Accurate estimation of the spatio-temporal distribution of snow water equivalent is essential given its global importance for understanding climate dynamics and climate change, and as a source of fresh water. Here, we explore the potential of using the Long Short-Term Memory (LSTM) network for continental and regional scale modeling of daily snow accumulation and melt dynamics at 4-km pixel resolution across the conterminous US (CONUS). To reduce training costs (data are available for ~0.31 million snowy pixels), we combine spatial sampling with stagewise model development, whereby the network is first pretrained across the entire CONUS and then subjected to regional fine-tuning. Accordingly, model evaluation is focused on out-of-sample predictive performance across space (analogous to the prediction in ungauged basins problem). We find that, given identical inputs (precipitation, temperature and elevation), a single CONUS-wide LSTM provides significantly better spatio-temporal generalization than a regionally calibrated version of the physical-conceptual temperature-index-based SNOW17 model. Adding more meteorological information (dew point temperature, vapor pressure deficit, longwave radiation and shortwave radiation) further improves model performance, while rendering redundant the local information provided by elevation. Overall, the LSTM exhibits better transferability than SNOW17 to locations that were not included in the training data set, reinforcing the advantages of structure learning over parameter learning. Our results suggest that an LSTM-based approach could be used to develop continental/global-scale systems for modeling snow dynamics.

This study investigates the impacts of climate seasonality, i.e., the seasonal cycle of precipitation (P) relative to that of potential evaporation (PET), on surface water supply and the long-term water partitioning and proposes an augmented aridity index considering climate seasonality in addition to climatic mean. Evaporation tends to be favored over streamflow at long-term timescales when both cycles occur in tandem (in-phase seasonality), while the opposite occurs (less evaporation, more streamflow) when the two cycles are out-of-phase. This study proposes a straightforward approach to incorporating the seasonality effects on the mean annual water balance into the Budyko framework, by revising the water availability (A) in the formulation of the aridity index (Φ). We hypothesize the Budyko curve represents catchments with uniform monthly values of P, leading to a mathematical formulation of A that better represents the coupled, land-atmosphere nature of the water availability. Our results also provide a simple mathematical framework for incorporating the seasonality into the aridity index, thus reducing the dimensionality of the long-term water balance problem through an aridity-seasonality index (Φ’). The formulation used here was able to improve the explanatory power of the Budyko framework for 328 catchments within the continental US, being proved as a useful strategy for the incorporating climate variations into its formulation in addition to climatic mean.

Water availability in the dry Western United States (US) under a warming climate and increasing water use demand has become a serious concern. Previous studies have projected future runoff changes across the Western US but ignored the impacts of ecosystem response to elevated CO2 concentration. Here, we aim to understand the impacts of elevated CO2 on future runoff changes through ecosystem responses to both rising CO2 and associated warming using the Noah-MP model with representations of vegetation dynamics and plant hydraulics. We first validated Noah-MP against observed runoff, LAI, and terrestrial water storage anomaly from 1980–2015. We then projected future runoff with Noah-MP under downscaled climates from three climate models under RCP8.5. The projected runoff declines variably from the Pacific Northwest by –11% to the Lower Colorado River basin by –92% from 2016–2099. To discern the exact causes, we conducted an attribution analysis of two additional sensitivity experiments: one with constant CO2 and another with monthly LAI climatology based on the Penman-Monteith equation. Results show that surface “greening” (due to the CO2 fertilization effect) and the stomatal closure effect are the second largest contributors to future runoff change, following the warming effect. These two counteracting CO2 effects are roughly compensatory, leaving the warming effect to remain the dominant contributor to the projected runoff declines at large river basin scales. This study suggests that both surface “greening” and stomatal closure effects are important factors and should be considered together in water resource projections.

Catchment-scale response functions, such as transit time distribution (TTD) and evapotranspiration time distribution (ETTD), are considered fundamental descriptors of a catchment’s hydrologic and ecohydrologic responses to spatially and temporally varying precipitation inputs. Yet, estimating these functions is challenging, especially in headwater catchments where data collection is complicated by rugged terrain, or in semi-arid or sub-humid areas where precipitation is infrequent. Hence, we developed practical approaches for estimating both TTD and ETTD from commonly available tracer flux data in hydrologic inflows and outflows without requiring continuous observations. Using the weighted wavelet spectral analysis method of Kirchner and Neal [2013] for δ18O in precipitation and stream water, we specifically calculated TTDs that contribute to streamflow via spatially and temporally variable flow paths in a sub-humid mountain headwater catchment in Arizona, USA. Our results indicate that composite TTDs most accurately represented this system for periods up to approximately one month and that a Gamma TTD was most appropriate thereafter. The TTD results also suggested that some contribution of subsurface water was beyond the applicable tracer range. For ETTD and using δ18O as a tracer in precipitation and xylem waters, a Gamma ETTD type best matched the observations, and stable water isotopes were capable tracers for the majority of vegetation source waters. This study contributes to a better understanding of a fundamental question in mountain catchment hydrology; namely, how tracer input fluxes are modulated by spatially and temporally varying subsurface flow paths that support evapotranspiration and streamflow at multiple time scales.