In recent years, wildfires in the western United States have occurred with increasing frequency and scale. Climate change scenarios in California predict prolonged periods of droughts with even greater potential for conditions amenable to wildfires. The Sierra Nevada Mountains provide 70% of water resources in California, yet how wildfires will impact watershed-scale hydrology is highly uncertain. In this work, we assess the impacts of wildfires perturbations on watershed hydrodynamics using a physically based integrated hydrologic model in a high-performance-computing framework. A representative Californian watershed, the Cosumnes River, is used to demonstrate how postwildfire conditions impact the water and energy balance.Results from the high-resolution model show counterintuitive feedbacks that occur following a wildfire and allow us to identify the regions most sensitive to wildfires conditions, as well as the hydrologic processes that are most affected. For example, whereas evapotranspiration generally decreases in the postfire simulations, some regions experience an increase due to changes in surface water run-off patterns in and near burn scars. Postfire conditions also yield greater winter snowpack and subsequently greater summer run-off as well as groundwater storage in the postfire simulations. Comparisons between dry and wet water years show that climate is the main factor controlling the timing at which some hydrologic processes occur (such as snow accumulation) whereas postwildfire changes to other metrics (such as streamflow) show seasonally dependent impacts primarily due to the timing of snowmelt, illustrative of the integrative nature of hydrologic processes across the Sierra Nevada-Central Valley interface. K E Y W O R D S climate extremes, integrated hydrologic model, vegetation changes, water management, watershed dynamics, wildfires

With the onset of climate change, regions relied upon for water supply are increasingly subject to end-member fluctuations between periods of severe drought followed by extreme precipitation. The impacts of these extreme conditions on watershed hydrodynamics in water-resource sensitive regions such as California are unknown despite their great importance for resilience and water management purposes. Understanding these impacts requires high-resolution physically based models to capture sharp variations of topography, land use, wetting fronts, etc. An integrated hydrologic model was used in a high-performance computing framework to study the complex nonlinear dynamics occurring at a representative Californian watershed. The Cosumnes Watershed, one of the last major rivers in California without a dam, offers a rare opportunity to isolate the effects of water management from climate extremes. Here, we show model validation with comparisons between model outputs and local measurements in addition to various satellite-based products including (1) Snow Water Equivalent (SWE) with Snow Data Assimilation System (SNODAS) and a reconstruction method by Bair and co-authors, (2) soil moisture with Soil Moisture Active Passive (SMAP), and (3) evapotranspiration (ET) with Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC). To assess changes in hydro-dynamic behavior during climate extremes and their transitions, a simulation spanning a recent drought followed by the highest precipitation year on record (2015-2017) is discussed. From these simulations, we are able to highlight regions that are the most sensitive to climate extremes, which depend on many factors including hydrologic connectivity, geology and topography. These analyses provide a better understanding of the physical phenomena occurring in the watershed, strengthening our knowledge of how the system may respond to extreme conditions which might become the "new normal.

High-Mountain Asia exhibits one of the highest increases in vegetation greenness on Earth, subsequently influencing the exchange of water and energy between the land surface and the atmosphere. Given the strong interactions between the hydrosphere, the biosphere, and the cryosphere, understanding the drivers of greening in this highly complex region with significant land cover heterogeneity is essential to assess the changes in the regional water budget. Here, we perform a holistic multivariate remote sensing analysis to simultaneously examine the primary components of the terrestrial water cycle from 2003 to 2020 and decipher the principal drivers of greening in High-Mountain Asia. We identified three drivers of greening: (1) precipitation drives greening in mid and low elevation areas covered by evergreen and mixed forests (e.g., Irrawaddy basin), (2) decreases in snow enhance greening in most of the hydrologic basins, and (3) irrigation induces greening in irrigated lands (Ganges–Brahmaputra and Indus).

Abstract. Projecting the spatiotemporal changes in water resources under a no-analog future climate requires physically based integrated hydrologic models which simulate the transfer of water and energy across the earth's surface. These models show promise in the context of unprecedented climate extremes given their reliance on the underlying physics of the system as opposed to empirical relationships. However, these techniques are plagued by several sources of uncertainty, including the inaccuracy of input datasets such as meteorological forcing. These datasets, usually derived from climate models or satellite-based products, are typically only resolved on the order of tens to hundreds of kilometers, while hydrologic variables of interest (e.g., discharge and groundwater levels) require a resolution at much smaller scales. In this work, a high-resolution hydrologic model is forced with various resolutions of meteorological forcing (0.5 to 40.5 km) generated by a dynamical downscaling analysis from the regional climate model Weather Research and Forecasting (WRF). The Cosumnes watershed, which spans the Sierra Nevada and Central Valley interface of California (USA), exhibits semi-natural flow conditions due to its rare undammed river basin and is used here as a test bed to illustrate potential impacts of various resolutions of meteorological forcing on snow accumulation and snowmelt, surface runoff, infiltration, evapotranspiration, and groundwater levels. Results show that the errors in spatial distribution patterns impact land surface processes and can be delayed in time. Localized biases in groundwater levels can be as large as 5–10 m and 3 m in surface water. Most hydrologic variables reveal that biases are seasonally and spatially dependent, which can have serious implications for model calibration and ultimately water management decisions.