWallis (1993, 1997) proposed homogeneity tests based on L-moment ratios such as L-CV alone (H1) and L-CV & L-skewness jointly (H2) which were also recently investigated by Viglione et al. (2007). In this paper a study, based on annual maximum series obtained from 85 Irish gauging stations, examines how successful a common method of identifying pooling group membership is in selecting groups that actually are homogeneous. Each station has its own unique pooling group selected by use of a Euclidean distance measure in catchment descriptor space, commonly denoted dij and with a minimum of 500 station years of data in the pooling group, which satisfies the 5T rule (FEH, 1999, 3, p. 169) for the 100 yr quantile. It was found that dij could be effectively defined in terms of catchment area, mean rainfall and baseflow index. The sampling distribution of L-CV (t2) in each pooling group and the 95% confidence limits about the pooled estimate of t2 are obtained by simulation. The t2 values of the selected group members are compared with these confidence limits both graphically and numerically. Of the 85 stations, only 1 station's pooling group members have all their t2 values within the confidence limits, while 7, 33 and 44 of them have 1, 2 or 3 or more, t2 values outside the confidence limits. The outcomes are also compared with the heterogeneity measures H1 and H2. The H1 values show an upward trend with the ranges of t2 values in the pooling group whereas the H2 values do not show any such dependency. A selection of 27 pooling groups, found to be heterogeneous, were further examined with the help of box-plots of catchment descriptor values and one particular case is considered in detail. Overall the results show that even with a carefully considered selection procedure, it is not certain that perfectly homogeneous pooling groups are identified. As a compromise it is recommended that a group containing more than 2 values of t2 outside the confidence limits should not be considered homogeneous.

Description: Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (krigings) are widely used in spatial interpolation from point measurement to continuous surfaces. However, the majority of existing geostatistical algorithms are available only for single-moment data. The first step in kriging computation is the semi-variogram modelling which usually uses only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. In this study, we used daily rainfall data from 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, Cressie's Approximate Weighted Least Squares method was used to fit seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) to daily sample semi-variogram on a daily basis. Seven selected raingages were used to compare the interpolation performance of these algorithms applied to many degenerated-raingage cases. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably interpolation with the Thiessen polygon that is commonly used in various hydrological models. Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) presented the highest Root Mean Square Error (RMSE) between the geostatistical and IDW methods. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases.

Description: The aim in this study is to simulate lake levels of Lake Tana by solving the water balance at daily time step. Since 42% of the basin is ungauged regionalisation procedures are applied. We examine the predictive capability of a regionalisation approach that combines multi-objective calibration of a simple conceptual model and multi regression analyses to establish relations between model parameters and catchment characteristics. Recently few studies are presented on lake level simulation of Lake Tana. In these studies the water balance of the lake is closed by estimation of runoff contributions from ungauged catchments. Studies partly relied on simple ad-hoc procedures of area comparison to estimate runoff from ungauged catchments. In this study a regional model is developed that relies on principles of similarity of catchments. For runoff modelling the HVB-96 model is selected while multi-objective model calibration is by a Monte Carlo procedure. Assessment of the lake water balance was established by comparing measured to estimated lake levels. Results of daily lake level simulation show a water balance closure term of 85 mm and a relative volume error of 2.17%. Results show runoff from ungauged catchments of 527 mm per year for the simulation period 1994 to 2003 that is approximately 30% of Lake Tana stream flow inflow. Compared to previous works this closure term is smallest.

Description: The Mediterranean climate together with the type of urban setting found in mountainous Middle Eastern cities generate much lower runoff yields than previously reported and than usually estimated for urban design. In fact, a close analysis shows that most of the rainwater remains within the cities as a possible source for urban groundwater recharge. The present study examined two locales – Ramallah, an old traditional Palestinian Arab town, and Modiin, a new township in Israel – both situated on the karstic Yarkon Taninim aquifer. This aquifer supplies the only high-quality drinking water in the region (one quarter of the Israeli-Palestinian water demand), which is characterized by dense populations and limited water resources. This paper provides the first measured information on the hydrological effects of urbanization in the area. It was found that the shift of the mountainous natural steep slopes into a series of closed-terraced homes and gardens created areas that are disconnected from the urban runoff response. Roofs drained into the attached gardens and created favorable recharge units. Mainly low-gradient roads became the principal source for urban runoff already following 1–4 mm of rainfall. Parallel roads converted single peak hydrographs towards multi-peak runoff responses, increasing flow duration and reducing peak discharges. The remaining urban area (public parks, natural areas, etc.) generated runoff only as a result of high-magnitude rainstorms. All of the above conditions limited urban runoff coefficients to an upper boundary of only 22% and 30% (Ramallah and Modiin, respectively). During extreme rainstorms (above 100 mm) similar runoff coefficients were measured in urban and natural catchments as a result of the limited areas contributing to runoff in the urban areas, while natural terrain does not have these artificial limits. Hence, it was found, the effects of urbanization decrease with event magnitude and there is significant potential for urban groundwater recharge. However, frequent low-magnitude rainstorms often generate highly polluted stormwater in urban sewer systems and this water should only be used with great caution.

Description: The paper tries interpreting how the method of SMOS observations realizes managing the problem of large scales and the target heterogeneity by means of employing the polarization angular signature. Land surface target on the Earth is naturally heterogeneous in its continuity of physical and biophysical properties. Soil moisture (SM) retrieval from SMOS data requires using the model CMEM to determine relations between the temperature brightness and water related properties and conditions, which are anchored to the ground by auxiliary data. SM retrieval must start from the conditions at least approaching physical reality. SMOS performs the data fusion in NRT (Nearly Real Time) in a very specific way, what is a new quality added to EO (Earth Observations). The paper demonstrates several effects of employing the SM retrievals from L1C data. Authors explain how they validate few selected test sites in Poland, and come to conclusions on choosing a strategy focused on validating single sites. Finally, they come to an understanding that SM retrieval is an advanced statistical method requiring good referencing to ground based physical conditions in large scales, worth confronting the shallow water content obtained from SMOS to that assessments of the total water content on continental scales, which available from effects of gravitational missions.

Description: Geomorphic processes that produce and transport sediment, and incise river valleys are complex; and often difficult to quantify over longer timescales of 103 to 105 years. Morphometric indices that describe the topography of hill slopes, valleys and river channels have commonly been used to compare morphological characteristics between catchments and to relate them to hydrological and erosion processes. This work focuses on a wide range of slope and river channel morphometric indices to study their behavior and strength in regions affected by low to moderate tectonic activity. We selected 10 catchments of about 150 to 250 km2 across the Ardennes Massif that cover various tectonic domains with uplift rates ranging from about 0.06 to 0.20 mm year−1 since mid-Pleistocene times. The morphometric analysis indicates that the slope and channel morphology of third-order catchments is not yet in topographic steady-state, and exhibits clear convexities in slope and river profiles. Our data indicate that the fluvial system is the main driver of topographic evolution and that the spatial pattern of uplift rates is reflected in the distribution of channel steepness and convexity. The spatial variation that we observe in slope and channel morphology between the 10 third-order catchments suggests that the response of the fluvial system was strongly diachronous, and that a transient signal of adjustment is migrating from the Meuse valley towards the Ardennian headwaters.

Description: The objectives of this work are: (a) to statistically test and quantify the decreasing trends of streamflow and sediment discharge in the middle reaches of the Yellow River in China during 1950–2008, (b) to identify change points or transition years of the decreasing trends, and (c) to diagnose whether the decreasing trends were caused by precipitation changes or human intervention, or both. The results show that significant decreasing trends in annual streamflow and sediment discharge have existed since the late 1950s in the middle reaches of the Yellow River (P=0.01). Change-point analyses further revealed that transition years existed and that abrupt decline in streamflow and sediment discharge began in 1985 and 1981, respectively, in the middle reaches of the Yellow River (P=0.05). Adoption of conservation measures in the 1980s and 1990s corroborates the identified transition years. Double-mass curves of precipitation vs. streamflow (sediment) for the periods before and after the transition year show remarkable decreases in proportionality of streamflow (sediment) generation. Compared with the period before the transition year, cumulative streamflow and cumulative sediment discharge reduced respectively by 17.8% and 28% during 1985–2008, which was caused by human intervention, in the middle reaches of the Yellow River. It is, therefore, concluded that human activities occupied a dominant position and played a major role in the streamflow and sediment discharge reduction in the middle reaches of the Yellow River.

Description: Quantitative evaluations of the impacts of climate change on water resources are primarily constrained by uncertainty in climate projections from GCMs. In this study we assess uncertainty in the impacts of climate change on river discharge in two catchments of the River Yangtze and Yellow Basins that feature contrasting climate regimes (humid and semi-arid). Specifically we quantify uncertainty associated with GCM structure from a subset of CMIP3 AR4 GCMs (HadCM3, HadGEM1, CCSM3.0, IPSL, ECHAM5, CSIRO, CGCM3.1), SRES emissions scenarios (A1B, A2, B1, B2) and prescribed increases in global mean air temperature (1 °C to 6 °C). Climate projections, applied to semi-distributed hydrological models (SWAT 2005) in both catchments, indicate trends toward warmer and wetter conditions. For prescribed warming scenarios of 1 °C to 6 °C, linear increases in mean annual river discharge, relative to baseline (1961–1990), for the River Xiangxi and River Huangfuchuan are +9% and 11% per +1 °C, respectively. Intra-annual changes include increases in flood (Q05) discharges for both rivers as well as a shift in the timing of flood discharges from summer to autumn and a rise (24 to 93%) in dry season (Q95) discharge for the River Xiangxi. Differences in projections of mean annual river discharge between SRES emission scenarios using HadCM3 are comparatively minor for the River Xiangxi (13% to 17% rise from baseline) but substantial (73% to 121%) for the River Huangfuchuan. With one minor exception of a slight (−2%) decrease in river discharge projected using HadGEM1 for the River Xiangxi, mean annual river discharge is projected to increase in both catchments under both the SRES A1B emission scenario and 2° rise in global mean air temperature using all AR4 GCMs on the CMIP3 subset. For the River Xiangxi, there is great uncertainty associated with GCM structure in the magnitude of the rise in flood (Q05) discharges (−1% to 41% under SRES A1B and −3% to 41% under 2° global warming) and dry season (Q95) discharges (2% to 55% under SRES A1B and 2% to 39% under 2° global warming). For the River Huangfuchuan, all GCMs project a rise in the Q05 flow but there is substantial uncertainty in the magnitude of this rise (7% to 70% under SRES A1B and 2% to 57% under 2° global warming). Greatest differences in the projected hydrologic changes are associated with GCMs in both catchments than emission scenarios and climate sensitivity. Critically, estimated uncertainty in projections of mean annual flows is less than that calculated for extreme (Q05, Q95) flows. This research suggest that the common approach of reporting of climate change impacts on river in terms of mean annual flows may mask the magnitude of uncertainty in flows of most importance to water managers.

Description: Design floods for EDF (Électricité de France, French electricity company) dam spillways are now computed using a probabilistic method named SCHADEX (Climatic-Hydrological Simulation of Extreme Floods) based on an extreme rainfall model named the MEWP (Multi Exponential Weather Pattern) distribution. This probabilistic model provides estimates of extreme rainfall quantiles using a mixture of exponential distributions. Each exponential distribution applies to a specific sub-sample of rainfall observations, corresponding to one of eight typical atmospheric circulation patterns that are relevant for France and the surrounding area. The aim of this paper is to validate the MEWP model by assessing its reliability and robustness with rainfall data from France, Spain and Switzerland. Data include 37 long series for the period 1904–2003, and a regional data set of 478 rain gauges for the period 1954–2005. Two complementary properties are investigated: (i) the reliability of estimates, i.e. the agreement between the estimated probabilities of exceedance and the actual exceedances observed on the dataset; (ii) the robustness of extreme quantiles and associated confidence intervals, assessed using various sub-samples of the long data series. New specific criteria are proposed to quantify reliability and robustness.The MEWP model is compared to standard models (seasonalised Generalised Extreme Value and Generalised Pareto distributions). In order to evaluate the suitability of the exponential model used for each weather pattern (WP), a general case of the MEWP distribution, using Generalized Pareto distributions for each WP, is also considered. Concerning the considered dataset, the exponential hypothesis of asymptotic behaviour of each seasonal and weather pattern rainfall records, appears to be reasonable. The results highlight: (i) the interest of WP sub-sampling that lead to significant improvement in reliability models performances; (ii) the low level of robustness of the models based on at-site estimation of shape parameter; (iii) the MEWP distribution proved to be robust and reliable, demonstrating the interest of the proposed approach.

