Irrigation expansion is often posed as a promising option to enhance food security. Here, we assess the influence of expansion of irrigation, primarily in rural areas of the contiguous United States (CONUS), on the intensification and spatial proliferation of surface freshwater scarcity. Our study shows that the rainfed to irrigation-fed (RFtoIF) transition of water-scarce croplands can impact scarcity in both transitioned and non-transitioned regions, with the magnitude of impact being dependent on multiple factors including local water demand, abstractions in the river upstream, and the buffering capacity of ancillary water sources to cities. Overall, RFtoIF transition will result in an additional 169.6 million hectares or 22% of the total CONUS land area facing moderate or severe water scarcity. Analysis of just the 53 large urban clusters with 146 million residents shows that the transition will result in 97 million urban population facing water scarcity for at least one month per year on average versus 82 million before the irrigation expansion. While these reported figures are subject to simulation uncertainties despite efforts to exercise due diligence, the study unambiguously underscores the need for strategies aimed at boosting crop productivity to incorporate the effects on water availability throughout the entire extent of the flow networks, instead of solely focusing on the local level. The results further highlight that if irrigation expansion is poorly managed, it may increase urban water scarcity, thus also possibly increasing the likelihood of water conflict between urban and rural areas.

Climate scenario dataset are indispensable for assessing future climate impacts. In this study, we developed statistically downscaled climate scenarios in Japan using modified bias correction method based on five general circulation models selected from the Coupled Model Intercomparison Project (CMIP) Phase 6 to facilitate impact assessments and adaptation strategies. Modification of time window of the original correction method results in successful agreement with the observed seasonal change of variables in each grid. The original CMIP6 models have a relatively small bias compared to CMIP5 models. The CMIP6-based bias-corrected scenarios are available for use with the emissions scenario of representative concentration pathway (RCP) 4.5 in addition to RCP2.6 and RCP8.5. Several temperature-related indices derived from the CMIP6-based climate scenarios agreed well with observations. The number of extremely hot days and nights increased nonlinearly in the future with additional global warming. An increase in the global warming level from 1°C to 2°C above the early 1900s would increase the probability of the number of extremely hot days per year exceeding the 2018 case by 4.1 times. The development of bias-corrected climate scenarios facilitates the study of various climate impacts on a CMIP6 basis.

The calibration of global hydrological models has been attempted for over two decades, but an effective and generic calibration method has not been proposed. In this study, we investigated the application of Approximate Bayesian Computation (ABC) to calibrate the H08 global hydrological model by running global simulations with 5000 randomly generated sets of four sensitive parameters. This yielded satisfactory results for 777 gauged watersheds, indicating that ABC can be used to optimize H08 parameters to calibrate individual watersheds. We tested the identifiability of the parameters to yield satisfactory representations of hydrological functions based on Köppen’s climate classification (“climate-based” calibrations hereafter) We aggregated 5000 simulation results per catchment based on the 11 Köppen climate classes, then selected the parameters that exceeded the Nash–Sutcliffe efficiency (NSE) scores predefined by the acceptance ratio for each climate class. Our results indicate that the number of stations showing satisfactory (NSE > 0.0) and good (NSE>0.5) performances were 480 and 234 (61.7% and 30.1% of total stations, respectively), demonstrating the effectiveness of climate-based calibration. We also showed that the climate-based parameters outperformed the default and global parameters in terms of representativeness (global-scale differences of hydrological properties among climate classes) and robustness (consistency in yielding satisfactory results for watersheds in the same climate class). The identified parameters for 11 Köppen climate classes showed consistency with the physical interpretation of soil formation and efficiencies in vapor transfer with a wide variety of vegetation types, corroborating the strong influence of climate on hydrological properties.