ALBERT

Description: Time variable gravity fields, reflecting variations of mass distribution in the system Earth is one of the key parameters to understand the changing Earth. Mass variations are caused either by redistribution of mass in, on or above the Earth's surface or by geophysical processes in the Earth's interior. The first set of observations of monthly variations of the Earth gravity field was provided by the US/German GRACE satellite mission beginning in 2002. This mission is still providing valuable information to the science community. However, as GRACE has outlived its expected lifetime, the geoscience community is currently seeking successor missions in order to maintain the long time series of climate change that was begun by GRACE. Several studies on science requirements and technical feasibility have been conducted in the recent years. These studies required a realistic model of the time variable gravity field in order to perform simulation studies on sensitivity of satellites and their instrumentation. This was the primary reason for the European Space Agency (ESA) to initiate a study on ''Monitoring and Modelling individual Sources of Mass Distribution and Transport in the Earth System by Means of Satellites''. The goal of this interdisciplinary study was to create as realistic as possible simulated time variable gravity fields based on coupled geophysical models, which could be used in the simulation processes in a controlled environment. For this purpose global atmosphere, ocean, continental hydrology and ice models were used. The coupling was performed by using consistent forcing throughout the models and by including water flow between the different domains of the Earth system. In addition gravity field changes due to solid Earth processes like continuous glacial isostatic adjustment (GIA) and a sudden earthquake with co-seismic and post-seismic signals were modelled. All individual model results were combined and converted to gravity field spherical harmonic series, which is the quantity commonly used to describe the Earth's global gravity field. The result of this study is a twelve-year time-series of 6-hourly time variable gravity field spherical harmonics up to degree and order 180 corresponding to a global spatial resolution of 1 degree in latitude and longitude. In this paper, we outline the input data sets and the process of combining these data sets into a coherent model of temporal gravity field changes. The resulting time series was used in some follow-on studies and is available to anybody interested.

Description: Data from General Circulation Models (GCMs) are often used to investigate hydrological impacts of climate change. However GCM data are known to have large biases, especially for precipitation. In this study the usefulness of GCM data for hydrological studies, with focus on discharge variability and extremes, was tested by using bias-corrected daily climate data of the 20CM3 control experiment from a selection of twelve GCMs as input to the global hydrological model PCR-GLOBWB. Results of these runs were compared with discharge observations of the GRDC and discharges calculated from model runs based on two meteorological datasets constructed from the observation-based CRU TS2.1 and ERA-40 reanalysis. In the first dataset the CRU TS 2.1 monthly timeseries were downscaled to daily timeseries using the ERA-40 dataset (ERA6190). This dataset served as a best guess of the past climate and was used to analyze the performance of PCR-GLOBWB. The second dataset was created from the ERA-40 timeseries bias-corrected with the CRU TS 2.1 dataset using the same bias-correction method as applied to the GCM datasets (ERACLM). Through this dataset the influence of the bias-correction method was quantified. The bias-correction was limited to monthly mean values of precipitation, potential evaporation and temperature, as our focus was on the reproduction of inter- and intra-annual variability. After bias-correction the spread in discharge results of the GCM based runs decreased and results were similar to results of the ERA-40 based runs, especially for rivers with a strong seasonal pattern. Overall the bias-correction method resulted in a slight reduction of global runoff and the method performed less well in arid and mountainous regions. However, deviations between GCM results and GRDC statistics did decrease for Q, Q90 and IAV. After bias-correction consistency amongst models was high for mean discharge and timing (Qpeak), but relatively low for inter-annual variability (IAV). This suggests that GCMs can be of use in global hydrological impact studies in which persistence is of less relevance (e.g. in case of flood rather than drought studies). Furthermore, the bias-correction influences mean discharges more than extremes, which has the positive consequence that changes in daily rainfall distribution and subsequent changes in discharge extremes will also be preserved when the bias-correction method is applied to future GCM datasets. However, it also shows that agreement between GCMs remains relatively small for discharge extremes. Because of the large deviations between observed and simulated discharge, in which both errors in climate forcing, model structure and to a lesser extent observations are accumulated, it is advisable not to work with absolute discharge values for the derivation of future discharge projections, but rather calculate relative changes by dividing the absolute change by the absolute discharge calculated for the control experiment.