Description: Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagatiom fuzzy neural network (CFNN) for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

Description: We present an evapotranspiration (ET) model developed in the framework of the EUMETSAT "Satellite Application Facility" (SAF) on Land Surface Analysis (LSA). The model is a simplified Soil-Vegetation-Atmosphere Transfer (SVAT) scheme that uses as input a combination of remote sensed data and atmospheric model outputs. The inputs based on remote sensing are LSA-SAF products: the Albedo (AL), the Downwelling Surface Shortwave Flux (DSSF) and the Downwelling Surface Longwave Flux (DSLF). They are available with the spatial resolution of the MSG SEVIRI instrument. ET maps covering the whole MSG field of view are produced by the model every 30 min, in near-real-time, for all weather conditions. This paper presents the adopted methodology and a set of validation results. The model quality is evaluated in two ways. First, ET results are compared with ground observations (from CarboEurope and national weather services), for different land cover types, over a full vegetation cycle in the Northern Hemisphere in 2007. This validation shows that the model is able to reproduce the observed ET temporal evolution from the diurnal to annual time scales for the temperate climate zones: the mean bias is less than 0.02 mm h−1 and the root-mean square error is between 0.06 and 0.10 mm h−1. Then, ET model outputs are compared with those from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Global Land Data Assimilation System (GLDAS). From this comparison, a high spatial correlation is noted, between 80 to 90%, around midday time frame. Nevertheless, some discrepancies are also observed and are due to the different input variables and parameterisations used.

Description: This paper presents the first inventory of dissolved minor and trace element (F, Al, Fe, Mn, Ba, Cd, Cr, Cu, Pb, Mo, Bi, Sr, Zn) concentrations in Lake Malawi, the second largest African lake. Sampling was carried out during 1993 dry season in the northern part of the lake. Trace metal concentrations were measured, together with Ca, Mg, Na, K, Cl, SO4, Alkalinity and Si, along three profiles in the lake northern extremity, in five tributaries and two on-land hydrothermal springs. Water profiles show similar elemental distributions and concentrations that are influenced by lake physical-chemical stratification. Stratification, assessed using temperature, conductivity, Si and Mn profiles, is characterised by two boundaries: the thermocline (70–90 m) and the oxicline (150–190 m). Elemental water concentrations are discussed using simple covariance analyse. Epilimnetic concentrations and distribution are also influenced by atmospheric deposition and river diving. Comparison of dissolved concentrations for potentially polluting elements with World Health Organisation Guidelines and those reported for other East African lakes shows that this reservoir is uncontaminated despite an increasing human stress. Major element behaviour is assessed through a 3 boxes model. In this model Cl and K are conservative elements whereas Si is removed from the solution by diatom productivity and sedimentation. Ca, Na, Mg and alkalinity show low reactivity. Evaporation is one of the controlling factors of lake element concentration that superimposes on the watershed control. Hydrothermal activity, not evidenced in the lake, controls the chemistry of one of the main northern tributary. Chemical comparison between Northern rivers and other tributaries characterises the geographical and geological specificity of studied northern watershed. Moreover the lake annual chemical budget shows that northern watershed generates the main elemental input to the lake, illustrating the dual importance of this area in terms of water and ionic recharge to the lake.

Description: Heterogeneity and complexity of hydrological processes offer substantial challenges to the hydrological modeller. Some hydrologists try to tackle this problem by introducing more and more detail in their models, or by setting-up more and more complicated models starting from basic principles at the smallest possible level. As we know, this reductionist approach leads to ever higher levels of equifinality and predictive uncertainty. On the other hand, simple, lumped and parsimonious models may be too simple to be realistic or representative of the dominant hydrological processes. In this commentary, a new model approach is proposed that tries to find the middle way between complex distributed and simple lumped modelling approaches. Here we try to find the right level of simplification while avoiding over-simplification. Paraphrasing Einstein, the maxim is: make a model as simple as possible, but not simpler than that. The approach presented is process based, but not physically based in the traditional sense. Instead, it is based on a conceptual representation of the dominant physical processes in certain key elements of the landscape. The essence of the approach is that the model structure is made dependent on a limited number of landscape classes in which the topography is the main driver, but which can include geological, geomorphological or land-use classification. These classes are then represented by lumped conceptual models that act in parallel. The advantage of this approach over a fully distributed conceptualisation is that it retains maximum simplicity while taking into account observable landscape characteristics.

Description: Climate change impacts in Pacific Northwest Region of North America (PNW) are projected to include increasing temperatures and changes in the seasonality of precipitation (increasing precipitation in winter, decreasing precipitation in summer). Changes in precipitation are also spatially varying, with the northwestern parts of the region generally experiencing greater increases in cool season precipitation than the southeastern parts. These changes in climate are projected to cause loss of snowpack and associated streamflow timing shifts which will increase cool season (October–March) flows and decrease warm season (April–September) flows and water availability. Hydrologic extremes such as the 100 year flood and extreme low flows are also expected to change, although these impacts are not spatially homogeneous and vary with mid-winter temperatures and other factors. These changes have important implications for natural ecosystems affected by water, and for human systems. The PNW is endowed with extensive water resources infrastructure and well-established and well-funded management agencies responsible for ensuring that water resources objectives (such as water supply, water quality, flood control, hydropower production, environmental services, etc.) are met. Likewise, access to observed hydrological, meteorological, and climatic data and forecasts is in general exceptionally good in the United States and Canada, and access to these products and services is often supported by federally funded programs that ensure that these resources are available to water resources practitioners, policy makers, and the general public. Access to these extensive resources support the argument that at a technical level the PNW has high capacity to deal with the potential impacts of natural climate variability on water resources. To the extent that climate change will manifest itself as moderate changes in variability or extremes, we argue that existing water resources infrastructure and institutional arrangements provide a solid foundation for coping with climate change impacts, and that the mandates of existing water resources policy and water resources management institutions are at least consistent with the fundamental objectives of climate change adaptation. A deeper inquiry into the underlying nature of PNW water resources systems, however, reveals significant and persistent obstacles to climate change adaptation, which will need to be overcome if effective use of the region's extensive water resources management capacity can be brought to bear on this problem. Primary obstacles include assumptions of stationarity as the fundamental basis of water resources system design, entrenched use of historic records as the sole basis for planning, problems related to the relatively short time scale of planning, lack of familiarity with climate science and models, downscaling procedures, and hydrologic models, limited access to climate change scenarios and hydrologic products for specific water systems, and rigid water allocation and water resources operating rules that effectively block adaptive response. Institutional barriers include systematic loss of technical capacity in many water resources agencies following the dam building era, jurisdictional fragmentation affecting response to drought, disconnections between water policy and practice, and entrenched bureaucratic resistance to change in many water management agencies. These factors, combined with a federal agenda to block climate change policy in the US during the Bush administration has (with some exceptions) led to institutional "gridlock" in the PNW over the last decade or so despite a growing awareness of climate change as a significant threat to water management. In the last several years, however, significant progress has been made in surmounting these obstacles, and the region's water resources agencies at all levels of governance are making progress in addressing the fundamental challenges inherent in adapting to climate change.

Description: Liquid and solid precipitation is abundant in the high elevation, upper reach of the Heihe basin. The development of modern irrigation schemes in the middle reach of the basin is taking up an increasing share of fresh water resources, endangering the oasis and traditional irrigation systems in the lower reach. In this study, the response of vegetation in the Ejina Oasis in the lower reach of the Heihe River to the water yield of the upper catchment was analyzed by time series analysis of monthly observations of precipitation in the upper and lower catchment, river streamflow downstream of the modern irrigation schemes and satellite observations of vegetation index. Firstly, remote sensing data were used to monitor the vegetation dynamic for a long time period. Due to cloud-contamination, atmospheric influence and different solar angles, however, the quality and consistence of time series of remote sensing data is degraded. In this research we used a Fourier Transform method – the Harmonic Analysis of Time Series (HANTS) algorithm – to reconstruct cloud-free NDVI time series data from the Terra-MODIS dataset. Anomalies in precipitation, streamflow, and vegetation index are detected by comparing each year with the average year. The relationship between the anomalies in vegetation growth, the local precipitation and upstream water yield were analyzed. The same approach is used to identify, remove and gap-filling cloud contaminated observations in the satellite data for each year in the dataset. The results showed that: the previous year total runoff had a significant relationship with the vegetation growth in Ejina Oasis and that anomalies in monthly runoff of the Heihe River influenced the phenology of vegetation in the entire oasis during drier years. The time of maximum green-up was uniform throughout the oasis during wetter years, but showed a clear S–N gradient (downstream) during drier years.

Description: The upper Blue Nile River Basin in Ethiopia is a largely untapped resource despite its huge potential for hydropower generation and irrigated agriculture. Controversies exist as to whether the numerous infrastructural development projects that are on the drawing board in Ethiopia will generate positive or negative externalities downstream in Sudan and Egypt. This study attempts at 1) examining the (re-)operation of infrastructures, in particular the proposed reservoirs in Ethiopia and the High Aswan Dam and 2) assessing the economic benefits and costs associated with the storage infrastructures in Ethiopia and their spatial and temporal distribution. To achieve this, a basin-wide integrated hydro-economic model has been developed. The model integrates essential hydrologic, economic and institutional components of the river basin in order to explore both the hydrologic and economic consequences of various policy options and planned infrastructural projects. Unlike most of the deterministic economic-hydrologic models reported in the literature, a stochastic programming formulation has been adopted in order to: i) understand the effect of the hydrologic uncertainty on management decisions, ii) determine allocation policies that naturally hedge against the hydrological risk, and iii) assess the relevant risk indicators. The study reveals that the development of four mega dams in the upper part of the Blue Nile Basin would change the drawdown refill cycle of the High Aswan Dam. Should the operation of the reservoirs be coordinated, they would enable an average annual saving of at least 2.5 billion m3 through reduced evaporation losses from the Lake Nasser. Moreover, the new reservoirs (Karadobi, Beko-Abo, Mandaya and Border) in Ethiopia would have significant positive impacts on hydropower generation and irrigation in Ethiopia and Sudan: at the basin scale, the annual energy generation is boosted by 38.5 TWh amongst which 14.2 TWh due to storage. Moreover, the regulation capacity of the above mentioned reservoirs would enable an increase of the Sudanese irrigated area by 5.5%.

