| publications-1591 |
PEER REVIEWED ARTICLE |
2016 |
Jaap Schellekens , Emanuel Dutra , Alberto MartÃnez-de la Torre , Gianpaolo Balsamo , Albert van Dijk , Frederiek Sperna Weiland , Marie Minvielle , |
A global water resources ensemble of hydrological models: theeartH2Observe Tier-1 dataset |
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10.5194/essd-2016-55 |
Data Management & Analytics |
Natural Water Bodies |
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Abstract. The dataset presented here consists of an ensemble of ten global hydrological and land surface models for the period 1979–2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state-of-the-art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominate regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not being reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3/yr (334 kg/m2/yr) while the ensemble mean of total evaporation was 537 kg/m2/yr. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu), and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The doi for the data is: doi:10.5281/zenodo.167070 |
603608 |
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| publications-1592 |
PEER REVIEWED ARTICLE |
2016 |
Simon Zwieback , Chun-Hsu Su , Alexander Gruber , Wouter A. Dorigo , Wolfgang Wagner |
The Impact of Quadratic Nonlinear Relations between Soil Moisture Products on Uncertainty Estimates from Triple Collocation Analysis and Two Quadratic Extensions |
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10.1175/jhm-d-15-0213.1 |
Data Management & Analytics |
Natural Water Bodies |
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Abstract The error characterization of soil moisture products, for example, obtained from microwave remote sensing data, is a key requirement for using these products in applications like numerical weather prediction. The error variance and root-mean-square error are among the most popular metrics: they can be estimated consistently for three datasets using triple collocation (TC) without assuming any dataset to be free of errors. This technique can account for additive and multiplicative biases; that is, it assumes that the three products are linearly related. However, its susceptibility to nonlinear relations (e.g., due to sensor saturation and scale mismatch) has not been addressed. Here, a simulation study investigates the impact of quadratic relations on the TC error estimates [also when the products are first rescaled using the nonlinear cumulative distribution function (CDF) matching technique] and on those by two novel methods. These methods—based on error-in-variables regression and probabilistic factor analysis—extend standard TC by also accounting for nonlinear relations using quadratic polynomials. The relative differences between the error estimates of the ASCAT remotely sensed product by the quadratic and the linear methods are predominantly smaller than 10% in a case study based on remotely sensed, reanalysis, and in situ measured soil moisture over the contiguous United States. Exceptions with larger discrepancies indicate that nonlinear relations can pose a challenge to traditional TC analyses, as the simulations show they can introduce biases of either sign. In such cases, the use of nonlinear methods may complement traditional approaches for the error characterization of soil moisture products. |
603608 |
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| publications-1593 |
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 |
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10.5194/hess-19-4081-2015 |
Data Management & Analytics |
Natural Water Bodies |
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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 |
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| publications-1594 |
PEER REVIEWED ARTICLE |
2016 |
T I E Veldkamp , Y Wada , J C J H Aerts , P J Ward |
Towards a global water scarcity risk assessment framework: incorporation of probability distributions and hydro-climatic variability |
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10.1088/1748-9326/11/2/024006 |
Data Management & Analytics |
Natural Water Bodies |
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No abstract available |
603608 |
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| publications-1595 |
PEER REVIEWED ARTICLE |
2017 |
T.I.E. Veldkamp , Y. Wada , J.C.J.H. Aerts , P. Döll , S. N. Gosling , J. Liu , Y. Masaki , T. Oki , S. Ostberg , Y. Pokhrel , Y. Satoh , H. Kim , P. |
Water scarcity hotspots travel downstream due to human interventions in the 20th and 21st century |
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10.1038/ncomms15697 |
Data Management & Analytics |
Natural Water Bodies |
