A nonstationary uncertainty framework for climate change impact projections – trading space-for-time to understand streamflow elasticity

Open Access
Author:
Singh, Riddhi
Graduate Program:
Civil Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
November 15, 2010
Committee Members:
  • Dr Thorsten Wagener, Thesis Advisor
Keywords:
  • climate change
  • streamflow elasticity
  • uncertainty
  • hydrologic model
Abstract:
Watershed models are used to simulate the streamflow for a given climatic scenarios and also for ungauged basins. They have become increasingly important to predict the behavior of watersheds under the expected climate change where the watersheds will experience climates which will be much different from their historical climate. Most of the waterheds models require a simulation based approach to arrive at optimal parameter sets. One of the major problems is dependence of watershed models on calibration, whose outcome is dependent on the climatic regime of the calibration data, or on a priori parameter estimates, which often perform poorly even in reproducing historical data. In addition there is an urgent need to for the estimation of uncertainty in climate change impact assessment. Streamflow elasticity is defined as the percent change in streamflow for a percent change in precipitation or temperature. It is an indicator of the sensitivity of streamflow to climate change. Elasticity can be derived from historical observations or from watershed model simulations. In this study we develop a new uncertainty framework utilizing trading-space-fortime to establish model constraints that reduce predictive uncertainty while accounting for the impact of climate nonstationarity on parameter estimates. The driving hypothesis is that observed spatial gradients in watershed signatures such as runoff ratio, baseflow index etc can be used as a proxy for temporal gradients. Thus, relationships developed over vast spatial extent spanning a variety of watersheds and climate, can be used to predict the nature of a watershed as it moves to climates that it never experienced before. The main conclusion of the study is that as we move towards more extreme climates, the importance of including nonstationarity in parameters increases. Moreover, drier climates are more sensitive to climate change than wetter climates for most of the watersheds considered in the study. The latter scenario is likely to be the case for many less developed countries, which typically already lie in regions where water availability is lower and climate variability is higher. The results shown here suggest that previously used frameworks will likely underestimate the hydrologically-controled risks posed by climate change!