Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets

Wartenburger, Richard; Seneviratne, Sonia I.; Hirschi, Martin; Chang, Jinfeng; Ciais, Philippe; Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Gosling, Simon N.; Gudmundsson, Lukas; Henrot, Alexandra‐Jane; Hickler, Thomas; Ito, Akihiko; Khabarov, Nikolay; Kim, Hyungjun; Leng, Guoyong; Liu, Junguo; Liu, Xingcai; Masaki, Yoshimitsu; Morfopoulos, Catherine; Müller, Christoph; Schmied, Hannes Müller; Nishina, Kazuya; Orth, René; Pokhrel, Yadu; Pugh, Thomas A. M.; Satoh, Yusuke; Schaphoff, Sibyll; Schmid, Erwin; Sheffield, Justin; Stacke, Tobias; Steinkamp, Jörg; Tang, Qiuhong; Thiery, Wim; Wada, Yoshihide; Wang, Xuhui; Weedon, Graham P.; Yang, Hong; Zhou, Tian

doi:10.1088/1748-9326/aac4bb

Cited by 54 publications

(45 citation statements)

References 105 publications

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“…The underestimation of runoff by certain models, particularly in the NDR hydrobelt, may be a result of excessive evapotranspiration (as reported for MATSIRO by [19]). Moreover, the simulation of evapotranspiration has been shown to vary widely between the ISIMIP2a global-scale hydrological models [63]. Several models struggle to accurately simulate the timing and magnitude of long-term MMR in all months of the year in NDR and the first and/or last months in other hydrobelts.…”

Section: Models' Performance Across Hydrobeltsmentioning

confidence: 99%

Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

Zaherpour

Gosling

Mount

et al. 2018

Environ. Res. Lett.

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112

113

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Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models' ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model-a finding that challenges the commonly held perception that model ensemble © 2018 The Author(s). Published by IOP Publishing Ltd Environ. Res. Lett. 13 (2018) 065015 estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.

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Section: Models' Performance Across Hydrobeltsmentioning

confidence: 99%

Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

Zaherpour

Gosling

Mount

et al. 2018

Environ. Res. Lett.

Self Cite

112

113

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“…ET simulation in LSMs is regulated by multiple biophysical and physiological properties or processes, including but not limited to stomatal conductance, leaf area, root water uptake, soil water, runoff and sometimes nutrient uptake (Famiglietti and Wood, 1991;Huang et al, 2016;Lawrence et al, 2007). Although almost all current LSMs have these components, different parameterization schemes result in substantial differences in ET estimation (Wartenburger et al, 2018). Therefore, in recent years, the multi-model ensemble approach has become popular in improving the accuracy of global terrestrial ET estimation (Mueller et al, 2011;Wartenburger et al, 2018).…”

Section: Process-based Land Surface Models (Lsms)mentioning

confidence: 99%

“…Knowledge of the uncertainties in global terrestrial ET estimates from different approaches is the prerequisite for future projection and many other applications. In recent years, several studies have compared multiple terrestrial ET estimates (Khan et al, 2018;Mueller et al, 2013;Wartenburger et al, 2018;Zhang et al, 2016b). However, most of these studies just analyzed multiple datasets of the same approach or focused on investigating similarities and differences among different approaches.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of global terrestrial evapotranspiration by state-of-the-art approaches in remote sensing, machine learning, and land surface models

