Statistical Modeling to Predict Climate Change Effects on Watershed Scale Evapotranspiration

Rajendra Khanal,Sulochan Dhungel,Simon C Brewer,Michael E Barber

doi:10.3390/atmos12121565

Abstract

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).

Highlights

Water resources are severely stressed in arid and semi-arid regions of the world with diverse competing pressures like agricultural, industrial, municipal, and environmental uses [1,2,3]
Mapping Evapotranspiration at Internalized Calibration (METRIC) ET was computed at pixel size of Landsat imagery (30 m), but for the purposes of developing the statistical model, it was upscaled from 30 m to 4 km by calculating total ET from each agricultural pixel with the 4 km grid
Climate change is likely to alter the dynamics of future water supply and demand around the globe, and increase the water stress

Summary

Introduction

Water resources are severely stressed in arid and semi-arid regions of the world with diverse competing pressures like agricultural, industrial, municipal, and environmental uses [1,2,3]. Climate change poses an additional risk to water stress and will probably exacerbate water management plans in the future [4,5]. This is because climate change is expected to alter the dynamics of spatiotemporal temperature and precipitation patterns, often resulting in increased evaporative demands [6]. With climate change expected to impose additional limitations on water management activities, it becomes increasingly necessary to predict changes in future agricultural water demands. Difficulty in estimating spatiotemporally varied evapotranspiration (ET) at field and watershed scales has led to inefficient water allocation, trading, and other planning operations, which are tremendous concerns in the face of climate variability, change and drought [12,13]

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Methods

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Discussion

Conclusion