Browse data: Variable
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Type: Is input for: (Click arrow to add another value) Showing below up to 20 results in range #1 to #20.
- CO2 concentration (Atmospheric CO2 concentration., type: model (from/to model))
- Change in soil properties - grid (Change in soil properties, such as clay/sand content, organic carbon content, soil depth (topsoil/subsoil)., type: model (from/to model))
- Cloudiness - grid (Percentage of cloudiness per month; assumed constant after the historical period, type: historical data)
- Demand for primary energy (Total demand for energy production. Sum of final energy demand and energy inputs into energy conversion processes., type: model (from/to model))
- Energy resources (Volume of energy resource per carrier, region and supply cost class (determines depletion dynamics)., type: driver)
- Irrigation water supply - grid (Water supplied to irrigated fields; equal to irrigation water withdrawal minus water lost during transport, depending on the conveyance efficiency., type: model (from/to model))
- Land cover, land use - grid (Multi-dimensional map describing all aspects of land cover and land use per grid cell, such as type of natural vegetation, crop and grass fraction, crop management, fertiliser and manure input, livestock density., type: model (from/to model))
- Land suitability - grid (Suitability of land in a grid cell for agriculture and forestry, as a function of accessibility, population density, slope and potential crop yields., type: model (from/to model))
- Land supply for bioenergy - grid (Land available for sustainable bioenergy production (abandoned agricultural land and non-forested land)., type: model (from/to model))
- Land systems - grid (Thirty land systems as defined in CLUMondo, characterized by specific levels of built-up area, cropland area, livestock density and management intensity., type: model (from/to model))
- Learning rate (Determines the rate of technology development in learning equations., type: driver)
- Management intensity crops (Management intensity crops, expressing actual yield level compared to potential yield. While potential yield is calculated for each grid cell, this parameter is expressed at the regional level. This parameter is based on data and exogenous assumptions - current practice and technological change in agriculture - and is endogenously adapted in the agro-economic model., type: model (from/to model))
- Number of wet days - grid (Number of days with a rain event, per month; assumed constant after the historical period, type: historical data)
- Potential bioenergy yield - grid (Potential yields of bioenergy crops., type: model (from/to model))
- Precipitation - grid (Monthly total precipitation., type: model (from/to model))
- Regression parameters (Regression parameters of suitability assessment., type: external parameter)
- Technology development of energy supply (Learning curves and exogenous learning that determine technology development., type: driver)
- Temperature - grid (Monthly average temperature., type: model (from/to model))
- Trade restriction (Trade tariffs and barriers limiting trade in energy carriers (in energy submodel)., type: driver)
- Water stress - grid (Water stress is a basin scale indicator of the mean annual water demand to availability ratio. This ratio gives an indication for the level of water stress experienced in the basin. Basins with a water demand to availability ratio above 0.2 are considered medium water stressed, basins with ratios above 0.4 are severely water stressed., type: model (end-indicator))