Forecasting Soil Moisture in Northern California Using a Linear Inverse Model

Abstract:

Soil moisture anomalies underpin a number of critical hydrological phenomena with socioeconomic consequences. Yet systematic studies of soil moisture predictability are often limited, and forecasts of the field often come with little or no indication of the confidence water managers should place in them. Here, we use a data-adaptive scheme, Linear Inverse Modeling (LIM), to investigate the predictability of soil moisture in northern California. This approach yields a model of soil moisture at a set of 10 stations in the region, with results that indicate the possibility of skillful forecasts at each for lead times of 1-2 weeks. But most importantly, the model enables a priori identification of forecasts of opportunity – conditions under which the model’s forecasts are expected to have particularly high skill. These forecasts are found to occur especially in the transition periods between California’s wet and dry seasons, suggesting the potential to aid stakeholder decision making during the most critical periods of water resource management.