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AbstractWater scarcity is rapidly increasing in many regions. In a novel, multi-model assessment, we examine how human interventions (HI: land use and land cover change, man-made reservoirs and human water use) affected monthly river water availability and water scarcity over the period 1971–2010. Here we show that HI drastically change the critical dimensions of water scarcity, aggravating water scarcity for 8.8% (7.4–16.5%) of the global population but alleviating it for another 8.3% (6.4–15.8%). Positive impacts of HI mostly occur upstream, whereas HI aggravate water scarcity downstream; HI cause water scarcity to travel downstream. Attribution of water scarcity changes to HI components is complex and varies among the hydrological models. Seasonal variation in impacts and dominant HI components is also substantial. A thorough consideration of the spatially and temporally varying interactions among HI components and of uncertainties is therefore crucial for the success of water scarcity adaptation by HI. |
603608 |
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| publications-1596 |
PEER REVIEWED ARTICLE |
2016 |
M. Kummu , J. H. A. Guillaume , H. de Moel , S. Eisner , M. Flörke , M. Porkka , S. Siebert , T. I. E. Veldkamp , P. J. Ward |
The world’s road to water scarcity: shortage and stress in the 20th century and pathways towards sustainability |
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10.1038/srep38495 |
Data Management & Analytics |
Natural Water Bodies |
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AbstractWater scarcity is a rapidly growing concern around the globe, but little is known about how it has developed over time. This study provides a first assessment of continuous sub-national trajectories of blue water consumption, renewable freshwater availability, and water scarcity for the entire 20th century. Water scarcity is analysed using the fundamental concepts of shortage (impacts due to low availability per capita) and stress (impacts due to high consumption relative to availability) which indicate difficulties in satisfying the needs of a population and overuse of resources respectively. While water consumption increased fourfold within the study period, the population under water scarcity increased from 0.24 billion (14% of global population) in the 1900s to 3.8 billion (58%) in the 2000s. Nearly all sub-national trajectories show an increasing trend in water scarcity. The concept of scarcity trajectory archetypes and shapes is introduced to characterize the historical development of water scarcity and suggest measures for alleviating water scarcity and increasing sustainability. Linking the scarcity trajectories to other datasets may help further deepen understanding of how trajectories relate to historical and future drivers, and hence help tackle these evolving challenges. |
603608 |
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| publications-1597 |
PEER REVIEWED ARTICLE |
2016 |
A.I. Gevaert , R.M. Parinussa , L.J. Renzullo , A.I.J.M. van Dijk , R.A.M. de Jeu |
Spatio-temporal evaluation of resolution enhancement for passive microwave soil moisture and vegetation optical depth |
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10.1016/j.jag.2015.08.006 |
Data Management & Analytics |
Natural Water Bodies |
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No abstract available |
603608 |
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| publications-1598 |
PEER REVIEWED ARTICLE |
2017 |
Ruud J. van der Ent , Obbe A. Tuinenburg |
The residence time of water in the atmosphere revisited |
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10.5194/hess-21-779-2017 |
Data Management & Analytics |
Natural Water Bodies |
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Abstract. This paper revisits the knowledge on the residence time of water in the atmosphere. Based on state-of-the-art data of the hydrological cycle we derive a global average residence time of 8.9 ± 0.4 days (uncertainty given as 1 standard deviation). We use two different atmospheric moisture tracking models (WAM-2layers and 3D-T) to obtain atmospheric residence time characteristics in time and space. The tracking models estimate the global average residence time to be around 8.5 days based on ERA-Interim data. We conclude that the statement of a recent study that the global average residence time of water in the atmosphere is 4–5 days, is not correct. We derive spatial maps of residence time, attributed to evaporation and precipitation, and age of atmospheric water, showing that there are different ways of looking at temporal characteristics of atmospheric water. Longer evaporation residence times often indicate larger distances towards areas of high precipitation. From our analysis we find that the residence time over the ocean is about 2 days less than over land. It can be seen that in winter, the age of atmospheric moisture tends to be much lower than in summer. In the Northern Hemisphere, due to the contrast in ocean-to-land temperature and associated evaporation rates, the age of atmospheric moisture increases following atmospheric moisture flow inland in winter, and decreases in summer. Looking at the probability density functions of atmospheric residence time for precipitation and evaporation, we find long-tailed distributions with the median around 5 days. Overall, our research confirms the 8–10-day traditional estimate for the global mean residence time of atmospheric water, and our research contributes to a more complete view of the characteristics of the turnover of water in the atmosphere in time and space. |
