Improving Meteorological Input for Surface Energy Balance System Utilizing Mesoscale Weather Research and Forecasting Model for Estimating Daily Actual Evapotranspiration

Abstract

Using Surface Energy Balance System (SEBS) to estimate actual evapotranspiration (ET) on a regional scale generally uses gridded meteorological data by interpolating data from meteorological stations with mathematical interpolation. The heterogeneity of underlying surfaces cannot be effectively considered when interpolating meteorological station measurements to gridded data only by mathematical interpolation. This study aims to highlight the improvement of modeled meteorological data from the Weather Research and Forecasting (WRF) mesoscale numerical model which fully considers the heterogeneity of underlying surfaces over the data from mathematical interpolation method when providing accurate meteorological input for SEBS model. Meteorological data at 1 km resolution in the Hotan Oasis were simulated and then were put into SEBS model to estimate the daily actual ET. The accuracy of WRF simulation was evaluated through comparison with data collected at the meteorological station. Results found that the WRF-simulated wind speed, air temperature, relative humidity and surface pressure correlate well with the meteorological stations measurements (R 2 are 0.628, 0.8242, 0.8089 and 0.8915, respectively). Comparison between ET calculated using the meteorological data simulated from the WRF (ETa-WRF) and meteorological data interpolated from measurements at met stations (ETa-STA) showed that ETa-WRF could better reflect the ET difference between different land cover, and capture the vegetation growing trend, especially in areas with sparse vegetation, where ETa-STA intends to overestimate. In addition, ETa-WRF has less noise in barren areas compared to ETa-STA. Our findings suggest that WRF can provide more reliable meteorological input for SEBS model than mathematical interpolation method.

Variation of Local Wind Fields under the Background of Climate Change and Its Impact on Algal Blooms in Lake Taihu, China

“…To further evaluate the impacts of near-surface wind field changes on algal blooms, the wind field in the Lake Taihu area was simulated using the weather research and forecasting model (WRF). The WRF is a mesoscale numerical weather forecasting model that can simulate meteorological elements with high spatio-temporal resolution, developed by the National Center for Atmospheric Research (NCAR) and the National Center for Environmental Forecasting (NCEP) in the United States of America [53]. In this study, WRF 3.8.1 was used to simulate the near-surface wind field from 08:00 on 3 September to 08:00 on 8 September 2010.…”

Machine Learning for Prediction of Energy in Wheat Production

“…The results were also validated. Wang et al [31] carried out efforts to improve the surface energy balance network's meteorological feedback using the mesoscale environment analysis and prevision model. Comparisons of data collected at the weather station were carried out to determine the quality of weather research and forecasting (WRF) simulation.…”

Evaluating Hydrological Responses to Climate Change

“…2.9. Improving Meteorological Input for Surface Energy Balance System Utilizing Mesoscale Weather Research and Forecasting Model for Estimating Daily Actual Evapotranspiration, by Wang et al (2020) This paper focused on developing techniques to improve the estimation of actual evapotranspiration estimated by the Surface Energy Balance System (SEBS) by incorporating the heterogeneity of underlying surfaces that are used in the Weather Research and Forecasting (WRF) model simulations that are so often used in dynamically downscaling future climate projections. The meteorological conditions of the Hotan Oasis in China were simulated by WRF with significant improvements over mathematical interpolation methods typically used and provided more reliable input for SEBS modeling of actual evapotranspiration.…”

“…Addressing different parts of the water balance, from precipitation [6] and evapotranspiration [9] to runoff [1][2][3]7,8] and impacts to water resources [2][3][4][5]7,8], these papers investigated the performance of hydrological models and GCMs, projected ecological and hydrological changes to basins [1,2,[4][5][6][7][8], and used a suite of analytical and numerical tools to improve both historical calibration/validation capabilities [2,8] and future climate modeling performance [4,5,9].…”