Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID â–Č Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-1511 PEER REVIEWED ARTICLE 2015 W.A. Dorigo , A. Gruber , R.A.M. De Jeu , W. Wagner , T. Stacke , A. Loew , C. Albergel , L. Brocca , D. Chung , R.M. Parinussa , R. Kidd Evaluation of the ESA CCI soil moisture product using ground-based observations 10.1016/j.rse.2014.07.023 Simulation & Modeling Precipitation & Ecological Systems No abstract available 603608
publications-1512 PEER REVIEWED ARTICLE 2016 Patricia LĂłpez LĂłpez , Niko Wanders , Jaap Schellekens , Luigi J. Renzullo , Edwin H. Sutanudjaja , Marc F. P. Bierkens Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations 10.5194/hess-20-3059-2016 Data Management & Analytics Precipitation & Ecological Systems Abstract. The coarse spatial resolution of global hydrological models (typically  >  0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (downscaled from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). Downscaled satellite-derived soil moisture (downscaled from  ∌  0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse-resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently made to move to global hyper-resolution modelling and can help to advance this research. 603608
publications-1513 PEER REVIEWED ARTICLE 2016 R.van der Schalie , Y.H. Kerr , J.P. Wigneron , N.J. RodrĂ­guez-FernĂĄndez , A. Al-Yaari , R.A.M.de Jeu Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model 10.1016/j.jag.2015.08.005 Data Management & Analytics Precipitation & Ecological Systems No abstract available 603608
publications-1514 PEER REVIEWED ARTICLE 2015 T. I. E. Veldkamp , S. Eisner , Y. Wada , J. C. J. H. Aerts , P. J. Ward Sensitivity of water scarcity events to ENSO-driven climate variability at the global scale 10.5194/hess-19-4081-2015 Simulation & Modeling Precipitation & Ecological Systems Abstract. Globally, freshwater shortage is one of the most dangerous risks for society. Changing hydro-climatic and socioeconomic conditions have aggravated water scarcity over the past decades. A wide range of studies show that water scarcity will intensify in the future, as a result of both increased consumptive water use and, in some regions, climate change. Although it is well-known that El Niño–Southern Oscillation (ENSO) affects patterns of precipitation and drought at global and regional scales, little attention has yet been paid to the impacts of climate variability on water scarcity conditions, despite its importance for adaptation planning. Therefore, we present the first global-scale sensitivity assessment of water scarcity to ENSO, the most dominant signal of climate variability. We show that over the time period 1961–2010, both water availability and water scarcity conditions are significantly correlated with ENSO-driven climate variability over a large proportion of the global land area (> 28.1 %); an area inhabited by more than 31.4 % of the global population. We also found, however, that climate variability alone is often not enough to trigger the actual incidence of water scarcity events. The sensitivity of a region to water scarcity events, expressed in terms of land area or population exposed, is determined by both hydro-climatic and socioeconomic conditions. Currently, the population actually impacted by water scarcity events consists of 39.6 % (CTA: consumption-to-availability ratio) and 41.1 % (WCI: water crowding index) of the global population, whilst only 11.4 % (CTA) and 15.9 % (WCI) of the global population is at the same time living in areas sensitive to ENSO-driven climate variability. These results are contrasted, however, by differences in growth rates found under changing socioeconomic conditions, which are relatively high in regions exposed to water scarcity events. Given the correlations found between ENSO and water availability and scarcity conditions, and the relative developments of water scarcity impacts under changing socioeconomic conditions, we suggest that there is potential for ENSO-based adaptation and risk reduction that could be facilitated by more research on this emerging topic. 603608
