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The transboundary Helmand River basin (HRB) is the main drainage system for large parts of Afghanistan and the Sistan region of Iran. Due to the reliance of this arid region on water from the Helmand River, a better understanding of hydrological-drought pattern and the underlying drivers in the region is critically required for effective management of the available water. The objective of this paper is therefore to analyze and quantify spatiotemporal pattern of drought and the underlying processes in the study region. More specifically we test for the Helmand River basin the following hypotheses for the 1970–2006 period: (1) drought characteristics, including frequency and severity, systematically changed over the study period; (2) the spatial pattern and processes of drought propagation through the Helmand River basin also changed; and (3) the relative roles of climate variability and human influence on changes in hydrological droughts can be quantified. It was found that drought characteristics varied throughout the study period but largely showed no systematic trends. The same was observed for the time series of drought indices SPI (standard precipitation index) and SPEI (standardized precipitation evapotranspiration index), which exhibited considerable spatial coherence and synchronicity throughout the basin, indicating that, overall, droughts similarly affect the entire HRB with few regional or local differences. In contrast, analysis of the SDI (streamflow drought index) exhibited significant negative trends in the lower parts of the basin, indicating an intensification of hydrological droughts. It could be shown that with a mean annual precipitation of ∼ 250 mm yr -1 , streamflow deficits and thus hydrological drought throughout the HRB are largely controlled by precipitation deficits, whose annual anomalies on average account for ±50 mm yr -1 , or ∼ 20 % of the water balance of the HRB, while anomalies of total evaporative fluxes on average only account for ±20 mm yr -1. Assuming no changes in the reservoir management practices over the study period, the results suggest that the two reservoirs in the HRB only played a minor role for the downstream propagation of streamflow deficits, as indicated by the mean difference between inflow and outflow during drought periods, which did not exceed ∼ 0.5 % of the water balance of the HRB. Irrigation water abstraction had a similarly limited effect on the magnitude of streamflow deficits, accounting for ∼ 10 % of the water balance of the HRB. However, the downstream parts of the HRB moderated the further propagation of streamflow deficits and associated droughts because of the minor effects of reservoir operation and very limited agricultural water in the early decades of the study period. This drought moderation function of the lower basin was gradually and systematically inverted by the end of the study period, when the lower basin eventually amplified the downstream propagation of flow deficits and droughts. Our results provide plausible evidence that this shift from drought moderation to drought amplification in the lower basin is likely a consequence of increased agricultural activity and the associated increases in irrigation water demand, from ∼ 13 mm yr -1 at the beginning of the study period to ∼ 23 mm yr -1 at the end, and thus in spite of being only a minor fraction of the water balance. Overall the results of this study illustrate that flow deficits and the associated droughts in the HRB clearly reflect the dynamic interplay between temporally varying regional differences in hydro-meteorological variables together with subtle and temporally varying effects linked to direct human intervention. [ABSTRACT FROM AUTHOR]

Recent advances in differentiable modeling, a genre of physics-informed machine learning that trains neural networks (NNs) together with process-based equations, have shown promise in enhancing hydrological models' accuracy, interpretability, and knowledge-discovery potential. Current differentiable models are efficient for NN-based parameter regionalization, but the simple explicit numerical schemes paired with sequential calculations (operator splitting) can incur numerical errors whose impacts on models' representation power and learned parameters are not clear. Implicit schemes, however, cannot rely on automatic differentiation to calculate gradients due to potential issues of gradient vanishing and memory demand. Here we propose a "discretize-then-optimize" adjoint method to enable differentiable implicit numerical schemes for the first time for large-scale hydrological modeling. The adjoint model demonstrates comprehensively improved performance, with Kling–Gupta efficiency coefficients, peak-flow and low-flow metrics, and evapotranspiration that moderately surpass the already-competitive explicit model. Therefore, the previous sequential-calculation approach had a detrimental impact on the model's ability to represent hydrological dynamics. Furthermore, with a structural update that describes capillary rise, the adjoint model can better describe baseflow in arid regions and also produce low flows that outperform even pure machine learning methods such as long short-term memory networks. The adjoint model rectified some parameter distortions but did not alter spatial parameter distributions, demonstrating the robustness of regionalized parameterization. Despite higher computational expenses and modest improvements, the adjoint model's success removes the barrier for complex implicit schemes to enrich differentiable modeling in hydrology. [ABSTRACT FROM AUTHOR]

An accurate representation of spatio-temporal characteristics of precipitation fields is fundamental for many hydro-meteorological analyses but is often limited by the paucity of gauges. Reanalysis models provide systematic methods of representing atmospheric processes to produce datasets of spatio-temporal precipitation estimates. The precipitation from the reanalysis datasets should, however, be evaluated thoroughly before use because it is inferred from physical parameterization. In this paper, we evaluated the precipitation dataset from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and compared it against (a) gauged point observations, (b) an interpolated gridded dataset based on gauged point observations (AWAP – Australian Water Availability Project), and (c) a global reanalysis dataset (ERA-Interim). We utilized a range of evaluation metrics such as continuous metrics (correlation, bias, variability, and modified Kling–Gupta efficiency), categorical metrics, and other statistics (wet-day frequency, transition probabilities, and quantiles) to ascertain the quality of the dataset. BARRA, in comparison with ERA-Interim, shows a better representation of rainfall of larger magnitude at both the point and grid scale of 5 km. BARRA also more closely reproduces the distribution of wet days and transition probabilities. The performance of BARRA varies spatially, with better performance in the temperate zone than in the arid and tropical zones. A point-to-grid evaluation based on correlation, bias, and modified Kling–Gupta efficiency (KGE ′) indicates that ERA-Interim performs on par or better than BARRA. However, on a spatial scale, BARRA outperforms ERA-Interim in terms of the KGE ′ score and the components of the KGE ′ score. Our evaluation illustrates that BARRA, with richer spatial variations in climatology of daily precipitation, provides an improved representation of precipitation compared with the coarser ERA-Interim. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions. [ABSTRACT FROM AUTHOR]

Over a long multi-year period, flood events can be classified according to their effectiveness in moving sediments. Efficiency depends both on the magnitude and frequency with which events occur. The effective (or dominant) discharge is the water discharge which corresponds to the maximum sediment supply. If its calculation is well documented in temperate or humid climates and large basins, it is much more difficult in small and semi-arid basins in which short floods with high sediment supplies occur. Using the example of 31 years of measurements in the Wadi Sebdou (north-west Algeria), this paper compares the two main statistical approaches to calculate the effective discharge (the empirical method based on histograms of sediment supply by discharge classes and an analytical calculation based on a hydrological probability distribution and on a sediment rating curve) to a very simple proxy: the half-load discharge, i.e. the flow rate corresponding to 50 % of the cumulative sediment yield. Three types of discharge subdivisions were tested. In the empirical approach, two subdivisions provided effective discharge close to the half-load discharge. Analytical solutions based on log-normal and log-Gumbel probability distributions were assessed but they highly underestimated the effective discharge, whatever the subdivision used to adjust the flow frequency distribution. Furthermore, annual series of maximum discharge and half-load discharge enabled the return period of hydrological years with discharge higher than the effective discharge (around 2 years) to be inferred and showed that more than half of the yearly sediment supply is carried by flows higher than the effective discharge only every 7 hydrological years. This study was the first to adapt the analytical approach in a semi-arid basin and to show the potentiality and limits of each method in a such climate. [ABSTRACT FROM AUTHOR]

All the major river systems in the contiguous United States (CONUS) (and many in the world) are impacted by dams, yet reservoir operations remain difficult to quantify and model due to a lack of data. Reservoir operation data are often inaccessible or distributed across many local operating agencies, making the acquisition and processing of data records quite time-consuming. As a result, large-scale models often rely on simple parameterizations for assumed reservoir operations and have a very limited ability to evaluate how well these approaches match actual historical operations. Here, we use the first national dataset of historical reservoir operations in the CONUS domain, ResOpsUS, to analyze reservoir storage trends and operations in more than 600 major reservoirs across the US. Our results show clear regional differences in reservoir operations. In the eastern US, which is dominated by flood control storage, we see storage peaks in the winter months with sharper decreases in the operational range (i.e., the difference between monthly maximum and minimum storage) in the summer. In the more arid western US where storage is predominantly for irrigation, we find that storage peaks during the spring and summer with increases in the operational range during the summer months. The Lower Colorado region is an outlier because its seasonal storage dynamics more closely mirrored those of flood control basins, yet the region is classified as arid, and most reservoirs have irrigation uses. Consistent with previous studies, we show that average annual reservoir storage has decreased over the past 40 years, although our analyses show a much smaller decrease than previous work. The reservoir operation characterizations presented here can be used directly for development or evaluation of reservoir operations and their derived parameters in large-scale models. We also evaluate how well historical operations match common assumptions that are often applied in large-scale reservoir parameterizations. For example, we find that 100 dams have maximum storage values greater than the reported reservoir capacity from the Global Reservoirs and Dams database (GRanD). Finally, we show that operational ranges have been increasing over time in more arid regions and decreasing in more humid regions, pointing to the need for operating policies which are not solely based on static values. [ABSTRACT FROM AUTHOR]

Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River basin, northwestern China, and expected to be vulnerable to climate change. It has been demonstrated that regional climate models (RCMs) provide more reliable results for a regional impact study of climate change (e.g., on water resources) than general circulation models (GCMs). However, due to their considerable bias it is still necessary to apply bias correction before they are used for water resources research. In this paper, after a sensitivity analysis on input meteorological variables based on the Sobol' method, we compared five precipitation correction methods and three temperature correction methods in downscaling RCM simulations applied over the Kaidu River basin, one of the headwaters of the Tarim River basin. Precipitation correction methods applied include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and quantile mapping (QM), while temperature correction methods are LS, variance scaling (VARI) and DM. The corrected precipitation and temperature were compared to the observed meteorological data, prior to being used as meteorological inputs of a distributed hydrologic model to study their impacts on streamflow. The results show (1) streamflows are sensitive to precipitation, temperature and solar radiation but not to relative humidity and wind speed; (2) raw RCM simulations are heavily biased from observed meteorological data, and its use for streamflow simulations results in large biases from observed streamflow, and all bias correction methods effectively improved these simulations; (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g., standard deviation, percentile values) while the LOCI method performed best in terms of the time-series-based indices (e.g., Nash-Sutcliffe coefficient, R2); (4) for temperature, all correction methods performed equally well in correcting raw temperature; and (5) for simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with those of corrected precipitation; i.e., the PT and QM methods performed equally best in correcting flow duration curve and peak flow while the LOCI method performed best in terms of the time-series-based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other areas and models. [ABSTRACT FROM AUTHOR]

Climate change accelerates the global water cycle. However, the relationships between climate change and hydrological processes in the alpine arid regions remain elusive. We sampled surface water and groundwater at high spatial and temporal resolutions to investigate these relationships in the Qaidam Basin, an extremely arid area in the northeastern Tibetan Plateau. Stable H–O isotopes and radioactive 3 H isotopes were combined with atmospheric simulations to examine hydrological processes and their response mechanisms to climate change. Contemporary climate processes and change dominate the spatial and temporal variations of surface water isotopes, specifically the westerlies moisture transport and the local temperature and precipitation regimes. The H–O isotopic compositions in the eastern Kunlun Mountains showed a gradually depleted eastward pattern, while a reverse pattern occurred in the Qilian Mountains water system. Precipitation contributed significantly more to river discharge in the eastern basin (approximately 45 %) than in the middle and western basins (10 %–15 %). Moreover, increasing precipitation and a shrinking cryosphere caused by current climate change have accelerated basin groundwater circulation. In the eastern and southwestern Qaidam Basin, precipitation and meltwater infiltrate along preferential flow paths, such as faults, volcanic channels, and fissures, permitting rapid seasonal groundwater recharge and enhanced terrestrial water storage. However, compensating for water loss due to long-term ice and snow melt will be a challenge under projected increasing precipitation in the southwestern Qaidam Basin, and the total water storage may show a trend of increasing before decreasing. Great uncertainty about water is a potential climate change risk facing the arid Qaidam Basin. [ABSTRACT FROM AUTHOR]

Sublimation is the main ablation component of snow in the upper areas of the semiarid Andes (∼ 26 to ∼ 32 ∘ S and ∼ 69 to ∼ 71 ∘ W). This region has elevations up to 6000 m , is characterized by scarce precipitation, high solar radiation receipt, and low air humidity, and has been affected by a severe drought since 2010. In this study, we suggest that most of the snowmelt runoff originates from specific areas with topographic and meteorological features that allow large snow accumulation and limited mass removal. To test this hypothesis, we quantify the spatial distribution of snowmelt runoff and sublimation in a catchment of the semiarid Andes using a process-based snow model that is forced with field data. Model simulations over a 2-year period reproduce point-scale records of snow depth (SD) and snow water equivalent (SWE) and are also in good agreement with an independent SWE reconstruction product as well as satellite snow cover area and indices of winter snow absence and summer snow persistence. We estimate that 50 % of snowmelt runoff is produced by 21 %–29 % of the catchment area, which we define as "snowmelt hotspots". Snowmelt hotspots are located at mid-to-lower elevations of the catchment on wind-sheltered, low-angle slopes. Our findings show that sublimation is not only the main ablation component: it also plays an important role shaping the spatial variability in total annual snowmelt. Snowmelt hotspots might be connected with other hydrological features of arid and semiarid mountain regions, such as areas of groundwater recharge, rock glaciers, and mountain peatlands. We recommend more detailed snow and hydrological monitoring of these sites, especially in the current and projected scenarios of scarce precipitation. [ABSTRACT FROM AUTHOR]

Although extreme El Niño events (EENEs) have always impacted hydrological anomalies and sediment transport in South America, their intensification by global warming and their association with changes in human activities and land cover after humid periods may lead to the acceleration of sediment transfers in river systems and dam reservoirs. This situation may threaten soil and water resources in arid and semiarid regions highly dependent on water originating from large dams. In this study, we investigated the sediment sequence accumulated in the Poechos Reservoir (northern Peru) and provided a retrospective reconstruction of the interactions of El Niño–Southern Oscillation (ENSO), agricultural practices and vegetation cover changes with sediment dynamics (1978–2019). To this end, a sediment core was dated and characterized by physical and chemical analyses (e.g., scanner tomography, X-ray fluorescence, particle size analysis) to estimate the evolution of sedimentation rates and changes in sediment sources during the last 5 decades. Sediment tracing results indicated the occurrence of changes in sediment sources associated with positive and negative phases of the Eastern Pacific index with a greater contribution of the lowland dry-forest area in comparison to that of the Andean area to sediment during the El Niño events (mean contribution of 76 %; up to 90 % during the coastal El Niño events (CENEs) of 2016–2017). This source contribution was mostly controlled by the stationary rainfall occurring during the EENEs in the lowland dry-forest area characterized by a low vegetation cover. Overall, after an extreme phase of ENSO, like after the EENE 1982–1983, the normal discharges and persistent sediment supplies from the middle- and upper-catchment parts led to river aggradation and the storage of substantial amounts of sediment in alluvial plains. In the absence of a significant EENE between 1983 and 1997, the large volume of sediment stored in the alluvial plains was exported by the EENE 1997–1998 resulting in an increase in sedimentation rate of 140 % after 1997 with a significant aggradation of the deltaic zone of the reservoir. In addition to the impact of extreme climate events on sediment dynamics, the development of agriculture along the riverine system after an extreme phase of ENSO increased the availability of sediments in the main channel of the rivers, easily transported by the next EENE. This study suggests that intensification of human activities associated with a higher frequency of extreme rainfall events amplified the quantity of sediment transported by the river system, which will significantly decrease the lifespan of the reservoir, which is essential to meeting the freshwater demands of the farmers and the populations living in this arid and semiarid region. [ABSTRACT FROM AUTHOR]

Terrestrial water storage (TWS) is an integrative hydrological state that is key for our understanding of the global water cycle. The TWS observation from the GRACE missions has, therefore, been instrumental in the calibration and validation of hydrological models and understanding the variations in the hydrological storage. The models, however, still show significant uncertainties in reproducing observed TWS variations, especially for the interannual variability (IAV) at the global scale. Here, we diagnose the regions dominating the variance in globally integrated TWS IAV and the sources of the errors in two data-driven hydrological models that were calibrated against global TWS, snow water equivalent, evapotranspiration, and runoff data. We used (1) a parsimonious process-based hydrological model, the Strategies to INtegrate Data and BiogeochemicAl moDels (SINDBAD) framework and (2) a machine learning, physically based hybrid hydrological model (H2M) that combines a dynamic neural network with a water balance concept. While both models agree with the Gravity Recovery and Climate Experiment (GRACE) that global TWS IAV is largely driven by the semi-arid regions of southern Africa, the Indian subcontinent and northern Australia, and the humid regions of northern South America and the Mekong River basin, the models still show errors such as the overestimation of the observed magnitude of TWS IAV at the global scale. Our analysis identifies modeling error hotspots of the global TWS IAV, mostly in the tropical regions including the Amazon, sub-Saharan regions, and Southeast Asia, indicating that the regions that dominate global TWS IAV are not necessarily the same as those that dominate the error in global TWS IAV. Excluding those error hotspot regions in the global integration yields large improvements in the simulated global TWS IAV, which implies that model improvements can focus on improving processes in these hotspot regions. Further analysis indicates that error hotspot regions are associated with lateral flow dynamics, including both sub-pixel moisture convergence and across-pixel lateral river flow, or with interactions between surface processes and groundwater. The association of model deficiencies with land processes that delay the TWS variation could, in part, explain why the models cannot represent the observed lagged response of TWS IAV to precipitation IAV in hotspot regions that manifest as errors in global TWS IAV. Our approach presents a general avenue to better diagnose model simulation errors for global data streams to guide efficient and focused model development for regions and processes that matter the most. [ABSTRACT FROM AUTHOR]

