•Human water abstractions and irrigation impact future hydrological drought.•Projections for future hydrological drought should include human influences.•The impact of human water use on the future ...low-flow regime is significant.
Climate change will very likely impact future hydrological drought characteristics across the world. Here, we quantify the impact of human water use including reservoir regulation and climate change on future low flows and associated hydrological drought characteristics on a global scale. The global hydrological and water resources model PCR-GLOBWB is used to simulate daily discharge globally at 0.5° resolution for 1971–2099. The model was forced with the latest CMIP5 climate projections taken from five General Circulation Models (GCMs) and four emission scenarios (RCPs), under the framework of the Inter-Sectoral Impact Model Intercomparison Project.
A natural or pristine scenario has been used to calculate the impact of the changing climate on hydrological drought and has been compared to a scenario with human influences. In the latter scenario reservoir operations and human water use are included in the simulations of discharge for the 21st century. The impact of humans on the low flow regime and hydrological drought characteristics has been studied at a catchment scale.
Results show a significant impact of climate change and human water use in large parts of Asia, Middle East and the Mediterranean, where the relative contribution of humans on the changed drought severity can be close to 100%. The differences between Representative Concentration Pathways are small indicating that human water use is proportional to the changes in the climate. Reservoirs tend to reduce the impact of drought by water retention in the wet season, which in turn will lead to increased water availability in the dry season, especially for large regions in Europe and North America. The impact of climate change varies throughout the season for parts of Europe and North-America, while in other regions (e.g. North-Africa, Middle East and Mediterranean), the impact is not influenced by seasonal changes.
This study illustrates that the impact of human water use and reservoirs is nontrivial and can vary substantially per region and per season. Therefore, human influences should be included in projections of future drought characteristics, considering their large impact on the changing drought conditions.
Mountain ranges in Asia are important water suppliers, especially if downstream climates are arid, water demands are high and glaciers are abundant. In such basins, the hydrological cycle depends ...heavily on high-altitude precipitation. Yet direct observations of high-altitude precipitation are lacking and satellite derived products are of insufficient resolution and quality to capture spatial variation and magnitude of mountain precipitation. Here we use glacier mass balances to inversely infer the high-altitude precipitation in the upper Indus basin and show that the amount of precipitation required to sustain the observed mass balances of large glacier systems is far beyond what is observed at valley stations or estimated by gridded precipitation products. An independent validation with observed river flow confirms that the water balance can indeed only be closed when the high-altitude precipitation on average is more than twice as high and in extreme cases up to a factor of 10 higher than previously thought. We conclude that these findings alter the present understanding of high-altitude hydrology and will have an important bearing on climate change impact studies, planning and design of hydropower plants and irrigation reservoirs as well as the regional geopolitical situation in general.
The assessment of return periods of extreme hydrological events often relies on statistical analysis using generalized extreme value (GEV) distributions. Here we compare the traditional GEV approach ...with a novel large ensemble approach to determine the added value of a direct, empirical distribution‐based estimate of extreme hydrological events. Using the global climate and hydrological models EC‐Earth and PCR‐GLOBWB, we simulate 2,000 years of global hydrology for a present‐day and 2 °C warmer climate. We show that the GEV method has inherent limitations in estimating changes in hydrological extremes, especially for compound hydrological events. The large ensemble method does not suffer from these limitations and quantifies the impacts of climate change with greater precision. The explicit simulation of extreme events enables better hydrological process understanding. We conclude that future studies focusing on the impact of climatic changes on hydrological extremes should use large ensemble techniques to properly account for these rare hydrological events.
Plain Language Summary
Extreme hydrological events such as droughts and floods can cause severe harm to people and nature. It is therefore important to understand why and how often they occur now and in the future. We compare two methods of studying these extreme events: a frequently used statistical method and a new direct simulation method (called “large ensemble simulations”). We show that this new method better represents the extreme events, that it reduces the uncertainties of the expected effects of climate changes on extreme events, and that it allows us to study why extreme events occur. We therefore are better capable to quantify the impact of climate change on hydrological extremes, and we recommend the large ensemble method for future studies on extreme events.
Key Points
Statistical models to describe extreme river discharge can be unreliable when multiple processes lead to extreme events
Large ensemble model experiments (many simulation years) are suitable for analysis of extreme events and do not rely on statistical models
Hydrological large ensembles provide better estimates of changes in extreme floods and droughts and their characteristics
We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the ...flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.
Large-scale hydrological drought studies have demonstrated spatial and temporal patterns in observed trends, and considerable difference exists among global hydrological models in their ability to ...reproduce these patterns. In this study a controlled modeling experiment has been set up to systematically explore the role of climate and physical catchment structure (soils and groundwater systems) to better understand underlying drought-generating mechanisms. Daily climate data (1958-2001) of 1495 grid cells across the world were selected that represent Köppen-Geiger major climate types. These data were fed into a conceptual hydrological model. Nine realizations of physical catchment structure were defined for each grid cell, i.e., three soils with different soil moisture supply capacity and three groundwater systems (quickly, intermediately and slowly responding). Hydrological drought characteristics (number, duration and standardized deficit volume) were identified from time series of daily discharge. Summary statistics showed that the equatorial and temperate climate types (A- and C-climates) had about twice as many drought events as the arid and polar types (B- and E-climates), and the durations of more extreme droughts were about half the length. Selected soils under permanent grassland were found to have a minor effect on hydrological drought characteristics, whereas groundwater systems had major impact. Groundwater systems strongly controlled the hydrological drought characteristics of all climate types, but particularly those of the wetter A-, C- and D-climates because of higher recharge. The median number of droughts for quickly responding groundwater systems was about three times higher than for slowly responding systems. Groundwater systems substantially affected the duration, particularly of the more extreme drought events. Bivariate probability distributions of drought duration and standardized deficit for combinations of Köppen-Geiger climate, soil and groundwater system showed that the responsiveness of the groundwater system is as important as climate for hydrological drought development. This urges for an improvement of subsurface modules in global hydrological models to be more useful for water resources assessments. A foreseen higher spatial resolution in large-scale models would enable a better hydrogeological parameterization and thus inclusion of lateral flow.
