Abstract

:In intensively managed watersheds, water scarcity is a product of interactions between complex biophysical processes and human activities. Understanding how intensively managed watersheds respond to climate change requires modeling these coupled processes. One challenge in assessing the response of these watersheds to climate change lies in adequately capturing the trends and variability of future climates. Here we combine a stochastic weather generator together with future projections of climate change to efficiently create a large ensemble of daily weather for three climate scenarios, reflecting recent past and two future climate scenarios. With a previously developed model that captures rainfall-runoff processes and the redistribution of water according to declared water rights, we use these large ensembles to evaluate how future climate change may impact satisfied and unsatisfied irrigation throughout the study area, the Treasure Valley in Southwest Idaho, USA. The numerical experiments quantify the changing rate of allocated and unsatisfied irrigation amount and reveal that the projected temperature increase more significantly influences allocated and unsatisfied irrigation amounts than precipitation changes. The scenarios identify spatially distinct regions in the study area that are at greater risk of the occurrence of unsatisfied irrigation. This study demonstrates how combining stochastic weather generators and future climate projections can support efforts to assess future risks of negative water resource outcomes. It also allows identification of regions in the study area that may be less suitable for irrigated agriculture in future decades, potentially benefiting planners and managers.

Highlights

  • Changes to precipitation and temperature regimes associated with global warming pose a particular challenge to intensively managed watersheds because of changes in climate impact both water supply and demand

  • To illustrate the degree to which the use of the stochastic weather generator captures variations in key climate parameters across General Circulation Model (GCM), we show the probability density functions (PDFs) of the output of the stochastic weather generator for annual precipitation amount, maximum temperature, and minimum temperature (Figure 4)

  • This study develops an ensemble approach for creating daily climate realizations combining a stochastic weather generator and downscaled General Circulation Model (GCM) projections

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Summary

Introduction

Changes to precipitation and temperature regimes associated with global warming pose a particular challenge to intensively managed watersheds because of changes in climate impact both water supply and demand. A significant body of work has sought to characterize the impacts of climate change on water supply through, for instance, quantifying changes in precipitation, snowpack dynamics, and runoff [1,2,3]. Considerable uncertainties in climate projections exist due to the internal variability of the climate system, equations used in different general circulation models, and the design of scenarios [5]. The wide spread of the future projections of precipitation and temperature, at regional scales, introduce difficulties in characterizing the impacts of climate change in intensively.

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