Modeling monthly actual evapotranspiration: an application of geographically weighted regression technique in the Passaic River Basin
Actual evapotranspiration (ET) is perhaps the most difficult quantity to directly measure among the major water balance components. Because of the high cost and labor constraints associated with the direct measurement of ET, empirical data-driven modeling has frequently been used to estimate ET. Bey...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-01-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/14/1/17 |
| Summary: | Actual evapotranspiration (ET) is perhaps the most difficult quantity to directly measure among the major water balance components. Because of the high cost and labor constraints associated with the direct measurement of ET, empirical data-driven modeling has frequently been used to estimate ET. Beyond the widely used traditional type regression that has the effect of producing ‘global’ parameter estimates, assumed to be uniform throughout an area, we utilized a more localized spatially non-stationary technique – the geographically weighted regression (GWR) – to estimate mean monthly ET in the Passaic River Basin (PRB). We identified the key environmental controls of ET and developed new sets of spatially varying empirical ET models based on variable combinations that produced the best-fit model. The analysis showed that temporal and spatial variabilities in ET over the PRB are driven by climatic and biophysical factors. We found that the key controlling factors were different from month to month, with wind speed being dominant throughout the year in the study basin. A monthly mean ET index map was further generated from the model to illustrate areas where ET exceeds precipitation. This will among others enable water loss due to evapotranspiration to be accounted for in future water supply plans for the basin.
HIGHLIGHTS
New set of empirical monthly actual ET models was developed.;
Use of spatially varying regression technique removes frequently overlooked stationarity assumption of ordinary least square models (OLS).;
The GWR technique reveals any rapid changes in environmental variables over an area, necessitating further investigation.;
Readily available data can be used to reasonably quantify monthly ET under mean climatic conditions.; |
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| ISSN: | 2040-2244 2408-9354 |