Dennis P. Lettenmaier Alan F. Hamlet JISAO Center for Science in the Earth System Climate Impacts Group and Department of Civil and Environmental Engineering University of Washington April, 2003 Considerations in Using Climate Change Information in Hydrologic Models and Water Resources Assessments

Climate Scenarios Transient GCM Simulations for Increasing CO2 and Aerosols Bias Correction And Downscaling Altered Precip, Temp Hydrologic Model Natural Streamflow Reservoir Model DamReleases, Regulated Streamflow Performance Measures Reliability of System Objectives Schematic Overview of Water Resources Assessments Using Climate Change Information

Technical issues 1) Climate model bias

GSM Regional Bias Problem: a spatial example Bias is removed at the monthly GSM-scale from the meteorological forecasts (so 3 rd column ~= 1 st column)

GSM Regional Bias Problem: one cell example For sample cell located over Ohio River basin, biases in monthly Ptot & Tavg are significant!

Problems of decadal scale variability and limited sample size when interpreting GCM simulations Statistical anomaly or systematic change in the climate system?

Technical issues 2) Interaction of natural variability at decadal time scales with climate change

Annual Flow at The Dalles events 2 events Problems with non-trivial interactions between climate variability and climate change

Technical issues 3) Effect of sequencing of events in the historic record

Problems with monthly sequencing and interannual variability in free running GCMs Do GCMs accurately reproduce the stochastic nature of the observed regional climate time series at seasonal time scales? (e.g. likelihood of dry fall following dry summer) Is the sequence of interannual events realistic (e.g. simulated ENSO variability and frequency and sequencing of droughts)? Should planning studies use the time series directly from the climate model, or should these time series come from other sources? How can various sources of information best be combined?

Overview of GCM Downscaling Techniques

Downscaling – bridging the scale gap

“Delta” Method Advantages: Relatively simple to interpret Simultaneously removes GCM bias and downscales Minimizes the effects of time series and spatial uncertainties in free running GCMs Disadvantages: Assumes time series and variability of monthly values like those in the historic record

Statistical Bias Correction and Downscaling Advantages: Computationally efficient in comparison with dynamic downscaling and has been shown to have comparable performance at large spatial scales when the spatial variability is principally controlled by topography. Includes potentially significant temporal, spatial or topographic variations in temperature and precipitation (or other variables) unlike those in the historic record. (Most sophisticated methods may include variation in number of sunny days, daily precipitation variability, etc.) Disadvantages: Inherits additional uncertainties from the large scale GCM forcing simulation (e.g. including spatial variability from the GCM also introduces uncertainty about the accuracy of the spatial variability in the simulations.)

Dynamic Downscaling Using Meso- Scale Climate Models Advantages: Includes potentially significant temporal, spatial or topographic variations in temperature and precipitation (or other variables) unlike those in the historic record. Spatial downscaling not required in some cases (depends on scale of interest) Produces physically-based simulations at small spatial scales (e.g. changes to topographically driven micro-climates may be more realistically simulated). Disadvantages: Computationally intensive Meso-scale simulations inherit uncertainties from the large scale GCM forcing Adds another stage of modeling with associated increases in cumulative bias

Some Considerations in Assessing Water Resources Impacts of Climate Change Bias correction is essential – any method has to reduce as a special case to reproducing the hydrologic history Critical period planning is a problem (even aside from climate change), but it is imbedded in most planning processes, and ignoring its use has hindered the incorporation of climate change information into the planning process. What is the planning horizon (is it just economic life expectancy)?