Description: We made a hydrological characterization of 32 watersheds (31–4350 km2) in the Blue Nile Basin, using data from a study of water and land resources in the Blue Nile Basin, Ethiopia published in 1964 by the US Bureau of Reclamation (USBR). The USBR document contains data on flow, climate, topography, geology, soil type, and land use for the period from 1959 to 1963. The aim of the study was to identify which combination of watershed variables best explain the variation in the hydrological regime, with special focus to low flow and, what kind of land use low flow might benefit from. Principal Component Analysis (PCA) and Partial Least Square (PLS) were used to analyze the relationship between hydrologic variables (total flow, maximum flow, minimum flow, runoff coefficient, and low flow index) and 30 potential watershed variables. We found that three groups of watershed variables – climate and topography, geology and soil, and land use had almost equal influence on the variation in the hydrologic variables (R2 values ranging from 0.3 to 0.5). The individual variables which were selected based on statistical significance from all groups of explanatory variables were better in explaining the variation. Low flow was positively correlated most strongly to wetland, wood land, rainfall, luvisols, and alluvial soils. Low flow was negatively correlated to grazing land, bush land, tuffs/basalts, eutric-vertisols and riverine forest. We concluded that low flow benefits from the land use types that preserve soil quality and water storage, such as wetland, savannah and woodland, while it was lower in land use resulting in soil degradation. Therefore it provides support to the theory that some land use such as grassland, can promote higher low flow

Description: The understanding of low flows in rivers is paramount more than ever as demand for water increases on a global scale. At the same time, limited streamflow data to investigate this phenomenon, particularly in the tropics, makes the provision of accurate estimations in ungauged areas an ongoing research need. This paper analysed the potential of climatic and terrain attributes of 167 tropical and sub-tropical unregulated catchments to predict baseflow recession rates. Climatic attributes included annual and seasonal indicators of rainfall and potential evapotranspiration. Terrain attributes included indicators of catchment shape, morphology, land cover, soils and geology. Stepwise regression was used to identify the best predictors for baseflow recession coefficients (kbf). Mean annual rainfall (MAR) and aridity index (AI) were found to explain 49% of the spatial variation of kbf. The rest of climatic indices plus average catchment slope (SLO) and tree cover were also good predictors, but co-correlated with MAR. Catchment elongation (CE), a measure of catchment shape, was also found to be statistically significant, although weakly correlated. An analysis of clusters of catchments of smaller size, showed that in these areas, presumably with some similarity of soils and geology due to proximity, residuals of the regression could be explained by SLO and CE. The approach used provides a~potential alternative for kbf parameterisation in ungauged areas.

Description: Explicit integration of seasonal precipitation forecasts into water resources operations and planning is practically nonexistent, even in regions of scarcity. This is often attributable to water manager's tendency to act in a risk averse manner, preferring to avoid consequences of poor forecasts, at the expense of unrealized benefits. Convincing demonstrations of forecast value are therefore desirable to support assimilation into practice. A dynamic coupled system, including forecast, rainfall-runoff, and hydropower models, is applied to the upper Blue Nile basin in Ethiopia to compare benefits and reliability generated by actual forecasts against a climatology-based approach, commonly practiced in most water resources systems. Processing one hundred decadal sequences demonstrates superior forecast-based benefits in 68 cases, a respectable advancement, however benefits in a few forecast-based sequences are noticeably low, likely to dissuade manager's adoption. A hydropower sensitivity test reveals a propensity toward poor-decision making when forecasts over-predict wet conditions. Tailoring the precipitation forecast to highlight critical dry predictions minimizes this inclination, resulting in 97% of the sequences favoring the forecast-based approach. Considering managerial risk preferences for the system, even risk-averse actions, if coupled with forecasts, exhibits superior benefits and reliability compared with risk-taking tendencies relying on climatology.

Description: Irrigation is the most important water use sector accounting for about 70% of the global freshwater withdrawals and 90% of consumptive water uses. While the extent of irrigation and related water uses are reported in statistical databases or estimated by model simulations, information on the source of irrigation water is scarce and very scattered. Here we present a new global inventory on the extent of areas irrigated with groundwater, surface water or non-conventional sources, and we determine the related consumptive water uses. The inventory provides data for 15 038 national and sub-national administrative units. Irrigated area was provided by census-based statistics from international and national organizations. A global model was then applied to simulate consumptive water uses for irrigation by water source. Globally, area equipped for irrigation is currently about 301 million ha of which 38% are equipped for irrigation with groundwater. Total consumptive groundwater use for irrigation is estimated as 545 km3 yr−1, or 43% of the total consumptive irrigation water use of 1 277 km3 yr−1. The countries with the largest extent of areas equipped for irrigation with groundwater, in absolute terms, are India (39 million ha), China (19 million ha) and the United States of America (17 million ha). Groundwater use in irrigation is increasing both in absolute terms and in percentage of total irrigation, leading in places to concentrations of users exploiting groundwater storage at rates above groundwater recharge. Despite the uncertainties associated with statistical data available to track patterns and growth of groundwater use for irrigation, the inventory presented here is a major step towards a more informed assessment of agricultural water use and its consequences for the global water cycle.

Description: This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.

Description: Rainfall-runoff models are common tools for river discharge estimation in the field of hydrology. In ungauged basins, the dependence on observed river discharge data for calibration restricts applications of rainfall-runoff models. The strong correlation between quantities of river cross-sectional water surface width obtained from remote sensing and corresponding in situ gauged river discharge has been verified by many researchers. In this study, a calibration scheme of rainfall-runoff models based on satellite observations of river width at basin outlet is illustrated. One distinct advantage is that this calibration is independent of river discharge information. The at-a-station hydraulic geometry is implemented to facilitate shifting calibration objective from river discharge to river width. The generalized likelihood uncertainty estimation methodology is applied to model calibration and uncertainty analysis. The calibration scheme is demonstrated through a case study for simulating river discharge at Pakse in the Mekong Basin. The effectiveness of calibration scheme and uncertainties associated with utilization of river width observations from space are examined from model input-state-output behaviour, capability of reproducing river discharge, and posterior parameter distribution. The results indicate that the satellite observation of river width is a competent surrogate of observed discharge for the calibration of rainfall-runoff model at Pakse and the proposed method has the potential for improving reliability of river discharge estimation in basins without any discharge gauging.

Description: A novel approach to consider additional spatial information in flood frequency analyses, especially for the estimation of discharges with recurrence intervals larger than 100 years, is presented. For this purpose, large flood quantiles, i.e. pairs of a discharge and its corresponding recurrence interval, as well as an upper bound discharge, are combined within a mixed bounded distribution function. Large flood quantiles are derived using probabilistic regional envelope curves (PRECs) for all sites of a pooling group. These PREC flood quantiles are introduced into an at-site flood frequency analysis by assuming that they are representative for the range of recurrence intervals which is covered by PREC flood quantiles. For recurrence intervals above a certain inflection point, a Generalised Extreme Value (GEV) distribution function with a positive shape parameter is used. This GEV asymptotically approaches an upper bound derived from an empirical envelope curve. The resulting mixed distribution function is composed of two distribution functions, which are connected at the inflection point. This method is applied to 83 streamflow gauges in Saxony/Germany. Our analysis illustrates that the presented mixed bounded distribution function adequately considers PREC flood quantiles as well as an upper bound discharge. The introduction of both into an at-site flood frequency analysis improves the quantile estimation. A sensitivity analysis reveals that, for the target recurrence interval of 1000 years, the flood quantile estimation is less sensitive to the selection of an empirical envelope curve than to the selection of PREC discharges and of the inflection point between the mixed bounded distribution function.

Description: The use of design storms can be very useful in many hydrological and hydraulic practices. In this study, the concept of a copula-based secondary return period in combination with the concept of mass curves is used to generate design storms. The analysis is based on storms selected from the 105 year rainfall time series with a 10 min resolution, measured at Uccle, Belgium. In first instance, bivariate copulas and secondary return periods are explained, together with a focus on which couple of storm variables is of highest interest for the analysis and a discussion of how the results might be affected by the goodness-of-fit of the copula. Subsequently, the fitted copula is used to sample storms with a predefined secondary return period for which characteristic variables such as storm duration and total storm depth can be derived. In order to construct design storms with a realistic storm structure, mass curves of 1st, 2nd, 3rd and 4th quartile storms are developed. An analysis shows that the assumption of independence between the secondary return period and the internal storm structure could be made. Based on the mass curves, a technique is developed to randomly generate an intrastorm structure. The coupling of both techniques eventually results in a methodology for stochastic design storm generation. Finally, its practical usefulness for design studies is illustrated based on the generation of design storm ensembles and rainfall-runoff modelling.

Description: The aim of this study is to propose methods to improve crop and water management in Mediterranean regions. At landscape scale, there is a very large spatial variability of agricultural practices, particularly for grasslands irrigated by flooding. These grasslands are harvested three times per year and produce high quality hay, but their productions decreased significantly these last few years because of the water scarcity. It is therefore important to assess the real water requirement for crops in order to predict productions in the case of agricultural practice modifications. Until now, the spatial variability of agricultural practices was obtained through surveys from farmers, but this method was tedious to describe an entire region. Thus, the specific aim of the study is to propose a new approach based on: 1) the feasibility of using optical remote sensing data acquired at high spatio-temporal resolutions for agricultural practice monitoring and, 2) the evaluation of a crop model, forced with this data, for estimating water balance and crop yield. We developed a methodology based on the combined use of FORMOSAT-2 images and STICS crop model to estimate production, evapotranspiration and drainage of irrigated grasslands in "the Crau" region in the South Eastern France. Numerous surveys and ground measurements were performed during an experiment conducted in 2006. Simple algorithms were developed to retrieve the dynamic of Leaf Area Index (LAI) for each plot and the main agricultural practices such as mowing and irrigation dates. This information was then used to parameterize STICS, applied at region scale to estimate the spatial variability of water budget associated with the biomass productions. Results are displayed at the farm scale. Satisfactory results were obtained when compared to ground measurements. The method for extrapolation to other regions or crops is discussed as regard to data available.

Description: For forecasting the maximum 5-d accumulated precipitation over the winter season at lead times of 3, 6, 9 and 12 months over Canada from 1950 to 2007, two nonlinear and two linear regression models were used, where the models were support vector regression (SVR) (nonlinear and linear versions), nonlinear Bayesian neural network (BNN) and multiple linear regression (MLR). The 118 stations were grouped into six geographic regions by K-means clustering. For each region, the leading principal components of the winter extreme precipitation were the predictands. Potential predictors included quasi-global sea surface temperature anomalies and 500 hPa geopotential height anomalies over the Northern Hemisphere, as well as six climate indices (the Niño-3.4 region sea surface temperature, the North Atlantic Oscillation, the Pacific-North American teleconnection, the Pacific Decadal Oscillation, the Scandinavia pattern, and the East Atlantic pattern). The results showed that in general the two robust SVR models tended to have better forecast skills than the two non-robust models (MLR and BNN), and the nonlinear SVR model tended to forecast slightly better than the linear SVR model. Among the six regions, the Eastern Prairies region displayed the highest forecast skills, and the Arctic region the second highest. The strongest nonlinearity was manifested over the Eastern Prairies and the weakest nonlinearity over the Arctic.

Description: Radar remote sensing has demonstrated its applicability to the retrieval of basin-scale soil moisture. The mechanism of radar backscattering from soils is complicated and strongly influenced by surface roughness. Furthermore, retrieval of soil moisture using AIEM-like models is a classic example of the underdetermined problem due to a lack of credible known soil roughness distributions at a regional scale. Characterization of this roughness is therefore crucial for an accurate derivation of soil moisture based on backscattering models. This study aims to directly obtain surface roughness information along with soil moisture from multi-angular ASAR images. The method first used a semi-empirical relationship that connects the roughness slope (Zs) and the difference in backscattering coefficient (Δσ) from ASAR data in different incidence angles, in combination with an optimal calibration form consisting of two roughness parameters (the standard deviation of surface height and the correlation length), to estimate the roughness parameters. The deduced surface roughness was then used in the AIEM model for the retrieval of soil moisture. An evaluation of the proposed method was performed in a grassland site in the middle stream of the Heihe River Basin, where the Watershed Allied Telemetry Experimental Research (WATER) was taken place. It has demonstrated that the method is feasible to achieve reliable estimation of soil water content. The key challenge to surface soil moisture retrieval is the presence of vegetation cover, which significantly impacts the estimates of surface roughness and soil moisture.

Description: SHETRAN is a physically-based distributed modelling system that gives detailed simulations in time and space of water flow and sediment and solute transport in river catchments. Standard algorithms for the automatic generation of river channel networks from digital elevation data are impossible to apply in SHETRAN and other similar models because the river channels are assumed to run along the edges of grid cells. In this work a new algorithm for the automatic generation of a river channel network in SHETRAN is described and its use in an example catchment demonstrated.

