Statistical Modeling to Predict Climate Change Effects on Watershed Scale Evapotranspiration
| Autor: | S. Dhungel, Michael E. Barber, Rajendra Khanal, Simon C. Brewer |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
agricultural water management
Atmospheric Science Watershed Calibration (statistics) Climate change Statistical model METRIC Environmental Science (miscellaneous) Yakima River basin Random forest Water resources Climatology Evapotranspiration consumptive use Meteorology. Climatology Environmental science Satellite QC851-999 random forest |
| Zdroj: | Atmosphere; Volume 12; Issue 12; Pages: 1565 Atmosphere, Vol 12, Iss 1565, p 1565 (2021) |
| ISSN: | 2073-4433 |
| DOI: | 10.3390/atmos12121565 |
| Popis: | Estimation of satellite-based remotely sensed evapotranspiration (ET) as consumptive use has been an integral part of agricultural water management. However, less attention has been given to future predictions of ET at watershed-scales especially since with a changing climate, there are additional challenges to planning and management of water resources. In this paper, we used nine years of total seasonal ET derived using a satellite-based remote sensing model, Mapping Evapotranspiration at Internalized Calibration (METRIC), to develop a Random Forest machine learning model to predict watershed-scale ET into the future. This statistical model used topographic and climate variables in agricultural areas of Lower Yakima, Washington and had a prediction accuracy of 88% for the region. This model was then used to predict ET into the future with changed climatic conditions under RCP4.5 and RCP8.5 emission scenarios expected by 2050s. The model result shows increases in seasonal ET across some areas of the watershed while decreases in other areas. On average, growing seasonal ET across the watershed was estimated to increase by +5.69% under the low emission scenario (RCP4.5) and +6.95% under the high emission scenario (RCP8.5). |
| Databáze: | OpenAIRE |
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