Investigating soil moisture and streamflow predictability with a simple water balance model: A case study
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Tuesday, 4 February 2014: 4:30 PM
Room C210 (The Georgia World Congress Center )
Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. We first investigate whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Our approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale (Orth et al. 2013). The validity of the approach is demonstrated with data from three heavily monitored catchments. In a second step we supplement the model with a snow module based on a degree-day approach and apply it to investigate the potential predictability of soil moisture and runoff in Switzerland (Orth and Seneviratne 2013). Our results show that simulated soil moisture and streamflow are well predictable (as indicated by significantly improved performance compared to climatology) until lead times of approximately one week and 2-3 days, respectively, when using initial soil moisture information and climatological atmospheric forcing. Using also initial snow information and seasonal weather forecasts as forcing, the predictable lead time doubles in case of soil moisture and triples for streamflow. The skill contributions of the additional information vary with altitude; at low altitudes the precipitation forecast is most important whereas in mountainous areas the temperature forecast and the initial snow information are the most valuable contributors. We find furthermore that the soil moisture and streamflow forecast skills increase with increasing initial soil moisture anomalies. Comparing the respective value of realistic initial conditions and state-of-the-art forcing forecasts, we show that the former is generally more important for soil moisture forecasts, whereas the latter information is more valuable for streamflow forecasts. We additionally illustrate the similarity between the concepts of memory and predictability as measures of persistence in the last part of this study.
Orth, R., R. D. Koster, and S. I. Seneviratne (2013), Inferring soil moisture memory from streamflow observations, J. Hydrometeorol., in press
Orth, R., and S. I. Seneviratne (2013), Predictability of soil moisture and streamflow on sub-seasonal timescales: A case study, J. Geophys. Res., in revision