Institute: North Dakota
Year Established: 2017 Start Date: 2017-03-01 End Date: 2018-02-28
Total Federal Funds: $8,190 Total Non-Federal Funds: $16,380
Principal Investigators: Xuefeng Chu
Abstract: Models in different scales suggest a trade-off between spatial scale and complexity of the models. Several macro-scale models have been developed to investigate particular hydrological processes; but more work is required to tackle important challenges such as scaling and parameter estimation, specifically in cold regions. The proposed study will develop a macro-scale physical-based gridded hydrologic model (GHM), which takes advantage of downscaled meteorological datasets. This study focuses on: (1) collecting the required data for the modeling, (2) incorporating unique algorithms for cold-climate conditions into the GHM, and (3) applying the GHM to macro-scale watersheds in North Dakota. In the monitoring effort, two wireless precipitation stations have been installed and precipitation data have been collected for a period of six months. More precipitation data will be collected and used for the modeling. In addition, water levels and bed topography of selected channels will be recorded by employing the HOBO water level data loggers and the River Surveyor, respectively. Eventually, different hydrological processes will be simulated by applying the GHM to the Devils Lake watershed in North Dakota. Results from the GHM can be linked to other models (ecological and climatic models) to provide the required information for decision makers and researchers.