ALBERT

Description: Saturated hydraulic conductivity (K), drainable porosity (f), and effective aquifer thickness (D) are essential hydrogeological parameters for hydrologic modelling and predicting. Streamflow recession analysis using analytical solutions of Boussinesq equation can yield estimated values for two of these three hydrogeological parameters when one is known a priori. In this study, we improved the inverse method for parameters estimation by combining the modified Kozeny–Carman equation with analytical solutions of Boussinesq equation to express the three hydrogeological parameters (K, f, and D) in relation to catchment characteristics and recession constants in a sloping aquifer. Here, the three parameters can be estimated simultaneously from streamflow recession analysis. Results of the estimated parameters are compared with the field measurements and the soil-texture based estimations in four small experimental catchments. It shows that our estimated values of these catchment-scale parameters can represent equivalent values in the measured aquifer profiles/sites. In hilly areas, the slope aquifer takes a vital effect on the estimates of K and f. Neglecting the sloping effect can lead to overestimation of K and underestimation of f in 1 ~ 2 orders of magnitude in the study catchments. However, even in the hilly catchments, the estimated aquifer thickness D is much greater than that from measurements on hillslopes while it approaches riparian thickness, indicating that the riparian zone takes a vital role on flow recession and the parameter estimations.

Description: Global hydroclimatic conditions have been significantly altered over the past century by anthropogenic influences that arise from the warming global climate and also from local/regional anthropogenic disturbances. Traditionally, studies have used coupling of multiple models to understand how land-surface fluxes vary due to changes in global climatic patterns and local land-use changes. We argue that Budyko's framework that relies on the supply and demand concept could be effectively adapted and extended to quantify the role of drivers – both changing climate and local human disturbances – in altering the land-surface response across the globe. We review the Budyko framework along with potential extensions with an intent to further the applicability of the framework to emerging hydrologic questions. Challenges in extending the Budyko framework over various spatio-temporal scales and evaluating the water balance at these various scales with global data sets are also discussed.

Description: Recent virtual and experimental investigations have shown that the young water fraction Fyw (i.e. the proportion of catchment outflow younger than circa 2–3 months) increases with discharge in most catchments. The discharge sensitivity of Fyw has been defined as the rate of increase in Fyw with increasing discharge (Q), and has been estimated by the linear regression slope between Fyw and Q, hereafter called DS(Q). The combined use of both metrics, Fyw and DS(Q), provides a promising method for catchment inter-comparison studies that seek to understand streamflow generation processes. Here we explore the discharge sensitivity of Fyw in the intensively sampled small Mediterranean research catchment Can Vila. Intensive sampling of high flows at Can Vila allows young water fractions to be estimated for the far upper tail of the flow frequency distribution. These young water fractions converge toward 1 at the highest flows, illustrating a conceptual limitation in the linear regression method for estimating DS(Q) as a metric of discharge sensitivity: Fyw cannot grow with discharge indefinitely, since the fraction of young water in discharge can never be larger than 1. Here we propose to quantify discharge sensitivity by the parameter of an exponential-type equation expressing how Fyw varies with discharge. The exponential parameter (Sd) approximates DS(Q) at moderate discharges where Fyw is well below 1; however, the exponential equation and its discharge sensitivity metric better capture the non-linear relationship between Fyw and Q and are robust with respect to changes in the range of sampled discharges, allowing comparisons between catchments with strongly contrasting flow regimes.

Description: Transit time distributions (TTDs) integrate information on timing, amount, storage, mixing and flow paths of water and thus characterize hydrologic and hydrochemical catchment response unlike any other descriptor. Here, we simulate the shape of TTDs in an idealized low-order catchment investigating whether it changes systematically with certain catchment and climate properties. To this end, we used a physically-based, spatially-explicit 3-D model, injected tracer with a precipitation event and recorded the resulting TTDs at the outlet of a small (~ 6000 m2) catchment for different scenarios. We found that the TTDs can be subdivided into four parts: 1) early part – controlled by soil hydraulic conductivity and antecedent soil moisture content, 2) middle part – transition zone with no clear pattern or control, 3) later part – influenced by soil hydraulic conductivity and subsequent precipitation amount and 4) very late tail of the breakthrough curve – governed by bedrock hydraulic conductivity. The modeled TTD shapes can be predicted using a dimensionless number: higher initial peaks are observed if the inflow of water to a catchment is not equal to its capacity to discharge water via subsurface flow paths, lower initial peaks are connected to increasing available storage. In most cases the modeled TTDs were humped with non-zero initial values and varying weights of the tails. Therefore, none of the best-fit theoretical probability functions could exactly describe the entire TTD shape. Still, we found that generally the Gamma and the Advection-Dispersion distribution work better for scenarios of low and high hydraulic conductivity, respectively.

