Fellow: Mohsen Tahmasebi Nasab, Ph.D. Student, Department of Civil and Environmental Engineering, North Dakota State University
Advisor: Xuefeng Chu, Ph.D., Associate Professor, Department of Civil and Environmental Engineering, North Dakota State University.

Models with different scales suggest a trade-off between spatial scales and complexity of the models. Some macro-scale models have been developed to simulate hydrologic processes; but more work is required to tackle important challenges such as scaling and parameter estimation, especially in areas with cold climate. This study is aimed to develop a macro-scale physically-based gridded hydrologic model (GHM), which takes advantage of downscaled meteorological datasets. Particularly, this study focuses on: (1) processing 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. Required input data such as land use and land cover, soil type distribution, and meteorological and topographic datasets were obtained from different sources. In addition, input data were processed to a compatible format for the GHM. Eventually, different hydrologic processes were simulated by applying the developed GHM to the Red River Basin and the state of North Dakota. Results from the GHM can be linked to other models (e.g., ecological and climatic models) to provide the required information for decision makers, as well as researchers.

Project Objectives:

This study is aimed to develop and apply the macro-scale physical-based GHM, which is coupled with downscaled meteorological datasets and can simulate the spatially and temporally varied hydrologic processes across large scales. This project is a part of my Ph.D. dissertation research. The specific objectives of my Ph.D. dissertation research are:

• to develop the macro-scale physically-based GHM that simulates cold-climate hydrologic processes which are specific to North Dakota;
• to test the GHM by coupling it with downscaled meteorological datasets, and applying it to different watersheds with various scales within the Souris-Red-Rainy region; and
• to study the related hydrologic issues associated with macro-scale modeling including the scaling issue and DEM resolution effects.

Achieving these objectives requires four tasks: (1) to complete the computer code; (2) to collect and process the modeling datasets; and (3) to test the GHM by applying it to different macro-scale watersheds.

Research Progress:

The initial version of GHM was developed and presented in forms of oral and poster presentations. In addition, the model was tested by applying it to (1) the Red River Basin, and (2) the state of North Dakota. The simulation results for the Red River Basin were compared with those from the widely-used SWAT model to confirm the accuracy of the most dominant simulated hydrologic processes. In addition to the following research outcomes, one paper is under review and two papers are under preparation (theoretical paper of the GHM and its application in the Red River Basin).

Significance:

The main goal of most hydrologic models is to project future changes in water resources, and to help decision makers to address hydrologic issues such as seasonal flood and contaminant transport problems. Since different earth systems and hydrologic processes are interdependent, hydrologic models are commonly used to simulate these processes and their interactions. However, inadequate understanding of the macro-scale hydrologic processes in a region may result in unfavorable modeling results. Recurring floods in the Red River Basin are an example of the hydrologic issues that require improved macro-scale hydrologic models.

A combination of macro-scale topographical, hydrological, and climatological factors can be responsible for flood events in the Red River basin: flat topography, frozen soil, high soil moisture before freezing, lower-than-normal temperatures, simultaneous occurrence of spring thaw with high discharge, and thick snowpack cover across the basin. Simulating these processes within a proper scale is highly important. Micro- and meso-scale models may be great choices for investigating hydrologic variations at local or slightly larger scales, but they are not efficient for macro-scale analyses. Macro-scale models, in contrast, take major hydrologic changes into account and are able to be linked to other models (e.g., meteorological, ecological, land-use change, climate-change, and economic models).

Peer-Reviewed Journal Papers:

     Tahmasebi Nasab, M., Zhang, J., and Chu, X. (2017). “A New Depression -Dominated Delineation (D-cubed) Method for Improved Watershed Modeling.” Hydrological Processes, 31(19), 3364–3378.

Conference:

     Tahmasebi Nasab, M., Grimm, K., Wang, N., and Chu, X. (2017). “Scale Analysis for Depression-Dominated Areas: How Does Threshold Resolution Represent a Surface?” Proceeding of World Environmental and Water Resources Congress 2017, American Society of Civil Engineers, Reston, VA, 164–174.

     Tahmasebi Nasab, M., Grimm, K., Wang, N., and Chu, X. (2017). “Scale Analysis for Depression-Dominated Areas: How Does Threshold Resolution Represent a Surface?” World Environmental and Water Resources Congress 2017, Sacramento, CA.

     Tahmasebi Nasab, M., Chu, X., Singh, V. (2017). “Depressions as Gatekeepers: How Do Depressions Control Hydrologic Modeling?” World Environmental and Water Resources Congress 2017, Sacramento, CA.

     Tahmasebi Nasab, M., Grimm, K., Wang, N., and Chu, X. (2017). “Hydrologic Monitoring and Modeling for Quantifying Prairie Pothole Dynamics” World Environmental and Water Resources Congress 2017, Sacramento, CA.

     Tahmasebi Nasab, M., and Chu, X. (2017). “A New Physical-Based Gridded Model for Macro-Scale Hydrologic Modeling” ND EPSCoR Annual Conference, Fargo, ND.