2016 Progress Report: An Integrated Modeling and Decision Framework to Evaluate Adaptation Strategies for Sustainable Drinking Water utility management under drought and climate changeEPA Grant Number: R835865
Title: An Integrated Modeling and Decision Framework to Evaluate Adaptation Strategies for Sustainable Drinking Water utility management under drought and climate change
Investigators: Ozekin, Kenan , Kasprzyk, Joseph Robert , Livneh, Benjamin , Rajagopalan, Balaji , Rosario-Ortiz, Fernando , Summers, R. Scott
Institution: Water Research Foundation , University of Colorado at Boulder
EPA Project Officer: Packard, Benjamin H
Project Period: September 1, 2015 through August 31, 2018 (Extended to August 31, 2019)
Project Period Covered by this Report: September 1, 2015 through August 31,2016
Project Amount: $1,250,000
RFA: National Priorities: Systems-Based Strategies to Improve The Nation’s Ability to Plan And Respond to Water Scarcity and Drought Due to Climate Change (2014) RFA Text | Recipients Lists
Research Category: Water
Drought due to climate change and other extreme events such as wildfire and floods challenge drinking water utilities' ability to treat water to meet regulatory and public health protection goals, with turbidity and disinfection byproducts (DBPs) control as the critical water quality (WQ) issues. The objectives of the research are to: (1) understand the flow and sediment generation from water supply watersheds in response to scenarios of hydro-climatological extremes and natural hazards; (2) understand the mobilization and transport of organic matter and sediments, and in some cases nutrients through the watershed and eventually to the water treatment plant (WTP); (3) develop source water thresholds for turbidity and DBP precursors based on finished water regulatory constraints and using stream WQ data with extreme value theory predict WQ threshold exceedances; and (4) evaluate a suite of adaptation and operation strategies (e.g., watershed management, wild fire mitigation, WTP modifications) along with their economic, societal and policy implications, with multi-objective optimization and multi-criteria analysis tools.
Major accomplishments in Year 1 (this report period) include:
- To understand the streamflow and sediment generation from water supply watersheds in response to scenarios of hydro-climatological extremes and natural hazards, a framework for comparing the algorithms from seven erosion and suspended sediment transport models. The models cover a broad range of empirical, stochastic, conceptual and physical components. Models implemented range in complexity, including: empirical equations such as (i) a rating curve; (ii) the Modified Universal Soil Loss Equation (MUSLE); (iii) stochastic multivariate regression, as well as conceptual components in (iv) Soil Water Assessment Tool (SWAT), to more physically based models, including (v) the Watershed Erosion Prediction Project (WEPP); (vi) the Precipitation Runoff Modeling System (PRMS); and (vii) the Distributed Hydrology Soil Vegetation Model (DHSVM). Preliminary uncalibrated DHSVM model runs over a test watershed exhibited a good match with observed flow rates; however, the sediment production magnitude was low with issues of lags in simulated peak timing relative to observations. We intend to implement each models algorithms within a consistent hydrologic framework in the Variable Infilrtration Capacity (VIC) model, and are exploring sensitivities to soil and meteorological parameters in order to obtain a better simulation of sediment.
- One of the goals of the project is to understand the mobilization of dissolved organic matter (DOM) and nutrients following a wildfire and the impact on formation of disinfection byproducts. As part of this goal, we have begun to characterize the changes in mobilization and composition of the dissolved organic carbon (DOC) after a wildfire utilizing samples that were thermally altered at 250 degrees C. Preliminary results show an increase in DOC after the soil has been heated, whereas the litter shows the opposite trend. However, the magnitude of the changes in DOC from the liter varies. These results indicate that burned soil results in a higher mobilization of carbon, with the opposite effect for litter. Additionally, we have collected samples to examine DBP precursor mobilization.
- We began our work on developing a decision tool to evaluate and suggest preferred alternatives for water treatment systems to adapt to extreme events. To that end, we have developed an initial problem formulation and are working with participating utilities to gather input on the approach we would like to take in the decision support system tool.
1. The team will explore sensitivities to soil and meteorological parameters in order to obtain a better simulation of sediment.
2. The team will continue to work on the examination of carbon mobilization in watersheds.
3. The team will continue to develop the statistical modeling framework.
4. The team will hold a workshop with project participants and begin work on the decision support system.
Integration. Work from the various activities will be integrated. For example, Activities 1 and 2 personnel will plan and conduct a field campaign to sample sediment in the CLP watershed. Modeling from Activities 1 and 3 will be combined to stochastically predict TOC in a similar framework to the multivariate regression for the sediment prediction, and work from all activities will contribute to the decision framework created in Activity 4.
Journal Articles on this Report : 2 Displayed | Download in RIS Format
|Other project views:||All 40 publications||5 publications in selected types||All 5 journal articles|
||Raseman WJ, Kasprzyk JR, Rosario-Ortiz FL, Stewart JR, Livneh B. Emerging investigators series: a critical review of decision support systems for water treatment: making the case for incorporating climate change and climate extremes. Environmental Science: Water Research and Technology 2017;3(1):18-36.||
||Samson CC, Rajagopalan B, Summers RS. Modeling source water TOC using hydroclimate variables and local polynomial regression. Environmental Science & Technology 2016;50(8):4413-4421.||