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Tushar Sinha Assistant Professor

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Presentation on theme: "Tushar Sinha Assistant Professor"— Presentation transcript:

1 Water Supply Forecasting and Analysis of Extreme Precipitation Events in South Texas
Tushar Sinha Assistant Professor Department of Environmental Engineering Texas A&M University - Kingsville

2 Monthly to Seasonal Water Supply Forecasting
Streamflow and soil moisture forecasts based on climate forecasts can be used to update water allocations and planning agricultural operations Monthly to seasonal water supply forecasts obtain their skills from Sea Surface Temperatures and initial land surface conditions Forecasting skill vary geographically and over different seasons

3 Climate to Water Supply Forecasting Framework
Precipitation and Temperature Forecasts from multiple Models (GCMs) Statistical Models Principal Component Regression Statistical Downscaling Meteorological Forcings Model Outputs Statistics (MOS) Streamflow, Soil moisture, Surface States Land Use, Topography and Soil Data Hydrologic Models VIC, Noah, SWAT, CLM, PIHM Forecasts are at coarser scale and can not be directly used for decision making. Both for forecasting and climate change, currently use spatio-temporal downscaling Reservoir Models Water Allocations

4 Predictors Model Predictand
Statistical Water Supply Forecasts Model Predictors Portal automatically downloads Updated Monthly/Seasonal Precipitation Forecasts from GCMs each month IRI Data Library Model Predictand GCMs Precipitation Forecasts (Pt) Statistical Downscaling Model (PCR) Forecasted Streamflow (Qt) Observed Streamflow (Qt-1) Training Period : Data up to previous year? Archived Forecasts : 1990-till date Storage Forecast (Reservoir Model) Low dimensional models to compare, National Weather River Forecasting Centers Climate Data (GCMs): ECHAM 4.5 Observed Streamflow: USACE Site

5 NC Inflow Forecasts Portal: Statistical Model
Model Description Individual Year Forecasts Retrospective Forecasts and Summary Existing Experimental Portal

6 Seasonal Forecasts (JFM)

7 Water Supply Forecasting for South Texas Nueces river near Tilden:
Llano river at Llano: Drainage area 4,197 mi2 94% Woody plants avg pcpt 26.5 in/yr Frio river near Derby Drainage area 3,429 mi2 79% Woody plants avg pcpt 23 in/yr Nueces river near Tilden: Drainage area 8,093 mi2 56% Woody plants avg pcpt 24 in/yr Months

8 Mean Square Skill Score: ENSO years
Elansary and Sinha, in preparation

9 NASA’s Land Information System (LIS)
Noah, CLM, Catchment, VIC Kumar et al., 2007

10 JFM Soil Moisture Deficit during 2007-2008 Drought
% Deviation in 2008 JFM soil moisture forecasts issued in Jan 2008 from soil moisture climatology (over ) using Noah model from LIS. Noah model captures spatial patterns from the US Drought Monitor Outlook issued at the middle of the season. Drought Monitor Outlook that I showed under Motivation, Soil Moisture deficits using NASA’s LIS, shows the scope of using LIS to develop large scale forecasts

11 South Texas Extreme Precipitation Events Analysis
Analyzed daily precipitation data from NOAA - National Climate Data Center Stations

12 Extreme Precipitation Events Analysis: McAllen

13 Summary: Seasonal Forecasting
Streamflow forecasts have better skill than climatological flows (no forecasts) for up to 3-months during fall and winter in South Texas Streamflow forecasts are better than climatological flows in winter for up to 5-months lead time under ENSO conditions NASA’s LIS offers scope of developing regional scale forecasts No significant changes in extreme precipitation trend, frequency and timings have been observed at 5 selected cities in South Texas

14 Final Thoughts Thank You
At seasonal time scale, holistic approach is needed for adaptive management Better predictability is needed in improving flood forecasts Despite no significant changes in extreme precipitation trends, frequency and timing, urbanization and initial conditions may result in increased flooding Thank You

15 Dominant Source of Error over the Sunbelt
Time Period 1991 to 2010 Forecasts issued at beginning of: Winter (JFM) Spring (AMJ) Summer (JAS) Fall (OND) Co-advised MS Thesis research, who defended last week Disaggregation errors are higher during summer and fall - West Large scale forecast errors are higher during winter and fall - East Mazrooei, Sinha, et al., J. Geophysical Research., 2015

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