Description: To ensure reliable model understanding of water movement and distribution in terrestrial systems, sufficient and good quality hydro-meteorological data are required. Limited availability of ground measurements in the vast majority of river basins world-wide increase the value of alternative data sources such as satellite observations in modelling. In the absence of directly observed river discharge data, other variables such as remotely sensed river water level may provide valuable information for the calibration and evaluation of hydrological models. This study investigates the potential of the use of remotely sensed river water level, i.e. altimetry observations, from multiple satellite missions to identify parameter sets for a hydrological model in the semi-arid Luangwa River Basin in Zambia. A distributed process-based rainfall runoff model with sub-grid process heterogeneity was developed and run on a daily timescale for the time period 2002 to 2016. Following a step-wise approach, various parameter identification strategies were tested to evaluate the potential of satellite altimetry data for model calibration. As a benchmark, feasible model parameter sets were identified using traditional model calibration with observed river discharge data. For the parameter identification using remote sensing, data from the Gravity Recovery and Climate Experiment (GRACE) were used in a first step to restrict the feasible parameter sets based on the seasonal fluctuations in total water storage. In a next step, three alternative ways of further restricting feasible model parameter sets based on satellite altimetry time-series from 18 different locations, i.e. virtual stations, along the Luangwa River and its tributaries were compared. In the calibrated benchmark case, daily river flows were reproduced relatively well with an optimum Nash-Sutcliffe efficiency of ENS,Q = 0.78 (5/95th percentiles of all feasible solutions ENS,Q,5/95 = 0.61 – 0.75). When using only GRACE observations to restrict the parameter space, assuming no discharge observations are available, an optimum of ENS,Q = −1.4 (ENS,Q,5/95 = −2.3 – 0.38) with respect to discharge was obtained. Depending on the parameter selection strategy, it could be shown that altimetry data can contain sufficient information to efficiently further constrain the feasible parameter space. The direct use of altimetry based river levels frequently over-estimated the flows and poorly identified feasible parameter sets due to the non-linear relationship between river water level and river discharge (ENS,Q,5/95 = −2.9 – 0.10); therefore, this strategy was of limited use to identify feasible model parameter sets. Similarly, converting modelled discharge into water levels using rating curves in the form of power relationships with two additional free calibration parameters per virtual station resulted in an over-estimation of the discharge and poorly identified feasible parameter sets (ENS,Q,5/95 = −2.6 – 0.25). However, accounting for river geometry proved to be highly effective; this included using river cross-section and gradient information extracted from global high-resolution terrain data available on Google Earth, and applying the Strickler-Manning equation with effective roughness as free calibration parameter to convert modelled discharge into water levels. Many parameter sets identified with this method reproduced the hydrograph and multiple other signatures of discharge reasonably well with an optimum of ENS,Q = 0.60 (ENS,Q,5/95 = −0.31 – 0.50). It was further shown that more accurate river cross-section data improved the water level simulations, modelled rating curve and discharge simulations during intermediate and low flows at the basin outlet at which detailed on-site cross-section information was available. For this case, the Nash-Sutcliffe efficiency with respect to river water levels increased from ENS,SM,GE = −1.8 (ENS,SM,GE,5/95 = −6.8 – −3.1) using river geometry information extracted from Google Earth to ENS,SM,ADCP = 0.79 (ENS,SM,ADCP,5/95 = 0.6 – 0.74) using river geometry information obtained from a detailed survey in the field. It could also be shown that increasing the number of virtual stations used for parameter selection in the calibration period can considerably improve the model performance in spatial split sample validation. The results provide robust evidence that in the absence of directly observed discharge data for larger rivers in data scarce regions, altimetry data from multiple virtual stations combined with GRACE observations have the potential to fill this gap when combined with readily available estimates of river geometry, thereby allowing a step towards more reliable hydrological modelling in poorly or ungauged basins.

