CROP AND LOCATION SPECIFIC AGRICULTURAL DROUGHT QUANTIFICATION: PART III. FORECASTING WATER STRESS AND YIELD TRENDS

Agriculture is the largest water consumer, with 70% of global water withdrawals being used for irrigation. Water scarcity issues are being exacerbated by drought and population increases, making efficient water resource management in agricultural production increasingly important. The objective of this article is to evaluate the use of short-term weather forecasts for agricultural drought prediction. A crop-specific, linear regression drought analysis technique was used in this study. This study takes place in the upper Colorado River basin (UCRB) in west Texas. Five variables associated with agricultural drought (precipitation, temperature, biomass production, soil moisture depletion, and transpiration) were scaled and used to estimate cotton yields. The yield percentiles were used as a drought index. Precipitation and temperature were forecasted with a two-week lead time using probable scenarios based on historical data. The other three variables were estimated using the SWAT model. Forecasts were generated for each week of the growing season from 2010 through 2013. Four statistics were used to evaluate model performance, including the Nash-Sutcliffe coefficient of efficiency (NSE), the coefficient of determination (R-2), and two error indices, the percent bias (PBIAS) and the RMSE-observations standard deviation ratio (RSR). Comparing the variables using the forecasted weather data to those using the observed weather data revealed that four of the five performed satisfactorily. Temperature performed the best statistically, with an NSE of 0.85 and PBIAS of 9.4%. Precipitation (NSE = 0.51, PBIAS = -34%), cumulative biomass (NSE = 0.69, PBIAS = -38%), and transpiration (NSE = 0.53, PBIAS = 11%) also performed well. However, the soil moisture depletion forecasts (NSE = 0.28, PBIAS = 11%) were unsatisfactory. The forecasted cotton yield trends (NSE = 0.72, PBIAS = -12%) and drought index (NSE = 0.76, PBIAS = -13%) both performed satisfactorily, indicating that this forecasting method may be used for decision making related to agricultural water management, including irrigation timing.