Impact of uncertainties in meteorological forcing data and land surface parameters on global estimates of terrestrial water balance components

Насонова, О. Н.; Gusev, Ye. M.; Kovalev, Ye. E.

doi:10.1002/hyp.7651

Cited by 41 publications

(38 citation statements)

References 37 publications

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“…Most of this work has used relatively simple lumped and semidistributed hydrological models that represent watersheds with area ranging between 100 and 10,000 km 2 (Sorooshian and Dracup, 1980;Gupta et al, 1998;Andréassian et al, 2001;Vrugt et al, 2003;Muleta and Nicklow, 2005;Balin et al, 2010;McMillan et al, 2010;Vaze et al, 2010, amongst many others Troy et al (2008), Gosling and Arnell (2011), Nasonova et al (2011) and Pappenberger et al (2011). Not only do GHMs pose significant computational challenges, they also require a wealth of input data to accurately characterize global scale variations in land-use, soil type, elevation, climate conditions, and groundwater table depths (amongst others).…”

Section: Introductionmentioning

confidence: 99%

“…The second study, published in Gosling and Arnell (2011) used an ensemble of 9 different scenarios from 21 different GCMs to analyze the impact of forcing data uncertainty. More recently, Nasonova et al (2011) investigated the effect of different forcing datasets on the SWAT simulated water balance. Results demonstrate that the simulated surface runoff strongly depends on the precipitation dataset being used.…”

Section: Introductionmentioning

confidence: 99%

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Significant uncertainty in global scale hydrological modeling from precipitation data errors

Weiland

Vrugt

Beek

et al. 2015

Journal of Hydrology

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Keywords:Global hydrological modeling Forcing uncertainty Model calibration Parameter uncertainty s u m m a r yIn the past decades significant progress has been made in the fitting of hydrologic models to data. Most of this work has focused on simple, CPU-efficient, lumped hydrologic models using discharge, water table depth, soil moisture, or tracer data from relatively small river basins. In this paper, we focus on largescale hydrologic modeling and analyze the effect of parameter and rainfall data uncertainty on simulated discharge dynamics with the global hydrologic model PCR-GLOBWB. We use three rainfall data products; the CFSR reanalysis, the ERA-Interim reanalysis, and a combined ERA-40 reanalysis and CRU dataset. Parameter uncertainty is derived from Latin Hypercube Sampling (LHS) using monthly discharge data from five of the largest river systems in the world. Our results demonstrate that the default parameterization of PCR-GLOBWB, derived from global datasets, can be improved by calibrating the model against monthly discharge observations. Yet, it is difficult to find a single parameterization of PCR-GLOBWB that works well for all of the five river basins considered herein and shows consistent performance during both the calibration and evaluation period. Still there may be possibilities for regionalization based on catchment similarities. Our simulations illustrate that parameter uncertainty constitutes only a minor part of predictive uncertainty. Thus, the apparent dichotomy between simulations of global-scale hydrologic behavior and actual data cannot be resolved by simply increasing the model complexity of PCR-GLOBWB and resolving sub-grid processes. Instead, it would be more productive to improve the characterization of global rainfall amounts at spatial resolutions of 0.5°and smaller.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Weiland

Vrugt

Beek

et al. 2015

Journal of Hydrology

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“…A number of studies have shown the very strong dependence of computed continental water flows on applied climate input (Biemans et al 2009;Döll and Fiedler 2008;Guo et al 2006;Müller Schmied et al 2014;Nasonova et al 2011). Not only precipitation, but also radiation data are strong drivers of water flows and storages around the globe, while temperature data have the strongest impact in case of snow and ice.…”

Section: Uncertain Climate Inputmentioning

confidence: 99%

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

Döll

Koshiro

Güntner

et al. 2016

Remote Sensing and Water Resources

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Quantification of spatially and temporally resolved water flows and water storage variations for all land areas of the globe is required to assess water resources, water scarcity and flood hazards, and to understand the Earth system. This quantification is done with the help of global hydrological models (GHMs). What are the challenges and prospects in the development and application of GHMs? Seven important challenges are presented. (1) Data scarcity makes quantification of human water use difficult even though significant progress has been achieved in the last decade. (2) series of water availability and use are needed to provide good indicators of water scarcity.(6) Integration of gradient-based groundwater modelling into GHMs is necessary for a better simulation of groundwater-surface water interactions and capillary rise. (7) Detection and attribution of human interference with freshwater systems by using GHMs are constrained by data of insufficient quality but also GHM uncertainty itself. Regarding prospects for progress, we propose to decrease the uncertainty of GHM output by making better use of in situ and remotely sensed observations of output variables such as river discharge or total water storage variations by multi-criteria validation, calibration or data assimilation. Finally, we present an initiative that works towards the vision of hyperresolution global hydrological modelling where GHM outputs would be provided at a 1-km resolution with reasonable accuracy.

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“…However, to obtain reliable hydrological simulations from LSMs, several uncertainties need to be tackled. Their main sources are: model physics 20 (Dirmeyer, 2011;Beck et al, 2016); land surface parameters (Douville, 1998;Milly and Shmakin 2002); and atmospheric forcing (Ngo-Duc et al, 2005;Decharme and Douville, 2006;Nasonova et al, 2011). The experiments of Guo et al (2006b) found that atmospheric forcing uncertainties affect LSM hydrological simulations as much as uncertainties stemming from the models themselves.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, we use long time series of remote sensing retrievals: GIMMS LAI data are available from 1981 to 2011; and ESA-CCI SSM covers the whole simulation period. Several studies evaluated the impacts of forcing uncertainties on large-scale LSM simulations: Guo et al (2006) tested the sensitivity of soil moisture 20 simulations forced by several meteorological forcing datasets; Decharme and Douville (2006) and Szczypta et al (2012) looked at the impact of forcing precipitation errors on river discharge simulations; Materia et al (2010) analysed the sensitivity of simulated river discharge to changes in the atmospheric forcing data; Nasonova et al (2011) investigated the impacts of forcing and surface parameter uncertainties on simulated runoff and evapotranspiration; Liu et al (2016) ran multi-forcing and multi-model experiments to compare simulated and observed evapotranspiration. To our best knowledge, 25 the extensiveness of the presented assessment exceeds those of previous studies focused on assessing LSM sensitivity to atmospheric forcing uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

Gelati¹,

Decharme²,

Calvet³

et al. 2017

Preprint

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Abstract. The understanding of land surface hydrology is critical for planning human activities involving freshwater 10 resources. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature, and SW and LW radiation. At the monthly time scale, SSM and LAI simulations are relatively insensitive to forcing uncertainties. Some discrepancies with ESA-CCI appear to be forcing-independent and may be due to different assumptions underlying the LSM and the remote sensing retrieval algorithm. All simulations overestimate average summer and early autumn LAI. Forcing uncertainty impacts on simulated 25 river discharge are larger on mean values and standard deviations than on correlations with GRDC data. Anomaly correlation coefficients are not inferior to those computed from raw monthly discharge time series, indicating that the model reproduces inter-annual variability fairly well. However, simulated river discharge time series generally feature larger variability compared to measurements. They also tend to overestimate winter-spring high flows and underestimate summer-autumn low flows. Considering that several differences emerge between simulations and reference data, which may not be completely 30 explained by forcing uncertainty, we suggest several research directions. These range from further investigating the Hydrol. Earth Syst. Sci. Discuss., https://doi

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Impact of uncertainties in meteorological forcing data and land surface parameters on global estimates of terrestrial water balance components

Cited by 41 publications

References 37 publications

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

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