More detailed information on ViWA
Models in ViWA
PROMET is a hydrological land surface process model (Mauser and Bach (2009)), which was extended by a bio-physical dynamic vegetation component to model crop growth and potential yield formation (Hank (2008), Hank (2015)). It uses first order physical and physiological principles to determine coupled water fluxes and net primary production (NPP) and respiration based on approaches from Farquhar et al.(1980) and Ball et al.(1987), combined with a phenology component, a 2-layer canopy architecture component (Xing & van Laar (2005)) and a 4-layer soil component. PROMET takes into account the dependency of NPP and phenology on environmental factors including meteorology, CO2 concentration for C3 and C4 pathways as well as water and temperature stress and nutrient deficits. The mass and energy balance of the canopy and underlying soil surface are iteratively closed for each simulation time step. The canopy and phenology component allocates assimilates into the different plant organs of the canopy depending on the phenological development. Assimilates that are accumulated within the fruit fraction during the growing period determine the dry biomass available for yield formation. A baseline documentation of the algorithms used can be found at here: http://www.geographie.uni-muenchen.de/department/fiona/forschung/projekte/promet_handbook/index.html .
PROMET contains parameters, which represent the sensitivity of the crops to environmental conditions (e.g. temperature or soil suction) or which determine phenological development.
PROMET has been intensively used within GLOWA-Danube for regional climate impact studies to investigate the change in hydrology of the Upper Danube through climate change (Mauser and Prasch (2015)). PROMET has also extensively been applied and carefully validated for yield simulations in the context of precision agriculture studies on field, farm and watershed scale under different climate conditions. Hank used remote sensing data to adjust model parameters to represent spatial heterogeneity on the field scale (Hank (2012), Hank (2015)). PROMET has also successfully been applied for global studies on agricultural yield potentials using a 30 arcsecond grid and random samples (Mauser et al, (2015). PROMET has successfully been run with multiple grid resolutions ranging from 10m to 2 km. It is run in ViWA for two purposes: 1) to simulate the global 250 member ensembles of crops and farming practices on a 30 arcsecond spatial resolution, 2) to simulate the land surface processes and runoff for the selected reference basins. For this purpose PROMET is dynamically coupled with MDOFLOW. Its results are compared with those of mhM. In both cases global geographical data on climate are used, which is dynamically downscaled with REMO. Soil information is derived from the Harmonized World Soil Database (HWSD,FAO (2012)) and topography is derived from the SRTM (Farr (2007)).
The simulations are performed on an hourly time step to account for non-linear reactions of plant growth to environmental factors (mainly light, water, temperature, wind and CO2). Depending on the reaction of the considered crop to meteorological and soil-specific conditions, the crop may either die due to water, heat or cold stress before being harvested or it may not reach maturity. In all cases, this results in total yield loss. If local conditions allow for a successful harvest, the simulation results is the potential agro-ecological yield for the respective location and the assumed nutrient level. Thereby, regarding our definition of potential crop production, we assume optimal crop management practices consisting of defined nutrient supply, optimal sowing and harvest dates, no harvest losses due to pests, diseases etc. In case of irrigation, we assume unlimited water availability for the irrigated area fraction according to Siebert et al. (2013)). A comparison of the simulated ensemble of growth curves with the measurements derived from SENTINEL data determines the actually applied nutrient level and farming practices (e.g. irrigation) at the globally distributed SENTINEL test sites.
Sowing dates and the number of harvests per season are determined by the length of the growing period that again depends on the seasonal course of both temperature and water supply. Optimal sowing dates are derived from Zabel et al. (2014). From these dates, the potential number of sowings per year for each crop at each sample location is determined. Depending on the simulated phenological progress, the model decides whether the potential number of crop cycles is realised or not. Thereby, we assume a defined crop specific time gap between harvest and replanting, accounting for technical field work, such as ploughing, harrowing, etc.
Multiple harvests and evapo-transpiration are accumulated over the year to produce the annual yield and water use efficiency.
The mesoscale hydrologic model (mHM) is a spatially explicit distributed regional to continental scale hydrologic model that uses grid cells as a primary hydrologic unit, and accounts for the following processes: canopy interception, snow accumulation and melting, soil moisture dynamics, infiltration and surface runoff, evapotranspiration, subsurface storage and discharge generation, deep percolation and baseflow and discharge attenuation and flood routine (Samaniego et al., 2010). mHM has proven to show a very good transferability (Kumar et al., 2013) to other catchments and spatial scales.
The model is driven by hourly or daily meteorological forcings (e.g., precipitation, temperature), and it utilizes observable basin physical characteristics (e.g., soil textural, vegetation, and geological properties) to infer the spatial variability of the required parameters. To date, the model has been successfully applied and tested in more than 300 Pan EU basins, as well as India, and USA, at various spatial resolutions (or grid size) which varied between 1 km and 100 km.
MODFLOW is the USGS’s three-dimensional (3D) finite difference groundwater model. It is considered as international standard for simulating and predicting groundwater condictions and groundwater/surface Interactions. The project VIVA aims at the development of a coupled version of mHM-MODFLOW to predict groundwater fluxes and storages under dynamic water management scenarios.
DART is a multi-sectoral, multi-regional, inter-temporal computable general equilibrium model for the world economy (Springer (2004)). The particular version used here (DART-BIO) contains especially detailed features concerning the agricultural sectors. 31 activities in agriculture (thereof ten crop sectors) are explicitly modelled which represent a realistic picture of the complex value chains in agriculture.
The model is based on microeconomic theory: in each of the regions, the economy is modelled as a competitive economy with flexible prices and market clearing. Agents represented in the model are consumers who maximise utility, producers who maximise profits and regional governments setting policy parameters such as taxes or tariffs. All industry sectors operate at constant returns to scale. Output is produced by the combination of energy, non-energy intermediate inputs and the primary factors of labour and capital. In addition, the agricultural sectors use land as an essential input. Producer goods are consumed by the representative household in each region, by governments, the investment sector, by other sectors as intermediates and the export sector. The representative household receives income from the provision of primary factors (capital, labour and land) to the production process. Consumers save a fixed share of income in each time period which is invested in producing investment goods, thus increasing the capital stock of the economy. The government provides a public good financed by tax and tariff revenues. The regions are connected via bilateral trade flows, where domestic and foreign goods are imperfect substitutes, distinguished by country of origin (Armington assumption). Factor markets are perfectly competitive and full employment of all factors is assumed. Labour and capital are assumed to be homogeneous goods, mobile across industries within regions but internationally immobile.
The primary factor land is used in agriculture and forestry and exogenously given. All 23 global regions are subdivided into AEZs which represent different productivity characteristics for agriculture based on soil, climate and other natural parameters. In each AEZ land enters the production of agricultural goods and earns the same land rent. The development of the economies over time is represented through a recursive-dynamic approach in DART-BIO. DART-BIO solves for a sequence of static one-period equilibria for future time periods. The transition from one period to the other is governed by a) capital accumulation, b) changes in labour supply and c) technological change. The regional capital accumulation itself is limited by the exogenously given regional saving rates which are assumed to change over time as an economy develops.
A global equilibrium is reached by simultaneously matching demand and supply for all goods, domestic and foreign, on all markets given the external restriction through tax and trade policies or other policy measures such as quota of emission trading. A detailed description of DART-BIO is available in Calcadilla et al. (2014).
Thus, DART-BIO is able to simulate different policy scenarios or the economic impact of climate change on the economics of land use decisions. In both cases the information provided by PROMET on yields are used by DART-BIO.