Precipitation is a key input variable in distributed surface water‐groundwater models, and its spatial variability is expected to impact watershed hydrologic response via changes in subsurface flow dynamics. Gridded precipitation data sets based on gauge observations, however, are plagued by uncertainty, especially in mountainous terrain where gauge networks are sparse. To examine the mechanisms via which uncertainty in precipitation data propagates through a watershed, we perform a series of numerical experiments using an integrated surface water‐groundwater hydrologic model, ParFlow.CLM. The Kaweah River watershed in California, USA, is used as our virtual catchment laboratory to characterize watershed response to variable precipitation forcing from headwaters to groundwaters. By applying the three‐cornered hat method, we quantify the spatially distributed uncertainty in four publically available precipitation forcing data sets and their simulated hydrology. Simulations demonstrate that uncertainty in the simulated groundwater storage is primarily a result of topographic redistribution of uncertainty in precipitation forcing. Soil water redistribution is the primary pathway that redistributes uncertainty downslope. We also find that topography exerts a larger impact than variable subsurface parameters on propagating uncertainty in simulated fluxes. Finally, we find that improvement in model performance metrics is higher for a single simulation forced with the mean precipitation from the available data sets than the averaged simulated results of separate simulations forced with each data set. Results from this study highlight the importance of topography‐moderated flow through the critical zone in shaping the groundwater response to climate variability.

Woody plant encroachment (WPE) into grasslands is a global phenomenon that is associated with land degradation via xerification, which replaces grasses with shrubs and bare soil patches. It remains uncertain how the global processes of WPE and climate change may combine to impact water availability for ecosystems. Using a process-based model constrained by watershed observations, our results suggest that both xerification and climate change augment groundwater recharge by increasing channel transmission losses at the expense of plant available water. Conversion from grasslands to shrublands without creating additional bare soil, however, reduces transmission losses. Model simulations considering both WPE and climate change are used to assess their relative roles in a late 21^stcentury condition. Results indicate that changes in focused channel recharge are determined primarily by the WPE pathway. As a result, WPE should be given consideration when assessing the vulnerability of groundwater aquifers to climate change.

High‐resolution characterizations and predictions are a grand challenge for ecohydrology. Recent advances in flight control, robotics and miniaturized sensors using unmanned aerial vehicles (UAVs) provide an unprecedented opportunity for characterizing, monitoring and modeling ecohydrologic systems at high‐resolution (<1 m) over a range of scales. How can the ecologic and hydrologic communities most effectively use UAVs for advancing the state of the art? This Innovative Viewpoints paper introduces the utility of two classes of UAVs for ecohydrologic investigations in two semiarid rangelands of the southwestern U.S. through two useful examples. We discuss the UAV deployments, the derived image, terrain and vegetation products and their usefulness for ecohydrologic studies at two different scales. Within a land‐atmosphere interaction study, we utilize high‐resolution imagery products from a rotary‐wing UAV to characterize an eddy covariance footprint and scale up environmental sensor network observations to match the time‐varying sampling area. Subsequently, in a surface and subsurface interaction study within a small watershed, we demonstrate the use of a fixed‐wing UAV to characterize the spatial distribution of terrain attributes and vegetation conditions which serve as input to a distributed ecohydrologic model whose predictions compared well with an environmental sensor network. We also point to several challenges in performing ecohydrology with UAVs with the intent of promoting this new self‐service (do‐it‐yourself) model for high‐resolution image acquisition over many scales. We believe unmanned aerial vehicles can fundamentally change how ecohydrologic science is conducted and offer ways to merge remote sensing, environmental sensor networks and numerical models.

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.

Soil moisture dynamics reflect the complex interactions of meteorological conditions with soil, vegetation and terrain properties. In this study, intermediate-scale soil moisture estimates from the cosmic-ray neutron sensing (CRNS) method are evaluated for two semiarid ecosystems in the southwestern United States: a mesquite savanna at the Santa Rita Experimental Range (SRER) and a mixed shrubland at the Jornada Experimental Range (JER). Evaluations of the CRNS method are performed for small watersheds instrumented with a distributed sensor network consisting of soil moisture sensor profiles, an eddy covariance tower, and runoff flumes used to close the water balance. We found a very good agreement between the CRNS method and the distributed sensor network (root mean square error (RMSE) of 0.009 and 0.013 m³ m⁻³ at SRER and JER, respectively) at the hourly timescale over the 19-month study period, primarily due to the inclusion of 5 cm observations of shallow soil moisture. Good agreement was also obtained in soil moisture changes estimated from the CRNS and watershed water balance methods (RMSE of 0.001 and 0.082 m³ m⁻³ at SRER and JER, respectively), with deviations due to bypassing of the CRNS measurement depth during large rainfall events. Once validated, the CRNS soil moisture estimates were used to investigate hydrological processes at the footprint scale at each site. Through the computation of the water balance, we showed that drier-than-average conditions at SRER promoted plant water uptake from deeper soil layers, while the wetter-than-average period at JER resulted in percolation towards deeper soils. The CRNS measurements were then used to quantify the link between evapotranspiration and soil moisture at a commensurate scale, finding similar predictive relations at both sites that are applicable to other semiarid ecosystems in the southwestern US.