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Reducing Drought Risks for Small- to Mid-Size Communities in the Southeast through Development of a Drought Index and Quantification of Its Value. Puneet Srivastava. Source: The Texas Tribune. Water Group Leader Southeast Climate Consortium (SECC). Professor of Ecological Engineering
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Reducing Drought Risks for Small- to Mid-Size Communities in the Southeast through Development of a Drought Index and Quantification of Its Value PuneetSrivastava Source: The Texas Tribune Water Group Leader Southeast Climate Consortium (SECC) Professor of Ecological Engineering Biosystems Engineering Department Auburn University
Collaborators Keith Ingram, University of Florida Latif Kalin, Auburn University David Stooksbury and Pam Knox, University of Georgia Muthuvel Chelliah, NOAA Climate Prediction Center Mattew Dunn, City of Auburn Richard Marcus, California State University
Outline • Motivation for this project • Development of the Community Water Deficit Index (CWDI) • Testing and verification • Quantification of the value of CWDI • Prototype decision support tool development • Next steps
Motivation – Climate Variability • High average annual precipitation in the Southeast (e.g, annually 55 inches in Alabama) • Large intra- and inter-annual variability in rainfall and stream flows • Water generally not available during growing season • Caused mainly by El Niño Southern Oscillation (ENSO) • Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO) might also modulate the effect of ENSO
Motivation – Climate Variability/Drought • In the Southeast US, precipitation, stream flow, and consequently, water availability is greatly affected by ENSO • La Niña brings warm and dry conditions (e.g., 1999 – 2001, 2007, 2010-2012) in the Southeast, especially in winter • El Niño is quite opposite
Motivation – Climate Variability/Drought • Small- to mid-size communities (pop. less than 100,000) are especially vulenrable to drought • Mainly depend on surface water sources • At least 75 such communities
Motivation – Available Indices • Currently available drought indices, including the US Drought Monitor, Lawn and Garden Moisture Index, Standard Precipitation Index, Palmer Drought Severity Index and others are not specific to municipal water systems in the Southeast • High spatial variability of the rainfall in the Southeast • Supply and demand balance not considered • No seasonal forecast • Need an index specific to municipal water systems, operates at high spatial resolution, considers supply and demand, and can forecast A multiagency effort since 1999
Objectives • Develop a drought index (CWDI) for forecasting drought for small- to mid-size communities of the Southeast United States using the ENSO impact in the region, and • Evaluate the value of the developed index for water resource managers of the region 8
Basic Principles • Supply and Demand Water Balance • Inputs • Runoff from the supply watershed • Base flow • Precipitation • Additional water supply sources (purchased water, groundwater, etc.) • Outputs • Total water withdrawal (demand) • Evaporation • Mandatory discharge 9
Community Water Deficit Index (CWDI) CWDI estimated as S = current storage (inputs – outputs) Sd = desired storage If CWDI < 0, => Drought If CWDI > 0, => No Drought • System Dynamics Program – STELLA® • Supply - surface (runoff) and groundwater (base flow) models • Demand • Evapotranspiration: Most important component • Dynamic demand • Irrigation of lawns and golf courses • Dependent on climate • Difference between Potential Evapotranspiration (PET) and Actual Evapotranspiration (AET) • Static Demand: Depends on population • Mandatory discharge
Case Study – City of Auburn, Alabama • Current average usage for the water system - 6 MGD • Current water supply capacity - 11.6 MGD • Can purchase up to 3.6 MGD from Opelika (used only at certain times during the year) Demand Area Lake Ogletree Supply Watershed
Case Study – City of Griffin, Georgia • Two withdrawal points on Flint River • Griffin (13.2 MGD) • Molena (50 MGD) • 7 customers • 80,000 people • City of Griffin • Wholesale customers • 24.5 MGD capacity
Validation - Auburn Full Pool Phase 1 Phase 2 Validation Calibration NSE = 0.81(very good) PBIAS = -0.12 (very good) RSR = 0.61 (satisfactory) Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and ratio of the root mean square error to the standard deviation of measured data (RSR)
CWDI during drought - Auburn Full Pool Phase 1 Phase 2 Phase 3
Drought Forecasting CWDI ENSO Outlook and Climate Variables CWDI Model Time
Lake Storage Forecast - Auburn Displays “skill”
CWDI Forecast - Auburn Aug 21,2011 Jan 1, 2012
Scenario Analysis Phase 3 CWDI – used to plan restrictions/conservation policies
Summary • Developed CWDI for forecasting drought for small to mid-size communities of the Southeast United States using ENSO • CWDI – a method for early detection, duration, severity and recovery from drought • Presents a tool for water resource managers to plan ahead for climate variability-induced droughts • CWDI can be used to ensure end of season water supply by invoking appropriate level of restrictions depending on ENSO forecast • City of Auburn used the results of this study to issue voluntary water use restriction before the growing season – resulted in successful management of 2011 drought
Value of CWDI-forecasted Drought • A pathway for increased adoption of drought forecasts requires • connecting seasonal forecast systems, e.g., CWDI, with the analysis of decision-making in the target system, and • quantification of value of these forecasts to the water resource managers • The efforts were focused on • determining the usefulness of drought information for municipal water management (as determined by the impact of drought on municipal water demand), • usefulness of water restrictions imposed by municipal water management, • and the extent to which advance knowledge of probabilistic drought forecast mitigates negative impacts
Value of CWDI-forecasted Drought • Model runs for 1999 – 2000 and 2007-2008 (drought years) suggests that CWDI forecasts and subsequently water use restrictions would have • helped reduce outdoor water use demand as compared to observed use • Auburn could have saved $1.2 million (1999 – 2000) and $0.5 million (2007-2008) during winter to summer months • Manuscript in review
Web-based CWDI Tool Deficit
Select weather stations Deficit
Next Steps • Take prototype back to city water managers • Revise accordingly • Repeat Auburn, AL process for Griffin, GA • Incorporate 180-day forecast using NOAA Climate Prediction Center’s Niño 3.4 forecasts
Acknowledgement Support for this research was provided by the Sectoral Applications Research Program (SARP) and the Regional Integrated Sciences and Assessments (RISA) of NOAA. We also wish to acknowledge City of Auburn, Alabama and City of Griffin, Gerogia for their help with this project.
Thanks! PuneetSrivastava (334) 844-7426 srivapu@auburn.edu