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The LIS-platform, a BIM-based software platform, is being developed to monitor various aspects of buildings. This platform provides a comprehensive overview of structu...
Many hydrological models exist nowadays provide us with answers to questions focusing on the impact of climate change, droughts, flooding, water availability, glacier melt, etc. Cons of many models are that they are difficult to use, not freely available, and/or require a vast amount of input data.
Developed by FutureWater
SPHY is a public domain model, that is easy to use, requires only precipitation and temperature as forcing, and can be applied for operational as well as strategic decision support. The SPHY model has been applied and tested in various studies ranging from real-time soil moisture predictions in flat lands, to operational reservoir inflow forecasting applications in mountainous catchments, and detailed climate change impact studies in the snow- and glacier-melt dominated regions.
See more information about this level and the TRL and SRL levels.
The SPHY model has been developed using the Python programming language and the PCRaster Dynamic Modelling Framework. The model can be downloaded from either the website (www.sphy.nl) or the Github repositor (https://github.com/FutureWater/SPHY). Several tutorials, manuals and casestudies have been developed to make the model accessible to a large user group worldwide. To make the model even more easier to use, we have developed a model interface (GUI) and pre-processor as plugins for QGIS. The model can be run using either these GUIs or through editing the model configuration file. Further details on how-to use and apply this tool can be found on the websites shown above.
Limitations/conditions under which this innovation does not work or is less effective
SPHY offers the flexibility to choose a user-defined spatial model resolution. Besides the several benefits this offers, it also has a limitation: if the spatial resolution is chosen too large, then processes may not be simulated correctly. This is mainly true for the routing of river discharge, and the processes related to the melting of snow and glaciers.
Added value
SPHY: (i) integrates most hydrologic processes, including glacier processes, (ii) has the flexibility to study a wide range of applications, including climate and land use change impacts, irrigation planning, and droughts, (iii) can be used for catchment- and river-basin-scale applications as well as farm- and country-level applications, and has a flexible spatial resolution, and (iv) can easily be implemented. Implementation of SPHY is relatively easy because (i) it is open source, (ii) input and output maps can directly be used in GIS, (iii) it is set up modular in order to switch on/off relevant/irrelevant processes and thus decreases model run time and data requirements, (iv) it needs only daily precipitation and temperature data as climate forcing, and (v) it can be forced with remote sensing data.
Many hydrological models exist nowadays provide us with answers to questions focusing on the impact of climate change, droughts, flooding, water availability, glacier melt, etc. Cons of many models are that they are difficult to use, not freely available, and/or require a vast amount of input data.
Developed by FutureWater
SPHY is a public domain model, that is easy to use, requires only precipitation and temperature as forcing, and can be applied for operational as well as strategic decision support. The SPHY model has been applied and tested in various studies ranging from real-time soil moisture predictions in flat lands, to operational reservoir inflow forecasting applications in mountainous catchments, and detailed climate change impact studies in the snow- and glacier-melt dominated regions.
The main components of the system have been tested separately, and an initial integration exercise has been conducted.
The SPHY model has been developed using the Python programming language and the PCRaster Dynamic Modelling Framework. The model can be downloaded from either the website (www.sphy.nl) or the Github repositor (https://github.com/FutureWater/SPHY). Several tutorials, manuals and casestudies have been developed to make the model accessible to a large user group worldwide. To make the model even more easier to use, we have developed a model interface (GUI) and pre-processor as plugins for QGIS. The model can be run using either these GUIs or through editing the model configuration file. Further details on how-to use and apply this tool can be found on the websites shown above.
Limitations/conditions under which this innovation does not work or is less effective
SPHY offers the flexibility to choose a user-defined spatial model resolution. Besides the several benefits this offers, it also has a limitation: if the spatial resolution is chosen too large, then processes may not be simulated correctly. This is mainly true for the routing of river discharge, and the processes related to the melting of snow and glaciers.
Added value
SPHY: (i) integrates most hydrologic processes, including glacier processes, (ii) has the flexibility to study a wide range of applications, including climate and land use change impacts, irrigation planning, and droughts, (iii) can be used for catchment- and river-basin-scale applications as well as farm- and country-level applications, and has a flexible spatial resolution, and (iv) can easily be implemented. Implementation of SPHY is relatively easy because (i) it is open source, (ii) input and output maps can directly be used in GIS, (iii) it is set up modular in order to switch on/off relevant/irrelevant processes and thus decreases model run time and data requirements, (iv) it needs only daily precipitation and temperature data as climate forcing, and (v) it can be forced with remote sensing data.
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