Model Library
Spatially Explicit Integrated Modeling System (SEIMS)
Model name: Spatially Explicit Integrated Modeling System (SEIMS)
Developed by: Dr. Liang-Jun Zhu, Dr. Junzhi Liu, Dr. Cheng-Zhi Qin, Dr. A-Xing Zhu and other contributors (Last update: 2024).
Model type: Lightweight, fully-distributed, dynamic, parallelized, process-based, hydrological, ecological, and biogeochemical, watershed model
Computational requirements: Windows, Linux, and macOS
Software requirements: GIS: required.
Capabilities and Limitations:
Capabilities
- SEIMS features a flexible modular structure, enabling customization for diverse watershed applications (Zhu et al., 2019);
- SEIMS takes grid cells as basic simulation units and allows users to combine different watershed process modules according to the characteristics of the study area and the application requirements in a plug-and-play fashion (Zhu et al., 2019);
- SEIMS includes tools for parallel computation, facilitating tasks such as sensitivity analysis and auto-calibration (Zhu et al., 2019);
- Two-level parallelization strategy of SEIMS offers superior scalability compared to parallelization at only the subbasin or basic-unit level (Liu et al., 2016).
- SEIMS has the capacity for efficient computing (Zhu et al., 2019).
Limitations
- SEIMS currently lacks full support for irregularly shaped fields and a multiple flow direction model, which are still under development (Zhu et al., 2019);
- Static task scheduling strategy based on the area of subbasins might produce a low speedup ratio (Zhu et al., 2019).
- Unlike semi-distributed models like SWAT, SEIMS assumes homogeneity within each pixel, potentially oversimplifying watershed processes (Risk, 2019);
- SEIMS cannot handle missing data values directly, requiring estimation, which may introduce uncertainty (Risk, 2019).
Model Inputs and Outputs:
Inputs
DEM, LULC, Soil characteristics, Meteorological variables and precipitation, Hydrological data (optional), Water quality data (optional)
Outputs
SEIMS simulates hydrological data, water quality parameters, erosion rates, sediment transport, plant growth, and BMP scenarios.
Examples:
References
Zhu, L.-J., Liu, J., Qin, C.-Z., & Zhu, A.-X. (2019). A modular and parallelized watershed modeling framework. Environmental Modelling & Software, 122, 104526. https://doi.org/10.1016/j.envsoft.2019.104526
Zhu, L.-J., Liu, J., & Qin, C.-Z. (2021). Spatial optimization of watershed best management
practice scenarios based on boundary-adaptive configuration units. Progress in Physical
Geography: Earth and Environment, 45(2), 207-227. https://doi.org/10.1177/0309133320939002
Objectives
This study aims to present an open-source, modular, and parallelized watershed modeling framework and demonstrate its application through a typical SEIMS-based watershed modeling procedure, encompassing model construction, parameter sensitivity analysis, and auto-calibration.
This study proposed an idea for dynamically adjusting the boundaries of one type of BMP configuration unit (i.e. slope position units) and then implement a new BMP scenario optimization approach based on these boundary-adaptive slope position units.