As the transition zone between the Tibetan Plateau and arid region, the Qilian Mountains are important ecological barriers and source regions of inland rivers in northwest China. In recent decades, drastic changes in the cryosphere have had a considerable impact on the formation process of water resources in the Qilian Mountains. In this study, 2164 environmental isotope samples were used to quantify the runoff components of 11 major rivers in the Qilian Mountains and investigate the influence of cryosphere changes on mountain runoff. The results show that the mountain runoff primarily originates in the cryosphere belt, which contributes approximately 82 %, 71 %, and 80 % to the Hexi inland water system, the upper stream of the Yellow River system, and the Qinghai inland river system, respectively. The maximum contribution ratio of glacier and snow meltwater to runoff occurred in May. The contribution of supra-permafrost water to runoff gradually increased from May and reached approximately 40 % in some rivers in October. Cryosphere degradation in the Qilian Mountains after the 1990s has caused a rapid increase in runoff, changes in the peak runoff time, and an increase in winter runoff. These changes in the hydrological processes bring opportunities and challenges to managing inland river water resources, and various adaptive measures to seek advantages and avoid disadvantages have been proposed. The findings from the environmental isotope analysis provide insights into understanding water resources and realizing the nexus of life, agriculture, industry, and ecological water use. [ABSTRACT FROM AUTHOR]

The "dry gets drier, and wet gets wetter" (DDWW) paradigm has been widely used to summarize the expected trends of the global hydrologic cycle under climate change. However, the paradigm is largely conditioned by choice of different metrics and datasets used and is still comprehensively unexplored from the perspective of terrestrial water storage anomalies (TWSAs). Considering the essential role of TWSAs in wetting and drying of the land system, here we built upon a large ensemble of TWSA datasets, including satellite-based products, global hydrological models, land surface models, and global climate models to evaluate the DDWW hypothesis during the historical (1985–2014) and future (2071–2100) periods under various scenarios with a 0.05 significance level (for trend estimates). We find that 11.01 %–40.84 % (range by various datasets) of global land confirms the DDWW paradigm, while 10.21 %–35.43 % of the area shows the opposite pattern during the historical period. In the future, the DDWW paradigm is still challenged, with the percentage supporting the pattern lower than 18 % and both the DDWW-validated and DDWW-opposed proportion increasing along with the intensification of emission scenarios. We show that the different choices of data sources can reasonably influence the test results up to a 4-fold difference. Our findings will provide insights and implications for global wetting and drying trends from the perspective of TWSA under climate change. [ABSTRACT FROM AUTHOR]

Adaptation to future climate change with limited water resources is a major global challenge to sustainable and sufficient crop production. However, the large-scale responses of the crop water footprint and its associated benchmarks under various irrigation regimes to future climate change scenarios remain unclear. The present study quantified the responses of the maize and wheat water footprint (WF) per unit yield (m 3 t -1) as well as the corresponding WF benchmarks under two Representative Concentration Pathway (RCP) scenarios, RCP2.6 and RCP8.5, in the 2030s, 2050s, and 2080s at a 5 arcmin grid level in China. The AquaCrop model with the outputs of six global climate models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5) as its input data was used to simulate the WFs of maize and wheat. The differences among rain-fed wheat and maize and furrow-, micro-, and sprinkler-irrigated wheat and maize were identified. Compared with the baseline year (2013), the maize WF will increase under both RCP2.6 and RCP8.5 (by 17 % and 13 %, respectively) until the 2080s. The wheat WF will increase under RCP2.6 (by 12 % until the 2080s) and decrease (by 12 %) under RCP8.5 until the 2080s, with a higher increase in the wheat yield and a decrease in the wheat WF due to the higher CO 2 concentration in 2080s under RCP8.5. The WF will increase the most for rain-fed crops. Relative to rain-fed crops, micro-irrigation and sprinkler irrigation result in the smallest increases in the WF for maize and wheat, respectively. These water-saving management techniques will mitigate the negative impact of climate change more effectively. The WF benchmarks for maize and wheat in the humid zone (an approximate overall average of 680 m 3 t -1 for maize and 873 m 3 t -1 for wheat at the 20th percentile) are 13 %–32 % higher than those in the arid zone (which experiences an overall average of 601 m 3 t -1 for maize and 753 m 3 t -1 for wheat). The differences in the WF benchmarks among various irrigation regimes are more significant in the arid zone, where they can be as high as 57 % for the 20th percentile: WF benchmarks of 1020 m 3 t -1 for sprinkler-irrigated wheat and 648 m 3 t -1 for micro-irrigated wheat. Nevertheless, the WF benchmarks will not respond to climate changes as dramatically as the WF in the same area, especially in areas with limited agricultural development. The present study demonstrated that the observed different responses to climate change in terms of crop water consumption, water use efficiency, and WF benchmarks under different irrigation regimes cannot be ignored. It also lays the foundation for future investigations into the influences of irrigation methods, RCPs, and crop types on the WF and its benchmarks in response to climate change in all agricultural regions worldwide. [ABSTRACT FROM AUTHOR]

With the rapid accumulation of water flux observations from global eddy-covariance flux sites, many studies have used data-driven approaches to model water fluxes, with various predictors and machine learning algorithms used. However, it is unclear how various model features affect prediction accuracy. To fill this gap, we evaluated this issue based on records of 139 developed models collected from 32 such studies. Support vector machines (SVMs; average R -squared = 0.82) and RF (random forest; average R -squared = 0.81) outperformed other evaluated algorithms with sufficient sample size in both cross-study and intra-study (with the same data) comparisons. The average accuracy of the model applied to arid regions is higher than in other climate types. The average accuracy of the model was slightly lower for forest sites (average R -squared = 0.76) than for croplands and grasslands (average R -squared = 0.8 and 0.79) but higher than for shrubland sites (average R -squared = 0.67). Using Rn/Rs , precipitation, Ta , and the fraction of absorbed photosynthetically active radiation (FAPAR) improved the model accuracy. The combined use of Ta and Rn/Rs is very effective, especially in forests, while in grasslands the combination of Ws and Rn/Rs is also effective. Random cross-validation showed higher model accuracy than spatial cross-validation and temporal cross-validation, but spatial cross-validation is more important in spatial extrapolation. The findings of this study are promising to guide future research on such machine-learning-based modeling. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) plays a vital role in Asian and even global atmospheric circulation, through the interactions between land and atmosphere. It has experienced significant climate warming and spatially and temporally variant wetting over the past half century. Because of the importance of land surface status to the interactions, determining the wetting and drying of the TP from individual changes in precipitation (Prep) or temperature is difficult. Soil moisture (SM) is the water synthesis of the surface status. The persistent deficit of SM (SM drought) is more sensitive to climate change than normal SM. This study first explored the climate wetting and drying of the TP from variations in historical SM droughts over 1961–2014, with a focus on spatiotemporal patterns, long-term variations, and climate causes of summer (May–September) SM droughts based on multiple observation and reanalysis data. The results showed comparatively frequent and severe droughts in the central and southern area, particularly in the semiarid and subhumid regions. SM drought exhibited an abrupt and significant (p < 0.05) alleviation in the interior and central-west TP in the middle to late 1990s. The prominent drought alleviation indicated a hydroclimate shift to a wetter plateau, not merely steady trends as given in the literature. We demonstrated that the wetting shift was dominated by Prep over potential evapotranspiration (PET). By contrast, the in-phase trends were combined forces of Prep and PET, with increased forces of PET after the wetting shift. Furthermore, the Prep dominance was largely attributed to a phase transition of the Atlantic multi-decadal oscillation from cold to warm since the mid-1990s. The PET impacts on the wetting trends were likely dominated by solar radiation, wind speed, and vapor pressure deficit. Regionally, the wetting shift was distinct from the arid to semiarid and semiarid to subhumid climate. Such spatiotemporal changes may affect the TP's atmospheric circulation and, subsequently, the Asian monsoon and global circulation, in addition to the fragile ecosystem in the TP. [ABSTRACT FROM AUTHOR]

The inherently dry summer climate of the Iberian Peninsula (IP) is undergoing drought exacerbated by more intense warming and reduced precipitation. Although many studies have studied changes in summer climate factors, it is still unclear how the changes in moisture contribution from the sources lead to the decrease in summer precipitation. This study investigates the differences in the IP precipitationshed from 1980 to 1997 and 1998 to 2019 using the Water Accounting Model-2layers with ERA5 data, and assesses the role of local moisture recycling and external moisture in reducing summer precipitation. Our findings indicate that the moisture contributions from the local IP, and from the west and the east of the precipitationshed, contributed 1.7, 3.6 and 1.1 mm per month less precipitation after 1997 than before 1997, accounting for 26 %, 57 % and 17 % of the main source supply reduction, respectively. The significant downward trend of the IP local moisture recycling closely links to the disappearance of the wet years after 1997 as well as the decrease in local contribution in the dry years. Moreover, the feedback between the weakened local moisture recycling and the drier land surface can exacerbate the local moisture scarcity and summer drought. [ABSTRACT FROM AUTHOR]

