Mofuss: Modeling fuelwood savings scenarios is a GIS-based open-source freeware developed to evaluate potential impacts of residential firewood use over the landscape. Users have different levels of interaction, from querying available results in a mapserver to uploading their own maps and parameters and ultimately affect underlying geoprocessing operations. Mofuss is developed and supported by the Environmental Geography Research Center (CIGA) at the National Autonomous University of Mexico (UNAM), in collaboration with the US Center of the Stockholm Environment Institute. The first version of Mofuss (version 1.0) was developed between September 2011 and April 2015 with funding from Global Alliance for Clean Cookstoves, Yale Institute for Biospheric Studies, Overlook International Foundation, ClimateWorks and UNAM’s PAPIIT.
Who should use the tool?
The tool was developed with a wide range of users in mind, ranging from academics and practitioners, to students and NGOs. Used correctly, it should help these stakeholders to: a) get more consistent estimates of fuelwood-related carbon savings within their interest areas, and b) plan sound and cost-effective intervention projects.
What type of inputs are needed ahead of time?
Inputs consist of maps depicting the spatial distribution of fuelwood sources and demand. Non-spatial assumptions (coefficients) are needed as well. A user-friendly interface helps the user to process all input data into the model, regardless of type (vector vs raster), projection (geographic vs planar coordinates) and/or geographical extent. See “Key data sources” below.
Key data sources
Mofuss has different levels of interaction with final users, from querying country-based datasets in the Internet, to affecting geo-processing operations in order to tune the model’s logic and performance in accordance to local patterns of fuelwood harvest and use. If no local maps and/or data are available or known for the study area, users have the possibility to use default datasets at varying levels of detail depending on the country of their interest.
Levels of interaction with end-users according to their expertise with spatial analysis and modeling software and techniques
Mofuss produces estimates of non-renewable biomass (NRB) at landscape level while allowing users to input the best available data for their area of interest, including project-specific maps and parameters.
Mofuss consists of several Windows-based “scripts” or list of commands that are executed by some other freeware and Windows OS. Users can estimate fuelwood sustainability for their study area of interest using a multilevel hierarchical structure:
LEVEL 1: Query global Tier 1 estimates of woodfuel sustainability within subnational administrative units using a web-based map. For few countries, Tier 2 and 3 data might be available as well.
LEVEL 2: Run Mofuss in fully default mode for a user-defined study area. Users input the area of interest into the model by manually drawing a polygon in Google Earth and saving it into the hard-drive. This, and subsequent levels, require to download free software and datasets from the internet and save them in a specified location on their hard drive, but do no further processing. After running the model, an automated pdf report is generated.
LEVEL 3: Users can alter a small set of input parameters related to woodfuel demand in Business as Usual and Intervention scenarios. This allows them to simulate the impact of their interventions by defining a rate of clean cookstove adoption.
LEVEL 4: Users can tune most built-in model parameters using locally available data such as biomass growth functions and baseline fuelwood per capita consumption as well as modify sensitivity analyses and change uncertainty parameters.
LEVEL 5: Add alternative maps (i.e. GIS layers) available for the study area, such a high resolution digital elevation models or land use/cover map – some basic GIS skills are needed.
LEVEL 6: Modify and/or add inner geoprocessing operations to account for site-specific processes, i.e. open source code – requires knowledge of GIS and R programming.