The water balance is an essential tool for hydrologic studies and quantifying waterbalance components is the focus of many research catchments. A fundamental question remains regarding the appropriateness of water-balance closure assumptions when not all components are available. In this study, we leverage in-situ measurements of water fluxes and storage from the Southern Sierra Critical Zone Observatory (SSCZO) and the Kings River Experimental Watersheds (KREW) to investigate annual water-balance closure errors across large (1016-5389 km 2 ) river basins and small (0.5-5 km 2 ) headwater-catchment scales in the southern Sierra Nevada. The

Crop models are widely used for the modeling and prediction of crop yields, as decision support tools, and to develop research questions. Though typically constructed as a set of dynamical equations, crop models are not often analyzed from a specifically dynamical systems point of view, despite its potential to elucidate the roles of feedbacks and internal and external forcings on system stability and the optimization of control protocols (e.g., irrigation and fertilization). Here we develop a minimal dynamical system, based in part on the widely known AquaCrop model, consisting of a set of ordinary differential equations (ODE's) describing the evolution of canopy cover, soil moisture, and soil nitrogen. These state variables are coupled through canopy growth and senescence, the evapotranspiration and percolation of soil moisture, and the uptake and leaching of soil nitrogen. The system is driven by random hydroclimatic forcing. Important crop model responses, such as biomass and yield, are calculated, and optimal yield and profitability under differing climate scenarios,

As climate change continues, forest vulnerability to droughts and heatwaves is increasing, but vulnerability varies regionally and locally through landscape position. Also, most models used in forecasting forest responses to heat and drought do not incorporate relevant spatial processes. In order to improve spatial predictions of tree vulnerability, we employed a nonlinear stochastic model of soil moisture dynamics accounting for landscape differences in aspect, topography and soils. Across a watershed in central Texas we modeled dynamic water stress for a dominant tree species, Juniperus ashei, and projected future dynamic water stress through the 21 century. Modeled dynamic water stress tracked spatial patterns of remotely sensed drought-induced canopy loss. Accuracy in predicting drought-impacted stands increased from 60%, accounting for spatially variable soil conditions, to 72% when also including lateral redistribution of water and radiation/temperature effects attributable to aspect. Our analysis also suggests that dynamic water stress will increase through the 21 century, with trees persisting at only selected microsites. Favorable microsites/refugia may exist across a landscape where trees can persist; however, if future droughts are too severe, the buffering capacity of an heterogeneous landscape could be overwhelmed. Incorporating spatial data will improve projections of future tree water stress and identification of potential resilient refugia.

Rainwater harvesting (RWH) has the potential to reduce water-related costs by providing an alternate source of water, in addition to relieving pressure on public water sources and reducing stormwater runoff. Existing methods for determining the optimal size of the cistern component of a RWH system have various drawbacks, such as specificity to a particular region, dependence on numerical optimization, and/or failure to consider the costs of the system. In this paper a formulation is developed for the optimal cistern volume which incorporates the fixed and distributed costs of a RWH system while also taking into account the random nature of the depth and timing of rainfall, with a focus on RWH to supply domestic, nonpotable uses. With rainfall inputs modeled as a marked Poisson process, and by comparing the costs associated with building a cistern with the costs of externally supplied water, an expression for the optimal cistern volume is found which minimizes the water-related costs. The volume is a function of the roof area, water use rate, climate parameters, and costs of the cistern and of the external water source. This analytically tractable expression makes clear the dependence of the optimal volume on the input parameters. An analysis of the rainfall partitioning also characterizes the efficiency of a particular RWH system configuration and its potential for runoff reduction.

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Soil formation results from opposite processes of bedrock weathering and erosion, whose balance may be altered by natural events and human activities, resulting in reduced soil depth and function. The impacts of vegetation on soil production and erosion and the feedbacks between soil formation and vegetation growth are only beginning to be explored quantitatively. Since plants require suitable soil environments, disturbed soil states may support less vegetation, leading to a downward spiral of increased erosion and decline in ecosystem function. We explore these feedbacks with a minimal model of the soil–plant system described by two coupled nonlinear differential equations, which include key feedbacks, such as plant‐driven soil production and erosion inhibition. We show that sufficiently strong positive plant–soil feedback can lead to a ‘humped’ soil production function, a necessary condition for soil depth bistability when erosion is assumed to vary monotonically with vegetation biomass. In bistable plant–soil systems, the sustainable soil condition engineered by plants is only accessible above a threshold vegetation biomass and occurs in environments where the high potential rate of erosion exerts a strong control on soil production and erosion. Vegetation removal for agriculture reduces the stabilizing effect of vegetation and lowers the system resilience, thereby increasing the likelihood of transition to a degraded soil state. Copyright © 2016 John Wiley & Sons, Ltd.

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.