Mountainous regions act as the water towers of the world by producing streamflow and groundwater recharge, a function that is particularly important in semiarid regions. Quantifying rates of mountain system recharge is difficult, and hydrologic models offer a method to estimate recharge over large scales. These recharge estimates are prone to uncertainty from various sources including model structure and parameters. The quality of meteorological forcing datasets, particularly in mountainous regions, is a large source of uncertainty that is often neglected in groundwater investigations. In this contribution, we quantify the impact of uncertainty in both precipitation and air temperature forcing datasets on the simulated groundwater recharge in the mountainous watershed of the Kaweah River in California, USA. We make use of the integrated surface water–groundwater model, ParFlow.CLM, and several gridded datasets commonly used in hydrologic studies, downscaled NLDAS-2, PRISM, Daymet, Gridmet, and TopoWx. Simulations indicate that, across all forcing datasets, mountain front recharge is an important component of the water budget in the mountainous watershed, accounting for 9 %–72 % of the annual precipitation and ∼90 % of the total mountain system recharge to the adjacent Central Valley aquifer. The uncertainty in gridded air temperature or precipitation datasets, when assessed individually, results in similar ranges of uncertainty in the simulated water budget. Variations in simulated recharge to changes in precipitation (elasticities) and air temperature (sensitivities) are larger than 1 % change in recharge per 1 % change in precipitation or 1 ∘ C change in temperature. The total volume of snowmelt is the primary factor creating the high water budget sensitivity, and snowmelt volume is influenced by both precipitation and air temperature forcings. The combined effect of uncertainty in air temperature and precipitation on recharge is additive and results in uncertainty levels roughly equal to the sum of the individual uncertainties depending on the hydroclimatic condition of the watershed. Mountain system recharge pathways including mountain block recharge, mountain aquifer recharge, and mountain front recharge are less sensitive to changes in air temperature than changes in precipitation. Mountain front and mountain block recharge are more sensitive to changes in precipitation than other recharge pathways. The magnitude of uncertainty in the simulated water budget reflects the importance of developing high-quality meteorological forcing datasets in mountainous regions. [ABSTRACT FROM AUTHOR]

The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman–Monteith–Leuning (PML) equation into the Distributed Time Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration (ET) process. We calibrated six key model parameters, grid by grid across China, using a multivariable calibration strategy which incorporates spatiotemporal runoff and ET datasets (0.25 ∘ ; monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters but performed better in humid rather than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at a national scale, though the improvement pertaining to watershed streamflow validation is not significant due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters from watershed properties, particularly in ungauged regions. [ABSTRACT FROM AUTHOR]

Understanding current and possible future alterations of water resources under climate change and increased water demand allows for better water and environmental management decisions in arid regions. This study aims at analyzing the impact of groundwater demand and climate change on groundwater sustainability and hydrologic regime alterations in a wadi system in central Iran. A hydrologic model is used to assess streamflow and groundwater recharge of the Halilrood Basin on a daily time step under five different scenarios over the baseline period (1979–2009) and for two future scenario periods (near future: 2030–2059 and far future: 2070–2099). The Indicators of Hydrologic Alteration (IHA) with a set of 32 parameters are used in conjunction with the Range of Variability Approach (RVA) to evaluate hydrologic regime change in the river. The results show that groundwater recharge is expected to decrease and is not able to fulfill the increasing water demand in the far future scenario. The Halilrood River will undergo low and moderate streamflow alteration under both stressors during the near future as RVA alteration is classified as "high" for only three indicators, whereas stronger alteration is expected in the far future, with 11 indicators in the high range. Absolute changes in hydrologic indicators are stronger when both climate change and groundwater demand are considered in the far future simulations, since 27 indicators show significant changes, and the RVA shows high and moderate levels of changes for 18 indicators. Considering the evaluated RVA changes, future impacts on the freshwater ecosystems in the Halilrood Basin will be severe. The developed approach can be transferred to other wadi regions for a spatially distributed assessment of water resources sustainability. [ABSTRACT FROM AUTHOR]

Elevation in atmospheric carbon dioxide concentration (eCO 2) affects vegetation water use, with consequent impacts on terrestrial runoff (Q). However, the sign and magnitude of the eCO 2 effect on Q are still contentious. This is partly due to eCO 2 -induced changes in vegetation water use having opposing responses at the leaf scale (i.e., water-saving effect caused by partially stomatal closure) and the canopy scale (i.e., water-consuming induced by foliage cover increase), leading to highly debated conclusions among existing studies. In addition, none of the existing studies explicitly account for eCO 2 -induced changes to plant rooting depth that is overwhelmingly found in experimental observations. Here we develop an analytical ecohydrological framework that includes the effects of eCO 2 on plant leaf, canopy density, and rooting characteristics to attribute changes in Q and to detect the eCO 2 signal on Q via vegetation feedbacks over 1982–2010. Globally, we detect a very small decrease of Q induced by eCO 2 during 1982–2010 (- 1.7 %). Locally, we find a small positive trend (p < 0.01) in the Q –eCO 2 response along a resource availability (β) gradient. Specifically, the Q –eCO 2 response is found to be negative (i.e., eCO 2 reduces Q) in low- β regions (typically dry and/or cold) and gradually changes to a small positive response (i.e., eCO 2 increases Q) in high- β areas (typically warm and humid). Our findings suggest a minor role of eCO 2 on changes in global Q over 1982–2010, yet we highlight that a negative Q –eCO 2 response in semiarid and arid regions may further reduce the limited water resource there. [ABSTRACT FROM AUTHOR]

Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P , 14 % to 26 % for ET, 28 % to 82 % for R , 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R , 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement. [ABSTRACT FROM AUTHOR]

The term "flash drought" is frequently invoked to describe droughts that develop rapidly over a relatively short timescale. Despite extensive and growing research on flash drought processes, predictability, and trends, there is still no standard quantitative definition that encompasses all flash drought characteristics and pathways. Instead, diverse definitions have been proposed, supporting wide-ranging studies of flash drought but creating the potential for confusion as to what the term means and how to characterize it. Use of different definitions might also lead to different conclusions regarding flash drought frequency, predictability, and trends under climate change. In this study, we compared five previously published definitions, a newly proposed definition, and an operational satellite-based drought monitoring product to clarify conceptual differences and to investigate the sensitivity of flash drought inventories and trends to the choice of definition. Our analyses indicate that the newly introduced Soil Moisture Volatility Index definition effectively captures flash drought onset in both humid and semi-arid regions. Analyses also showed that estimates of flash drought frequency, spatial distribution, and seasonality vary across the contiguous United States depending upon which definition is used. Definitions differ in their representation of some of the largest and most widely studied flash droughts of recent years. Trend analysis indicates that definitions that include air temperature show significant increases in flash droughts over the past 40 years, but few trends are evident for definitions based on other surface conditions or fluxes. These results indicate that "flash drought" is a composite term that includes several types of events and that clarity in definition is critical when monitoring, forecasting, or projecting the drought phenomenon. [ABSTRACT FROM AUTHOR]

Desertification in semi-arid regions is currently a global environmental and societal problem. This research attempts to understand whether a 40-year-old rain-fed Artemisia sphaerocephala Krasch sand-fixing land as part of the Three North Shelterbelt Program (3NSP) of China can be developed sustainably or not using a newly designed lysimeter to monitor the precipitation-induced deep soil recharge (DSR) at 220 cm of depth. Evapotranspiration is calculated through a water balance equation when precipitation and soil moisture data are collected. A comparison of soil particle sizes and soil moisture distributions in artificial sand-fixing land and neighboring bare land is made to assess the impact of sand-fixing reforestation. Results show that such a sand-fixing reforestation results in a root system being mainly developed in the horizontal direction and a changed soil particle distribution. Specifically, the sandy soil with 50.53 % medium sand has been transformed into a sandy soil with 68.53 % fine sand. Within the Artemisia sphaerocephala Krasch sand-fixing experimental area, the DSR values in the bare sand plot and Artemisia sphaerocephala Krasch plot are respectively 283.6 and 90.6 mm in wet years, reflecting a difference of more than 3 times. The deep soil layer moisture in semi-arid sandy land is largely replenished by precipitation-induced infiltration. The DSR values of the bare sandy land plot and Artemisia sphaerocephala Krasch plot are respectively 51.6 and 2 mm in dry years, a difference of more than 25 times. The proportions of DSR reduced by Artemisia sphaerocephala Krasch are 68.06 % and 96.12 % in wet and dry years, respectively. This research shows that Artemisia sphaerocephala Krasch in semi-arid regions can continue to grow and has the capacity to fix sand. It consumes a large amount of precipitated water and reduces the amount of DSR considerably. [ABSTRACT FROM AUTHOR]

Identifying and quantifying recharge processes linked to ephemeral surface water features is challenging due to their episodic nature. We use a combination of well-established near-surface geophysical methods to provide evidence of a surface and groundwater connection under a small ephemeral recharge feature in a flat, semi-arid region near Adelaide, Australia. We use a seismic survey to obtain P-wave velocity through travel-time tomography and S-wave velocity through the multichannel analysis of surface waves. The ratios between P-wave and S-wave velocities are used to calculate Poisson's ratio, which allow us to infer the position of the water table. Separate geophysical surveys were used to obtain electrical conductivity measurements from time-domain electromagnetics and water contents from downhole nuclear magnetic resonance. The geophysical observations provide evidence to support a groundwater mound underneath a subtle ephemeral surface water feature. Our results suggest that recharge is localized and that small-scale ephemeral features may play an important role in groundwater recharge. Furthermore, we show that a combined geophysical approach can provide a perspective that helps shape the hydrogeological conceptualization of a semi-arid region. [ABSTRACT FROM AUTHOR]