Pan¹,

Pan²,

Tian³

et al. 2019

Preprint

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Abstract. Evapotranspiration (ET) is a critical component in global water cycle and links terrestrial water, carbon and energy cycles. Accurate estimate of terrestrial ET is important for hydrological, meteorological, and agricultural research and applications, such as quantifying surface energy and water budgets, weather forecasting, and scheduling of irrigation. However, direct measurement of global terrestrial ET is not feasible. Here, we first gave a retrospective introduction to the basic theory and recent developments of state-of-the-art approaches for estimating global terrestrial ET, including remote sensing-based physical models, machine learning algorithms and land surface models (LSMs). Then, we utilized six remote sensing-based models (including four physical models and two machine learning algorithms) and fourteen LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the mean annual global terrestrial ET ranged from 50.7 × 103 km3 yr−1（454 mm yr−1）to 75.7 × 103 km3 yr−1 (6977 mm yr−1), with the average being 65.5 × 103 km3 yr−1 (588 mm yr−1), during 1982–2011. LSMs had significant uncertainty in the ET magnitude in tropical regions especially the Amazon Basin, while remote sensing-based ET products showed larger inter-model range in arid and semi-arid regions than LSMs. LSMs and remote sensing-based physical models presented much larger inter-annual variability (IAV) of ET than machine learning algorithms in southwestern U.S. and the Southern Hemisphere, particularly in Australia. LSMs suggested stronger control of precipitation on ET IAV than remote sensing-based models. The ensemble remote sensing-based physical models and machine-learning algorithm suggested significant increasing trends in global terrestrial ET at the rate of 0.62 mm yr−2 (p 0.05), even though most of the individual LSMs reproduced the increasing trend. Moreover, all models suggested a positive effect of vegetation greening on ET intensification. Spatially, all methods showed that ET significantly increased in western and southern Africa, western India and northeastern Australia, but decreased severely in southwestern U.S., southern South America and Mongolia. Discrepancies in ET trend mainly appeared in tropical regions like the Amazon Basin. The ensemble means of the three ET categories showed generally good consistency, however, considerable uncertainties still exist in both the temporal and spatial variations in global ET estimates. The uncertainties were induced by multiple factors, including parameterization of land processes, meteorological forcing, lack of in situ measurements, remote sensing acquisition and scaling effects. Improvements in the representation of water stress and canopy dynamics are essentially needed to reduce uncertainty in LSM-simulated ET. Utilization of latest satellite sensors and deep learning methods, theoretical advancements in nonequilibrium thermodynamics, and application of integrated methods that fuse different ET estimates or relevant key biophysical variables will improve the accuracy of remote sensing-based models.

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“…Müller Schmied et al () showed that by taking into account uncertainties of climate and land cover data, global discharge was changed by 5% whereas actual evapotranspiration was unaffected. In a number of studies, the performance of the WGHM in the simulation of discharge, reservoir, radiation, and evapotranspiration was evaluated, and it was found that the model estimate in the gridded and basin scales generally outperforms other global models (Masaki et al, ; Müller Schmied et al, 2016b; Veldkamp et al, ; Wartenburger et al, ; Zaherpour et al, , ; Zhao et al, ). The comparison of seasonal amplitudes of the model to GRACE data showed that WGHM underestimates TWSA in the Southern Hemisphere and the tropics but compares favourably with GRACE in the Northern Hemisphere (Scanlon et al, ).…”

Section: Discussionmentioning

confidence: 99%

Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada

Bahrami

Goı̈ta

Magagi

2019

Hydrological Processes

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In this study, the spatial and temporal variabilities of terrestrial water storage anomaly (TWSA) and snow water equivalent anomaly (SWEA) information obtained from the Gravity Recovery and Climate Experiment (GRACE) twin satellites data were analysed in conjunction with multisource snow products over several basins in the Canadian landmass. Snow water equivalent (SWE) data were extracted from three different sources: Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E), and Canadian Meteorological Centre (CMC). The objective of the study was to understand whether SWE variations have a significant contribution to terrestrial water storage anomalies in the Canadian landmass. The period was considered from December 2002 to March 2011. Significant relationships were observed between TWSA and SWEA for most of the 15 basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products compared with satellite‐based SWE data. Stronger relationships were found in snow‐dominated basins (Rs > = 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in the north of Quebec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behaviour was observed in the Western Hudson Bay basin. In both regions, it was found that the contribution of non‐SWE compartments including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the Water‐Global Assessment and Prognosis Global Hydrology Model (WGHM) simulations and then subtracted from GRACE observations. The GRACE‐derived SWEA correlation results showed improved relationships with three SWEA products. The improvement is particularly important in the sub‐basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE‐derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). Overall, the results indicated the important role of SWE on terrestrial water storage variations.

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Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets

Cited by 54 publications

References 105 publications

Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

Evaluation of global terrestrial evapotranspiration by state-of-the-art approaches in remote sensing, machine learning, and land surface models

Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada

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