Description: The SMOSMANIA soil moisture network in Southwestern France is used to evaluate synthetic and remotely sensed soil moisture products. The surface soil moisture (SSM) measured in situ at 5 cm permits to evaluate synthetic SSM from the SIM operational hydrometeorological model of Météo-France and to perform a cross-evaluation of the normalised SSM estimates derived from coarse-resolution (25 km) active microwave observations from the ASCAT scatterometer instrument (C-band, onboard METOP), issued by EUMETSAT and resampled to the Discrete Global Grid (DGG, 12.5 km grid spacing) by TU-Wien (Vienna University of Technology) over a two year period (2007–2008). A downscaled ASCAT product at one kilometre scale is evaluated as well, together with operational soil moisture products of two meteorological services, namely the ALADIN numerical weather prediction model (NWP) and the Integrated Forecasting System (IFS) analysis of Météo-France and ECMWF, respectively. In addition to the operational SSM analysis of ECMWF, a second analysis using a simplified extended Kalman filter and assimilating the ASCAT SSM estimates is tested. The ECMWF SSM estimates correlate better with the in situ observations than the Météo-France products. This may be due to the higher ability of the multi-layer land surface model used at ECMWF to represent the soil moisture profile. However, the SSM derived from SIM corresponds to a thin soil surface layer and presents good correlations with ASCAT SSM estimates for the very first centimetres of soil. At ECMWF, the use of a new data assimilation technique, which is able to use the ASCAT SSM, improves the SSM and the root-zone soil moisture analyses.

Description: Global freshwater resources are sensitive to changes in climate, land cover and population density and distribution. The Land-surface Processes and eXchanges Dynamic Global Vegetation Model (LPX-DGVM) is a development of the Lund-Potsdam-Jena model with improved representation of fire-vegetation interactions. It allows simultaneous consideration of the effects of changes in climate, CO2 concentration, natural vegetation and fire regime shifts on the continental hydrological cycle. Here the model is assessed for its ability to simulate large-scale spatial and temporal runoff patterns, in order to test its suitability for modelling future global water resources. Comparisons are made against observations of streamflow and a composite dataset of modelled and observed runoff (1986–1995). The model captures the main features of the geographical distribution of global runoff, but tends to overestimate runoff in much of the Northern Hemisphere (where this can be largely accounted for by freshwater extractions and the unrealistic accumulation of the simulated winter snowpack in permafrost regions) and the southern tropics. Interannual variability is represented reasonably well at the large catchment scale, as are seasonal flow timings and monthly high and low flow events. Further improvements to the simulation of intra-annual runoff might be achieved via the addition of river flow routing. Overestimates of runoff in some basins could likely be corrected by the inclusion of transmission losses and direct-channel evaporation.

Description: Impacts of climate variability and land use change on catchment runoff of the Meki River basin were assessed using hydrological modeling. The Modular Modeling System (MMS) was used to build a suitable Precipitation Runoff Modeling System (PRMS) for the study area, perform sensitivity analysis, model calibration and validation, and scenario analysis. The model calibration and validation periods in this study were divided into three. The calibration period was a five years period (1981–1986). The validation period was divided into two: validation 1 (1986–1991) and validation 2 (1996–2002). Model performance was evaluated by using joint plots of daily and monthly observed and simulated runoff hydrographs and the Nash-Sutcliffe coefficient of efficiency to statistically analyze model performance in simulating daily and monthly runoff. Daily observed and simulated hydrographs showed a reasonable agreement for both calibration and validation periods. The model coefficients of efficiency were 0.71 for the calibration period and 0.69 and 0.66 for validation period 1 and 2, respectively. Simulated runoff was generally greater than the observed runoff values for the calibration and validation periods. The model was also limited in its capability of simulating complex hydrograph shapes and peak discharge values. However, the model performed well in simulating dry season flows for both validation and calibration periods. A 20% of change in rainfall and 1.5 °C increase in temperature was considered for climatic scenarios and one land use change scenario were used to assess the likely impacts of these changes on the runoff of the Meki River. The results of the scenario analysis showed that the basin is more sensitive to increase in rainfall (+80% for +20%) than to a decrease (−62% for −20%). Increase in temperature has also a significant impact both on the potential evapotranspiration and stream flow of the basin. Increase by 1.5 °C in temperature resulted in increase in potential evapotranspiration (6.02%) and decrease in stream flow (13%). The proposed land use scenario of converting areas between 2000 to 3000 m a.s.l. to woodland also resulted in a significant decrease in stream flow (11.8%) and increase in evapotranspiration (2.2%) of the study area.

Description: Regionalization of physical land surface models requires the supply of detailed land cover information. Numerous global and regional land cover maps already exist, but generally they do not resolve arable land into different crop types. However, the characteristic phenological behaviour of different crops affects the mass and energy fluxes on the land surface and thus its hydrology. The objective of this study is the generation of a land cover map for Central Europe based on CORINE Land Cover 2000, merged with CORINE Switzerland, but distinguishing different crop types. Accordingly, an approach was developed, subdividing the land cover class arable land into the regionally most relevant subclasses for Central Europe using statistical data from EUROSTAT. This database was analysed concerning the acreage of different crop types, taking a multiseasonal series of MERIS Normalized Difference Vegetation Index (NDVI) into account. The satellite data were used for the separation of spring and summer crops. The hydrological impact of the improved land cover map was modelled exemplarily for the Upper Danube catchment.

Description: This work investigates the added value of ensembles constructed from seventeen lumped hydrological models against their simple average counterparts. It is thus hypothesized that there is more information provided by all the outputs of these models than by their single aggregated predictors. For all available 1061 catchments, results showed that the mean continuous ranked probability score of the ensemble simulations were better than the mean average error of the aggregated simulations, confirming the added value of retaining all the components of the model outputs. Reliability of the simulation ensembles is also achieved for about 30% of the catchments, as assessed by rank histograms and reliability plots. Nonetheless this imperfection, the ensemble simulations were shown to have better skills than the deterministic simulations at discriminating between events and non-events, as confirmed by relative operating characteristic scores especially for larger streamflows. From 7 to 10 models are deemed sufficient to construct ensembles with improved performance, based on a genetic algorithm search optimizing the continuous ranked probability score. In fact, many model subsets were found improving the performance of the reference ensemble. This is thus not essential to implement as much as seventeen lumped hydrological models. The gain in performance of the optimized subsets is accompanied by some improvement of the ensemble reliability in most cases. Nonetheless, a calibration of the predictive distribution is still needed for many catchments.

Description: This paper presents the development and implementation of a distributed model of coupled water nutrient processes, based on the representative elementary watershed (REW) approach, to the Upper Sangamon River Basin, a large, tile-drained agricultural basin located in Central Illinois, Mid-West of USA. Comparison of model predictions with the observed hydrological and biogeochemical data, as well as regional estimates from literature studies, shows that the model is capable of capturing the dynamics of water, sediment and nutrient cycles reasonably well. The model is then used as a tool to gain insights into the physical and chemical processes underlying the inter- and intra-annual variability of water and nutrient balances. Model predictions show that about 80% of annual runoff is contributed by tile drainage, while the remainder comes from surface runoff (mainly saturation excess flow) and subsurface runoff. It is also found that, at the annual scale nitrogen storage in the soil is depleted during wet years, and is supplemented during dry years. This carryover of nitrogen storage from dry year to wet year is mainly caused by the lateral loading of nitrate. Phosphorus storage, on the other hand, is not affected much by wet/dry conditions simply because the leaching of it is very minor compared to the other mechanisms taking phosphorous out of the basin, such as crop harvest. The analysis then turned to the movement of nitrate with runoff. Model results suggested that nitrate loading from hillslope into the channel is preferentially carried by tile drainage. Once in the stream it is then subject to in-stream denitrification, the significant spatio-temporal variability of which can be related to the variation of the hydrologic and hydraulic conditions across the river network.

Description: Assessing and quantifying natural water storage is becoming increasingly important as nations develop strategies for economic growth and adaptations measures for climate change. The Gravity Recovery and Climate Experiment (GRACE) data provide a new opportunity to gain a direct and independent measure of water mass variations on a regional scale. Hydrological models are required to interpret these mass variations and partition them between different parts of the hydrological cycle, but groundwater storage has generally been poorly constrained by such models. This study focused on the Nile basin, and used a groundwater recharge model ZOODRM (Zoomable Object Oriented Distributed Recharge Model) to help interpret the seasonal variation in terrestrial water storage indicated by GRACE. The recharge model was constructed using almost entirely remotely sensed input data and calibrated to observed hydrological data from the Nile. GRACE data for the Nile Basin indicates an annual terrestrial water storage of approximately 200 km3: water input is from rainfall, and much of this water is evaporated within the basin since average annual outflow of the Nile is less than 30 km3. Total annual recharge simulated by ZOODRM is 400 km3/yr; 0–50 mm/yr within the semi arid lower catchments, and a mean of 250 mm/yr in the sub-tropical upper catchments. These results are comparable to the few site specific studies of recharge in the basin. Accounting for year-round discharge of groundwater, the seasonal groundwater storage is 100–150 km3/yr and seasonal change in soil moisture, 30 km3/yr. Together, they account for between 50 and 90% of the annual water storage in the catchment. The annual water mass variation (200 km3/yr) is an order of magnitude smaller than the rainfall input into the catchment (2000 km3/yr), which could be consistent with a high degree of moisture recycling within the basin. Future work is required to advance the calibration of the ZOODRM model, particularly improving the timing of runoff routing.

Description: Snow redistribution by wind and the resulting accumulation regimes were simulated for two winters over an alpine ridge transect located in the Canada Rocky Mountains. Simulations were performed using physically based blowing snow and snowmelt models. A hydrological response unit (HRU)-based spatial discretization was used rather than a more computationally expensive fully-distributed one. The HRUs were set up to follow an aerodynamic sequence, whereby eroded snow was transported from windswept, upwind HRUs to drift accumulating, downwind HRUs. HRUs were selected by examining snow accumulation patterns from manual snow depth measurements. Simulations were performed using two sets of wind speed forcing: (1) station observed wind speed, and (2) modelled wind speed from a widely applied empirical, terrain-based windflow model. Best results were obtained when using the site meteorological station wind speed data. The windflow model performed poorly when comparing the magnitude of modelled and observed wind speeds, though over-winter snow accumulation results obtained when using the modelled wind speeds were reasonable. However, there was a notable discrepancy (17%) between blowing snow sublimation quantities estimated when using the modelled and observed wind speeds. As a result, the end-of-winter snow accumulation was considerably underestimated (32%) when using the modelled wind speeds. That snow redistribution by wind can be adequately simulated in computationally efficient HRUs over this alpine ridge has important implications for representing snow transport in large-scale hydrology models and land surface schemes. Snow redistribution by wind was shown to significantly impact snow accumulation regimes in mountainous environments as snow accumulation was reduced to less than one-third of snowfall on windswept landscapes and nearly doubled in certain lee slope and treeline areas. Blowing snow sublimation losses were shown to be significant (approximately one-quarter of snowfall or greater).