Gullies lead to land degradation and desertification as well as increasing environmental and societal threats, especially in arid and semiarid regions. Despite this fact, there is a lack of related research initiatives. In an effort to better understand soil loss in these systems, we studied small permanent gullies, which are a recurrent problem in the Brazilian northeastern semiarid region. The increase in sediment connectivity and the reduction of soil moisture, among other deleterious consequences, endanger this desertification-prone region and reduce its capacity to support life and economic activities. Thus, we propose a model to simulate gully-erosion dynamics, which is derived from the existing physically based models of and. The models were adapted so as to simulate long-term erosion. A threshold area shows the scale dependency of gully-erosion internal processes (bed scouring and wall erosion). To validate the model, we used three gullies that were over 6 decades old in an agricultural basin in the Brazilian state of Ceará. The geometry of the channels was assessed using an unmanned aerial vehicle and the structure from motion technique. Laboratory analyses were performed to obtain soil properties. Local and regional rainfall data were gauged to obtain sub-daily rainfall intensities. The threshold value (cross-section area of 2 m 2) characterizes when erosion in the walls, due to loss of stability, becomes more significant than sediment detachment in the wet perimeter. The 30 min intensity can be used when no complete hydrographs from rainfall are available. Our model could satisfactorily simulate the gully-channel cross-section area growth over time, yielding a Nash–Sutcliffe efficiency of 0.85 and an R2 value of 0.94. [ABSTRACT FROM AUTHOR]

The main objective of this work is to question the representation of the energy budget in soil–vegetation–atmosphere transfer (SVAT) models for the prediction of the turbulent fluxes in the case of irrigated crops with a complex structure (row) and under strong transient hydric regimes due to irrigation. To this end, the Interaction between Soil, Biosphere, and Atmosphere (ISBA-A-g s) is evaluated at a complex open olive orchard and, for the purposes of comparison, on a winter wheat field taken as an example of a homogeneous canopy. The initial version of ISBA-A-g s , based on a composite energy budget (hereafter ISBA-1P for one patch), is compared to the new multiple energy balance (MEB) version of ISBA that represents a double source arising from the vegetation located above the soil layer. In addition, a patch representation corresponding to two adjacent, uncoupled source schemes (hereafter ISBA-2P for two patches) is also considered for the olive orchard. Continuous observations of evapotranspiration (ET), with an eddy covariance system and plant transpiration (Tr) with sap flow and isotopic methods were used to evaluate the three representations. A preliminary sensitivity analyses showed a strong sensitivity to the parameters related to turbulence in the canopy introduced in the new ISBA–MEB version. For wheat, the ability of the single- and dual-source configuration to reproduce the composite soil–vegetation heat fluxes was very similar; the root mean square error (RMSE) differences between ISBA-1P, ISBA-2P and ISBA–MEB did not exceed 10 W m -2 for the latent heat flux. These results showed that a composite energy balance in homogeneous covers is sufficient to reproduce the total convective fluxes. The two configurations are also fairly close to the isotopic observations of transpiration in spite of a light underestimation (overestimation) of ISBA-1P (ISBA–MEB). At the olive orchard, contrasting results are obtained. The dual-source configurations, including both the uncoupled (ISBA-2P) and the coupled (ISBA–MEB) representations, outperformed the single-source version (ISBA-1P), with slightly better results for ISBA–MEB in predicting both total heat fluxes and evapotranspiration partition. Concerning plant transpiration in particular, the coupled approach ISBA–MEB provides better results than ISBA-1P and, to a lesser extent, ISBA-2P with RMSEs of 1.60, 0.90, and 0.70 mm d -1 and R2 of 0.43, 0.69, and 0.70 for ISBA-1P, ISBA-2P and ISBA–MEB, respectively. In addition, it is shown that the acceptable predictions of composite convective fluxes by ISBA-2P for the olive orchard are obtained for the wrong reasons as neither of the two patches is in agreement with the observations because of a bad spatial distribution of the roots and a lack of incoming radiation screening for the bare soil patch. This work shows that composite convection fluxes predicted by the SURFace EXternalisée (SURFEX) platform and the partition of evapotranspiration in a highly transient regime due to irrigation is improved for moderately open tree canopies by the new coupled dual-source ISBA–MEB model. It also points out the need for further local-scale evaluations on different crops of various geometry (more open rainfed agriculture or a denser, intensive olive orchard) to provide adequate parameterisation to global database, such as ECOCLIMAP-II, in the view of a global application of the ISBA–MEB model. [ABSTRACT FROM AUTHOR]

The temporal and spatial distributions of regional irrigation water productivity (RIWP) are crucial for making decisions related to agriculture, especially in arid irrigated areas with complex cropping patterns. Thus, in this study, we developed a new RIWP model for an irrigated agricultural area with complex cropping patterns. The model couples the irrigation- and drainage-driven soil water and salinity dynamics and shallow groundwater movement in order to quantify the temporal and spatial distributions of the target hydrological and biophysical variables. We divided the study area into 1 km × 1 km hydrological response units (HRUs). In each HRU, we considered four land use types: sunflower fields, wheat fields, maize fields, and uncultivated lands (bare soil). We coupled the regional soil hydrological processes and groundwater flow by taking a weighted average of the water exchange between unsaturated soil and groundwater under different land use types. The RIWP model was calibrated and validated using 8 years of hydrological variables obtained from regional observation sites in a typical arid irrigation area in North China, the Hetao Irrigation District. The model simulated soil moisture and salinity reasonably well as well as groundwater table depths and salinity. However, overestimations of groundwater discharge were detected in both the calibration and validation due to the assumption of well-operated drainage ditch conditions; regional evapotranspiration (ET) was reasonably estimated, whereas ET in the uncultivated area was slightly underestimated in the RIWP model. A sensitivity analysis indicated that the soil evaporation coefficient and the specific yield were the key parameters for the RIWP simulation. The results showed that the RIWP decreased from maize to sunflower to wheat from 2006 to 2013. It was also found that the maximum RIWP was reached when the groundwater table depth was between 2 and 4 m, regardless of the irrigation water depth applied. This implies the importance of groundwater table control on the RIWP. Overall, our distributed RIWP model can effectively simulate the temporal and spatial distribution of the RIWP and provide critical water allocation suggestions for decision-makers. [ABSTRACT FROM AUTHOR]

Sand dams, a popular water harvesting structure employed by rural communities, capture and store water for use during the dry season in arid and semi-arid regions. Most sand dam research has been performed on the "ideal" sand dam, despite approximately 50 % of sand dams not functioning as intended. This research involves a 1-year long, in-depth field study of three sand dams in Tanzania, one of which is essentially non-functioning. The study investigated a sand dam's impact on macroinvertebrate habitat, vegetation, and streambank erosion and explored a sand dam's water loss mechanisms. Surveys of macroinvertebrate assemblage were performed each season. Vegetation surveys were performed every other month, and erosion was recorded semi-monthly. Water table monitoring wells were installed at each sand dam, and measurements were taken twice a day. The study found that sand dams are too homogeneous to provide the sustenance and refugia macroinvertebrates need at different life stages. The non-functioning sand dam has a thick layer of silt preventing infiltration of rainwater. The functioning sand dams store a significant amount of water, but most is lost to evapotranspiration within a few months of the last rainfall. Unlike the non-functioning sand dam, the functioning sand dams have a positive impact on local vegetation and minimal impact on erosion. Sand dams can increase the water security of a community, but site characteristics and construction methods must be strongly considered to maximize the sand dam's positive impact. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm yr -1 (6.56×104 km 3 yr -1) to 617.1 mm yr -1 (6.87×104 km 3 yr -1). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm yr -2 with a significance level of p<0.05 and 0.38 mm yr -2 with a significance level of p<0.05 , respectively). In contrast, the ensemble mean of the LSMs showed no statistically significant change (0.23 mm yr -2 , p>0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm yr -1. Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model–data fusion will advance our predictive understanding of global terrestrial ET. [ABSTRACT FROM AUTHOR]

The hydrology of Morocco is characterized by significant spatial variability. Precipitation follows a sharp gradient, decreasing from the north to the south. In order to redistribute the available water, a project has been proposed to transfer 860×106 m 3 yr-1 from the wet north to the arid southern regions, namely the "Water Highway" project. The present study aims to address the viability of the project after accounting for the impacts of climate change in the watersheds located in the north. We perform regional climate model (RCM) simulations over the study region using boundary conditions from five different global circulation models (GCMs) and assuming two different emissions scenarios – RCP4.5 (with mitigation) and RCP8.5 (business as usual). The impact on precipitation and temperature are assessed, and the decrease in the available water quantity is estimated. Under RCP8.5, the project is likely not feasible. However, under the RCP4.5, a rescaled version of this project may be feasible depending on how much water is allocated to satisfy the local water demand in the north. [ABSTRACT FROM AUTHOR]

Across the arid regions of water-stressed countries of Asia, groundwater production for irrigated agriculture has led to water-level declines that continue to worsen. For India, China, Pakistan, Iran, and others, it is unrealistic to expect groundwater sustainability in a verifiable sense to emerge. Fragmented governance and the general inability to bring traditional socio-economic tools to bear on reducing groundwater demands have impeded progress to groundwater sustainability. For India and Pakistan, where operational management is at the level of states and provinces, there is no capacity to regulate. Also in both China and India, the tremendous numbers of groundwater users, large and small, confound regulation of groundwater. With business as usual, groundwater-related problems receive insufficient attention, a situation referred to as an "accelerating and invisible groundwater crisis" (Biswas et al., 2017). Another obstacle to sustainability comes from trying to manage something you do not understand. With sustainable management, there are significant burdens in the needed technical know-how, in collecting necessary data, and in funding advanced technologies. Thus, there are risks that Iran, India, and Pakistan will run short of groundwater from over-pumping in some places and will also be adversely affected by global climate change. [ABSTRACT FROM AUTHOR]

