Copyright (C) 2013-2023 LREIS, NJNU, and LZU. All rights reserved.

• SEIMS GitHub
• SEIMS Documentations
  • Archieved PDF version in English
  • Online versions: English version, 简体中文版 (尚未完成!)

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, include Hydrology, Erosion, Nutrient, Plant Growth, BMP Management, etc. Algorithms are integrated from SWAT, LISEM, WetSpa Extension, DHSVM, CASC2D, etc.

SEIMS is still being developing and any constructive feedback (issues or push requests) will be welcome and appreciated.

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.

• 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.

• 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.

• 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 Syst. Sci. Data, 14: 3791–3805.

SEIMS is an open source software. Support is provided through the Github issues and Email of present developers.

• Issues: https://github.com/lreis2415/SEIMS/issues
• Emails of present developers:
  • Dr. Liang-Jun Zhu ([email protected])
  • Prof. Junzhi Liu ([email protected])