Description: During the past decades, human water use has more than doubled, yet available freshwater resources are finite. As a result, water scarcity has been prevalent in various regions of the world. Here, we present the first global assessment of past development of water stress considering not only climate variability but also growing water demand, desalinated water use and non-renewable groundwater abstraction over the period 1960–2001 at a spatial resolution of 0.5°. Agricultural water demand is estimated based on past extents of irrigated areas and livestock densities. We approximate past economic development based on GDP, energy and household consumption and electricity production, which are subsequently used together with population numbers to estimate industrial and domestic water demand. Climate variability is expressed by simulated blue water availability defined by freshwater in rivers, lakes, wetlands and reservoirs by means of the global hydrological model PCR-GLOBWB. We thus define blue water stress by comparing blue water availability with corresponding net total blue water demand by means of the commonly used, Water Scarcity Index. The results show a drastic increase in the global population living under water-stressed conditions (i.e. moderate to high water stress) due to growing water demand, primarily for irrigation, which has more than doubled from 1708/818 to 3708/1832 km3 yr−1 (gross/net) over the period 1960–2000. We estimate that 800 million people or 27% of the global population were living under water-stressed conditions for 1960. This number is eventually increased to 2.6 billion or 43% for 2000. Our results indicate that increased water demand is a decisive factor for heightened water stress in various regions such as India and North China, enhancing the intensity of water stress up to 200%, while climate variability is often a main determinant of extreme events. However, our results also suggest that in several emerging and developing economies (e.g. India, Turkey, Romania and Cuba) some of past extreme events were anthropogenically driven due to increased water demand rather than being climate-induced.

Description: As an initial step in assessing the prospect of using global hydrological models (GHMs) for hydrological forecasting, this study investigates the skill of the GHM PCR-GLOBWB in reproducing the occurrence of past extremes in monthly discharge on a global scale. Global terrestrial hydrology from 1958 until 2001 is simulated by forcing PCR-GLOBWB with daily meteorological data obtained by downscaling the CRU dataset to daily fields using the ERA-40 reanalysis. Simulated discharge values are compared with observed monthly streamflow records for a selection of 20 large river basins that represent all continents and a wide range of climatic zones. We assess model skill in three ways all of which contribute different information on the potential forecasting skill of a GHM. First, the general skill of the model in reproducing hydrographs is evaluated. Second, model skill in reproducing significantly higher and lower flows than the monthly normals is assessed in terms of skill scores used for forecasts of categorical events. Third, model skill in reproducing flood and drought events is assessed by constructing binary contingency tables for floods and droughts for each basin. The skill is then compared to that of a simple estimation of discharge from the water balance (P−E). The results show that the model has skill in all three types of assessments. After bias correction the model skill in simulating hydrographs is improved considerably. For most basins it is higher than that of the climatology. The skill is highest in reproducing monthly anomalies. The model also has skill in reproducing floods and droughts, with a markedly higher skill in floods. The model skill far exceeds that of the water balance estimate. We conclude that the prospect for using PCR-GLOBWB for monthly and seasonal forecasting of the occurrence of hydrological extremes is positive. We argue that this conclusion applies equally to other similar GHMs and LSMs, which may show sufficient skill to forecast the occurrence of monthly flow extremes.

Description: The current generation of large-scale hydrological models does not include a groundwater flow component. Large-scale groundwater models, involving aquifers and basins of multiple countries, are still rare mainly due to a lack of hydro-geological data which are usually only available in developed countries. In this study, we propose a novel approach to construct large-scale groundwater models by using global datasets that are readily available. As the test-bed, we use the combined Rhine-Meuse basin that contains groundwater head data used to verify the model output. We start by building a distributed land surface model (30 arc-second resolution) to estimate groundwater recharge and river discharge. Subsequently, a MODFLOW transient groundwater model is built and forced by the recharge and surface water levels calculated by the land surface model. Results are promising despite the fact that we still use an offline procedure to couple the land surface and MODFLOW groundwater models (i.e. the simulations of both models are separately performed). The simulated river discharges compare well to the observations. Moreover, based on our sensitivity analysis, in which we run several groundwater model scenarios with various hydro-geological parameter settings, we observe that the model can reasonably well reproduce the observed groundwater head time series. However, we note that there are still some limitations in the current approach, specifically because the offline-coupling technique simplifies the dynamic feedbacks between surface water levels and groundwater heads, and between soil moisture states and groundwater heads. Also the current sensitivity analysis ignores the uncertainty of the land surface model output. Despite these limitations, we argue that the results of the current model show a promise for large-scale groundwater modeling practices, including for data-poor environments and at the global scale.

Description: Hydrological processes control the behaviour of many unstable slopes, and their importance for landslide activity is generally accepted. The presence of fissures influences the storage capacity of a soil and affects the infiltration processes of rainfall. The effectiveness of the fissure network depends upon fissure size, their spatial distribution, and connectivity. Moreover, fissure connectivity is a dynamic characteristic, depending on the degree of saturation of the medium. This research aims to investigate the influence of the fissure network on hydrological responses of a landslide. Special attention is given to spatial and temporal variations in fissure connectivity, which makes fissures act both as preferential flow paths for deep infiltration (disconnected fissures) and as lateral groundwater drains (connected fissures). To this end, the hydrological processes that control the exchange of water between the fissure network and the matrix have been included in a spatially distributed hydrological and slope stability model. The ensuing feedbacks in landslide hydrology were explored by running the model with one year of meteorological forcing. The effect of dynamic fissure connectivity was evaluated by comparing simulations with static fissure patterns to simulations in which these patterns change as a function of soil saturation. The results highlight that fissure connectivity and fissure permeability control the water distribution within landslides. Making the fissure connectivity function of soil moisture results in composite behaviour spanning the above end members and introduces stronger seasonality of the hydrological responses.