An Integrative Framework for Water Quantity and Quality Decision Making in the face of Climate Variability

Balaji Rajagopalan1, Edie Zagona2 and Martyn Clark3

1 Civil and Environmental Engg., & CIRES

2 CADSWES/Civil and Environmental Engg.

3 CSTPR/CIRES

Preamble

River and reservoir managers around the world, and in particular, the Colorado River Basin face increased pressure to manage water for multiple purposes (water supply, hydropower, irrigation, recreation, fish and wildlife). Reservoir management becomes a formidable challenge under conditions of increasing climate variability and regional growth. The current dry season in terms of low snow pack in the Colorado basin, the water issues in the Klamath and Truckee river basin involving competing objectives of water quality (for fish habitat) and water quantity (for irrigation), highlights the importance of efficient water management. Water management typically entails making several decisions at reservoirs related to water quantity and quality at the beginning of a season - e.g., planning the amount of releases for multiple purposes, emptying the reservoir in anticipation of future flows, scheduling the releases for water quality and quantity purposes over the entire season, etc. Clearly, in this context, climate information on longer lead times and at relevant decision process scales has the potential to influence and expand the response options available to reservoir managers. Our preliminary work to understand the decision process and climate-streamflow relationships suggests that the decision and planning process is amenable to use of climate information

Proposed Research

Several studies in the recent past have focused on identifying relationships between large-scale climate and streamflows (i.e. climate diagnostics) and then, use this information in assessing the seasonal streamflow and subsequently modify the decision making. Often, the weakest link in these studies has been the decision making aspect. This interdisciplinary research effort seeks to provide a framework for systematic quantitative assessment of the potential for the use of climate information for multi-objective water resources management.

The proposed framework is as follows:

1. Climate information for a given season is obtained from NOAA. Typically, these are probabilistic forecasts of precipitation and temperature over a given region for a season. Conditioned upon this information, ensembles of daily weather are generated for all the days in the season via a stochastic weather generator (Rajagopalan and Lall, 1999), at several locations in the river basin.

2. The ensembles of daily weather ensembles are passed through a physical watershed model Modular Modeling System (MMS) developed by Leavesley, 1996. This generates ensembles of daily streamflow and consequently, ensembles of monthly and seasonal streamflows likely to enter the reservoirs from the watershed.

3. The streamflow ensembles are then fed into a decision support system (DSS) called RIVERWARE (Zagona et al., 2001) - developed by CADSWES/USBR for reservoir management and is widely used by reservoir managers in the Western US. The DSS contains all the reservoir operation rules (such as water rights, mandatory releases etc.) The DSS then provides ensembles of decision options (i.e. ensembles of various decision variables such as releases, yield, storage etc.) that will aid in improved decision making.

We propose to demonstrate this framework on the Truckee River basin near Reno, NV. This basin has an interesting water quality and quantity problem that USBR are currently working on. The USBR acquired rights for extra water to be used in mitigating stream temperatures in the summer that are detrimental to fish habitat. A decision-making framework for efficient planning and use of this water is critical. The DSS developed by Neuman (2001) in RIVERWARE will be utilized in this demonstration.

Synergistic Component

The proposed research for short-term water management complements very well the WWA research on long term water policy decision making under socio-economic and climate variability. In fact, the proposed framework can be used for policy decisions as well, and in this regard we will collaborate with the SSUN group of WWA.

Innovative Component

The proposed framework is unique in that, it provides a quantitative framework to integrate climate information (physical sciences) all the way through to the decision making process (social sciences). This has the potential to be a model for various decision making scenarios.

Interdisciplinary

The research work proposed is clearly interdisciplinary in nature - the collaborating personnel clearly highlight this aspect. The research requires knowledge of large-scale climate, stochastic hydroclimatology, watershed hydrology, reservoir operation/management and decision making.

Work Plan

Edie Zagona, will help with using the DSS (RIVERWARE) for decision making. Martyn Clark will help with the implementation of the watershed model (MMS). Balaji Rajagopalan, will be responsible for the overall co-ordination of the research. In addition he will be working on the climate diagnostics and weather generator component to couple with MMS. There will be a graduate student working with us on this project in developing the framework.

Budget

Graduate student for 12 months $22,192

Tuition $4,140

Supplies, Software license, Services provided

By CADSWES Research Center $3668

Total $30,000

Reference:

Leavesley, G.H., Restrepo, P.J., Markstrom, S.L., Dixon, M. and Stannard, L.G., 1996: The modular modeling system – MMS: User’s manual, US Geological Survey Open File Report, 96-151, 142pp.

Neumann, D, 2001: An operations model for temperature management of the Truckee River above Reno, NV. MS thesis, University of Colorado, Boulder, CO.

Rajagopalan and Lall, 1999: A nearest neighbor bootstrap resampling scheme for resampling daily precipitation and other weather variables, Water Resources Research, 35(10), 3089-3101.

Zagona, E. A., T. J. Fulp, R. Shane, T. M. Magee, and H. M. goranflo, 2001: RiverWare: A generalized tool for complex reservoir system modeling. Journal of the American Water Resources Association, 37(4), August.