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This paper compared future runoff projections using a Budyko type equation with respect to projections by a global hydrological model (GHM). The comparison was made for the annual mean runoff projections for a future period (2060-2100) after the Budyko parameter was set based on hydrologic model outputs at a present period (1960-2000). The objective of this study was to investigate the performance of the Budyko equation with respect to the hydrologic model at different climate regions. To address the question this study used the spatial average of runoff for the 35 largest basins in the world. According to the comparison, the projections by the two approaches agreed well (R2 = 0.983), in particular in humid tropic region (R2 = 0.986), but with consistent underestimation of future runoff (Median Error, ME = -0.042) by the Budyko equation. In subarctic region the performance of the Budyko equation was low (R2 = 0.599) due to the overestimation of future runoff (ME = 0.110). The results in the dry and temperate regions also showed some discrepancy (R2 = 0.931 and 0.724) without apparent patterns in the errors. The paper discussed possible reasons for the errors with respect to water and energy seasonality and changes in storage component contributions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Five discharge datasets have median correlation coefficients > 0.6 with the observations. • Overestimation of monthly mean flow is more common than underestimation. • GRFR showed the best estimate at around 40% of the locations. • None of the models provides good estimates for rivers and streams in arid regions. High-resolution global flow discharge datasets are critical to understand the terrestrial water cycle and river ecosystem services. Recently, a number of global hydrological models have been developed to capture global river flow dynamics at locally relevant scales. However, the relative accuracy of these simulated flow discharges is largely unknown. In this paper, using the largest observed streamflow datasets from > 26,000 gauging stations, we evaluated the performance of monthly and sub-monthly flow variation (1980–2014) generated from five major global hydrological models, Catchment-based Macro-scale Floodplain model (CaMa-Flood), Global Flood Awareness System (GloFAS-reanalysis), Global Reach-level Flood Reanalysis (GRFR), PCRaster GLOBal Water Balance model (PCR-GLOBWB), and Water Global Assessment and Prognosis (WaterGAP). Significant spatial heterogeneity in model performance was identified. We found that estimates by all five discharge datasets have a relatively high correlation with the observations (medians > 0.6). But none of them showed adequate correlations between simulated and observed flows for systems in Southwest or Central U.S.A., South Africa, or South Australia. Across all models, overestimation of monthly mean flow was more common than underestimation and high flows at a sub-monthly scale were more accurately characterized than low flows. Performance of all models was poor in Africa and Oceania. No single model performed universally best at all gauging stations, but GRFR showed the best estimate at around 40 % of the locations. None of the models provided satisfying estimates for rivers and streams in arid regions. We also found a significant negative effect of flow regulation on the predictive accuracy of all models. Our study highlights the importance of thorough evaluation of global discharge datasets before their local and regional application and provides guidance for future enhancement of global hydrological models. [ABSTRACT FROM AUTHOR]

Objectives: The gravity variation caused by hydrometeorological factors is an important factor affecting the interpretation of seismic gravity variation results. However, quantitative analysis of gravity field changes caused by hydrometeorological factors is rarely carried out. Methods: This paper takes the northern Xinjiang, China as the research area and uses the global hydrological model data of global land data assimilation system (GLDAS) and an atmospheric model to calculate the gravity influence of hydrometeorological factors on the research area from January 2016 to December 2017. Results: The results show that the annual change of terrestrial water influence is 1.3 μGal, and the difference in spatial distribution of terrestrial water influence between two adjacent periods is lower than 1 μGal. The annual change of atmospheric influence is as high as 8 μGal, and the difference in spatial distribution of atmospheric influence between two adjacent periods is up to 6 μGal. Conclusions: The gravity variations before and after deducting hydrometeorological factors were compared using the mobile gravity measurement data of three periods, i. e., April 2016, August 2016 and June 2017. The comparison results show that in the processing of mobile gravity data in the northern Xinjiang, the large⁃scale hydrological factor can be ignored and the meteorological factor should be taken into account. Meanwhile, for a better analysis of the mobile gravity variation, soil moisture and atmospheric pressure near the measuring point should be observed during the mobile gravity measurement. [ABSTRACT FROM AUTHOR]

It has been shown that the changes in global terrestrial water storage (TWS) are strongly linked to the teleconnections (TCs) that induce large-scale climate variations. However, the contributions of the different TCs to global changes of TWS and its components (water storage components, WSCs) remain undetermined. To fill this gap, we systematically assess the relationships between six major ocean-related TCs and different WSCs derived from the Gravity Recovery and Climate Experiment (GRACE) mission and hydrological models under different timescales. Additionally, the interrelationships of the TCs are also analyzed via the independent component analysis for further investigation. The results allow an improved understanding of the hydrometeorological process controlling WSC changes. Specifically, the annual timescale analysis can constrain high-frequency noises and retain the informative fluctuations of WSC residuals. ENSO and AMO are found to be the two most dominant TCs controlling the variations of WSCs globally. TWS and groundwater storage (GWS) are the two WSCs most correlated with the dominant TCs. The WSCs at shallow depths, which are largely affected by strongly hysteretic controls of TCs, are more closely linked to the TCs with many high-frequency components that tend to have weak hysteresis on WSCs. As for the interrelationships of TCs, the independent component, which is highly correlated with all six TCs, has a predominant influence on WSCs. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an assessment of the impacts of climate change on a series of indicators of hydrological regimes across the global domain, using a global hydrological model run with climate scenarios constructed using pattern-scaling from 21 CMIP3 (Coupled Model Intercomparison Project Phase 3) climate models. Changes are compared with natural variability, with a significant change being defined as greater than the standard deviation of the hydrological indicator in the absence of climate change. Under an SRES (Special Report on Emissions Scenarios) A1b emissions scenario, substantial proportions of the land surface (excluding Greenland and Antarctica) would experience significant changes in hydrological behaviour by 2050; under one climate model scenario (Hadley Centre HadCM3), average annual runoff increases significantly over 47% of the land surface and decreases over 36%; only 17% therefore sees no significant change. There is considerable variability between regions, depending largely on projected changes in precipitation. Uncertainty in projected river flow regimes is dominated by variation in the spatial patterns of climate change between climate models (hydrological model uncertainty is not included). There is, however, a strong degree of consistency in the overall magnitude and direction of change. More than two-thirds of climate models project a significant increase in average annual runoff across almost a quarter of the land surface, and a significant decrease over 14%, with considerably higher degrees of consistency in some regions. Most climate models project increases in runoff in Canada and high-latitude eastern Europe and Siberia, and decreases in runoff in central Europe, around the Mediterranean, the Mashriq, central America and Brasil. There is some evidence that project change in runoff at the regional scale is not linear with change in global average temperature change. The effects of uncertainty in the rate of future emissions is relatively small. [Copyright &y& Elsevier]

