A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

Schellekens, Jaap; Dutra, Emanuel; Torre, Alberto Martínez-de la; Balsamo, Gianpaolo; Dijk, Albert I. J. M. van; Weiland, Frederiek Sperna; Minvielle, Marie; Calvet, Jean‐Christophe; Decharme, Bertrand; Eisner, Stephanie; Fink, Gabriel; Flörke, Martina; Peßenteiner, Stefanie; Beek, Rens van; Polcher, Jan; Beck, Hylke E.; Orth, René; Calton, Ben; Burke, Sophia; Dorigo, Wouter; Weedon, Graham P.

doi:10.5194/essd-9-389-2017

Cited by 210 publications

(197 citation statements)

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“…4). While the mean seasonal variations of both observational data streams are relatively robust and have been used for model evaluation before (Alkama et al, 2010;Schellekens et al, 2017;Zhang et al, 2017), their inter-annual variations are more uncertain and contain considerable noise. This clearly reduces the information content in the observational data, so that we evaluate the IAV in more qualitative terms.…”

Section: Performance Of the Spatially Integrated Simulationsmentioning

confidence: 99%

“…In order to benchmark our model against current stateof-the-art hydrological models, we compared its simulations with the multi-model ensemble of the global hydrological and land surface models of the eartH2Observe dataset (Schellekens et al, 2017). This ensemble includes HTESSEL-CaMa (Balsamo et al, 2009), JULES Clark et al, 2011), LISFLOOD (van der Knijff et al, 2010), ORCHIDEE (Krinner et al, 2005;Ngo-Duc et al, 2007;d'Orgeval et al, 2008), SURFEX-TRIP (Alkama et al, 2010;Decharme et al, 2013), W3RA (van Dijk andWarren, 2010;van Dijk et al, 2014), WaterGAP3 (Flörke et al, 2013;Döll et al, 2009), PCR-GLOBWB (van Beek et al, 2011Wada et al, 2014), and SWBM (Orth et al, 2013).…”

Section: Evaluation Of Model Performancementioning

confidence: 99%

“…This is the case for snow-dominated regions as the northern high latitudes (Schellekens et al, 2017), which are among the areas most prone to the impacts of climate change (Tallaksen et al, 2015). These regions have been experiencing the strongest surface warming over the last century globally (IPCC, 2014), a trend which is expected to be exacerbated in the future and to significantly change hydrological patterns (AMAP, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…To facilitate insight into the underlying processes, hydrological models are frequently used to separate the measured TWS into its different components such as groundwater, soil moisture, and snowpacks (Felfelani et al, 2017). However, as a consequence of uncertain model structure, forcing, and parametrization, model-based partitioning is ambiguous (Güntner, 2008) and may lead to diverging conclusions, especially on regional scale (Long et al, 2015;Schellekens et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Due to their global coverage and independence from surface conditions, the data represent a unique opportunity to quantify spatio-temporal variations of the Earth's water resources (Alkama et al, 2010;Werth et al, 2009). Therefore, GRACE data have been widely used to diagnose patterns of hydrological variability (Seo et al, 2010;Rodell et al, 2009;Ramillien et al, 2006;Feng et al, 2013), to validate and improve model simulations Güntner, 2008;Werth and Güntner, 2010;Chen et al, 2017;Eicker et al, 2014;Girotto et al, 2016;Schellekens et al, 2017), and to enhance our understanding of the water cycle on regional to global scales (Syed et al, 2009;Felfelani et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Understanding terrestrial water storage variations in northern latitudes across scales

