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Exploring the Implications of Modeling Choices on Prediction of Water Savings
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  • Chinedum Eluwa,
  • Baptiste Francois,
  • Alec Bernstein,
  • Casey M Brown
Chinedum Eluwa
University of Massachusetts, Amherst

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Baptiste Francois
University of Massachusetts, Amherst
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Alec Bernstein
University of Massachusetts, Amherst
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Casey M Brown
University of Massachusetts Amherst
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Abstract

Improvements in irrigation technology are expected to yield water savings. Recent research highlights the need for accompanying institutional conditions (e.g., restricting irrigation expansion). However, estimating the expected quantity of water savings remains uncertain, even under such institutional conditions. This is because estimates of the water savings resulting from improved irrigation technology are subject to several methodological (sometimes arbitrary) choices. Three key choices are: (1) the underlying hydrologic model used to partition irrigation water into consumed (e.g., evapotranspiration) and non-consumed (e.g., runoff) components, (2) the selected hydrologic model parameters, and (3) the convention used to represent non-beneficial losses (e.g., non-crop evaporative losses during channel conveyance, on-farm application, off-farm storage, or unrecoverable seepage). This study is the first to explore the combined implications of these choices as regards predicting water savings. It is also the first to attribute the uncertainty in expected water savings to each of these choices. To explore these implications, we use an ensemble of water savings under all possible combinations of three different conceptual hydrologic model structures (HYMOD, HBV, SAC-SMA), a hundred equifinal parameter sets (for each model), and two conventions for representing non-beneficial losses - a total of 600 scenarios. The results show that parameter selection and alternative conventions of representing non-beneficial losses are the largest sources of uncertainty in water savings, contributing ~49% and ~33% respectively to overall uncertainty. These results provide a quantitative estimate for the minimum range of uncertainty one may expect when considering policy options that depend on quantified estimates of water savings.