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Variability in soil moisture has implications for regional terrestrial environments under a warming climate. This paper focuses on the spatiotemporal variability in the intra-annual persistence of soil moisture in China using the fifthgeneration reanalysis dataset by the European Centre for Medium-Range Weather Forecasts for the period 1979-2018. The results show that in China, the mean intra-annual persistence in the humid to arid zones increased from 60 to 115 days in the lower layer but decreased from 19 to 13 days and from 25 to 14 days in the upper and root layers, respectively. However, these changes were strongly attenuated in extremely dry and wet regions due to the scarcity of soil moisture anomalies. Large changes in persistence occurred in the lower soil layer in dryland areas, with a mean difference of up to 40 days between the 2010s and the 1980s. Overall increasing trends dominated the large-scale spatial features, despite regional decreases in the eastern arid zone and the North and Northeast China plains. In the root layer, the two plains experienced an expanded decrease while on the Tibetan Plateau it was dominated by decadal variability. These contrasting changes between the lower and root layers along the periphery of the transition zone was a reflection of the enhanced soil hydrological cycle in the root layer. The enhanced persistence in drylands lower layer is an indication of the intensified impacts of soil moisture anomalies (e.g., droughts) on terrestrial water cycle. These findings may help the understanding of climate change impacts on terrestrial environments. [ABSTRACT FROM AUTHOR]

The majority of the world's population growth to 2050 is projected to occur in the tropics. Hence, there is a serious need for robust methods for undertaking water resource assessments to underpin the sustainable management of water in tropical regions. This paper describes the largest and most comprehensive assessment of the future impacts of runoff undertaken in a tropical region using conceptual rainfall-runoff models (RRMs). Five conceptual RRMs were calibrated using data from 115 streamflow gauging stations, and model parameters were regionalized using a combination of spatial proximity and catchment similarity. Future rainfall and evapotranspiration projections (denoted here as GCMES) were transformed to catchment-scale variables by empirically scaling (ES) the historical climate series, informed by 15 global climate models (GCMs), to reflect a 1°C increase in global average surface air temperature. Using the best-performing RRM ensemble, approximately half the GCMES used resulted in a spatially averaged increase in mean annual runoff (by up to 29%%) and half resulted in a decrease (by up to 26%%). However, ~70%% of the GCMES resulted in a difference of within ±5%% of the historical rainfall (1930-2007). The range in modeled impact on runoff, as estimated by five RRMs (for individual GCMES), was compared to the range in modeled runoff using 15 GCMES (for individual RRMs). For mid- to high runoff metrics, better predictions will come from improved GCMES projections. A new finding of this study is that in the wet-dry tropics, for extremely large runoff events and low flows, improvements are needed in both GCMES and rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

Assessing the status of water resources is essential for long-term planning related to water and many other needs of a country. According to climate reports, climate change is on the rise in all parts of the world; however, this phenomenon will have more consequences in arid and semi-arid regions. The aim of this study is to evaluate the effects of climate change on groundwater, surface water, and their exchanges in Shazand plain in Iran, which has experienced a significant decline in streamflow and groundwater level in recent years. To address this issue, we propose the use of the integrated hydrological model MODFLOW-OWHM to simulate groundwater level, surface water routing, and their interactions; a climate model, NorESM, under scenario SSP2, for climate data prediction; and, finally, the HEC-HMS model to predict future river discharge. The results predict that, under future climate conditions, the river discharges at the hydrometric stations of the region may decrease by 58%, 63%, 75%, and 81%. The average groundwater level in 2060 may decrease significantly by 15.1 m compared to 2010. The results of this study reveal the likely destructive effects of climate change on water resources in this region and highlight the need for sustainable management methods to mitigate these future effects. [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]

Climate change has a significant impact on water resources. Forecasts and simulations of climate runoff processes are essential for assessing the impact of global climate change on runoff variations. This study focuses on the upper Taohe River Basin, which is an important watershed in the semi-arid regions of northwest China. To assess the runoff in the upper Taohe River Basin and the responses to climate change, the SWAT hydrological model was used to analyze future climate change scenarios and their effects on water resources. The results indicate that the minimum temperature would increase gradually in the 21st century and that the minimum temperature change would be more significant than the maximum temperature change, which indicates that minimum temperature changes would make an obvious contribution to future regional warming. Under RCP2.6, the average precipitation would decrease; at the same time, under RCP4.5 and RCP8.5, the average precipitation would increase. In the future, under different climate scenarios, the runoff will exhibit droughts and flood disasters. These research results provide scientific support for water resource utilization and management in the Taohe River Basin. [ABSTRACT FROM AUTHOR]

Dryland regions are characterised by water scarcity and are facing major challenges under climate change. One difficulty is anticipating how rainfall will be partitioned into evaporative losses, groundwater, soil moisture, and runoff (the water balance) in the future, which has important implications for water resources and dryland ecosystems. However, in order to effectively estimate the water balance, hydrological models in drylands need to capture the key processes at the appropriate spatio-temporal scales. These include spatially restricted and temporally brief rainfall, high evaporation rates, transmission losses, and focused groundwater recharge. Lack of available input and evaluation data and the high computational costs of explicit representation of ephemeral surface–groundwater interactions restrict the usefulness of most hydrological models in these environments. Therefore, here we have developed a parsimonious distributed hydrological model for DRYland Partitioning (DRYP). The DRYP model incorporates the key processes of water partitioning in dryland regions with limited data requirements, and we tested it in the data-rich Walnut Gulch Experimental Watershed against measurements of streamflow, soil moisture, and evapotranspiration. Overall, DRYP showed skill in quantifying the main components of the dryland water balance including monthly observations of streamflow (Nash–Sutcliffe efficiency, NSE, ∼ 0.7), evapotranspiration (NSE > 0.6), and soil moisture (NSE ∼ 0.7). The model showed that evapotranspiration consumes > 90 % of the total precipitation input to the catchment and that < 1 % leaves the catchment as streamflow. Greater than 90 % of the overland flow generated in the catchment is lost through ephemeral channels as transmission losses. However, only ∼ 35 % of the total transmission losses percolate to the groundwater aquifer as focused groundwater recharge, whereas the rest is lost to the atmosphere as riparian evapotranspiration. Overall, DRYP is a modular, versatile, and parsimonious Python-based model which can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland regions. [ABSTRACT FROM AUTHOR]

