Ph.D. Utah State University, 1995
Associate Professor of Civil, Environmental and Architectural Engineering
Office: ECOT 541
Web: Prof. Rajagopalan
(Dept. of Civil, Environmental, and Architectural Engineering)
- Stochastic Hydrology and Hydroclimatology
- Nonparametric functional estimation techniques (probability density Functions, regression, scenarios generation, forecasting)
- Understanding low frequency climate variability and its signatures on regional hydrology
- Incorporating climate information in water resources/hydrologic decision making
- Understanding spatio-temporal variability in Indian summer monsoon
- Nonlinear Dynamics - recovering dynamics from data
- Bayesian techniques for optimal combination of information from multiple sources and decision making
Statistical climate modeling and its application to hydrology, water resources eng. related issues; Stochastic modeling of rainfall and other weather variables; scaling issues in rainfall; Spatial estimation of hydro-climate variables; nonparametric estimation of density and regression functions for Multivariate Time series analysis of climate data; Identifying inter-annual variability in hydro climate variables and nonlinear dynamical modeling and forecasting; Inferring long range climate variability through statistical analysis of paleo proxy data.
Current Research: Climate Variability, Source Water Quality, and Utility Planning
— with graduate stduent Erin Towler
Climate variability and change affect the hydrologic cycle, which influences the quality of drinking source waters. Shifts in water quality can limit the use of source waters, affecting treatment and management decisions. The confluence of economic growth, increasing demands, and decreasing water supply and quality due to climate fluctuations makes it imperative to develop tools for sustainable development and management of water resources. Efforts are underway to incorporate climate information into water-availability planning, but little has been done to extend this to water quality. To address this urgent need, we are developing an integrated set of tools that translate climate information into water quantity and quality.
Water quality is often evaluated in terms of thresholds set by regulatory agencies or identified as limits to particular treatment options. Thus, probabilities of threshold exceedances are important for decision making. Examples of our work to inform planning decisions at seasonal and interdecadal time scales are briefly described below.
1) Water Quality Forecasting. To improve efficiency, water utilities require skillful water quality forecasts—and although seasonal climate forecasts have improved, that has not yet led to improved seasonal water quality forecasts. We developed a local logistic regression approach to generate ensembles of water quality variables conditioned on seasonal climate forecast. We demonstrated the useability of the approach for a water utility in Oregon, whose managers sought to understand turbidity exceedances.
2) Understanding Extremes. Large-scale climate drives hydrologic extremes and consequently water quality extremes. We developed a nonstationary generalized extreme value model to estimate the probability distribution of hydrologic and water-quality extremes. The method was used to project extremes under future climate change, for the same Oregon water provider (Figure 1).
3) Planning for New Water Sources. New water source development is the most common solution to meet increasing demand, and it involves significant capital investment. We developed an integrated stochastic framework with simulation techniques to generate paired projections of streamflow and water quality under a range of climate scenarios. The framework was demonstrated on a municipal water provider in Colorado that is developing a new water source with variable salinity.
4) Evaluating Approaches. When developing or blending in new water sources, utilities need to evaluate the relative costs of necessary treatment. We propose an approach to assess the potential treatment and residential costs associated with blending, under climate uncertainty. Figure 2 shows one way to present and evaluate options. With the proposed blending strategies, a 30-percent reduction in annual flow due to climate change means a 12-percent treatment cost increase and a 22-percent increase in residential cost.
Our work is described in two papers in press at Water Resources Research, and a third in review at Water Research.
Rajagopalan is a professor at the University of Colorado at Boulder.