Electricity production by hydropower is negatively affected by drought. To understand and quantify risks of less than normal streamflow for hydroelectricity production (HP) at the global scale, we developed an HP model that simulates time series of monthly HP worldwide and thus enables analyzing the impact of drought on HP. The HP model is based on a new global hydropower database (GHD), containing 8,716 geo‐localized plant records, and on monthly streamflow values computed by the global hydrological model WaterGAP with a spatial resolution of 0.5°. The GHD includes 44 attributes and covers 91.8% of the globally installed capacity. The HP model can reproduce HP trends, seasonality, and interannual variability that was caused by both (de)commissioning of hydropower plants and hydrological variability. It can also simulate streamflow drought and its impact on HP reasonably well. Global risk maps of HP reduction were generated for both 0.5° grid cells and countries, revealing that 67 out of the 134 countries with hydropower suffer, in 1 out of 10 years, from a reduction of more than 20% of mean annual HP and 18 countries from a reduction of more than 40%. The developed HP model enables advanced assessments of drought impacts on hydroelectricity at national to international levels. Key Points: A new global hydropower database and a hydroelectricity production model were developed and validatedThe model simulates the impact of streamflow drought on monthly hydroelectricity production worldwideThe 1‐in‐10 year risk of hydroelectricity production reduction due to drought is assessed at both 0.5° grid cell and country levels [ABSTRACT FROM AUTHOR]

Global hydrological models (GHMs) are important tools for addressing worldwide change-related water resource problems from a global perspective. However, the development of these models has long been hindered by their low accuracy. In order to improve the streamflow simulation accuracy of GHMs, we developed a GHM—the FLEX-Global—based on the regionalization of hydrological model parameters. The FLEX-Global model is primarily based on the framework of the FLEX hydrological model coupled with the HAND-based Storage Capacity curve (HSC) runoff generation module to calculate net rainfall, and uses the global river-routing CaMa-Flood model to calculate river network routing. This new model allows for streamflow simulation at a spatial resolution of 0.5°×0.5° and a temporal resolution of 1 day in global catchments. To validate FLEX-Global accuracy, the FLEX-Global-simulated streamflow of 26 major rivers distributed in five different climate zones was compared with the observed data from the Global Runoff Data Center (GRDC). Next, the model performance of FLEX-Global was further verified by comparing it with that of seven existing GHMs with varying accuracy in the five climate zones. Multi-metric evaluation indicated that the streamflow simulation accuracy was improved by the FLEX-Global model with regionalized parameters, especially in the tropical and dry climate zones. This newly-developed GHM with regionalized parameters can provide scientific support for the assessment of climate change impact, optimization of global water resource mangement, simulation of Earth's multi-sphere coupling, and implementation of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP). [ABSTRACT FROM AUTHOR]

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) Uncertainty of meteorological input data strongly affects model outputs. (3) The reaction of vegetation to changing climate and CO concentrations is uncertain and not taken into account in most GHMs that serve to estimate climate change impacts. (4) Reasons for discrepant responses of GHMs to changing climate have yet to be identified. (5) More accurate estimates of monthly time 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. [ABSTRACT FROM AUTHOR]

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The number of global open-source hydrometeorological datasets and models is large and growing. However, with a constantly growing demand for services and tools from stakeholders, not only in the water sector, we still lack simple solutions, which are easy to use for nonexperts. The new R package incorporates the BROOK90 hydrologic model and global open-source datasets used for parameterization and forcing. The aim is to estimate the vertical water fluxes within the soil–water–plant system of a single site or of a small catchment (<100 km2). This includes data scarce regions where no hydrometeorological measurements or reliable site characteristics can be obtained. The end-user only needs to provide a location and the desired period. The package automatically downloads the necessary datasets for elevation (Amazon Web Service Terrain Tiles), land cover (Copernicus: Land Cover 100 m), soil characteristics (ISRIC: SoilGrids250), and meteorological forcing (Copernicus: ERA5 reanalysis). Subsequently these datasets are processed, specific hydrotopes are created, and BROOK90 is applied. In a last step, the output data of all desired variables on a daily scale as well as time-series plots are stored. A first daily and monthly validation based on five catchments within various climate zones shows a decent representation of soil moisture, evapotranspiration, and runoff components. A considerably better performance is achieved for a monthly scale. [ABSTRACT FROM AUTHOR]