SEIMS: A modular and parallelized watershed modeling framework
Copyright (C) 2013-2026 LREIS, NJNU, and LZU. All rights reserved.
Build Status
Brief Introduction
The Spatially Explicit Integrated Modeling System (SEIMS), is a lightweight, modular, and parallelized watershed modeling framework, that focusing on build and perform watershed process models in a plug-and-play way, and conduct scenario optimization of watershed best management practices (BMPs).
SEIMS is implemented using standard C++ and Python to be cross-platform compatible. SEIMS uses CMake to manage the entire project for compatibility on mainstream compilation environments. The compiled C++ programs include the SEIMS main programs (the OpenMP version and the MPI&OpenMP version), SEIMS module library (i.e., dynamic/shared libraries), and executable programs for data preprocessing. Python is used for utility tools including data preprocessing, calibration, sensitivity analysis, scenario analysis, and so on.
SEIMS contains several module categories, including Hydrology, Erosion, Nutrient, Plant Growth, BMP Management, etc. Algorithms are integrated from SWAT, LISEM, WetSpa Extension, DHSVM, CASC2D, etc.
SEIMS is still being developed and any constructive feedback (issues or push requests) will be welcome and appreciated.
Installation
Users are highly recommended to take a look at the automatic workflow of installation and testing of SEIMS on Windows, Linux, and macOS through GitHub actions. The configuration yml scripts are located in SEIMS/.github/workflows. The detailed instruction for installing SEIMS can be found here.
Selected peer-reviewed papers
Full list can be found here.
Reviews and opinions
Watershed modeling framework
- Wang, Y.-J., Zhu, L.-J., Qin, C.-Z., Zhu, A-X., 2026. A spatially hybrid hydrological modeling approach using subbasin-specific model structures. Environmental Modelling & Software 200, 106944.
- Liu, Junzhi, Liu, Jiaojiao, Liu, M., Tian, S., Liu, Y., Yang, W., Liu, Y., 2025. Development of an alpine hydrological model considering the recharge of stream water to alluvial plain aquifers. Environmental Modelling & Software 192, 106567.
- Liu, Junzhi, Liu, Jiaojiao, Zhang, B., Xu, J., Wang, H., Zheng, X., Liu, C., Guo, Z., Ma, J., Xiao, D., Long, P., Lianghao, Z., Liu, Y., Yang, W., 2025. WISE: A spatially explicit carbon cycling model at the watershed scale. Information Geography 1(1), 100010.
- Zhu, L.-J., Liu, J., Qin, C.-Z., Zhu, A-X., 2019. A modular and parallelized watershed modeling framework. Environmental Modelling & Software 122, 104526.
- Liu, J., Zhu, A-X., Qin, C.-Z., Wu, H., Jiang, J., 2016. A two-level parallelization method for distributed hydrological models. Environmental Modelling & Software 80: 175–184.
- Liu, J., Zhu, A-X., Liu, Y., Zhu, T., Qin, C.-Z., 2014. A layered approach to parallel computing for spatially distributed hydrological modeling. Environmental Modelling & Software 51: 221–227.
- Liu, J., Zhu, A-X., Qin, C.-Z., 2013. Estimation of theoretical maximum speedup ratio for parallel computing of grid-based distributed hydrological models. Computers & Geosciences 60: 58–62.
Scenario optimization of BMPs
- Wu, T., Zhu, L.-J., Shen, S., Qin, C.-Z., Zhu, A.-X., 2026. [Spatiotemporal optimization method for watershed management practice scenarios within a simulation–optimization framework](). Water Resource Management.
- Shen, S., Qin, C.-Z., Zhu, L.-J., Zhu, A-X., 2023. Optimizing the implementation plan of watershed best management practices with time-varying effectiveness under stepwise investment. Water Resources Research 59(6), e2022WR032986.
- Zhu, L.-J., Qin, C.-Z., and Zhu, A-X., 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.
- Zhu, L.-J., Qin, C.-Z., Zhu, A-X., Liu, J., Wu, H., 2019. Effects of different spatial configuration units for the spatial optimization of watershed best management practice scenarios. Water 11(2), 262.
- Qin, C.-Z., Gao, H.-R., Zhu, L.-J., Zhu, A-X., Liu, J.-Z., Wu, H., 2018. Spatial optimization of watershed best management practices based on slope position units. Journal of Soil and Water Conservation 73(5): 504–517.
- Wu, H., Zhu, A-X., Liu, J., Liu, Y., Jiang, J., 2018. Best Management Practices Optimization at Watershed Scale: Incorporating Spatial Topology among Fields. Water Resource Management 32: 155–177.
Participatory Decision Support System
Data sets
- Liu, J., Zhang, B., Que, Y., Xu, J., Hou, W., Yang, W., 2026. RiverLakeBasins: a global dataset of nested river-watersheds and lake-hillslopes. International Journal of Geographical Information Science.
- Liu, J., Fang, P., Que, Y., Zhu, L.-J., Duan, Z., Tang, G., Liu, P., Ji, M., Liu, Y., 2022. A dataset of lake-catchment characteristics for the Tibetan Plateau. Earth System Science Data 14(8): 3791–3805.
Support
SEIMS is an open source software. Support is provided through the Github issues and Email of present developers.
