The challenges in this case study are to project the impact of climate change on the foundation for agricultural production. Climate change is expected to affect soil wetness during winter and spring, where more precipitation is foreseen, and dryness during summer and early fall, where less precipitation is expected. More wetness/higher groundwater levels during winter and spring will adversely affect the field work in connection with sowing as well as crop growth on water logged fields leading to needs for increased drainage of fields, while dryer summers will adversely affect crop yield and lead to needs for increased irrigation. Hence, both flooding and drought will be examined together with the resulting effect on the root zone moisture content, the groundwater level and the river discharge. Focus is given to uncertainty of the projections of future conditions which is a function of both emission scenario, choice of climate model and agro-hydrological model.
Decision support to client
The results generated will be available for decisions concerning future land use management, including drainage, irrigation, choice of crop, time of sowing and harvest and land use (nature, farming, urban, etc.).
Temporal and spatial scale
Results will be generated for the period up to 2100 with a temporal resolution of daily values. Spatially, the analysis will be carried out at catchment scale (~1000 km2) with a resolution in the order of 100m.
Pan-European and local indicators
Climate indicators will be flood and drought indicators related to depth to groundwater table and root zone soil moisture.
Case study workflow
Step 1: CC projections (including bias-corrections).Climate model projections of precipitation, temperature and reference evapotranspiration from a collection of different climate model (combinations of GCMs and RCMs) are selected. Bias correction using a direct method (e.g., the so-called distribution based scaling using dual gamma distributions) is carried out on each model against a dense network of observational data (~10 km grid). An appropriate ensemble of models will be selected for the subsequent analysis of uncertainty.
Step 2: Hydrological impacts (run hydrological models). Alternative hydrological models are developed for a catchment in the study area. The models will be different with respect to conceptualization of the hydrological system. Some models will be relatively simple with lumped descriptions of the processes, while other will be more sophisticated with distributed descriptions. The models will be calibrated against hydrological observation using alternative metrics and calibration schemes. Based on the different models (with respect to conceptualization and calibration) uncertainty on outputs will be assessed.
Step 3: Assess risks of floods and droughts (indicators). Based on selected indicators the risk of floods and droughts will be assessed as a function of climate model, hydrological model and calibration methodology. Results characterized by different levels of uncertainty will be produced.
Step 4: Plan adaptation measures. This will include an evaluation of how uncertainty - and reduced uncertainty – affects the choice of adaptation measures.
Importance and Relevance of Adaptation
Traditionally, a single result (emission scenario, climate model, hydrological model) was used to quantify the impact of climate change. Hence, no measure of the uncertainty of the projections was provided.
Read latest progress here!
Consultancy or Knowledge Purveyors
Flemming Gertz, Senior specialist in Plants and Environment, SEGES
Christen Børgesen, Senior Researcher in Nature, Environment and Farming, University of Aarhus
Jes Pedersen, Senior Consultant in Climate adaptation and groundwater, Central Denmark Region
Torben O. Sonnenborg, GEUS, Denmark