Description: River flow regimes, including long-term average flows, seasonality, low flows, high flows and other types of flow variability, play an important role for freshwater ecosystems. Thus, climate change affects freshwater ecosystems not only by increased temperatures but also by altered river flow regimes. However, with one exception, transferable quantitative relations between flow alterations and ecosystem responses have not yet been derived. While discharge decreases are generally considered to be detrimental for ecosystems, the effect of future discharge increases is unclear. As a first step towards a global-scale analysis of climate change impacts on freshwater ecosystems, we quantified the impact of climate change on five ecologically relevant river flow indicators, using the global water model WaterGAP 2.1g to simulate monthly time series of river discharge with a spatial resolution of 0.5 degrees. Four climate change scenarios based on two global climate models and two greenhouse gas emissions scenarios were evaluated. We compared the impact of climate change by the 2050s to the impact of water withdrawals and dams on natural flow regimes that had occurred by 2002. Climate change was computed to alter seasonal flow regimes significantly (i.e. by more than 10%) on 90% of the global land area (excluding Greenland and Antarctica), as compared to only one quarter of the land area that had suffered from significant seasonal flow regime alterations due to dams and water withdrawals. Due to climate change, the timing of the maximum mean monthly river discharge will be shifted by at least one month on one third on the global land area, more often towards earlier months (mainly due to earlier snowmelt). Dams and withdrawals had caused comparable shifts on less than 5% of the land area only. Long-term average annual river discharge is predicted to significantly increase on one half of the land area, and to significantly decrease on one quarter. Dams and withdrawals had led to significant decreases on one sixth of the land area, and nowhere to increases. Thus, by the 2050s, climate change will have impacted ecologically relevant river flow characteristics much more strongly than dams and water withdrawals have up to now. The only exception refers to the decrease of the statistical low flow Q90, with significant decreases both by past water withdrawals and future climate change on one quarter of the land area. Considering long-term average river discharge, only a few regions, including Spain, Italy, Iraq, Southern India, Western China, the Australian Murray Darling Basin and the High Plains Aquifer in the USA, all of them with extensive irrigation, are expected to be less affected by climate change than by past anthropogenic flow alterations. In some of these regions, climate change will exacerbate the discharge reduction. Emissions scenario B2 leads to only slightly reduced alterations of river flow regimes as compared to scenario A2 even though emissions are much smaller. The differences in alterations resulting from the two applied climate models are larger than those resulting from the two emissions scenarios. Based on general knowledge about ecosystem responses to flow alterations and data related to flow alterations by dams and water withdrawals, we expect that the computed climate change induced river flow alterations will impact freshwater ecosystems more strongly than past anthropogenic alterations.

Description: The aim of this study was to estimate the impacts of anticipated global climate change on runoff and evapotranspiration in small-forested catchments. The investigated Lysina and Pluhův Bor catchments are situated in the Slavkov Forest in the western part of the Czech Republic. To forecast hydrological patterns for the period 2071–2100, outputs from two general circulation models, HadAM3H and ECHAM4/OPYC3, were downscaled by an RCAO (regional climate model) which ran the SRES emission scenarios A2 and B2 for each model. Bias-corrected RCAO daily outputs were used in combination with the hydrological model Brook90. Annual runoff is predicted to decline by 6–45%, and impacts on the distribution of monthly flow are predicted to be significant, with summer-autumn decreases of 29–96%, and winter increases of up to ~48% compared to mean flow from 1967–1990. Mean daily flows are estimated to decrease by 63–94% from August to November. These changes would have serious ecological consequences, since streams could regularly dry-up for short periods of time.

Description: The Chi-Chi Earthquake of September 1999 in Central Taiwan registered a moment magnitude MW of 7.6 on the Richter scale, causing widespread landslides. Subsequent typhoons associated with heavy rainfalls triggered the landslides. The study investigates multi-temporal landslide images from spatial analysis between 1996 and 2005 in the Chenyulan Watershed, Taiwan. Spatial patterns in various landslide frequencies were detected using landscapes metrics. The logistic regression results indicate that frequency of occurrence is an important factor in assessing landslide hazards. Low-occurrence landslides sprawl the catchment while the sustained (frequent) landslide areas cluster near the ridge as well as the stream course. From those results, we can infer that landslide area and mean size for each landslide correlates with the frequency of occurrence. Although negatively correlated with frequency in the low-occurrence landslide, the mean size of each landslide is positively related to frequency in the high-occurrence one. Moreover, this study determines the spatial susceptibilities in landslides by performing logistic regression analysis. Results of this study demonstrate that the factors such as elevation, slope, lithology, and vegetation cover are significant explanatory variables. In addition to the various frequencies, the relationships between driving factors and landslide susceptibility in the study area are quantified as well.

Description: Agricultural practices and water resources management in the central highlands of Ethiopia is highly dependant and associated with climatic resources and their pattern and hence wise use of those resources is a priority for the region. Accordingly, a study was conducted to asses and critically quantity the climate resources of the central high lands of Ethiop, Bishoftu district. Thirty three years of weather record data has been used for the work. The onset, duration and end of the growing seasons were defined and quantified based on FAO and Reddy models while the dry and wet spell distributions and the drought events were calculated using the Markov chain models and the standardized precipitation index (SPI) respectively. The results revealed that the mean onset of the main (Kiremt) growing season was found to occur during the second meteorological decade and ended during the end of September. Similarly, though unreliable and only few occurred during the entire study period, the mean onset of the shorter (Belg) season was found to occur during the beginning of the first decade of April. The length of the growing season during the main rainy season, (Kiremt,) ranged from 112 to 144 days with a standard deviation of 9.6 days and coefficient of variation of 7.5%. However, the mean growing length during the Belg season was found to be 22.4 days with a standard deviation of 27 days and coefficient of variation of 122%. The results of analysis obtained both from the Markov Chain and Reddy models indicated higher probabilities of dry spell occurrences during the shorter season (Belg) but the occurrences of the same in the main rainy season (Kiremt) was very minimal. Like wise, the SPI model detected some drought events ranging from mild to severe classes in both seasons based on one a month time scale analysis. A considerable attention of maximizing crop harvest during the main rainy season is practically important.

Description: Three statistical downscaling methods were applied to NCEP/NCAR reanalysis (used as a surrogate for the best possible general circulation model), and the downscaled meteorology was used to drive a hydrology model over California. The historic record was divided into an "observed" period of 1950–1976 to provide the basis for downscaling, and a "projected" period of 1977–1999 for assessing skill. The downscaling methods included a bias-correction/spatial downscaling method (BCSD), which relies solely on monthly large scale meteorology and resamples the historical record to obtain daily sequences, a constructed analogues approach (CA), which uses daily large-scale anomalies, and a hybrid method (BCCA) using a quantile-mapping bias correction on the large-scale data prior to the CA approach. At 11 sites we compared three simulated daily flow statistics: streamflow timing, 3-day peak flow, and 7-day low flow. While all downscaling methods produced reasonable streamflow statistics at most locations, the BCCA method consistently outperformed the other methods, capturing the daily large-scale skill and translating it to simulated streamflows that more skillfully reproduced observationally-driven streamflows.

Description: In this study, a new analytical solution for describing the tide-induced groundwater fluctuations in oceanic islands with finite length and different slopes of the beaches is developed. Unlike previous solutions, the present solution is not only applicable for a semi-infinite coastal aquifer, but also for an oceanic island with finite length and different sloping beaches. The solution can be used to investigate the effect of higher-order components and beach slopes on the water table fluctuations. The results demonstrate the effect of higher-order components increases with the shallow water parameter or amplitude parameter and the water table level increases as beach slopes decrease.

Description: Simulation of areal snowmelt and snow-cover depletion over time can be carried out by applying point-scale melt rate computations to distributions of snow water equivalent (SWE). In alpine basins, this can be done by considering these processes separately on individual slope units. However, differences in melt timing and rates arise at smaller spatial scales due to the variability in SWE and snowpack cold content, which affects the timing of melt initiation, depletion of the snow-cover and spatial extent of the snowmelt runoff contributing area (SRCA). This study examined the effects of variability in SWE, internal energy and applied melt energy on melt rates and timing, and snow-cover depletion in a small cold regions alpine basin over various scales ranging from point to basin. Melt rate computations were performed using a physically based energy balance snowmelt routine (Snobal) in the Cold Regions Hydrological Model (CRHM) and compared with measurements at three meteorological stations over a ridge within the basin. At the point scale, a negative association between daily melt rates and SWE was observed in the early melt period, with deeper snow requiring greater energy inputs to initiate melt. SWE distributions over the basin (stratified by slope) were measured using snow surveys and repeat LiDAR depth estimates, and used together with computed melt rates to simulate the areal snow-cover depletion. Comparison with observations from georeferenced oblique photographs showed an improvement in simulated areal snow-cover depletion curves when accounting for the variability in melt rate with depth of SWE in the early melt period. Finally, the SRCA was characterized as the product of the snow-covered area and the fraction of the SWE distribution undergoing active melt on each slope unit. Results for each slope were then aggregated to give the basin scale SRCA. The SRCA is controlled by the variability of melt amongst slope units and over individual SWE distributions, the variability of SWE and the resulting snow-cover depletion patterns over the basin.

Description: Drawing upon numerous field studies and modelling exercises of snow processes, the Cold Regions Hydrological Model (CRHM) was developed to simulate the four season hydrological cycle in cold regions. CRHM includes modules describing radiative, turbulent and conductive energy exchanges to snow in forest and open environments, as well as provide account for losses from canopy snow sublimation and rain evaporation. Due to the physical-basis and rigorous testing of each module, there is a minimal need for model calibration. To evaluate CRHM, simulations of snow accumulation and melt were compared to observations collected at paired forest and clearing sites of varying latitude, elevation, forest cover density, and climate. Overall, results show that CRHM is capable of characterising the variation of snow accumulation between forest and open sites, achieving a model efficiency of 0.57, with the lowest efficiencies at the forest sites. Simulations of canopy sublimation losses slightly overestimated observed losses from a weighed cut tree, giving a model efficiency of 0.41 for daily losses. Good model performance was demonstrated in simulating energy fluxes to snow at the clearings, but performance was degraded from this under forest canopies due to errors in simulating daily net longwave radiation. However, expressed as cumulative energy to snow over the winter, simulated values were 96% and 98% of that observed at forest and clearing sites, respectively. Overall, good model prediction of the substantial variations in mass and energy between forest and clearing sites suggests that CRHM may be useful as an analytical or predictive tool for snow processes in needleleaf forests.

Description: Soil moisture is a key parameter for land surface water resource and climate change monitoring. ESA's Soil Moisture and Ocean Salinity (SMOS) mission will deliver, as one of its main goals, global fields of surface soil moisture with an accuracy better than 0.04 m3 m−3. SMOS relies on an L-band (1.4 GHz) interferometric radiometer. Within the context of the preparation for this mission over land, the Valencia Anchor Station (VAS) experimental site, in Spain, was selected to be one of the main test sites in Europe for the SMOS Calibration/Validation (Cal/Val) activities. This study presents preliminary analysis of PERSIANN in the framework of SMOS Cal/Val activities at the Valencia Anchor Station. The PERSIANN database is an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks. The real interest of using the PERSIANN database into the hydrologic applications is mainly with the goal of having access to the spatial and temporal distribution of precipitation over a significant area (typically here an area equivalent to a SMOS pixel). The goal of this study is to quantify the gain of using PERSIANN instead of distributing sparse rain gauge measurements. The interest of using satellite rainfall estimates as well as the influence that the precipitation events can induce on the modelling of the water content in the soil was depicted by a comparison between different soil moisture products. Having an accurate estimation of the amount and temporal/spatial distribution of precipitation is a critical issue so as to have a faithful representation of the soil moisture distribution.

Description: This paper presents methods to extract badlands thalwegs network from regular grid DTM by combining terrain morphology indices to a drainage algorithm. The computation of a continuous vector network will permit the study of the badlands spatial patterns. These methods aim at delineating a thalweg only where the DTM denotes a significant curvature with respect to DTM accuracy. It relies on three major steps. Firstly, discontinuous concave areas are detected from the DTM using morphological criteria; the plan curvature and the convergence index. Secondly, the concave areas are connected using a drainage algorithm which provides continuous and tree-structured thick scheme. We assume that these areas present physical significance and correspond to a gully floor area. Finally, the thick path is reduced to its main curve and vectorised to obtain a thalwegs network. The methods are applied on both virtual and actual cases DTM. The actual case is a LiDAR DTM of Draix Badlands (French Alps). The obtained networks are quantitatively compared both to the one obtained with usual drainage area criteria and to a reference network. The networks comparison shows the great potential of the convergence index based method for thalwegs network extraction.

