Hydrologic responses to climate warming for a snow‐dominated watershed and a transient snow watershed in the California Sierra

Abstract

Climate warming will have substantial impacts on hydrological fluxes in the California Sierra. A commonly used approach for assessing these impacts, particularly in mountain watersheds, is to substitute space for time. This conceptual model assumes that with warming, the hydrologic behaviour of higher elevation snow dominated watersheds (SDWs) will converge to the hydrologic behaviour of lower elevation transient snow watersheds (TSWs). To investigate the efficacy of this conceptual model, a process‐based model (RHESSys) was applied to a TSW and a SDW with a mean annual temperature 2 °C lower than the TSW in the Sierra National forest, California. This study investigated the effect of climate warming (2 and 4 °C) on the model estimates of snow water equivalent (SWE), streamflow, evapotranspiration (ET), and moisture deficit in the two watersheds. Modelling results show that SDW under 2 °C warming scenarios generates monthly SWE similar in magnitude and timing as TSW under historic conditions. However, SDW under 2 °C warming scenarios generates higher annual and summer streamflow due to shallower groundwater storage and experiences less water limitation due to lower ET, compared with TSW under historical climate conditions. In both watersheds, leaf area index and wetness index are primary factors controlling spatial patterns of seasonal ET under both historical climate conditions and warming scenarios. Climate warming increases the spatial variability in monthly ET, especially in the summer period. These modelling results suggest that vegetation structure and subsurface properties may be as important as climate in explaining hydrologic response to climate warming in small Sierra Nevada watersheds.

The Counteracting Effects of Snowmelt Rate and Timing on Runoff

“…RHESSys has been used for hydrologic simulations in a variety of environments (Bart et al, 2016; Christensen et al, 2008; Grant et al, 2013; Hartman et al, 1999; Jefferson et al, 2008; Son, 2015; e.g., Tague & Grant, 2009; Tague & Peng, 2013; Tague et al, 2007, 2009, 2013). Numerous studies have evaluated RHESSys estimates of ecohydrologic fluxes in seasonally water‐limited Mediterranean environments by comparison with a broad set of relevant observations, including streamflow (Boisramé et al, 2019; e.g., Tague & Peng, 2013), snowmelt (Son & Tague, 2018), ET (Tague & Moritz, 2019; Zierl et al, 2007), interannual variation in tree rings (Vicente‐Serrano et al, 2015), and leaf‐scale conductance measurements (Tsamir et al, 2019). These comparisons suggest that RHESSys accurately represents the dominant controls on seasonal and interannual variation in ET for these systems.…”

The Regional Hydro-Ecological Simulation System for 30 Years: A Systematic Review

“…Ecohydrological fluxes incorporated in RHESSys, e.g., streamflow, evapotranspiration, and net primary productivity under climate change, can provide crucial data to support watershed sustainable management under climate change. For example, Son et al [31] examined the shift in snowmelt, runoff, and evaporation between a snow-dominated basin and rainfall-dominated watershed under projected global warming of 2 or 4 • C, California, USA. Besides, they identified that vegetation structures and soil properties are also crucial factors for ecohydrological responses to climate change.…”

“…Previous studies have often estimated and calibrated the snow-related parameters based on R 2 (determination coefficient) for snow depth, a snow-water equivalent. For example, Son et al conducted the calibration of snow-related parameters by comparing measured snow depth data and modeled snow-water equivalent, which results in an R 2 of 0.87 and 0.70, implying that the model reproduced the real snowmelt process [31,37]. For studies that require detailed ecological outputs, vegetation-related parameters are manually calibrated with remote sensing or field-observed Leaf Area Index (LAI) data [18,38].…”

“…For water quality studies, DOC and NO 3 have been used to validate simulations. Son et al [31] performed WRTD (Weighted Regression Model) statistical analysis between site-monitored and simulated dissolved organic carbon to calculate the NSe and LogNSe. Bell et al [44] evaluated the performance in the monthly NO 3 concentration and showed that the correlation coefficient and NSe were 0.82 and 0.64, respectively.…”

“…As a physical process-based model, RHESSys simulates the ecohydrological processes at the expense of involving substantial parameters and data support, which impedes the application of RHESSys and induces more uncertainties [43,71]. Son et al [31] found difficulties in determining snow and soil parameters for dissolved carbon simulations, which may cause a bias in model outputs. Martin et al [6] pointed out the lack of detailed rainfall intensity and urban drainage data, which may induce underestimating the peak streamflow.…”