We assessed and quantified future projected changes in terrestrial evaporative demand by calculating Potential Evapotranspiration (PET) for the North American Monsoon region in the Southwestern U.S. and Mexico. The PET projections were calculated using the daily Penman–Monteith equation. The terrestrial meteorological variables needed for the equation (i.e., minimum and maximum daily temperature, specific humidity, wind speed, incoming shortwave radiation, and pressure) were obtained from the North American–CORDEX initiative. We used dynamically downscaled projections of three CMIP5 GCMs for RCP8.5 emission scenarios (i.e., HadGEM2-ES, MPI-ESM-LR, and GFDL-ESM2M), and each was dynamically downscaled to ~25 km by two RCMs (i.e., WRF and regCM4). All terrestrial annual PET projections showed a statistically significant increase when comparing the historical period (1986–2005) to future projections (2020–2039 and 2040–2059). The regional spatial average of the six GCM-RCM combinations projected an increase in the annual PET of about +4% and +8% for 2020–2039 and 2040–2059, respectively. The projected average 20-year annual changes over the study area range for the two projection periods were +1.4%–+8.7% and +3%–+14.2%, respectively. The projected annual PET increase trends are consistent across the entire region and for the six GCM-RCM combinations. Higher annual changes are projected in the northeast part of the region, while smaller changes are projected along the pacific coast. The main drivers for the increase are the projected warming and increase in the vapor pressure deficit. The projected changes in PET, which represent the changes in the atmospheric evaporative demand, are substantial and likely to impact vegetation and the hydrometeorological regime in the area. Quantitative assessments of the projected PET changes provided by this study should be considered in upcoming studies to develop resilience plans and adaptation strategies for mitigating the projected future changes.

Operational forecasters desire information about how their reservoir and riverine systems will evolve over monthly to seasonal timescales. Seasonal traces of hydrometeorological variables at a daily or sub-daily resolution are needed to drive hydrological models at this timescale. Operationally available models such as the Climate Forecast System (CFS) provide seasonal precipitation forecasts, but their coarse spatial scale requires further processing for use in local or regional hydrologic models. We focus on three methods to generate such forecasts: (1) a bias-adjustment method, in which the CFS forecasts are bias-corrected by ground-based observations; (2) a weather generator (WG) method, in which historical precipitation data, conditioned on an index of the El Niño–Southern Oscillation, are used to generate synthetic daily precipitation time series; and (3) a historical analog method, in which the CFS forecasts are used to condition the selection of historical satellite-based mean areal precipitation (MAP) traces. The Yaque del Norte River basin in the Dominican Republic is presented herein as a case study, using an independent dataset of rainfall and reservoir inflows to assess the relative performance of the methods. The methods showed seasonal variations in skill, with the MAP historical analog method having the strongest overall performance, but the CFS and WG methods also exhibited strong performance during certain seasons. These results indicate that the strengths of each method may be combined to produce an ensemble forecast product.

Mountain snowpack is a critical freshwater reservoir that sustains ecosystems and provides water for hydropower, agriculture, and urban uses. Accurate basin‐scale snowpack estimates are essential for ecosystem and water resources management. Our ability to predict snowpack evolution is hampered by the low density of precipitation gauges in high mountain regions, as most precipitation gauges are installed in easily accessible areas, which are often below the snowline. These measurements are typically spatially interpolated to fill data gaps in high mountain areas, resulting in a range of gridded products with considerable differences. On the other hand, natural resources agencies rely on a few strategically placed snow pillows and snow courses to infer basin‐scale snow water equivalent (SWE). In the Kings River Basin, California, there is an opportunity to combine precipitation and snow pillow measurements to improve basin‐scale snowpack estimates. In this study, we blend precipitation gauge observations with snowpack measurements to force a spatially distributed, process‐based snowpack evolution model in order to improve basin‐scale precipitation and snowpack predictions. We test the blended precipitation (Blended scenario) against other gridded precipitation products, which are derived from precipitation gauges only (Gauge scenario) and the PRISM precipitation product (PRISM scenario). Our results show that the Blended scenario had improved snowpack predictions (NSE = 0.85) over the Gauge and PRISM scenarios (NSE = 0.37 and 0.81) when compared to observed SWE, which can be attributed to better representation of precipitation at high elevations. The Blended scenario was also the only scenario to produce sufficient surface water input (SWI) to account for the combined estimates of full natural flow and evapotranspiration in the basin. We also examine the model sensitivity to warming and discuss the impact of warming on SWE and SWI and implications for water management. The results underscore the need for improved snow observation networks at high elevations that can be used to improve the representation of precipitation patterns, which is critical for the hydrologic modelling of mountainous basins.

Glacial lake outburst floods (GLOFs) pose significant hazards to downstream communities and infrastructure, necessitating effective early warning systems. Warming global temperatures have led to widespread glacial retreat, increasing the size and number of glacial lakes worldwide. In situ measurements supporting high-resolution glacio-hydrologic models can help to meet this need, but their cost, the large number of glacial lakes, and the remote terrain where GLOFs occur ensure that many glacial lakes will not be monitored. This study explores the ability of an existing hydrometeorological warning system, the Flash Flood Guidance System (FFGS), to characterize the risk of GLOF occurrence, in combination with other available data sources. While the FFGS was not designed to predict GLOFs, it provides forecasters with ready access to relevant information and has extensive coverage in areas where they commonly occur, such as High Mountain Asia. We utilize inventories of glacial lakes and historical GLOF occurrences and extract FFGS hydrometeorological outputs and satellite-based remote sensing products at these locations to understand the prevailing hydrometeorological conditions leading up to these events, and develop a statistical characterization of GLOF risk. This case study points to a promising, low-cost means to give forecasters a better indication of the threats posed by GLOFs, particularly in regions with minimal existing capacity to forecast these events.