MIKE SHE

MIKE SHE
Name	MIKE SHE
Developer	DHI
Latest release	(proprietary; periodic updates)
Programming language	Fortran/C++ (historical)
Operating system	Windows
Genre	Integrated hydrological modelling software
License	Proprietary

MIKE SHE

MIKE SHE is an integrated, physically-based hydrological modelling system used for simulating surface water and groundwater processes. It couples modules representing precipitation, evapotranspiration, overland flow, unsaturated zone flow, groundwater flow, and channel routing to represent catchment-scale water dynamics. The software is developed for use in water resources planning, flood forecasting, environmental impact assessment, and research, and is widely applied by consultancies, utilities, and academic groups.

Overview

MIKE SHE is designed to represent hydrological processes across scales, linking spatially distributed components for United Nations Environment Programme, World Bank, European Commission projects and national agencies such as Environment Agency (England and Wales), US Army Corps of Engineers, and Australian Bureau of Meteorology. The model couples solvers for overland flow, subsurface flow, and river networks, and interoperates with geographic information systems such as ArcGIS, QGIS, and remote sensing platforms from Landsat, Sentinel-2, and MODIS. MIKE SHE has been applied in basins associated with rivers like the Thames, Murray River, Colorado River, Ganges, and Nile River.

History and Development

MIKE SHE originated from research programs in Europe focused on distributed hydrological modelling in the late 1970s and 1980s, influenced by foundational work at institutions including DHI Group, Imperial College London, ETH Zurich, University of Cambridge, and Utrecht University. Early influences include conceptual and physically-based formulations from researchers associated with US Geological Survey (USGS), Institut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture (IRSTEA), and National Centre for Atmospheric Research (NCAR). The model evolved through successive releases driven by operational needs at European Space Agency (ESA) projects and international consulting practices, incorporating numerical schemes from computational hydraulics developed alongside codes like HEC-RAS, MODFLOW, and SWAT.

Model Structure and Components

MIKE SHE’s architecture comprises modules for atmospheric input, land surface processes, vadose zone flow, saturated groundwater flow, and channel routing. Atmospheric forcing can be provided by datasets from European Centre for Medium-Range Weather Forecasts (ECMWF), NOAA, Met Office, and observation networks such as Global Precipitation Measurement. Land-surface components implement evapotranspiration algorithms linked to parameterizations used in models like Penman-Monteith and formulations employed in land surface models used by NASA—implemented for use with soil characterizations from databases like FAO and ISRIC. Unsaturated flow solvers use Richards’ equation analogues similar to methods applied in HYDRUS, while groundwater components solve three-dimensional diffusion-advection equations comparable to MODFLOW and integrate channel hydraulics comparable to HEC-RAS and Mike 11. Numerical schemes include finite difference and finite element discretizations, and coupling frameworks permit two-way interactions among components.

Applications and Case Studies

MIKE SHE has been deployed in urban drainage projects for cities such as London, Sydney, New York City, and Singapore; in transboundary basin planning for the Mekong River Commission, Nile Basin Initiative, and Indus River System Authority; and in environmental assessments for mining and infrastructure projects involving entities like Rio Tinto and BP. Research case studies include groundwater–surface water interaction analyses in catchments studied at University of Bristol, University of Saskatchewan, and U.S. Geological Survey laboratories, as well as climate change impact studies linked to assessments by Intergovernmental Panel on Climate Change (IPCC) workflows. MIKE SHE has also been integrated into operational flood forecasting systems for agencies such as Met Éireann and the Scottish Environment Protection Agency.

Comparison with Other Hydrological Models

Compared with lumped or semi-distributed models such as HBV, TOPMODEL, and Xinanjiang, MIKE SHE offers a more physically-explicit representation akin to integrated models such as HydroGeoSphere, ParFlow, and OpenLISEM. Relative to catchment-scale models like SWAT and river-network models like HEC-RAS, MIKE SHE emphasizes two-way coupling between surface and subsurface domains and more detailed vadose-zone physics. In operational contexts, MIKE SHE is often contrasted with open-source alternatives such as MODFLOW (groundwater-focused), Delft3D (hydrodynamics and morphology), and RIBASIM (river basin simulation) on the basis of process detail, computational cost, and licensing.

Software Implementation and Licensing

MIKE SHE is implemented and distributed by DHI Group as part of a suite of products that may interoperate with modules like MIKE 11, MIKE FLOOD, and MIKE URBAN. The software is proprietary and licensed commercially; deployments often require technical support agreements and periodic updates managed by the developer. Data exchange and pre-/post-processing workflows typically employ GIS tools including ArcGIS, QGIS, and scripting environments such as Python and MATLAB for automation and coupling with external datasets from Copernicus and national meteorological services like Met Office and NOAA.

Limitations and Criticisms

Critiques of MIKE SHE focus on computational intensity for high-resolution distributed simulations, steep learning curves cited by users from institutions such as University of Leeds and Technical University of Denmark, and constraints imposed by proprietary licensing versus open-source models promoted by communities around USGS and Open-source Geospatial Foundation (OSGeo). Model parameterization and calibration have been challenged in comparative studies with GLUE and PEST frameworks for uncertainty analysis, and some researchers argue that process complexity does not always yield commensurate predictive gains compared with simpler models in data-poor settings.

Category:Hydrological models