Methodological choices can have strong effects on projections of climate change impacts on hydrology. In this study, we investigate the ways in which four different steps in the modeling chain influence the spread in projected changes of different aspects of hydrology. To form the basis of these analyses, we constructed an ensemble of 160 simulations from permutations of two Representative Concentration Pathways, 10 global climate models, two downscaling methods, and four hydrologic model implementations. The study is situated in the Pacific Northwest of North America, which has relevance to a diverse, multinational cast of stakeholders. We analyze the effects of each modeling decision on changes in gridded hydrologic variables of snow water equivalent and runoff, as well as streamflow at point locations. Results show that the choice of representative concentration pathway or global climate model is the driving contributor to the spread in annual streamflow volume and timing. On the other hand, hydrologic model implementation explains most of the spread in changes in low flows. Finally, by grouping the results by climate region the results have the potential to be generalized beyond the Pacific Northwest. Future hydrologic impact assessments can use these results to better tailor their modeling efforts. Plain Language Summary: Future climate change will affect water resources throughout the Pacific Northwest of North America. Simulation experiments and recent observations agree that there will be less snow and it will melt earlier, which will impact the timing and amount of streamflow. However, the magnitudes of these changes are uncertain. In this study, we analyzed the spread among 160 different simulated scenarios of the hydrologic future. We show that the ways we represent the future atmosphere and land surface can have strong effects on our final predictions. Specifically, the way that we model the land surface has a large impact on predictions in arid zones or during dry periods. However, the way we model the atmosphere affects our predictions of changes in snow, snowmelt, and streamflow timing. Our findings are helpful for understanding future hydrologic change more thoroughly, which is of particular importance given international agreements in the Columbia River Basin. Key Points: Methodological choices can have large impacts on projected changes in hydrology, at times affecting the sign of change in streamflow volumeChanges in flow timing are mostly explained by modeled changes in climate while changes in low flows by hydrologic model choiceThe spread among our projected changes remains large, but it is possible to focus modeling efforts to better understand the spread [ABSTRACT FROM AUTHOR]

Arid and semiarid lowland areas of central Asia are largely dependent on fluvial water originating from the Tian Shan. Mountain glaciers contribute significantly to runoff, particularly in summer. With global warming, the total glacier area in the Kunma Like River catchment declined by 13.2% during 1990-2007. For future water resources, it is essential to quantify the responses of hydrologic processes to both climate change and shrinking glaciers in glacierized catchments, such as the headwaters of the Tarim River. Thus, a degree-day glacier melt algorithm was integrated into the macroscale hydrologic Variable Infiltration Capacity model (VIC). Good results were obtained for monthly runoff simulations in the Kunma Like River catchment, which suggest that the extended VIC has acceptable performance. Because of increased precipitation and air temperature, annual runoff in the catchment has increased by about 4.07 × 108 m3 decade−1 during 1984/85-2006/07. Under the assumption of the same climatic conditions, sensitivity analyses indicated that annual and summer river runoff volumes would decrease by 9.3% and 10.4%, respectively, for reductions in glacier area of 13.2%. The variation coefficient of annual runoff also increased because of shrinking glaciers. Runoff scenarios for warmer future climate and various deglaciation situations suggest that reductions in glacier area by >30% will likely produce less meltwater in summer and river runoff will decline. Consequently, the annual total discharge of the Kunma Like River is projected to decrease by 2.8%-19.4% in the 2050s scenario because of glacier shrinking. [ABSTRACT FROM AUTHOR]

Water managers across the United States face the need to make informed policy decisions regarding long-term impacts of climate change on water resources. To provide a scientifically informed basis for this, the evolution of important components of the basin-scale water balance through the end of the twenty-first century is estimated. Bias-corrected and spatially downscaled climate projections, from phase 3 of the Coupled Model Intercomparison Project (CMIP3) of the World Climate Research Programme, were used to drive a spatially distributed Variable Infiltration Capacity (VIC) model of hydrologic processes in the Salt-Verde basin in the southwestern United States. From the suite of CMIP3 models, the authors select a five-model subset, including three that best reproduce the historical climatology for the study region, plus two others to represent wetter and drier than model average conditions, so as to represent the range of GCM prediction uncertainty. For each GCM, data for three emission scenarios (A1B, A2, and B1) were used to drive the hydrologic model into the future. The projections of this model ensemble indicate a statistically significant 25% decrease in streamflow by the end of the twenty-first century. The primary cause for this change is due to projected decreases in winter precipitation accompanied by significant (temperature driven) reductions in storage of snow and increased winter evaporation. The results show that water management in central Arizona is highly likely to be impacted by changes in regional climate. [ABSTRACT FROM AUTHOR]