Regional Precipitation and ET Patterns: Impacts on Agricultural Water Management

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Regional Precipitation and ET Patterns: Impacts on Agricultural Water Management Christopher H. Hay, PhD, PE Ag. and Biosystems Engineering South Dakota State University 23 November 2010 Photo: USDA-ARS

Precipitation has increased over the last two decades over much of South Dakota 2 Increase in Mean Annual Precipitation (in inches) 1991 2008 compared to 1961 1990

James River was above flood stage for nearly 1-½ years 3

The James River Basin presents some unique challenges 4 World s longest un-navigable river Shallowest gradient river in North America

2010 spring flooding south of Huron, SD (20 Mar) Image: NASA EO

Spring flooding on the James River near Scotland (24 Jun) Image: NASA EO

Big Sioux River peaked in September 2010 7

SD has had large areas of prevented planting acres 8 Data: USDA RMA

Interest in agricultural drainage in SD has increased at the same time that irrigation development continues Too much rain at the wrong time Increased commodity prices Increased land prices Photo: USDA-NRCS 9

Eastern SD lies in a transitional region from humid to semiarid conditions 10 Keim (2010)

Thus, SD is in a transition zone from demand to supply limitations on ET 11 M Jung et al. Nature 000, 1-4 (2010) doi:10.1038/nature09396

Temperatures have generally increased in the region 12

Precipitation has increased over the northern plains/upper Midwest 13 Global Climate Change Impacts in the United States, Thomas R. Karl, Jerry M. Melillo, and Thomas C. Peterson, (eds.). Cambridge University Press, 2009.

There has been a significant regional trend in dew point over the 1973 2008 period in SD Median trend 0.37 C/10 yrs Kendall's τ 0.255 S 1132 p-value < 0.0001 14

Row crops have altered the hydrologic cycle Adapted from Schaffer (2005) 15

This creates water management challenges and opportunities 16 Adapted from G. Sands

This talk focuses comparisons of precipitation and evapotranspiration patterns ET estimation Regional precipitation and ET patterns 17 Station precipitation and ET patterns and trends

Temperature and precipitation data used were from the US Historical Climatology Network Subset of the US Coop Observer Network (NOAA NWS) Stations selected based on period of record and percentage of missing values Extensive QA/QC for errors and bias M. J. Menne, C. N. Williams, Jr., and R. S. Vose, 2010. United States Historical Climatology Network (USHCN) Version 2 Serial Monthly Dataset. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee. Last updated June 2010. M. J. Menne, C. N. Williams, Jr., and R. S. Vose, 2010. United States Historical Climatology Network Daily Temperature, Precipitation, and Snow Data. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee. Last updated June 2010. 18

Data from 100 stations were used for IA, MN, ND, and SD 19 77 for Reference ET

ET estimation data and methods USHCN and SD AWDN daily data Hargreaves-Samani and ASCE Standardized Reference ET equation Aggregated into weekly, monthly, and annual sums Graphical analysis 20 Photo: Lameck Odhiambo

SD Automatic Weather Data Network stations 21

ASCE Standardized Reference Evapotranspiration Equation 22 ASCE-EWRI (2005)

Hargreaves-Samani equation R a calculated based on latitude and date Tends to underpredict under high wind conditions Tends to overpredict under high humidity conditions Recommended time step of 5 days or longer 23 Hargreaves and Samani (1985), Hargreaves and Allen (2003), and FAO 56 (Allen et al., 1998)

The Hargreaves-Samani equation performed reasonably well compared to the ASCE Standardized ET ref equation Daily ET Weekly ET 24 Brookings

Weekly reference ET estimate comparisons Station Ratio (HS/ASCE) RMSD (mm/wk) R 2 Beresford 1.04 5.02 0.70 Bowdle 0.95 4.94 0.79 Britton 0.98 4.19 0.75 Brookings 1.03 3.94 0.76 Dell Rapids 1.07 3.99 0.81 Gettysburg 0.95 4.87 0.77 Highmore 0.93 4.95 0.79 Oak Lake 0.99 3.31 0.80 Pierre 0.95 5.32 0.74 Redfield 1.00 5.56 0.64 South Shore 0.99 4.36 0.74 Average 0.99 4.59 0.75 25

Crop coefficients to estimate actual ET Corn Soybean ET c = ET ref x K c 26 Non-growing season K c = 0.44 (Hay and Irmak, 2009)

Precipitation has increased in the last 2 decades over much of the eastern Dakotas, southern MN and eastern IA 27 Change in mean annual precipitation (inches) 1991-2009 as compared to 1961-1990

Winter precipitation has also increased across the eastern Dakotas 29 Change in winter (DJF) precipitation (%) 1991-2009 as compared to 1961-1990

Spring precipitation has increased across Iowa 30 Change in spring (MAM) precipitation (%) 1991-2009 as compared to 1961-1990

Summer precipitation increases have helped extend the corn belt north and west 31 Change in summer (JJA) precipitation (%) 1991-2009 as compared to 1961-1990

Large increases in fall precip. across the eastern Dakotas 32 Change in fall (SON) precipitation (%) 1991-2009 as compared to 1961-1990

Increases in fall precipitation have been particularly dramatic 33

There has generally been a slight decline in reference ET 34 Change in mean annual reference ET (inches) 1991-2009 as compared to 1961-1990

Growing season ET ref has been flat to a slight decline Change in growing season (May-Sep) reference ET (%) 1991-2009 as compared to 1961-1990

Non-growing season ET ref has been flat 36 Change in non-growing season (Oct-Apr) reference ET (%) 1991-2009 as compared to 1961-1990

Station analyses Example Station Aberdeen Northeast SD Annual precipitation and ET Average weekly P & ET corn (full water supply) over last 19 years and prior normal 37

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Limitations and future needs Preliminary results based on exploratory analysis ET ref only validated for eastern SD No accounting for other factors influencing ET (other than empirically) Surface solar radiation Vapor pressure deficit Wind No soil moisture storage accounting Water excess/deficit Water limitations on actual ET 42 No accounting for snow water storage and melt

Recent work has suggested a declining global trend in actual terrestrial ET 43 M Jung et al. Nature 000, 1-4 (2010) doi:10.1038/nature09396

Precipitation is predicted to increase during the nongrowing season 44 Global Climate Change Impacts in the United States, Thomas R. Karl, Jerry M. Melillo, and Thomas C. Peterson, (eds.). Cambridge University Press, 2009.

Heavier, more intense precipitation is increasing 45 Global Climate Change Impacts in the United States, Thomas R. Karl, Jerry M. Melillo, and Thomas C. Peterson, (eds.). Cambridge University Press, 2009.

In summary, there has been a shift in precipitation patterns over the last few decades across the region Upward trend in precipitation, particularly outside the growing season Decline in evaporative demand (though slight) If climate model predictions hold, recent water management issues could persist in the future Questions?