Sustainable irrigation with treated wastewater (TWW) is a promising solution for water scarcity in arid and semi-arid regions. Soil aquifer treatment (SAT) provides a solution for both the need for tertiary treatment and seasonal storage of wastewater. Stresses over land use and the need to control the obtained water quality makes the optimization of SAT of great importance. This study looks into the influence of SAT systems' operational dynamics (i.e., flooding and drying periods) as well as some aspects of the inflow biochemical composition on their biogeochemical state and the ultimate outflow quality. A series of four long-column experiments was conducted, aiming to examine the effect of different flooding/drying period ratios on dissolved oxygen (DO) concentrations, oxidation–reduction potential (ORP) and outflow composition. Flooding periods were kept constant at 60 min for all experiments while drying periods (DPs) were 2.5 and 4 times the duration of the flooding periods. Our results show that the longer DPs had a significant advantage over the shorter periods in terms of DO concentrations and ORP in the upper parts of the column as well as in the deeper parts, which indicates that larger volumes of the profile were able to maintain aerobic conditions. DO concentrations in the deeper parts of the column stabilized at ∼3 –4 mg L -1 for the longer DPs compared to ∼1 –2 mg L -1 for the shorter DPs. This advantage was also evident in outflow composition that showed significantly lower concentrations of NH4+ -N, dissolved organic carbon (DOC) and total Kjeldahl nitrogen (TKN) for the longer DPs (∼0.03 , ∼1.65 and ∼0.62 mg L -1 respectively) compared to the shorter DPs (∼0.5 , ∼4.4 and ∼3.8 mg L -1 , respectively). Comparing experimental ORP values in response to different DPs to field measurements obtained in one of the SAT ponds of the SHAFDAN, Israel, we found that despite the large-scale differences between the experimental 1-D system and the field 3-D conditions, ORP trends in response to changes in DP, qualitatively match. We conclude that longer DP not only ensure oxidizing conditions close to the surface, but also enlarge the active (oxidizing) region of the SAT. While those results still need to be verified at full scale, they suggest that SAT can be treated as a pseudo-reactor that to a great extent could be manipulated hydraulically to achieve the desired water quality while increasing the recharge volumes. [ABSTRACT FROM AUTHOR]

Groundwater plays an important role in the terrestrial water cycle, interacting with the land surface via vertical fluxes through the water table and distributing water resources spatially via gravity-driven lateral transport. It is therefore essential to have a correct representation of groundwater processes in land surface models, as land–atmosphere coupling is a key factor in climate research. Here we use the LEAFHYDRO land surface and groundwater model to study the groundwater influence on soil moisture distribution and memory, and evapotranspiration (ET) fluxes in the Iberian Peninsula over a 10-year period. We validate our results with time series of observed water table depth from 623 stations covering different regions of the Iberian Peninsula, showing that the model produces a realistic water table, shallower in valleys and deeper under hilltops. We find patterns of shallow water table and strong groundwater–land surface coupling over extended interior semi-arid regions and river valleys. We show a strong seasonal and interannual persistence of the water table, which induces bimodal memory in the soil moisture fields; soil moisture "remembers" past wet conditions, buffering drought effects, and also past dry conditions, causing a delay in drought recovery. The effects on land–atmosphere fluxes are found to be significant: on average over the region, ET is 17.4 % higher when compared with a baseline simulation with LEAFHYDRO's groundwater scheme deactivated. The maximum ET increase occurs in summer (34.9 %; 0.54 mm d -1). The ET enhancement is larger over the drier southern basins, where ET is water limited (e.g. the Guadalquivir basin and the Mediterranean Segura basin), than in the northern Miño/Minho basin, where ET is more energy limited than water limited. In terms of river flow, we show how dry season baseflow is sustained by groundwater originating from accumulated recharge during the wet season, improving significantly on a free-drain approach, where baseflow comes from water draining through the top soil, resulting in rivers drying out in summer. Convective precipitation enhancement through local moisture recycling over the semi-arid interior regions and summer cooling are potential implications of these groundwater effects on climate over the Iberian Peninsula. Fully coupled land surface and climate model simulations are needed to elucidate this question. [ABSTRACT FROM AUTHOR]

Stemflow is important for recharging root-zone soil moisture in arid regions. Previous studies have generally focused on stemflow volume, efficiency and influential factors but have failed to depict stemflow processes and quantify their relations with rainfall characteristics within events, particularly for xerophytic shrubs. Here, we measured the stemflow volume, intensity, funneling ratio and time lags to rain at two dominant shrub species (Caragana korshinskii and Salix psammophila) and rainfall characteristics during 54 events at the semiarid Liudaogou catchment of the Loess Plateau, China, during the 2014–2015 rainy seasons. The funneling ratio was calculated as the ratio between stemflow and rainfall intensities at the inter- and intra-event scales. Our results indicated that the stemflow of C. korshinskii and S. psammophila , on average, started at 66.2 and 54.8 min, maximized 109.4 and 120.5 min after rain began, and ended 20.0 and 13.5 min after rain ceased. The two shrubs had shorter stemflow duration (3.8 and 3.4 h) and significantly larger stemflow intensities (517.5 and 367.3 mm h -1) than those of rain (4.7 h and 4.5 mm h -1). As branch size increased, both species shared the decreasing funneling ratios (97.7–163.7 and 44.2–212.0) and stemflow intensities (333.8–716.2 and 197.2–738.7 mm h -1). Tested by the multiple correspondence analysis and stepwise regression, rainfall amount and duration controlled stemflow volume and duration, respectively, at the event scale by linear relations (p < 0.01). Rainfall intensity and raindrop momentum controlled stemflow intensity and time lags to rain for both species within the event by linear or power relationships (p < 0.01). Rainfall intensity was the key factor affecting stemflow process of C. korshinskii , whereas raindrop momentum had the greatest influence on stemflow process of S. psammophila. Therefore, rainfall characteristics had temporally dependent influences on corresponding stemflow variables, and the influence also depended on specific species. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a major component of the land surface process involved in energy fluxes and energy balance, especially in the hydrological cycle of agricultural ecosystems. While many models have been developed as powerful tools to simulate ET, there is no agreement on which model best describes the loss of water to the atmosphere. This study focuses on two aspects, evaluating the performance of four widely used ET models and identifying parameters, and the physical mechanisms that have significant impacts on the model performance. The four tested models are the Shuttleworth–Wallace (SW) model, Penman–Monteith (PM) model, Priestley–Taylor and Flint–Childs (PT–FC) model, and advection–aridity (AA) model. By incorporating the mathematically rigorous thermodynamic integration algorithm, the Bayesian model evidence (BME) approach is adopted to select the optimal model with half-hourly ET observations obtained at a spring maize field in an arid region. Our results reveal that the SW model has the best performance, and the extinction coefficient is not merely partitioning the total available energy into the canopy and surface but also including the energy imbalance correction. The extinction coefficient is well constrained in the SW model and poorly constrained in the PM model but not considered in PT–FC and AA models. This is one of the main reasons that the SW model outperforms the other models. Meanwhile, the good fitting of SW model to observations can counterbalance its higher complexity. In addition, the detailed analysis of the discrepancies between observations and model simulations during the crop growth season indicate that explicit treatment of energy imbalance and energy interaction will be the primary way of further improving ET model performance. [ABSTRACT FROM AUTHOR]

With the aim to understand the spatial and temporal variability of groundwater recharge, a high-resolution, spatially distributed numerical model (MIKE SHE) representing surface water and groundwater was used to simulate responses to precipitation in a 2.16 km 2 upland catchment on fractured sandstone near Los Angeles, California. Exceptionally high temporal and spatial resolution was used for this catchment modeling: hourly climate data, a 20m×20m grid in the horizontal plane, and 240 numerical layers distributed vertically within the thick vadose zone and in the upper part of the groundwater zone. The finest practical spatial and temporal resolutions were selected to accommodate the large degree of surface and subsurface variability of catchment features. Physical property values for the different lithologies were assigned based on previous on-site investigations, whereas the parameters controlling streamflow and evapotranspiration were derived from calibration to continuous streamflow at the outfall and to average hydraulic heads from 17 wells. Confidence in the calibrated model was enhanced by validation through (i) comparison of simulated average recharge to estimates based on the applications of the chloride mass-balance method to data from the groundwater and vadose zones within and beyond the catchment, (ii) comparison of the water isotope signature (18O and 2H) in shallow groundwater to the variability of isotope signatures for precipitation events over an annual cycle, and (iii) comparison of simulated recharge time series and observed fluctuation of water levels. The average simulated recharge across the catchment for the period 1995–2014 is 16 mm yr -1 (4 % of the average annual precipitation), which is consistent with previous estimates obtained by using the chloride mass balance method (4.2 % of the average precipitation). However, one of the most unexpected results was that local recharge was simulated to vary from 0 to >1000 mm yr -1 due to episodic precipitation and overland runoff effects. This recharge occurs episodically with the major flux events at the bottom of the evapotranspiration zone, as simulated by MIKE SHE and confirmed by the isotope signatures, occurring only at the end of the rainy season. This is the first study that combines MIKE SHE simulations with the analysis of water isotopes in groundwater and rainfall to determine the timing of recharge in a sedimentary bedrock aquifer in a semiarid region. The study advances the understanding of recharge and unsaturated flow processes and enhances our ability to predict the effects of surface and subsurface features on recharge rates. This is crucial in highly heterogeneous contaminated sites because different contaminant source areas have widely varying recharge and, hence, groundwater fluxes impacting their mobility. [ABSTRACT FROM AUTHOR]

