Estimating evapotranspiration using the complementary relationship can serve as a proxy to more sophisticated physically based approaches and can be used to better understand water and energy budget feedbacks. The authors investigated the existence of complementarity between actual evapotranspiration (ET) and potential ET (ET p ) over natural vegetation in semiarid desert ecosystems of southern Idaho using only the forcing data and simulated fluxes obtained from Noah land surface model (LSM) and North American Regional Reanalysis (NARR) data. To mitigate the paucity of long-term meteorological data, the Noah LSM-simulated fluxes and the NARR forcing data were used in the advection-aridity (AA) model to derive the complementary relationship (CR) for the sagebrush and cheatgrass ecosystems. When soil moisture was a limiting factor for ET, the CR was stable and asymmetric, with b values of 2.43 and 1.43 for sagebrush and cheatgrass, respectively. Higher b values contributed to decreased ET and increased ET p , and as a result ET from the sagebrush community was less compared to that of cheatgrass. Validation of the derived CR showed that correlations between daily ET from the Noah LSM and CR-based ET were 0.76 and 0.80 for sagebrush and cheatgrass, respectively, while the root-mean-square errors were 0.53 and 0.61 mm day 21 .
SummaryClimate change in the Pacific Northwest and in particular, the Salmon River Basin (SRB), is expected to bring about 3 -5 °C rise in temperatures and an 8% increase in precipitation. In order to assess the impacts due to these changes at the basin scale, this study employed an improved version of Variable Infiltration Capacity (VIC) model, which includes a parallel version of VIC combined with a comprehensive parameter estimation technique, Shuffled Complex Evolution (SCE) to estimate the streamflow and other water balance components. Our calibration (1955-75) and validation (1976-99) of the model at the outlet of the basin, White Bird, resulted in an r 2 value of 0.94 which was considered satisfactory. Subsequent center of timing analysis showed that a gradual advancement of snowmelt induced-peak flow advancing by about 10 days in the future. Historically, the flows have shown a general decline in the basin, and in the future while the magnitudes might not be greatly affected, decreasing runoff of about 3 % over the next 90 years could be expected and timing of peak flow would shift by approximately 10 days. Also, a significant reduction of snow water equivalent up to 25%, increased evapotranspiration up to 14%, and decreased soil moisture storages of about 2 % is predicted by the model. A steady decline in SWE/P from the majority of climate model projections for the basin was also evident. Thus, the earlier snowmelt, decreasing soil moisture and increased evapotranspiration collectively implied the potential to trigger drought in the basin and could affect the quality of aquatic habitats and their spawning and a detailed investigation on these impacts is warranted.
Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E) soil moisture data products are proven to be useful across various regions around the world. However, numerous studies have suggested that validation and bias‐correction at the local scale is important to use them with confidence for numerical weather prediction, land surface energy, and water balance assessment, including infiltration and drainage. Here we investigate the cumulative distribution function (CDF) to correct AMSR‐E surface soil moisture data using field observations of soil moisture for 37 sites from Nebraska's Automated Weather Data Network (AWDN) and model‐simulated soil moisture from southwestern Idaho. We explore the scaling of AMSR‐E data using simulated soil moisture from three hydrological models, Variable Infiltration Capacity (VIC), Noah Land Surface Model (Noah LSM) and Robinson Hubbard 1‐D (RH1D) models. We hypothesize that these calibrated hydrological models can substitute for field observations to represent continuous surface of soil moisture fields over space and time when used in conjunction with daily AMSR‐E data. Our results suggest that it is necessary to have the AMSR‐E data bias‐corrected based on either field observations or model estimates. The magnitude of values for corrected AMSR‐E soil moisture and observed soil moisture showed better correlation for the growing seasons between 2003 and 2005. It is also shown that well‐calibrated hydrological models can be useful to provide correction for the AMSR‐E product thereby adding value to the AMSR‐E soil moisture datasets.
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