Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use

Schmied, Hannes Müller; Adam, L.; Eisner, Stephanie; Fink, G.; Flörke, Martina; Kim, Hyungjun; Oki, T.; Portmann, F. T.; Reinecke, Robert D.; Riedel, C.; Song, Q.; Zhang, J.; Döll, Petra

doi:10.5194/hess-2015-527

Cited by 34 publications

(48 citation statements)

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“…For example, LPJmL, H08 and WaterGAP use climate conditions to simulate crop calendars (Bondeau et al, 2007;Hanasaki et al, 2010), while PCR-GLOBWB use the crop calendar data from Portmann et al (2010). In addition, the uncertainty arising from climate forcing is small in most of regions (CV<0.25) due to the high 5 agreement of historical climate datasets (Müller Schmied et al, 2016). Therefore, it is evident that the uncertainty from model structure is larger than that induced by forcing data.…”

Section: Uncertainties In Reconstructed Irrigation Water Withdrawalmentioning

confidence: 99%

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Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

Huang¹,

Hejazi²,

Li³

et al. 2017

Preprint

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20Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scale, due to a lack of observations at local and seasonal time scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5 degree) sectoral water 25 withdrawal dataset for the period 1971-2010, which distinguishes six water use sectors, i.e. irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that global total water withdrawal has increased significantly during 1971-2010, mainly driven by the increase of irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern 30 China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of annual total irrigation water withdrawal in mid and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-551 Manuscript under review for journal Hydrol. Earth Syst. Sci. Discussion started: 15 September 2017 c Author(s) 2017. CC BY 4.0 License.2 withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual scales. The reconstructed gridded water withdrawal dataset is open-access, and can be used for examining issues related to water withdrawals at fine spatial, temporal and sectoral scales.

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Section: Uncertainties In Reconstructed Irrigation Water Withdrawalmentioning

confidence: 99%

“…This is mainly because of the high agreement in monthly variation of air temperature among the four different data sources (i.e. WFDEI, WATCH, GSWP3, Princeton) as all of them are bias corrected to (different) versions of the CRU time series (Müller Schmied et al, 2016). 10 …”

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confidence: 99%

Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

Huang¹,

Hejazi²,

Li³

et al. 2017

Preprint

Self Cite

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“…WaterGAP was developed to assess water availability and water scarcity and was applied in a range of studies with historic meteorological forcings [10,27,[36][37][38][39] and climate change [40][41][42][43] scenarios.…”

Section: Watergap Global Hydrology Model (Wghm)mentioning

confidence: 99%

Evaluation of Radiation Components in a Global Freshwater Model with Station-Based Observations

Schmied

Müller

Sánchez-Lorenzo

et al. 2016

Water

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Abstract:In many hydrological models, the amount of evapotranspired water is calculated using the potential evapotranspiration (PET) approach. The main driver of several PET approaches is net radiation, whose downward components are usually obtained from meteorological input data, whereas the upward components are calculated by the model itself. Thus, uncertainties can be large due to both the input data and model assumptions. In this study, we compare the radiation components of the WaterGAP Global Hydrology Model, driven by two meteorological input datasets and two radiation setups from ERA-Interim reanalysis. We assess the performance with respect to monthly observations provided by the Baseline Surface Radiation Network (BSRN) and the Global Energy Balance Archive (GEBA). The assessment is done for the global land area and specifically for energy/water limited regions. The results indicate that there is no optimal radiation input throughout the model variants, but standard meteorological input datasets perform better than those directly obtained by ERA-Interim reanalysis for the key variable net radiation. The low number of observations for some radiation components, as well as the scale mismatch between station observations and 0.5 • × 0.5 • grid cell size, limits the assessment.

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“…That is, no specific preferences to utilize the CSR-M solutions in this study over another mascons. Precipitation data acquired from the Global Precipitation Climatology Centre (GPCC), the GPCC gauge-corrected precipitation dataset from the Deutscher Wetterdienstt (DWD) [70,71]; surface runoff (R), and groundwater data (GW) from the WaterGAP hydrological model outputs (WGHM; [72][73][74]). Actual (ET) and reference (PET) evapotranspiration estimates were obtained using the approach described by [75], the data are internally stored at the Hydrometeorology and Remote Sensing Laboratory (HyDROS) server.…”

Section: Nile River Basinmentioning

confidence: 99%

Trend Dynamics of GRACE Terrestrial Water Storage in the Nile River Basin

Hasan¹,

Tarhule²

2019

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GRACE-derived Terrestrial Water Storage Anomalies (TWSA) continue to be used in an expanding array of studies to analyze numerous processes and phenomena related to terrestrial water storage dynamics, including groundwater depletions, lake storage variations, snow, and glacial mass changes, as well as floods, droughts, among others. So far, however, few studies have investigated how the factors that affect total water storage (e.g., precipitation, runoff, soil moisture, evapotranspiration) interact and combine over space and time to produce the mass variations that GRACE detects. This paper is an attempt to fill that gap and stimulate needed research in this area. Using the Nile River Basin as case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to TWS in different climatic zones in the Nile River Basin. The analytic method employed the trends in both the dependent and independent variables applying two geographically multiple regression (GMR) approaches: (i) an unweighted or ordinary least square regression (OLS) model in which the contributions of all variables to TWS variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on the occurrence of trend clusters, determined by Moran’s cluster index. In both cases, model efficacy was investigated using standard goodness of fit diagnostics. The OLS showed that trends in five variables (i.e., precipitation, runoff, surface water soil moisture, and population density) significantly (p<0.0001) explain the trends in TWSA for the basin at large. However, the models R2 value is only 0.14. In contrast, the GWR produced R2 values ranging between 0.40 and 0.89, with an average of 0.86 and normally distributed standard residuals. The models retained in the GWR differ by climatic zone. The results showed that all nine variables contribute significantly to the trend in TWS in the Tropical region; population density is an important contributor to TWSA variability in all zones; ET and Population density are the only significant variables in the semiarid zone. This type of information is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE mission and for gap-filling between GRACE and GRACE-FO.

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Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use

Cited by 34 publications

References 0 publications

Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

Evaluation of Radiation Components in a Global Freshwater Model with Station-Based Observations

Trend Dynamics of GRACE Terrestrial Water Storage in the Nile River Basin

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