Precipitation is an important hydro-meteorological variable, and is a primary driver of the water cycle. In large parts of the world, real-time ground-based observations of precipitation are sparse ...and satellite-derived precipitation products are the only information source.
We used changes in satellite-derived soil moisture (SM) and land surface temperature (LST) to reduce uncertainties in the real-time TRMM Multi-satellite Precipitation Analysis product (TMPA-RT). The Variable Infiltration Capacity (VIC) model was used to model the response of LST and SM on precipitation, and a particle filter was used to update TMPA-RT. Observations from AMSR-E (LPRM and LSMEM), ASCAT, SMOS and LST from AMSR-E were assimilated to correct TMPA-RT over the continental United States.
Assimilation of satellite-based SM observations alone reduced the false detection of precipitation (by 85.4%) and the uncertainty in the retrieved rainfall volumes (5%). However, a higher number of observed rainfall events were not detected after assimilation (34%), compared to the original TMPA-RT (46%). Noise in the retrieved SM changes resulted in a relatively low potential to reduce uncertainties. Assimilation of LST observations alone increased the rainfall detection rate (by 51%), and annual precipitation totals were closer to ground-based precipitation observations. Combined assimilation of both satellite SM and LST, did not significantly reduce the uncertainties compared to the original TMPA-RT, because of the influence of satellite SM over LST. However, in central United States improvements were found after combined assimilation of SM and LST observations. This study shows the potential for reducing the uncertainties in TMPA-RT estimates over sparsely gauged areas.
•Particle filter approach to correct for uncertainties in satellite precipitation•Satellite land surface temperatures can reduce precipitation uncertainties.•Satellite soil moisture can be used to correct to satellite precipitation.•Impact of satellite soil moisture is limited due to retrieval noise.
Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use (IWU) by crop. The goal of this study is to estimate ...crop‐specific IWU from surface water withdrawals (SWW), total groundwater withdrawals (GWW), and nonrenewable groundwater depletion (GWD). To do this, we employ the PCR‐GLOBWB 2 global hydrology model to partition irrigation information from the U.S. Geological Survey Water Use Database to specific crops across the Continental United States (CONUS). We incorporate high‐resolution input data on agricultural production and climate within the CONUS to obtain crop‐specific irrigation estimates for SWW, GWW, and GWD for 20 crops and crop groups from 2008 to 2020 at county spatial resolution. Over the study period, SWW decreased by 20%, while both GWW and GWD increased by 3%. On average, animal feed (alfalfa/hay) uses the most irrigation water across all water sources: 33 from SWW, 13 from GWW, and 10 km3/yr from GWD. Produce used less SWW (43%), but more GWW (57%), and GWD (27%) over the study time‐period. The largest changes in IWU for each water source between the years 2008 and 2020 are: rice (SWW decreased by 71%), sugar beets (GWW increased by 232%), and rapeseed (GWD increased by 405%). These results present the first national‐scale assessment of irrigation by crop, water source, and year. In total, we contribute nearly 2.5 million data points to the literature (3,142 counties; 13 years; 3 water sources; and 20 crops).
Key Points
A national database of crop‐specific irrigation water use by source is developed
Animal feed uses the most irrigation water compared to other crops across all water sources
Rice decreased surface water use by 71%, sugar beets increased groundwater use by 232%, and rapeseed increased groundwater depletion by 405% from 2008 to 2020
Large‐scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains ...uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large‐scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR‐E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land‐surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10–30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas.
Key Points
Satellite soil moisture holds potential for calibration of hydrological models
Soil moisture observations enable better calibration of land‐surface parameters
Errors in discharge simulations are reduced using soil moisture for calibration
Abstract
Large-ensemble climate model simulations can provide deeper understanding of the characteristics and causes of extreme events than historical observations, due to their larger sample size. ...However, adequate evaluation of simulated ‘unseen’ events that are more extreme than those seen in historical records is complicated by observational uncertainties and natural variability. Consequently, conventional evaluation and correction methods cannot determine whether simulations outside observed variability are correct for the right physical reasons. Here, we introduce a three-step procedure to assess the realism of simulated extreme events based on the model properties (step 1), statistical features (step 2), and physical credibility of the extreme events (step 3). We illustrate these steps for a 2000 year Amazon monthly flood ensemble simulated by the global climate model EC-Earth and global hydrological model PCR-GLOBWB. EC-Earth and PCR-GLOBWB are adequate for large-scale catchments like the Amazon, and have simulated ‘unseen’ monthly floods far outside observed variability. We find that the realism of these simulations cannot be statistically explained. For example, there could be legitimate discrepancies between simulations and observations resulting from infrequent temporal compounding of multiple flood peaks, rarely seen in observations. Physical credibility checks are crucial to assessing their realism and show that the unseen Amazon monthly floods were generated by an unrealistic bias correction of precipitation. We conclude that there is high sensitivity of simulations outside observed variability to the bias correction method, and that physical credibility checks are crucial to understanding what is driving the simulated extreme events. Understanding the driving mechanisms of unseen events may guide future research by uncovering key climate model deficiencies. They may also play a vital role in helping decision makers to anticipate unseen impacts by detecting plausible drivers.
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