Description: Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems and the population’s traditional livelihoods. In the Lena River catchment, Eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We focus on the Lena River catchment, where significant environmental changes are already apparent. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scale. The method yields dissimilarities or emergence levels ranging from 0 to 100 % and the direction of change as continuous time series itself. For the Lena River catchment, on average, emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by 2040. For precipitation the analysis is less conclusive because of high uncertainties in existing reanalysis datasets that also impede an evaluation of the climate models. Model projections suggest hardly any emergence by 2000 but a strong emergence thereafter, reaching 60 % by the end of the investigated period (2089). The presented ToE method provides more versatility than traditional parametric approaches and allows for a detailed temporal analysis of climate signal evolutions. An original strategy to select the most realistic model simulations based on the available observational data significantly reduces the uncertainties resulting from the spread in the 65 climate models used. The method comes as a toolbox available at https://github.com/pohleric/toe_tools.

Description: Despite being the main drinking water resource for over five million people, the water balance of the Eastern Mountain Aquifer system on the western side of the Dead Sea is poorly understood. The regional aquifer consists of fractured and karstified limestone – aquifers of Cretaceous age and can be separated in Cenomanian aquifer (upper aquifer) and Albian aquifer (lower aquifer). Both aquifers are exposed along the mountain ridge around Jerusalem, which is the main recharge area. From here, the recharged groundwater flows in a highly karstified aquifer system towards the east, to discharge in springs in the Lower Jordan Valley and Dead Sea region. We investigated the Eastern Mountain Aquifer system on groundwater flow, groundwater age and potential mixtures, and groundwater recharge. We combined 36Cl/Cl, tritium and the anthropogenic gases SF6, CFC-12 and CFC-11, CFC-113 as dating tracers to estimate the young water components inside the Eastern Mountain Aquifer system. By application of lumped parameter models, we verified young groundwater components from the last 10 to 30 years and an admixture of a groundwater component older than about 70 years. Concentrations of nitrate, Simazine® (Pesticide), Acesulfame K® (artificial sweetener) and Naproxen® (drug) in the groundwater were further indications of infiltration during the last 30 years. The combination of multiple environmental tracers and lumped parameter modelling helped to understand the groundwater age distribution and to estimate recharge despite scarce data in this very complex hydrogeological setting. Our groundwater recharge rates support groundwater management of this politically difficult area and can be used to inform and calibrate ongoing groundwater flow models.

Description: Surface electrical resistivity tomography (ERT) is a widely used tool to study seawater intrusion (SWI). It is noninvasive and offers a high spatial coverage at a low cost, but it is strongly affected by decreasing resolution with depth. We conjecture that the use of CHERT (cross-hole ERT) can partly overcome these resolution limitations since the electrodes are placed at depth, which implies that the model resolution does not decrease in the zone of interest. The objective of this study is to evaluate the CHERT for imaging the SWI and monitoring its dynamics at the Argentona site, a well-instrumented field site of a coastal alluvial aquifer located 40 km NE of Barcelona. To do so, we installed permanent electrodes around boreholes attached to the PVC pipes to perform time-lapse monitoring of the SWI on a transect perpendicular to the coastline. After two years of monitoring, we observe variability of SWI at different time scales: (1) natural seasonal variations and aquifer salinization that we attribute to long-term drought and (2) short-term fluctuations due to sea storms or flooding in the nearby stream during heavy rain events. The spatial imaging of bulk electrical conductivity allows us to explain non-trivial salinity profiles in open boreholes (step-wise profiles really reflect the presence of fresh water at depth). By comparing CHERT results with traditional in situ measurements such as electrical conductivity of water samples and bulk electrical conductivity from induction logs, we conclude that CHERT is a reliable and cost-effective imaging tool for monitoring SWI dynamics.

Description: Floods continue to devastate societies and their economies. Resilient societies commonly incorporate flood forecasting into their strategy to mitigate the impact of floods. Hydrological models which simulate the rainfall-runoff process are at the core of flood forecasts. To date operational flood forecasting models use areal rainfall estimates that are based on geographical features. This paper introduces a new methodology to optimally blend the weighting of gauges for the purpose of obtaining superior flood forecasts. For a selection of 7 Australian catchments this methodology was able to yield improvements of 15.3 % and 7.1 % in optimization and evaluation periods respectively. Catchments with a low gauge density, or an overwhelming majority of gauges with a low proportion of observations available, are not well suited to this new methodology. Models which close the water balance and demonstrate internal model dynamics that are consistent with a conceptual understanding of the rainfall-runoff process yielded consistent improvement in streamflow simulation skill.

Description: Fully-coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations and derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the ten sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlation at neighbouring grid cells to one another. The global Moran's I indicates that at the global scale anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells, while the local Moran's I reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate center exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least one of the ten sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.