Here’s a shout out to Xi Chen, a visiting scholar from Hohai University for publishing his first journal article. His research on forecasting summer rainfall in China’s Huai River Basin using large scale climate information is now published in Hydrology and Earth System Sciences (HESS)journal. HESS is an open access journal that is operated by the European Geophysical Union. Xi Chen led the project from data collection and quality control to model development and validations. During his visit, he worked on several projects on climate forecasts, water sustainability, and hydrologic extremes in droughts and floods. This is the first of a series of publications he is working on. The full article can be found here. A brief description about the project and its importance:
Huai River Basin is densely inhabited and serves as one of the main cropping area in China. The region has 36 reservoirs designed for supplying water for various needs and for controlling floods. One of the main issues in managing water in this basin is periodic droughts and floods caused by high variations in rainfall. In this work, we developed a statistical model that will forecast the amount of total summer rainfall before the season begins; i.e. the probable rainfall for the months of June, July and August every year will be predicted at the beginning of May. This one month lead time will enable water managers to make decisions on whether to release more water during the season (if there is a forecast of good rainfall) or to store more water in the dams (if there is a forecast of drought). Farmers can use this forecast information and the lead time to make choices on what type of crop to grow and secure the sources of irrigation.
Since we are interested in predicting the rainfall and river flows in 14 different locations in the basin, we develop a multivariate regression model that relates the rainfall and flows with identified pre-season climate predictors (like El-Nino Southern Oscillations). Knowledge on the evolving conditions in the tropical Pacific Ocean (climate predictor) will inform the atmospheric moisture tracks and ultimately the amount of rainfall. This is a high dimensional problem that required accurate representation of uncertainties and simultaneous predictions at multiple locations. Given these challenges, we used a Hierarchical Bayesian Model to explicitly quantify the parameter uncertainty through each estimation stage using appropriate conditional and prior distributions. It allows for grouping of information across the different locations. The covariance structure will provide the ability to properly represent the cross site correlation.
We are now using these forecasts along with changing demand through adaptive human behavior to specify dynamic rules for operating multiple reservoir systems in the basin. This will allow for better management of deficits from the reservoirs.