603608 |
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| publications-1599 |
PEER REVIEWED ARTICLE |
2017 |
Patricia López López , Edwin H. Sutanudjaja , Jaap Schellekens , Geert Sterk , Marc F. P. Bierkens |
Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products |
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10.5194/hess-21-3125-2017 |
AI & Machine Learning |
Water Distribution Networks |
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Abstract. A considerable number of river basins around the world lack sufficient ground observations of hydro-meteorological data for effective water resources assessment and management. Several approaches can be developed to increase the quality and availability of data in these poorly gauged or ungauged river basins; among them, the use of Earth observations products has recently become promising. Earth observations of various environmental variables can be used potentially to increase knowledge about the hydrological processes in the basin and to improve streamflow model estimates, via assimilation or calibration. The present study aims to calibrate the large-scale hydrological model PCRaster GLOBal Water Balance (PCR-GLOBWB) using satellite-based products of evapotranspiration and soil moisture for the Moroccan Oum er Rbia River basin. Daily simulations at a spatial resolution of 5  ×  5 arcmin are performed with varying parameters values for the 32-year period 1979–2010. Five different calibration scenarios are inter-compared: (i) reference scenario using the hydrological model with the standard parameterization, (ii) calibration using in situ-observed discharge time series, (iii) calibration using the Global Land Evaporation Amsterdam Model (GLEAM) actual evapotranspiration time series, (iv) calibration using ESA Climate Change Initiative (CCI) surface soil moisture time series and (v) step-wise calibration using GLEAM actual evapotranspiration and ESA CCI surface soil moisture time series. The impact on discharge estimates of precipitation in comparison with model parameters calibration is investigated using three global precipitation products, including ERA-Interim (EI), WATCH Forcing methodology applied to ERA-Interim reanalysis data (WFDEI) and Multi-Source Weighted-Ensemble Precipitation data by merging gauge, satellite and reanalysis data (MSWEP). Results show that GLEAM evapotranspiration and ESA CCI soil moisture may be used for model calibration resulting in reasonable discharge estimates (NSE values from 0.5 to 0.75), although better model performance is achieved when the model is calibrated with in situ streamflow observations. Independent calibration based on only evapotranspiration or soil moisture observations improves model predictions to a lesser extent. Precipitation input affects discharge estimates more than calibrating model parameters. The use of WFDEI precipitation leads to the lowest model performances. Apart from the in situ discharge calibration scenario, the highest discharge improvement is obtained when EI and MSWEP precipitation products are used in combination with a step-wise calibration approach based on evapotranspiration and soil moisture observations. This study opens up the possibility of using globally available Earth observations and reanalysis products of precipitation, evapotranspiration and soil moisture in large-scale hydrological models to estimate discharge at a river basin scale. |
603608 |
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| publications-1600 |
PEER REVIEWED ARTICLE |
2017 |
Jaap Schellekens , Emanuel Dutra , Alberto MartÃnez-de la Torre , Gianpaolo Balsamo , Albert van Dijk , Frederiek Sperna Weiland , Marie Minvielle , |
A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset |
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10.5194/essd-9-389-2017 |
AI & Machine Learning |
Water Distribution Networks |
|
Abstract. The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979–2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominated regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3 yr−1 (334 kg m−2 yr−1), while the ensemble mean of total evaporation was 537 kg m−2 yr−1. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu), and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The DOI for the data is https://doi.org/10.1016/10.5281/zenodo.167070. |
603608 |
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