The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach. Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality. Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models. [ABSTRACT FROM AUTHOR]

There is a growing desire for reliable 21st-century projections of water availability at the regional scale. Global climate models (GCMs) are typically used together with global hydrological models (GHMs) to generate such projections. GCMs alone are unsuitable, especially if they have biased representations of aridity. The Budyko framework represents how water availability varies as a non-linear function of aridity and is used here to constrain projections of runoff from GCMs, without the need for computationally expensive GHMs. Considering a Chinese case study, we first apply the framework to observations to show that the contribution of direct human impacts (water consumption) to the significant decline in Yellow River runoff was greater than the contribution of aridity change by a factor of approximately 2, although we are unable to rule out a significant contribution from the net effect of all other factors. We then show that the Budyko framework can be used to narrow the range of Yellow River runoff projections by 34 %, using a multi-model ensemble and the high-end Representative Concentration Pathway (RCP8.5) emissions scenario. This increases confidence that the Yellow River will see an increase in runoff due to aridity change by the end of the 21st century. Yangtze River runoff projections change little, since aridity biases in GCMs are less substantial. Our approach serves as a quick and inexpensive tool to rapidly update and correct projections from GCMs alone. This could serve as a valuable resource when determining the water management policies required to alleviate water stress for future generations. [ABSTRACT FROM AUTHOR]

We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0° S) and elevation (0 to 6993ma.s.l.) ranges, and it relies on multiple data sources (including ground data, remotesensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basinaveraged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydroclimatological indices using these time series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset and promote common standards in large sample studies, we computed most catchment attributes introduced by Addor et al. (2017) in their Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r > 0:91) and lower correlation (r < 0:76) in hyperarid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. [ABSTRACT FROM AUTHOR]

A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil. [ABSTRACT FROM AUTHOR]

The magnitude and sign of soil moisture--precipitation coupling (SMPC) is investigated using a probability-based approach and 10 years of daily microwave satellite data across North Africa at a 1° horizontal scale. Specifically, the co-existence and co-variability of spatial (i.e. using soil moisture gradients) and temporal (i.e. using soil moisture anomaly) soil moisture effects on afternoon rainfall is explored. The analysis shows that in the semi-arid environment of the Sahel, the negative spatial and the negative temporal coupling relationships do not only co-exist, but are also dependent on one another. Hence, if afternoon rain falls over temporally drier soils, it is likely to be surrounded by a wetter environment. Two regions are identified as SMPC "hot spots". These are the south-western part of the domain (7-15° N, 10°W-7° E), with the most robust negative SMPC signal, and the South Sudanese region (5-13° N, 24-34° E). The sign and significance of the coupling in the latter region is found to be largely modulated by the presence of wetlands and is susceptible to the number of long-lived propagating convective systems. The presence of wetlands and an irrigated land area is found to account for about 30% of strong and significant spatial SMPC in the North African domain. This study provides the first insight into regional variability of SMPC in North Africa, and supports the potential relevance of mechanisms associated with enhanced sensible heat flux and mesoscale variability in surface soil moisture for deep convection development. [ABSTRACT FROM AUTHOR]

Spatiotemporal patterns of rainfall are important characteristics that influence runoff generation and flashflood magnitude and require high-resolution measurements to be adequately represented. This need is further emphasized in arid climates, where rainfall is scarce and highly variable. In this study, 24 years of corrected and gaugeadjusted radar rainfall estimates are used to (i) identify the spatial structure and dynamics of convective rain cells in a dry climate region in the Eastern Mediterranean, (ii) to determine their climatology, and (iii) to understand their relation with the governing synoptic systems and with flashflood generation. Rain cells are extracted using a segmentation method and a tracking algorithm, and are clustered into three synoptic patterns according to atmospheric variables from the ERA-Interim reanalysis. On average, the cells are about 90 km2 in size, move 13ms-1 from west to east, and live for 18 min. The Cyprus low accounts for 30% of the events, the low to the east of the study region for 44 %, and the Active Red Sea Trough for 26 %. The Active Red Sea Trough produces shorter rain events composed of rain cells with higher rain intensities, longer lifetime, smaller area, and lower velocities. The area of rain cells is positively correlated with topographic height. The number of cells is negatively correlated with the distance from the shoreline. Rain-cell intensity is negatively correlated with mean annual precipitation. Flash-flood-related events are dominated by rain cells of large size, low velocity, and long lifetime that move downstream with the main axis of the catchments. These results can be further used for stochastic simulations of convective rain storms and serve as input for hydrological models and for flash-flood nowcasting systems. [ABSTRACT FROM AUTHOR]

Rain-fed farming is the primary livelihood of semi-arid west Africa. Changes in land cover have the potential to affect precipitation, the critical resource for production. Turbulent flux measurements from two eddy-covariance towers and additional observations from a dense network of small, wireless meteorological stations combine to relate land cover (savanna forest and agriculture) to evaporation in a small (3.5 km2) catchment in Burkina Faso, west Africa. We observe larger sensible and latent heat fluxes over the savanna forest in the headwater area relative to the agricultural section of the watershed all year. Higher fluxes above the savanna forest are attributed to the greater number of exposed rocks and trees and the higher productivity of the forest compared to rain-fed, hand-farmed agricultural fields. Vegetation cover and soil moisture are found to be primary controls of the evaporative fraction. Satellite-derived vegetation index (NDVI) and soil moisture are determined to be good predictors of evaporative fraction, as indicators of the physical basis of evaporation. Our measurements provide an estimator that can be used to derive evaporative fraction when only NDVI is available. Such large-scale estimates of evaporative fraction from remotely sensed data are valuable where groundbased measurements are lacking, which is the case across the African continent and many other semi-arid areas. Evaporative fraction estimates can be combined, for example, with sensible heat from measurements of temperature variance, to provide an estimate of evaporation when only minimal meteorological measurements are available in remote regions of the world. These findings reinforce local cultural beliefs of the importance of forest fragments for climate regulation and may provide support to local decision makers and rural farmers in the maintenance of the forest areas. [ABSTRACT FROM AUTHOR]

Water resources, which are considerably affected by land use/land cover (LULC) and climate changes, are a key limiting factor in highly vulnerable ecosystems in arid and semi-arid regions. The impacts of LULC and climate changes on water resources must be assessed in these areas. However, conflicting results regarding the effects of LULC and climate changes on runoff have been reported in relatively large basins, such as the Jinghe River basin (JRB), which is a typical catchment (> 45 000 km²) located in a semi-humid and arid transition zone on the central Loess Plateau, northwest China. In this study, we focused on quantifying both the combined and isolated impacts of LULC and climate changes on surface runoff. We hypothesized that under climatic warming and drying conditions, LULC changes, which are primarily caused by intensive human activities such as the Grain for Green Program, will considerably alter runoff in the JRB. The Soil and Water Assessment Tool (SWAT) was adopted to perform simulations. The simulated results indicated that although runoff increased very little between the 1970s and the 2000s due to the combined effects of LULC and climate changes, LULC and climate changes affected surface runoff differently in each decade, e.g., runoff increased with increased precipitation between the 1970s and the 1980s (precipitation contributed to 88% of the runoff increase). Thereafter, runoff decreased and was increasingly influenced by LULC changes, which contributed to 44% of the runoff changes between the 1980s and 1990s and 71% of the runoff changes between the 1990s and 2000s. Our findings revealed that large-scale LULC under the Grain for Green Program has had an important effect on the hydrological cycle since the late 1990s. Additionally, the conflicting findings regarding the effects of LULC and climate changes on runoff in relatively large basins are likely caused by uncertainties in hydrological simulations. [ABSTRACT FROM AUTHOR]

Droughts are serious natural hazards, especially in semi-arid regions. They are also difficult to characterize. Various summary metrics representing the dryness level, denoted drought indices, have been developed to quantify droughts. They typically lump meteorological variables and can thus directly be computed from the outputs of regional climate models in climate-change assessments. While it is generally accepted that drought risks in semi-arid climates will increase in the future, quantifying this increase using climate model outputs is a complex process that depends on the choice and the accuracy of the drought indices, among other factors. In this study, we compare seven meteorological drought indices that are commonly used to predict future droughts. Our goal is to assess the reliability of these indices to predict hydrological impacts of droughts under changing climatic conditions at the annual timescale. We simulate the hydrological responses of a small catchment in northern Spain to droughts in present and future climate, using an integrated hydrological model calibrated for different irrigation scenarios. We compute the correlation of meteorological drought indices with the simulated hydrological time series (discharge, groundwater levels, and water deficit) and compare changes in the relationships between hydrological variables and drought indices. While correlation coefficients linked with a specific drought index are similar for all tested land uses and climates, the relationship between drought indices and hydrological variables often differs between present and future climate. Drought indices based solely on precipitation often underestimate the hydrological impacts of future droughts, while drought indices that additionally include potential evapotranspiration sometimes overestimate the drought effects. In this study, the drought indices with the smallest bias were the rainfall anomaly index, the reconnaissance drought index, and the standardized precipitation evapotranspiration index. However, the efficiency of these drought indices depends on the hydrological variable of interest and the irrigation scenario. We conclude that meteorological drought indices are able to identify years with restricted water availability in present and future climate. However, these indices are not capable of estimating the severity of hydrological impacts of droughts in future climate. A wellcalibrated hydrological model is necessary in this respect. [ABSTRACT FROM AUTHOR]