publications-1515 PEER REVIEWED ARTICLE 2016 E. Cattani , A. Merino , V. Levizzani Evaluation of Monthly Satellite-Derived Precipitation Products over East Africa 10.1175/jhm-d-15-0042.1 Simulation & Modeling Precipitation & Ecological Systems Abstract East Africa experienced in the 2001–11 time period some of the worst drought events to date, culminating in the high-impact drought of 2010/11. Long-term monitoring of precipitation is thus essential, and satellite-based precipitation products can help in coping with the relatively sparse rain gauge ground networks of this area of the world. However, the complex topography and the marked geographic variability of precipitation in the region make precipitation retrieval from satellites problematic and product validation and intercomparison necessary. Six state-of-the-art monthly satellite precipitation products over East Africa during the 2001–09 time frame are evaluated. Eight areas (clusters) are identified by investigating the precipitation seasonality through the Global Precipitation Climatology Centre (GPCC) climatological gauge data. Seasonality was fully reproduced by satellite data in each of the GPCC-identified clusters. Not surprisingly, complex terrain (mountain regions in particular) represents a challenge for satellite precipitation estimates, as demonstrated by the standard deviations of the six-product ensemble. A further confirmation comes from the comparison between satellite estimates and rain gauge measurements as a function of terrain elevation. The 3B42 product performs best, although the satellite–gauge comparative analysis was not completely independent since a few of the products include a rain gauge bias correction. 603608
publications-1516 PEER REVIEWED ARTICLE 2016 Jimmy de Fouw , Laura L. Govers , Johan van de Koppel , Jim van Belzen , Wouter Dorigo , Mohammed A. Sidi Cheikh , Marjolijn J.A. Christianen Drought, Mutualism Breakdown, and Landscape-Scale Degradation of Seagrass Beds 10.1016/j.cub.2016.02.023 Simulation & Modeling Precipitation & Ecological Systems No abstract available 603608
publications-1517 PEER REVIEWED ARTICLE 2016 Richard de Jeu , Wouter Dorigo On the importance of satellite observed soil moisture 10.1016/j.jag.2015.10.007 Simulation & Modeling Uncategorized No abstract available 603608
publications-1518 PEER REVIEWED ARTICLE 2016 Wouter Dorigo , Richard de Jeu Satellite soil moisture for advancing our understanding of earth system processes and climate change 10.1016/j.jag.2016.02.007 Simulation & Modeling Precipitation & Ecological Systems No abstract available 603608
publications-1519 PEER REVIEWED ARTICLE 2016 A. Gruber , C.-H. Su , W. T. Crow , S. Zwieback , W. A. Dorigo , W. Wagner Estimating error cross-correlations in soil moisture data sets using extended collocation analysis 10.1002/2015jd024027 Simulation & Modeling Precipitation & Ecological Systems AbstractGlobal soil moisture records are essential for studying the role of hydrologic processes within the larger earth system. Various studies have shown the benefit of assimilating satellite‐based soil moisture data into water balance models or merging multisource soil moisture retrievals into a unified data set. However, this requires an appropriate parameterization of the error structures of the underlying data sets. While triple collocation (TC) analysis has been widely recognized as a powerful tool for estimating random error variances of coarse‐resolution soil moisture data sets, the estimation of error cross covariances remains an unresolved challenge. Here we propose a method—referred to as extended collocation (EC) analysis—for estimating error cross‐correlations by generalizing the TC method to an arbitrary number of data sets and relaxing the therein made assumption of zero error cross‐correlation for certain data set combinations. A synthetic experiment shows that EC analysis is able to reliably recover true error cross‐correlation levels. Applied to real soil moisture retrievals from Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) C‐band and X‐band observations together with advanced scatterometer (ASCAT) retrievals, modeled data from Global Land Data Assimilation System (GLDAS)‐Noah and in situ measurements drawn from the International Soil Moisture Network, EC yields reasonable and strong nonzero error cross‐correlations between the two AMSR‐E products. Against expectation, nonzero error cross‐correlations are also found between ASCAT and AMSR‐E. We conclude that the proposed EC method represents an important step toward a fully parameterized error covariance matrix for coarse‐resolution soil moisture data sets, which is vital for any rigorous data assimilation framework or data merging scheme. 603608
publications-1520 PEER REVIEWED ARTICLE 2016 A. Gruber , C.-H. Su , S. Zwieback , W. Crow , W. Dorigo , W. Wagner Recent advances in (soil moisture) triple collocation analysis 10.1016/j.jag.2015.09.002 Data Management & Analytics River Basins No abstract available 603608