Description: The spatial and temporal variations of snowcover distribution, and snowmelt runoffs are considered as sensitive indicators for climatic change. The purpose of this paper is to analyze and forecast the responses of snowmelt runoff to climate change. The upstream of Heihe River Basin in Northwestern China was chose as the representative catchments, and the observation data of the meteorological and hydrological stations were utilized to analyze the status and the regularity for the climatic change from 1956 to 2008. Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to develop an optimized technology for snow mapping in the mountainous region. Snowmelt Runoff Model (SRM) was chose to simulate snowmelt runoff and scenario forecast the change trend of snowmelt runoff in catchment scale for the mountainous region in Northwestern China. The results show that climatic warming was apparent in the upstream of Heihe River Basin in the past 50a. Annual average air temperature of three different weather stations located in the basin has increased 2.1 °C, 2.6 °C and 2.9 °C, respectively. The snowmelt runoff has increased obviously from 1970 to present. With different warming climate scenarios, the results by SRM simulating showed that the first occurred time of snowmelt runoff shift ahead and discharge become larger as responses of snowmelt runoff to air temperature increasing, and the influence of temperature rising on average discharge of the whole snow season is not obvious. On the other hand, simulated discharge showed a marked increase trend with the increase of precipitation. And, the simulated results show that the increase of precipitation almost has no influence on the occurring time of snowmelt runoff.

Description: Discharge in rivers is commonly estimated by the use of a rating curve constructed from pairs of measured water elevations and discharges at a specific location. The Bayesian approach has been successfully applied to estimate discharge rating curves that are based on the standard power-law. In this paper the standard power-law model is extended by adding a B-spline function. The extended model is compared to the standard power-law model by applying the models to discharge data sets from sixty one different rivers. In addition four rivers are analyzed in detail to demonstrate the benefit of the extended model. The models are compared using two measures, the Deviance Information Criterion (DIC) and Bayes factor. The former provides robust comparison of fit adjusting for the different complexity of the models and the latter measures the evidence of one model against the other. The extended model captures deviations in the data from the standard power-law but reduces to the standard power-law when that model is adequate. The extended model provides substantially better fit than the standard power-law model for about 30% of the rivers and performs better for 60% of the rivers when extrapolating large discharge values.

Description: The process of transformation of rainfall into runoff over a catchment is very complex and exhibits both temporal and spatial variability. However, in a semi-arid area this variability is mainly controlled by the physical and chemical properties of the soil surface. Developing an accurate and easily-used model that can appropriately determine the runoff generation value is of strong demand. In this study a simple, an empirically based model developed to explore effect of soil properties on runoff generation. Thirty six dry-farming lands under follow conditions in a semi-arid agricultural zone in Hashtroud, NW Iran were considered to installation of runoff plots. Runoff volume was measured at down part of standard plots under natural rainfall events from March 2005 to March 2007. Results indicated that soils were mainly clay loam having 36.7% sand, 31.6% silt and 32.0% clay, and calcareous with about 13% lime. During a 2-year period, 41 natural rainfall events produced surface runoff at the plots. Runoff was negatively (R2=0.61, p

Description: Increasing water scarcity has posed a major constraint to sustain food production in many parts of the world. To study the situation at the regional level, we took Iran as an example and analyzed how an intra-country "virtual water trade strategy" (VWTS) may help improve cereal production as well as alleviate the water scarcity problem. This strategy calls, in part, for the adjustment of the structure of cropping pattern (ASCP) and interregional food trade where crop yield and crop water productivity as well as local economic and social conditions are taken into account. We constructed a systematic framework to assess ASCP at the provincial level under various driving forces and constraints. A mixed-integer, multi-objective, linear optimization model was developed and solved by linear programming. Data from 1990–2004 were used to account for yearly fluctuations of water availability and food production. Five scenarios were designed aimed at maximizing the national cereal production while meeting certain levels of wheat self-sufficiency under various water and land constraints in individual provinces. The results show that under the baseline scenario, which assumes a continuation of the existing water use and food policy at the national level, some ASCP scenarios could produce more wheat with less water. Based on different scenarios in ASCP, we calculated that 31% to 100% of the total wheat shortage in the deficit provinces could be supplied by the wheat surplus provinces. As a result, wheat deficit provinces would receive 3.5 billion m3 to 5.5 billion m3 of virtual water by importing wheat from surplus provinces.

Description: The aim of this study is to estimate the green, blue and grey water footprint of wheat in a spatially-explicit way, both from a production and consumption perspective. The assessment is global and improves upon earlier research by taking a high-resolution approach, estimating the water footprint of the crop at a 5 by 5 arc minute grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid cell. In addition, the water pollution associated with the use of nitrogen fertilizer in wheat production is estimated for each grid cell. We have used the water footprint and virtual water flow assessment framework as in the guideline of the Water Footprint Network. The global wheat production in the period 1996–2005 required about 1088 billion cubic meters of water per year. The major portion of this water (70%) comes from green water, about 19% comes from blue water, and the remaining 11% is grey water. The global average water footprint of wheat per ton of crop was 1830 m3/ton. About 18% of the water footprint related to the production of wheat is meant not for domestic consumption but for export. About 55% of the virtual water export comes from the USA, Canada and Australia alone. For the period 1996–2005, the global average water saving from international trade in wheat products was 65 Gm3/yr. A relatively large total blue water footprint as a result of wheat production is observed in the Ganges and Indus river basins, which are known for their water stress problems. The two basins alone account for about 47% of the blue water footprint related to global wheat production. About 93% of the water footprint of wheat consumption in Japan lies in other countries, particularly the USA, Australia and Canada. In Italy, with an average wheat consumption of 150 kg/yr per person, more than two times the word average, about 44% of the total water footprint related to this wheat consumption lies outside Italy. The major part of this external water footprint of Italy lies in France and the USA.

Description: Pakistan is highly dependent on water resources originating in the mountain sources of the upper Indus for irrigated agriculture which is the mainstay of its economy. Hence any change in available resources through climate change or socio-economic factors could have a serious impact on food security and the environment. In terms of both ratio of withdrawals to runoff and per-capita water availability, Pakistan's water resources are already highly stressed and will become increasingly so with projected population changes. Potential changes to supply through declining reservoir storage, the impact of waterlogging and salinity or over-abstraction of groundwater, or reallocations for environmental remediation of the Indus Delta or to meet domestic demands, will reduce water availability for irrigation. The impact of climate change on resources in the Upper Indus is considered in terms of three hydrological regimes – a nival regime dependent on melting of winter snow, a glacial regime, and a rainfall regime dependent on concurrent rainfall. On the basis of historic trends in climate, most notably the decline in summer temperatures, there is no strong evidence in favour of marked reductions in water resources from any of the three regimes. Evidence for changes in trans-Himalayan glacier mass balance is mixed. Sustainability of water resources appears more threatened by socio-economic changes than by climatic trends. Nevertheless, analysis and the understanding of the linkage of climate, glaciology and runoff is still far from complete; recent past climate experience may not provide a reliable guide to the future.

Description: The changing availability of freshwater resources is likely to be one of the most important consequences of projected 21st century climate change for both human and natural systems. However, substantial uncertainty remains regarding the precise impacts of climate change on water resources, due in part to uncertainty in GCM projections of climate change. Here we explore the potential impacts of climate change on water resources in a humid, tropical catchment (the River Mitano) in the Upper Nile Basin of Uganda. Uncertainty associated with GCM structure and climate sensitivity is explored, as well as from parameter specification within hydrological models. This is achieved by running pattern-scaled GCM output through a semi-distributed hydrological model (developed using SWAT) of the catchment. Importantly, use of pattern-scaled GCM output allows investigation of specific thresholds of global climate change including the purported 2 °C threshold of "dangerous" climate change. In-depth analysis of results based on HadCM3 climate scenarios shows that annual river discharge first increases, then declines with rising global mean air temperature. A coincidental shift from a bimodal to unimodal discharge regime also results from a projected reduction in baseflow (groundwater discharge). Both of these changes occur after a 4 °C rise in global mean air temperature. These results are, however, highly GCM dependent in both the magnitude and direction of change. This dependence stems primarily from projected differences in GCM scenario precipitation rather than temperature. GCM-related uncertainty is far greater than that associated with climate sensitivity or hydrological model parameterisation.

Description: Wetlands are undergoing considerable degradation in South Africa. As interventions are often technical and costly, there is a requirement to develop conceptual process models for these wetland systems so that rehabilitation attempts will be successful. This paper presents an approach using the geophysical methods of Electrical Resistivity Imaging (ERI) and Induced Polarization (IP) to delineate sub-surface hydro-geomorphic controls that maintain equilibrium disconnectivity of wetland-catchment processes, which through gully erosion are increasing the catchments connectivity through loss of water and sediment. The findings presented here give insight into the geomorphic processes that maintain the wetland in an un-degraded state, this allows for the development of a conceptual model outlining the wetland forming processes. The analysis suggests that sub-surface clay-plugs, within an otherwise sandy substrate are created by illuviation of clays from the surrounding hillslopes particularly at zones of valley confinement.

Description: The Oligocene-Miocene turbidite sandstones of fore-deep in the Northern Apennines form a very great aquifer that originally, before the reduction by Plio-Pleistocene erosion, is extended over an area of 60 000 sq/km (minimum) to 1.5 km–4.5 km tickness. The spatial relationships between the open fractures and mineralized fracture (veins) in the outcrops of foredeep's sandstones (Macigno) along the Tuscany coast, between Leghorn and Piombino (Northern Apennines), are analyzed and discussed. Also is discussed a conceptual model that allows a virtual surface of separation between an upper zone in open fractures and a fracture in the lower zone mineralization. The position of this surface than the topography surface, depends on the difference between the velocity of erosion and the velocity development of open fractures by reduction of the lithostatic load, during the exhumation of the system. The lack of the open fractured zone, below this surface suggests that the deep water circulation into the Macigno sandstones along the coast area, depends exclusively on the connection between the major faults and the primary discontinuity (stratification). Based on the results of fracturing analysis of the coastal Macigno the authors aim to extend the research to internal areas, and in particular to the ridge of the Northern Apennines, where the foredeep's sandstones are well developed and continued.

Description: Stormwater detention tanks are widely used for mitigating impacts of combined sewer overflows (CSO) from urban catchments into receiving water bodies. The optimal size of detention tanks depends on climate and sewer system behaviours and can be estimated by using derived distribution approaches. They are based on using a stochastic model to fit the statistical pattern of observed rainfall records and a urban hydrology model to transform rainfall in sewer discharge. A key issue is the identification of the optimal structure of the stochastic rainfall model. Point processes are frequently applied where rainfall events are schematised through the occurrence of rectangular pulses, which are governed by rainfall descriptors. In the model herein used these latter descriptors are the interevent time (duration of the dry period between consecutive storms), event rainfall depth and event rainfall duration. This paper focuses on the analytical derivation of the probability distribution of the number and volume of overflows from the storm tank to the receiving water body for different and non-standard shapes of the probability distribution for above mentioned descriptors. The proposed approach is applied to 2 different sites in Spain: Valencia and Santander located on the Mediterranean and northern Atlantic coastline, respectively. For both cases, it turned out that Pareto and Gamma-2 probability distributions for rainfall depth and duration provided better fit than the exponential model, widely used in previous studies. A comparison between the two climatic zones, humid and semiarid, respectively, proves the key role played by climatic conditions for storm detention tanks sizing.