Interactions between soil moisture and terrestrial evaporation affect water cycle behaviour and responses between the land surface and the atmosphere across scales. With strong heterogeneities at the land surface, the inherent spatial variability in soil moisture makes its representation via point-scale measurements challenging, resulting in scale mismatch when compared to coarser-resolution satellitebased soil moisture or evaporation estimates. The Cosmic Ray Neutron Probe (CRNP) was developed to address such issues in the measurement and representation of soil moisture at intermediate scales. Here, we present a study to assess the utility of CRNP soil moisture observations in validating model evaporation estimates. The CRNP soil moisture product from a pasture in the semi-arid central west region of New South Wales, Australia, was compared to evaporation derived from three distinct approaches, including the Priestley-Taylor (PT-JPL), Penman-Monteith (PM-Mu), and Surface Energy Balance System (SEBS) models, driven by forcing data from local meteorological station data and remote sensing retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Pearson's correlations, quantile-quantile (Q-Q) plots, and analysis of variance (ANOVA) were used to qualitatively and quantitatively evaluate the temporal distributions of soil moisture and evaporation over the study site. The relationships were examined against nearly 2 years of observation data, as well as for different seasons and for defined periods of analysis. Results highlight that while direct correlations of raw data were not particularly instructive, the Q-Q plots and ANOVA illustrate that the root-zone soil moisture represented by the CRNP measurements and the modelled evaporation estimates reflect similar distributions under most meteorological conditions. The PT-JPL and PM-Mu model estimates performed contrary to expectation when high soil moisture and cold temperatures were present, while SEBS model estimates displayed a disconnect from the soil moisture distribution in summers with long dry spells. Importantly, no single evaporation model matched the statistical distribution of the measured soil moisture for the entire period, highlighting the challenges in effectively capturing evaporative flux response within changing landscapes. One of the outcomes of this work is that the analysis points to the feasibility of using intermediatescale soil moisture measurements to evaluate gridded estimates of evaporation, exploiting the independent, yet physically linked nature of these hydrological variables. [ABSTRACT FROM AUTHOR]

To achieve water resource sustainability in the water-limited southwestern US, it is critical to understand the potential effects of proposed forest thinning on the hydrology of semi-arid basins, where disturbances to headwater catchments can cause significant changes in the local water balance components and basinwise streamflows. In Arizona, the Four Forest Restoration Initiative (4FRI) is being developed with the goal of restoring 2.4 million acres of ponderosa pine along the Mogollon Rim. Using the physically based, spatially distributed triangulated irregular network (TIN)-based Real-time Integrated Basin Simulator (tRIBS) model, we examine the potential impacts of the 4FRI on the hydrology of Tonto Creek, a basin in the Verde-Tonto-Salt (VTS) system, which provides much of the water supply for the Phoenix metropolitan area. Long-term (20-year) simulations indicate that forest removal can trigger significant shifts in the spatiotemporal patterns of various hydrological components, causing increases in net radiation, surface temperature, wind speed, soil evaporation, groundwater recharge and runoff, at the expense of reductions in interception and shading, transpiration, vadose zone moisture and snow water equivalent, with south-facing slopes being more susceptible to enhanced atmospheric losses. The net effect will likely be increases in mean and maximum streamflow, particularly during El Niño events and the winter months, and chiefly for those scenarios in which soil hydraulic conductivity has been significantly reduced due to thinning operations. In this particular climate, forest thinning can lead to net loss of surface water storage by vegetation and snowpack, increasing the vulnerability of ecosystems and populations to larger and more frequent hydrologic extreme conditions on these semi-arid systems. [ABSTRACT FROM AUTHOR]

The Budyko hypothesis (BH) is an effective approach to investigating long-term water balance at large basin scale under steady state. The assumption of steady state prevents applications of the BH to basins, which is unclosed, or with significant variations in root zone water storage, i.e., under unsteady state, such as in extremely arid regions. In this study, we choose the Heihe River basin (HRB) in China, an extremely arid inland basin, as the study area. We firstly use a calibrated and then validated monthly water balance model, i.e., the abcd model, to quantitatively determine annual and monthly variations of water balance for the sub-basins and the whole catchment of the HRB, and find that the roles of root zone water storage change and that of inflow from upper sub-basins in monthly water balance are significant. With the recognition of the inflow water from other regions and the root zone water storage change as additional possible water sources to evapotranspiration in unclosed basins, we further define the equivalent precipitation (Pe) to include local precipitation, inflow water and root zone water storage change as the water supply in the Budyko framework. With the newly defined water supply, the Budyko curve can successfully describe the relationship between the evapotranspiration ratio and the aridity index at both annual and monthly timescales, whilst it fails when only the local precipitation being considered. Adding to that, we develop a new Fu-type Budyko equation with two non-dimensional parameters (ω and λ) based on the deviation of Fu's equation. Over the annual timescale, the new Fu-type Budyko equation developed here has more or less identical performance to Fu's original equation for the sub-basins and the whole catchment. However, over the monthly timescale, due to large seasonality of root zone water storage and inflow water, the new Fu-type Budyko equation generally performs better than Fu's original equation. The new Fu-type Budyko equation (ω and λ) developed here enables one to apply the BH to interpret regional water balance over extremely dry environments under unsteady state (e.g., unclosed basins or sub-annual timescales). [ABSTRACT FROM AUTHOR]

The understanding of runoff generation mechanisms is crucial for the sustainable management of river basins such as the allocation of water resources or the prediction of floods and droughts. However, identifying the mechanisms of runoff generation has been a challenging task, even more so in arid and semi-arid areas where high rainfall and streamflow variability, high evaporation rates, and deep groundwater reservoirs may increase the complexity of hydrological process dynamics. Isotope and hydrochemical tracers have proven to be useful in identifying runoff components and their characteristics. Moreover, although widely used in humid temperate regions, isotope hydrograph separations have not been studied in detail in arid and semiarid areas. Thus the purpose of this study is to determine whether isotope hydrograph separations are suitable for the quantification and characterization of runoff components in a semi-arid catchment considering the hydrological complexities of these regions. Through a hydrochemical characterization of the surface water and groundwater sources of the catchment and two- and three-component hydrograph separations, runoff components of the Kaap catchment in South Africa were quantified using both isotope and hydrochemical tracers. No major disadvantages while using isotope tracers over hydrochemical tracers were found. Hydrograph separation results showed that runoff in the Kaap catchment is mainly generated by groundwater sources. Two-component hydrograph separations revealed groundwater contributions of between 64 and 98% of total runoff. By means of threecomponent hydrograph separations, runoff components were further separated into direct runoff, shallow and deep groundwater components. Direct runoff, defined as the direct precipitation on the stream channel and overland flow, contributed up to 41% of total runoff during wet catchment conditions. Shallow groundwater defined as the soil water and nearsurface water component (and potentially surface runoff) contributed up to 45% of total runoff, and deep groundwater contributed up to 84% of total runoff. A strong correlation for the four studied events was found between the antecedent precipitation conditions and direct runoff. These findings suggest that direct runoff is enhanced by wetter conditions in the catchment that trigger saturation excess overland flow as observed in the hydrograph separations. [ABSTRACT FROM AUTHOR]

The effect of future climate scenarios on surface and groundwater resources was simulated using a modeling approach for an artificial recharge area in arid southern Iran. Future climate data for the periods of 2010-2030 and 2030-2050 were acquired from the Canadian Global Coupled Model (CGCM 3.1) for scenarios A1B, A2, and B1. These scenarios were adapted to the studied region using the delta-change method. A conceptual rainfall-runoff model (Qbox) was used to simulate runoff in a flash flood prone catchment. The model was calibrated and validated for the period 2002-2011 using daily discharge data. The projected climate variables were used to simulate future runoff. The rainfall-runoff model was then coupled to a calibrated groundwater flow and recharge model (MODFLOW) to simulate future recharge and groundwater hydraulic heads. As a result of the rainfall-runoff modeling, under the B1 scenario the number of floods is projected to slightly increase in the area. This in turn calls for proper management, as this is the only source of fresh water supply in the studied region. The results of the groundwater recharge modeling showed no significant difference between present and future recharge for all scenarios. Owing to that, four abstraction and recharge scenarios were assumed to simulate the groundwater level and recharge amount in the studied aquifer. The results showed that the abstraction scenarios have the most substantial effect on the groundwater level and the continuation of current pumping rate would lead to a groundwater decline by 18 m up to 2050. [ABSTRACT FROM AUTHOR]