Trautmann

Koirala

Eicker

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The GRACE satellites provide signals of total terrestrial water storage (TWS) variations over large spatial domains at seasonal to inter-annual timescales. While the GRACE data have been extensively and successfully used to assess spatio-temporal changes in TWS, little effort has been made to quantify the relative contributions of snowpacks, soil moisture, and other components to the integrated TWS signal across northern latitudes, which is essential to gain a better insight into the underlying hydrological processes. Therefore, this study aims to assess which storage component dominates the spatio-temporal patterns of TWS variations in the humid regions of northern mid-to high latitudes.To do so, we constrained a rather parsimonious hydrological model with multiple state-of-the-art Earth observation products including GRACE TWS anomalies, estimates of snow water equivalent, evapotranspiration fluxes, and gridded runoff estimates. The optimized model demonstrates good agreement with observed hydrological spatio-temporal patterns and was used to assess the relative contributions of solid (snowpack) versus liquid (soil moisture, retained water) storage components to total TWS variations. In particular, we analysed whether the same storage component dominates TWS variations at seasonal and inter-annual temporal scales, and whether the dominating component is consistent across small to large spatial scales.Consistent with previous studies, we show that snow dynamics control seasonal TWS variations across all spatial scales in the northern mid-to high latitudes. In contrast, we find that inter-annual variations of TWS are dominated by liquid water storages at all spatial scales. The relative contribution of snow to inter-annual TWS variations, though, increases when the spatial domain over which the storages are averaged becomes larger. This is due to a stronger spatial coherence of snow dynamics that are mainly driven by temperature, as opposed to spatially more heterogeneous liquid water anomalies, that cancel out when averaged over a larger spatial domain. The findings first highlight the effectiveness of our model-data fusion approach that jointly interprets multiple Earth observation data streams with a simple model. Secondly, they reveal that the determinants of TWS variations in snow-affected northern latitudes are scale-dependent. In particular, they seem to be not merely driven by snow variability, but rather are determined by liquid water storages on interannual timescales. We conclude that inferred driving mechanisms of TWS cannot simply be transferred from one scale to another, which is of particular relevance for understanding the short-and long-term variability of water resources.

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Section: Performance Of the Spatially Integrated Simulationsmentioning

confidence: 99%

Section: Evaluation Of Model Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding terrestrial water storage variations in northern latitudes across scales

Trautmann

Koirala

Eicker

et al. 2018

Hydrol. Earth Syst. Sci.

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Phosphorus Loadings to the World's Largest Lakes: Sources and Trends

Fink

Alcamo

Flörke

et al. 2018

Global Biogeochemical Cycles

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116

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Eutrophication is a major water quality issue in lakes worldwide and is principally caused by the loadings of phosphorus from catchment areas. It follows that to develop strategies to mitigate eutrophication, we must have a good understanding of the amount, sources, and trends of phosphorus pollution. This paper provides the first consistent and harmonious estimates of current phosphorus loadings to the world's largest 100 lakes, along with the sources of these loadings and their trends. These estimates provide a perspective on the extent of lake eutrophication worldwide, as well as potential input to the evaluation and management of eutrophication in these lakes. We take a modeling approach and apply the WorldQual model for these estimates. The advantage of this approach is that it allows us to fill in large gaps in observational data. From the analysis, we find that about 66 of the 100 lakes are located in developing countries and their catchments have a much larger average phosphorus yield than the lake catchments in developed countries (11.1 versus 0.7 kg TP km−2 year−1). Second, the main source of phosphorus to the examined lakes is inorganic fertilizer (47% of total). Third, between 2005–2010 and 1990–1994, phosphorus pollution increased at 50 out of 100 lakes. Sixty percent of lakes with increasing pollution are in developing countries. P pollution changed primarily due to changing P fertilizer use. In conclusion, we show that the risk of P‐stimulated eutrophication is higher in developing countries.

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Global Flood Models

Trigg

Bernhofen

Maréchal³

et al. 2021

Geophysical Monograph Series

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Flooding is the most damaging natural hazard, both economically and by population affected. Flood models are important tools for evaluating the risks associated with flooding. Historically, the modeling domain has been limited in scale; however, advancements in computing power and global data sets have led to the development of global flood models (GFMs). This global modeling capability has benefited scientific studies of exposure and climate change impact, the insurance industry, and intergovernmental disaster risk reduction efforts. Global flood modeling has now progressed beyond its infancy to a point where coordinated and targeted model development can take place based on collective studies. This chapter provides a detailed summary of the current global flood modeling state-of-affairs. It begins with a summary of the history and challenges of GFM development. This is followed by a review of current GFMs and their structures, applications, and credibility. A section is also dedicated to describing global flood modeling in the context of the insurance catastrophe model, an important GFM category that is less visible due to their proprietary nature. The chapter concludes by looking to the future and highlighting how GFMs need to improve and the new data sets and methods that could con tribute to their continued development.

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A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

Cited by 210 publications

References 87 publications

Understanding terrestrial water storage variations in northern latitudes across scales

Understanding terrestrial water storage variations in northern latitudes across scales

Phosphorus Loadings to the World's Largest Lakes: Sources and Trends

Global Flood Models

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