Description: With the onset of new satellite radar constellations (e.g. Sentinel-1) and advances in computational science (e.g. grid computing) enabling the supply and processing of multi-mission satellite data at a temporal frequency that is compatible with real-time flood forecasting requirements, this study presents a new concept for the sequential assimilation of Synthetic Aperture Radar (SAR)-derived water stages into coupled hydrologic-hydraulic models. The proposed methodology consists of adjusting storages and fluxes simulated by a coupled hydrologic-hydraulic model using a Particle Filter-based data assimilation scheme. Synthetic observations of water levels, representing satellite measurements, are assimilated into the coupled model in order to investigate the performance of the proposed assimilation scheme as a function of both accuracy and frequency of water level observations. The use of the Particle Filter provides flexibility regarding the form of the probability densities of both model simulations and remote sensing observations. We illustrate the potential of the proposed methodology using a twin experiment over a widely studied river reach located in the Grand-Duchy of Luxembourg. The study demonstrates that the Particle Filter algorithm leads to significant uncertainty reduction of water level and discharge at the time step of assimilation. However, updating the storages of the model only improves the model forecast over a very short time horizon. A more effective way of updating thus consists in adjusting both states and inputs. The proposed methodology, which consists in updating the biased forcing of the hydrodynamic model using information on model errors that is inferred from satellite observations, enables persistent model improvement. The present schedule of satellite radar missions is such that it is likely that there will be continuity for SAR-based operational water management services. This research contributes to evolve reactive flood management into systematic or quasi-systematic SAR-based flood monitoring services.

Description: Interception losses are a major influence in the water yield of vegetated areas. For most storms, interception results in less water reaching the ground. However, fog drip or occult precipitation can result in negative interception because small drops are deposited on all plant surfaces and subsequently fall to the ground once vegetation storage capacities are exceeded. Fog drip is normally disregarded, but for some plant communities, it could be a mechanism offsetting evaporation losses. Tillandsia recurvata is a cosmopolitan epiphyte adapted to arid habitats where fog may be an important water source. Therefore, the interception storage capacity by T. recurvata was measured in controlled conditions through applying simulated rain or fog. The storage capacity was proportional to dry weight mass. Nocturnal stomatic opening in T. recurvata is not only relevant for CO2 but for water vapor, as suggested by the higher weight change of specimens wetted with fog for 1 h at dark in comparison to those wetted during daylight (543±77 vs. 325±56 mg, p=0.048). The coefficients obtained in the laboratory were used together with biomass measurements for T. recurvata in a xeric scrub to calculate the depth of water intercepted. Interception storage capacity (Cmin) was 0.19 and 0.54 mm for rainfall and fog respectively. T. recurvata contributed 20% to the rain interception of their shrub hosts: Acacia farnesiana and Prosopis laevigata. Meteorological data registered during one year at Cadereyta, México showed that radiative fog formation was possible during the dry season. The results showed the potential role of T. recurvata in capturing fog, which probably is a main source of water during the dry season that supports their reproductive and physiological activity at that time. The storage capacity of T. recurvata leaf surfaces could increase the amount of water available for evaporation, but as this species colonise montane forests, the effect could be negative on water recharge, because in the laboratory experiments it took up to 12 h to reach saturation conditions when fog was applied.

Description: In this study we propose a multi-source data approach for quantifying long-term flooding and aquifer recharge in ungauged ephemeral rivers. The methodology is applied to the Buffels River, at 9000 km2 the largest ephemeral river in Namaqualand (NW South Africa), a region with scarce stream flow records limiting research investigating hydrological response to global change. Daily discharge and annual flood series (1965–2006) were estimated from a distributed rainfall-runoff hydrological model (TETIS) using rainfall gauge records located within the catchment. The model was calibrated and validated with data collected during a two year monitoring programme (2005–2006) at two stream flow stations, one each in the upper and lower reaches of the catchment. In addition to the modelled flow records, non-systematic flood data were reconstructed using both sedimentary and documentary evidence. The palaeoflood record identified at least 25 large floods during the last 700 yr; with the largest events reaching a minimum discharge of 255 m3 s−1 (450 yr return period) in the upper basin, and 510 m3 s−1 (100 yr return period) in the lower catchment. Since 1925 AD, the flood hydrology of the Buffels River has been characterised by a decrease in the magnitude and frequency of extreme events, with palaeoflood discharges five times greater than the largest modelled floods during the period 1965–2006. Large floods generated the highest hydrograph volumes, however their contribution to aquifer recharge is limited as this depends on other factors such as flood duration and storage capacity of the unsaturated zone prior to the flood. Floods reaching flows associated with 5–10 yr return periods (120–140 m3 s−1) and flowing for 12 days are able to fully saturate the Spektakel aquifer in the lower Buffels River basin.

Description: Agricultural activity is the dominant factor affecting water quality and nitrate export, which causes eutrophication and episodic acidification in downstream water bodies (e.g., reservoirs, lakes, and coastal zones). However, in subtropical mountainous areas such environmental impact due to the land use change was rarely documented. In this study, we investigated 16 sub-catchments during 2007 and 2008 in the Chi-Chia-Wan catchment where is the sole habitat for the endemic species, Formosan landlocked salmon (Oncorhynchus masou formosanus). The results revealed that the NO3-N concentration in pristine catchments varied from 0.144 to 0.151 mg/L without significant seasonal variation. This concentration was comparable with other forestry catchments around the world. However, the annual nitrate export was around 375.3–677.1 kg/km2/yr, much higher than other catchments due to the greater amount of rainfall. This is an important baseline for comparisons with other climate areas. As for the impact of agricultural activities, the catchments with some human disturbance, ~5.2% of the catchment area, might yield 5947.2 kg N/km2/yr – over 10-times higher than that of pristine catchment. Such high export caused by such a low level of disturbance might indicate that subtropical mountainous area is highly sensitive to agricultural activities. As for the land-use effect on nitrate yield, the forestry land might yield 488.5 ± 325.1 kg/km2/yr and the vegetable farm could yield 298 465.4 ± 3347.2 kg/km2/yr – 1000-times greater than the forestry. The estimated nitrate yields for land use classes were a crucial basis and useful for the land manager to assess the possible impacts (e.g., non-point source pollution evaluation and the recovery of land expropriation).

Description: The Gironde estuary, one of the largest European ones, presents temporary low dissolved oxygen content in its fluvial section close to the Bordeaux urban area. In a context of population growth and of long-term environmental changes, the development of a high-frequency monitoring programme of the fluvial-estuarine system of the Gironde, called MAGEST (MArel Gironde ESTuary), had appeared essential to address current and future water-quality issues/evaluations. The objectives of the MAGEST survey programme are to establish a reference database to improve the knowledge of the Gironde estuary functioning, encompassing the aspects of hydrology, sediment dynamics and biogeochemistry. Through examples of results from intratidal to seasonal time scales, we demonstrate how such a time-series is of valuable interest to extract the main trends of its functioning and of the water quality in relation to external forcings (climatology, urban wastes, land use, ...) and to predict the future evolution of the Gironde estuary with global and environmental changes.

Description: This article describes the measurement setup that is used for green roof experiments in a tropical environment, the required data treatment to obtain reliable values of rainfall, runoff and evapotranspiration, and how to deal with external disturbances that can influence the experiment results. High resolution rainfall runoff measurements to identify, understand and properly model the relevant runoff processes in a green roof require both tailored equipment and data treatment. A tipping bucket rain gauge is calibrated for and installed to measure minute based rain intensities. A runoff measuring setup is developed that can accurately quantify the runoff up to 6 l/min, and has a high resolution in both time and volume. Two different measuring setups are used to verify the evapotranspiration that is derived from the rainfall and runoff measurements.

Description: The work aims at discussing the role of predictive uncertainty in flood forecasting and flood emergency management, its relevance to improve the decision making process and the techniques to be used for its assessment. Real time flood forecasting requires taking into account predictive uncertainty for a number of reasons. Deterministic hydrological/hydraulic forecasts give useful information about real future events, but their predictions, as usually done in practice, cannot be taken and used as real future occurrences but rather used as pseudo-measurements of future occurrences in order to reduce the uncertainty of decision makers. Predictive uncertainty (PU) is in fact defined as the probability of occurrence of a future value of a predictand (such as water level, discharge or water volume) conditional upon prior observations and knowledge as well as on all the information we can obtain on that specific future value from model forecasts. When dealing with commensurable quantities, as in the case of floods, PU must be quantified in terms of a probability distribution function which will be used by the emergency managers in their decision process in order to improve the quality and reliability of their decisions. After introducing the concept of PU, the presently available processors are introduced and discussed in terms of their benefits and limitations. In this work the Model Conditional Processor has been extended to the possibility of using a joint truncated normal distribution, in order of improving adaptation to low and high flows. The paper concludes by showing the results of the application of the MCP on the Baron Fork River, OK, USA. The data set provided by the NOAA's National Weather Service, within the DMIP 2 Project, allowed two physically based models, the TOPKAPI model and TETIS model, to be calibrated and a data driven model to be implemented using the Artificial Neural Network. The three model forecasts have been combined with the aim of reducing the PU and improving the probabilistic forecast taking advantage of the different capabilities of each model approach.

Description: The groundwater fluctuations affected by tidal variations at an observation well in a coastal aquifer can be used to determine the tidal characteristics and aquifer parameters without conducting an aquifer test. In this study, a method, comprised of Jeng et al.'s solution (2005) and simulated annealing (SA) algorithm, is developed to determine the coastal aquifer parameters (namely, hydraulic diffusivity, beach slope, and aquifer thickness) as well as the tidal characteristics (namely, bichromatic-tide amplitudes, bichromatic-tide wave frequencies, and tidal phase lag) from the analysis of the tide-induced well-water-level (WWL) data. Two data sets, i.e., synthetic WWL data generated from Jeng et al.'s solution (2005) with assumed parameter values and field data obtained from Barrenjoey beach in Australia, are analyzed. The estimated parameter values obtained from analyzing synthetic WWL data by the present method show good agreements with the previously assumed parameter values. The parameter estimation procedure may however fail in the case of a large shallow-water parameter which in fact violates the constraint on the use of Jeng et al.'s solution (2005). In the analysis of field WWL data, the results indicate that the estimated aquifer parameters from the present method are significantly different from those given in Nielsen (1990). Inspecting the observed WWL data and the WWL data predicted from Jeng et al.'s solution (2005) reveals that the present method may provide better estimations for the aquifer parameters than those given in Nielsen (1990).

Description: The availability of data is a major challenge for hydrological modelling in large parts of the world. Remote sensing data can be exploited to improve models of ungauged or poorly gauged catchments. In this study we combine three datasets for calibration and validation of a rainfall-runoff model of the ungauged Okavango catchment in Southern Africa: (i) Surface soil moisture (SSM) estimates derived from SAR measurements onboard the Envisat satellite; (ii) Radar altimetry measurements by Envisat providing river stages in the tributaries of the Okavango catchment, down to a minimum width of about one hundred meters; and (iii) Temporal changes of the Earth's gravity field recorded by the Gravity Recovery and Climate Experiment (GRACE) caused by total water storage changes in the catchment. The SSM data are compared to simulated moisture conditions in the top soil layer. They cannot be used for model calibration but support bias identification in the precipitation data. The accuracy of the radar altimetry data is validated on gauged subbasins of the catchment and altimetry data of an ungauged subbasin is used for model calibration. The radar altimetry data are important to condition model parameters related to channel morphology such as Manning's roughness. GRACE data are used to validate the model and to condition model parameters related to various storage compartments in the hydrological model (e.g. soil, groundwater, bank storage etc.). As precipitation input the FEWS-Net RFE, TRMM 3B42 and ECMWF ERA-Interim data sets are considered and compared.

Description: The present study describes the assimilation of river water level observations and the resulting improvement of the river flood forecast. The BLUE algorithm was built on top of the one-dimensional hydraulics model MASCARET. The assimilation algorithm folds in two steps: the first one is based on the assumption that the upstream flow can be adjusted using a three-parameter correction, the second one consists in directly correcting the hydraulic state. This procedure is applied on a four-day sliding window over the whole flood event. The background error covariances for water level and discharge are represented with asymmetric correlation functions where the upstream correlation length is bigger than the downstream correlation length. This approach is motivated by the implementation of a Kalman Filter algorithm on top of an advection-diffusion toy model. The assimilation study with MASCARET is carried out on the Adour and the Marne Vallage (France) catchments. The correction of the upstream flow as well as the control of the hydraulic state along the flood event leads to a significant improvement of the water level and discharge in analysis and forecast modes.

