Improved Methods for Assessment of Hydrologic Vulnerability to Climate ChangeEPA Grant Number: R824802
Title: Improved Methods for Assessment of Hydrologic Vulnerability to Climate Change
Investigators: Lettenmaier, Dennis P. , Wood, Eric F.
Current Investigators: Lettenmaier, Dennis P. , Palmer, Richard , Wood, Eric F.
Institution: University of Washington , Princeton University
EPA Project Officer: Chung, Serena
Project Period: October 1, 1995 through March 31, 2000
Project Amount: $463,762
RFA: Regional Hydrologic Vulnerability to Global Climate Change (1995) Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Global Climate Change , Water , Climate Change
The two major objectives of this project are: a) to develop an approach that resolves the present climate bias problem in stochastic weather generators used to downscale GCM climate predictions; and b) to study the interaction of the climatic dependence of water supply and demand for water resource systems where municipal and industrial, agricultural water supply, and energy generation are the major system uses. The work in the first area has focused on off-line simulation of global soil moisture, which is required to produce GCM runs in which the effect of drift in land (and sea) surface conditions is eliminated.
The general approach is to run the land surface scheme (VIC-2L) using observed precipitation and temperature at two degree latitude by longitude spatial resolution, and daily temporal resolution. An approach will be developed to form gridded daily precipitation and temperature for the global land areas for the study period. It consists of disaggregating existing gridded monthly precipitation and temperature using a procedure that reproduces the intra-monthly statistics and spatial correlations derived and/or interpolated from daily observations. The procedure reproduces the observed sequences of daily precipitation and temperature, averaged over two degree grid cells, for cells where a minimum of seven observing stations are available.
For other grid cells, the temporal probability distributions of daily precipitation and temperature are reproduced. The daily precipitation and temperature sequences derived using this method will be used to simulate the daily water balance of each grid cell for the period 1979-93. The seasonal distribution of soil wetness in the upper layer did a good job of representing large-scale patterns and their seasonal cycle, e.g., the dryness of North Africa (Sahara), Arabia and Central Asia, Central and West Australia, and the relative wetness of the tropical belt and the mid- latitude storm belt regions. The major differences between the derived fields and previously developed global surface soil wetness products include the use of a specific simulation period as opposed to climatologies, the daily time step, and the use of a more sophisticated atmosphere-land surface model.
To address the second objective, a comparative analysis of the sensitivities of the Columbia River system and the Boston water supply system to climatic and demand uncertainties will be initiated. For each of these two systems, hydrologic models will be implemented to simulate reservoir system inflow, and water resource system models will be implemented to simulate reservoir system performance. For the Columbia River system, algorithms will be implemented to assess the sensitivity of system performance to system demand and demand uncertainty. For the MWRA system, available models of the hydrology (water supply), as well as the reservoir system, will be used. An available a water demand model will be used, which reflects the interaction of climatic factors (e.g., temperature) and the effect of various water use sectors on water demand. This chain of models will be adapted to investigate the relative sensitivities of the MWRA system to climatic and demand uncertainties. Comparison of the MWRA and Columbia River systems is expected to provide a useful contrast between a western and eastern U.S. system with contrasting hydrologies, operating objectives, and demand trends.