Effect of Forecast-Based Pricing on Irrigated Agriculture: A Simulation

Seasonal climate prediction offers potentially useful information for water managers. However, implementing forecast information is challenging due to the probabilistic nature of forecasts and limited demonstrations of usefulness. In this study, an adaptive groundwater pricing model utilizing operational seasonal climate forecasts was evaluated for groundwater management. The price for groundwater in the upcoming season was selected according to an algorithm that incorporates the current groundwater elevation and a prediction of seasonal rainfall. A simulation based on 37 years of rainfall data and operational monsoon forecasts for Tamil Nadu, India, was conducted to assess the effect of forecast-based pricing on societal benefits, groundwater elevation, and farmer income. Results indicate the adaptive pricing model is far more effective at maintaining groundwater elevations and maximizing societal benefits than a static groundwater price. Current groundwater elevation was a more effective input to the pricing algorithm than the forecast of seasonal rainfall when evaluated separately. A comparison of forecast use by water managers to select prices and use by farmers to choose crop patterns found similar societal benefits for each approach. Controlling demand for groundwater through pricing will cause hardship for current groundwater users who currently access groundwater without tariffs. Water managers should consider using tariff revenue to provide drought relief and transitional assistance.