Description: Vegetation indices (VIs) have been traditionally used for quantitative monitoring of vegetation. Remotely sensed radiometric measurements of visible and infrared solar energy, which is reflected or emitted by plant canopies, can be used to obtain rapid, non-destructive estimates of certain canopy attributes and parameters. One parameter of special interest for water management applications, is the crop coefficient employed by the FAO-56 model to derive actual crop evapotranspiration (ET). The aim of this study was to evaluate a methodology that combines the basal crop coefficient derived from VIs with a daily soil water balance in the root zone to estimate daily evapotranspiration rates for corn and wheat crops at field scale. The ability of the model to trace water stress in these crops was also assessed. Vegetation indices were first retrieved from field hand-held radiometer measurements and then from Landsat 5 and 7 satellite images. The results of the model were validated using two independent measurement systems for ET and regular soil moisture monitoring, in order to evaluate the behavior of the soil and atmosphere components of the model. ET estimates were compared with latent heat flux measured by an eddy covariance system and with weighing lysimeter measurements. Average overestimates of daily ET of 8 and 11% were obtained for corn and wheat, respectively, with good agreement between the estimated and measured root-zone water deficit for both crops when field radiometry was employed. Satellite remote-sensing inputs overestimated ET by 4 to 9%, showing a non-significant lost of accuracy when the satellite sensor data replaced the field radiometry data. The model was also used to monitor the water stress during the 2009 growing season, detecting several periods of water stress in both crops. Some of these stresses occurred during stages like grain filling, when the water stress is know to have a negative effect on yield. This fact could explain the lower yield reached compared to local yield statistics for wheat and corn. The results showed that the model can be used to calculate the water requirements of these crops in irrigated areas and that its ability to monitor water stress deserves further research.

Description: An assessment of regional similarity in catchment stream response is often needed for accurate predictions in ungauged catchments. However, it is not clear whether similarity among catchments is preserved at all flow conditions. We address this question through the analysis of flow duration curves for 25 gauged catchments located across four river basins in the northeast United States. The coefficient of variation of streamflow percentiles is used as a measure of variability among catchments across flow conditions. Results show that similarity in catchment stream response is dynamic and highly dependent on flow conditions. Specifically, within each of the four basins, the coefficient of variation is high at low flow percentiles and gradually reduces for higher flow percentiles. Analysis of the inter-annual variation in streamflow percentiles shows a similar reduction in variability from low flow to high flow percentiles. Greater similarity in streamflows is observed during the winter and spring (wet) seasons compared to the summer and fall (dry) seasons. Results suggest that the spatial variability in streamflow at low flows is primarily controlled by the dominance of high evaporative demand during the warm period. On the other hand, spatial variability at high flows during the cold period is controlled mostly by the increased dominance of precipitation input over evapotranspiration. By evaluating variability over the entire range of streamflow percentiles, this work explores the nature of hydrologic similarity from an inter-seasonal perspective.

Description: The hydrologic cycle of high mountainous catchments is frequently simulated with simple precipitation-discharge models representing the snow accumulation and ablation behavior of a very complex environment with a set of lumped equations accounting for altitudinal temperature and precipitation gradients. In this study, we present a methodology to include sparse snow depths measurements into the calibration process. Based on this methodology, we assess for a case study, the Rhonegletscher catchment (Switzerland), how much observed information we need to reliably calibrate the model, such that it reproduces the dominant system dynamics, discharge, as well as glacier mass balance. Here, we focus on the question whether observed discharge is sufficient as a calibration variable or whether we need annual or even seasonal glacier mass balance data. Introducing seasonally variable accumulation and ablation parameters is sufficient to enable the simple model to reproduce observed seasonal mass balances for the Rhonegletscher. Furthermore, our results suggest that calibrating the hydrological model exclusively on discharge can lead to wrong representations of the intra-annual accumulation and ablation processes and to a strong bias in long term glacier mass balance simulations. Adding only a few annual mass balance observations considerably reduces this bias. Calibrating exclusively on annual balance data can, in turn, lead to wrong seasonal mass balance simulations. Even if these results are case study specific, our conclusions provide valuable new insights into the benefit of different types of observations for calibrating hydrological models in glacier catchments. The presented multi-signal calibration framework and the simple method to calibrate a semi-lumped model on point observations has potential for application in other modeling contexts.

Description: Observing and monitoring the different components of the global water cycle and their dynamics are essential steps to understand the climate of the Earth, forecast the weather, predict natural disasters like floods and droughts, and improve water resources management. Earth observation technology is a unique tool to provide a global understanding of many of the essential variables governing the water cycle and monitor their evolution over time from global to basin scales. In the coming years an increasing number of Earth observation missions will provide an unprecedented capacity to quantify several of these variables on a routine basis. In this context, the European Space Agency (ESA), in collaboration with the Global Energy and Water Cycle Experiment (GEWEX) of the World Climate Research Program (WCRP), launched the Water Cycle Multi-Mission Observation Strategy (WACMOS) project in 2009. The project aims at developing and validating a novel set of geo-information products relevant to the water cycle covering the following thematic areas: evapotranspiration, soil moisture, cloud characterization and water vapour. The generation of these products is based on a number of innovative techniques and methods aiming at exploiting the synergies of different types of Earth observation data available today to the science community. This paper provides an overview of the major findings of the project with the ultimate goal of demonstrating the potential of innovative multi-mission based strategies to improve current observations by maximizing the synergistic use of the different types of information provided by the currently available observation systems.

Description: Artificial Neural Networks (ANN) have been found efficient, particularly in problems where characteristics of the processes are stochastic and difficult to describe using explicit mathematical models. However, time series prediction based on ANN algorithms is fundamentally difficult and faces problems. One of the major shortcomings is the over-fitting while training session and occurs when a ANN loses its generalization. In this research, Generalized Neural Network (GNN) model is developed to overcome the drawbacks of conventional forecasting techniques. Using GNN helped avoid over-fitting of training data which was observed as a limitation of classical ANN models. Real inflow data collected over the last 130 years at Lake Nasser was used to train, test and validate the proposed model. Results show that the proposed GNN model outperforms Non-Generalized Neural Network and conventional auto-regressive models and it could provide accurate inflow forecasting.

Description: Groundwater contribution to river flows, generally called base flows, often accounts for a significant proportion of total flow rate, especially during the dry season. The objective of this work is to test simple approaches requiring limited data to understand groundwater contribution to river flows. The Noire river basin in southern Quebec is used as a case study. A lumped conceptual hydrological model (the MOHYSE model), a groundwater flow model (MODFLOW) and hydrograph separation are used to provide estimates of base flow for the study area. Results show that the methods are complementary. Hydrograph separation and the MOHYSE surface flow model provide similar annual estimates for the groundwater contribution to river flow, but monthly base flows can vary significantly between the two methods. Both methods have the advantage of being easily implemented. However, the distinction between aquifer contribution and shallow subsurface contribution to base flow can only be made with a groundwater flow model. The aquifer renewal rate estimated with the MODFLOW model for the Noire River is 30% of the recharge estimated from base flow values. This is a significantly difference which can be crucial for regional-scale water management.

Description: The objective of this study was to evaluate the accuracy of high resolution satellite-based rainfall estimates (SREs) across six river basins within Ethiopia during the major (Kiremt) and minor (Belg) rainy seasons for the years 2003 to 2007. The six regions, the Awash, Baro Akobo, Blue Nile, Genale Dawa, Rift Valley and Wabi Shebele River Basins surround the Ethiopian Highlands, which produces different topographical features, as well as spatial and temporal rainfall patterns. Precipitation estimates for the six regions were taken from three widely used high resolution SREs: the Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Neural Networks (PERSIANN) and the real-time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT. All three SREs show the natural northwest-southeast precipitation gradient, but exhibit different spatial (mean annual total and number of rainy days) and temporal (monthly) totals. When compared to ground based rain gauges throughout the six regions, and for the years of interest, the performance of the three SREs were found to be season independent. The results varied for lower elevations, with CMORPH and TMPA 3B42RT performing better than PERSIANN in the southeast, while PERSIANN provided more accurate results in the northwest. At higher elevations, PERSIANN consistently underestimated while the performance of CMORPH and TMPA 3B42RT varied.

Description: In arid contexts, water rises from the saturated level of a shallow aquifer to the drying soil surface where evaporation takes place. This process plays important roles in terms of plant survival, salt balance and aquifer budget. A new field quantification method of this capillary rise flow is proposed using micro-injections (6 μL) of deuterium-enriched solution (δ value of 63 000‰ vs. V-SMOW) into unsatured soil at 1 m depth. Evaluation of peak displacement from a profile sampling 35 days later, delivered estimates that were compared with outputs of numerical simulation based on laboratory hydrodynamic measurements. A rate of 3.7 cm y−1 was observed in a Moroccan site where the aquifer level was 2.44 m deep. This value was higher, than other estimates based on natural diffusion with the same depth of aquifer, but lower than the estimates established using integration of van Genutchen closed-form functions for soil hydraulic conductivity and retention curve.

Description: Rain can be measured and represented in many ways such as point data from rain gauges, grid data from meteorological radar, or interpolated data. In this paper we represent rain fields by implementing a rain cell model of convective rain cells. The rain fields are used as an input to a hydrological model to test the watershed response to spatial and temporal characteristics of the rain cells. As a case study we tested an extreme storm event over a semi-arid watershed in southern Israel. The rain cell model was found to simulate the rain storm adequately. The use of these modeled cells allowed us to test the sensitivity of the watershed hydrological response to rain cell characteristics and it was found that the watershed is mainly sensitive to the starting location of the rain cell. Relatively small changes in the rain cell's location, speed and direction may increase watershed peak discharge by three-fold.

Description: This work aims to develop a semi-deterministic multiplicative cascade method for producing reliable short-term (sub-daily) rainfall sequences. The scaling feature of sub-daily rainfall sequences is analysed over the timescales of interest (i.e., 5 min to hourly in this research) to help derive the crucial parameters, i.e., the fragmentation ratios, for the proposed method. These derived ratios are then further used to stochastically disaggregate hourly rainfall sequences to 5-min using the multiplicative cascade process. The log-Poisson distributed cascade method is involved in this work to validate the proposed methodology by comparing certain statistics of the generated rainfall sequences over a specific range of timescales. The results demonstrate that the proposed methodology in general has the ability to reproduce the patterns of sub-daily rainfall observations from Greenwich raingauge station in London.

Description: Stemflow of xerophytic shrubs represents a significant component of water replenishment to the soil-root systems influences water utilization of plant roots at the stand scale, especially for water scarce desert ecosystems. In this study, stemflow of Caragana korshinskii was quantified by aluminum foil collar collection method at re-vegetated sand dunes of the Shapotou restored desert ecosystem in Northwestern China. Meanwhile, time domain reflectometry probes were inserted horizontally at 20 different soil profile depths under the C. korshinskii shrub to monitor soil moisture variation at hourly intervals. Results indicated that 2.2 mm precipitation were necessary for the generation of stemflow for C. korshinskii. Stemflow averaged 8% of the gross precipitation, and the average funneling ratio was as high as 90. The soil moisture in the uppermost soil profile was strongly correlated with individual rainfall and the stemflow strengthened this relationship. Therefore, it is favorable for infiltrated water redistribution in the deeper soil profile of the root zone. We conclude that stemflow contributes significantly to a positive soil moisture balance in the root zone and the replenishment of soil moisture at deeper soil layers. This plays an important role in plant survival and the general ecology of arid desert environments.