expand to see the list of Publications (Since 2016)
Tahmasebi Nasab, M., & Chu, X. (2023). Impacts of Temperature Data Sets on Macroscale Snowmelt Simulations in the Missouri River Basin. Journal of Cold Regions Engineering, 37(2).
Zolghadr-Asli, B., Naghdyzadegan Jahromi, M., Wan, X., Enayati, M., Naghdizadegan Jahromi, M., Tahmasebi Nasab, M., ... & Pourghasemi, H. R. (2023). Uncovering the Depletion Patterns of Inland Water Bodies via Remote Sensing, Data Mining, and Statistical Analysis. Water, 15(8), 1508.
Tahmasebi Nasab, M., Berg, S. S., Comba, L., Sellner, B., & Epperson, C. (2022). Impacts of seasonally frozen ground on streamflow recession in the Red River of the North Basin. River Research and Applications, 38(7), 1277-1284.
Enayati, M., Bozorg-Haddad, O., Pourgholam-Amiji, M., Zolghadr-Asli, B., Tahmasebi Nasab, M. (2022). Decision Tree (DT): A Valuable Tool for Water Resources Engineering. In: Bozorg-Haddad, O., Zolghadr-Asli, B. (eds) Computational Intelligence for Water and Environmental Sciences. Studies in Computational Intelligence, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-19-2519-1_10
Enayati, M., Bozorg-Haddad, O., & Tahmasebi Nasab, M. (2021). Conflict Resolution: The Gist of the Matter in Water Resources Planning and Management. In Essential Tools for Water Resources Analysis, Planning, and Management (pp. 263-273). Springer, Singapore.
Tahmasebi Nasab, M., & Chu, X. (2021). Do sub-daily temperature fluctuations around the freezing temperature alter macro-scale snowmelt simulations?. Journal of Hydrology, 596, 125683.
Tahmasebi Nasab, M., & Chu, X. (2020). Macro-HyProS: A new macro-scale hydrologic processes simulator for depression-dominated cold climate regions. Journal of Hydrology, 580, 124366.
Chu, X., Lin, Z., Tahmasebi Nasab, M., Zeng, L., Grimm, K., Bazrkar, M. H., ... & Zheng, H. (2019). Macro-scale grid-based and subbasin-based hydrologic modeling: joint simulation and cross-calibration. Journal of Hydroinformatics, 21(1), 77-91.
Tahmasebi Nasab, M., Grimm, K., Bazrkar, M. H., Zeng, L., Shabani, A., Zhang, X., & Chu, X. (2018). SWAT modeling of non-point source pollution in depression-dominated basins under varying hydroclimatic conditions. International journal of environmental research and public health, 15(11), 2492.
Grimm, K., Tahmasebi Nasab, M., & Chu, X. (2018). TWI computations and topographic analysis of depression-dominated surfaces. Water, 10(5), 663.
Tahmasebi Nasab, M., Zhang, J., & Chu, X. (2017). A new depression‐dominated delineation (D‐cubed) method for improved watershed modelling. Hydrological Processes, 31(19), 3364-3378.
Tahmasebi Nasab, M., Singh, V., & Chu, X. (2017). SWAT modeling for depression-dominated areas: how do depressions manipulate hydrologic modeling?. Water, 9(1), 58.
Habtezion, N., Tahmasebi Nasab, M., & Chu, X. (2016). How does DEM resolution affect microtopographic characteristics, hydrologic connectivity, and modelling of hydrologic processes?. Hydrological Processes, 30(25), 4870-4892.
Conference Proceeding Papers
Conzet, X., LaFavor, I., Abdimuhsin, M., Niaghi, A. R., & Tahmasebi Nasab, M. (2021) Analysis and Modeling of Frozen Ground and Soil Temperature in North Dakota. In World Environmental and Water Resources Congress 2021 (pp. 545-551).
Tahmasebi Nasab, M., & Chu, X. (2018). Topo-Statistical Analyses of Ponding Area versus Ponding Storage of Depression-Dominated Regions for Macro-Scale Hydrologic Modeling. In World Environmental and Water Resources Congress 2018 (pp. 415-424).
Tahmasebi Nasab, M., Grimm, K., Wang, N., & Chu, X. (2017). Scale analysis for depression-dominated areas: how does threshold resolution represent a surface?. In World Environmental and Water Resources Congress 2017 (pp. 164-174).
Tahmasebi Nasab, M., Jia, X., & Chu, X. (2016). Modeling of subsurface drainage under varying microtopographic, soil and rainfall conditions. In 2016 10th International Drainage Symposium Conference, 6-9 September 2016.
Undergraduate Student researchers
Featured Research projects
Analysis and Modeling of Frozen Ground and Soil Temperature
Long periods of snow-covered frozen ground affect the generation of surface runoff by altering the infiltration process. Physically based approaches to identify frozen ground are relatively data-intensive since they incorporate heat transfer procedures for computing the energy flux of the soil. In order to simplify the identification of frozen ground, the majority of the macro-scale hydrologic models utilize empirical frost indices to estimate the frozen ground condition. The main objectives of this study are to provide statistical analyses of the frozen ground condition and to evaluate the performance of a continuous frozen ground index (CFGI) methodology by using measured soil temperature data.
Horizontal structure of Macro-HyProS: a LEGO-fashion RGB (Red, Green, and Blue) block layout, in which Red Block represents the developed area, Green Block represents the vegetated area, and Blue Block represents the wetted area.
Macro-HyProS: A new macro-scale hydrologic processes simulator for depression-dominated cold climate regions
Macro-HyProS is a grid-based hydrologic model of a unique structure to deal with hydrologic complexities in depression-dominated cold climate regions. The model runs on a daily time step and incorporates a LEGO-fashion horizontal layout to account for sub-grid land-use heterogeneity. On the vertical layout, each grid consists of different bands, each of which is responsible for simulating specific hydrologic processes. Macro-HyProS employs improved methodologies to account for snow accumulation and ablation, depressions, and frozen ground condition.
A new daily macro-scale grid-based snow model was developed to simulate the dynamics of snow accumulation and ablation processes. Unlike other macro-scale models that rely upon a single daily average temperature, the developed model takes into account sub-daily temperature fluctuations by considering minimum and maximum temperatures and their occurrence timing. The model was applied to the Missouri River Basin for water years 2011 and 2012, which represent two contrasting wet and dry years, respectively. The results were compared with those from the SNODAS snowmelt data to ensure that the HTIM provided comparable snowmelt results.
Monthly comparisons of the snowmelt coverages simulated by using the hybrid temperature index method (HTIM), the standard temperature index method (TIM), and the Snow Data Assimilation System (SNODAS) based on three classes of snowmelt (Class 1: low, Class 2: moderate, and Class 3: high) in December 2010 (a, b, c, and d), March 2012 (e, f, g, and h), and April 2011 (I, j, k, and l).