BI-DE/18-19-008: Stochastic rainfall models for rainfall erosivity evaluation
|Lead partner:||University of Ljubljana, Faculty of Civil and Geodetic Engineering|
|Project leader:||Professor Matjaž Mikoš, PhD (UL FGG)|
|Project partner:||Leibniz Universität Hannover, Institute of Hydrology and Water Resources Management|
|Source of finance:|
|Key words:||rainfall, rainfall erosivity, erosion|
Soil erosion is one of the risks that can have negative impact on the soil and can consequently also have an influence on human activities and environment (e.g., agriculture). Different factors have an influence on the water erosion, among which rainfall erosivity has the highest variability and has often the dominant impact on the soil erosion. It is determined by the rainfall kinetic energy and can be estimated using highfrequency rainfall data (e.g., data with 5-minute time step). For example, mean rainfall erosivity in Europe is about 720 (MJ mm) / (ha h yr), whereas maximum annual rainfall erosivity values in Europe can exceed 8000 (MJ mm) / (ha h yr) and Slovenia is one of the countries with the highest rainfall erosivity in Europe. This kind of high-frequency data is often very rare and not available for all areas and climate regions. Even in the case that high-frequency rainfall data is available the rainfall series are usually shorter than 10 or 15 years. Thus, stochastic rainfall models that enable to create longer rainfall series using a set of mathematical definitions are a promising option in order to increase the length of high-frequency rainfall data and consequently decrease the uncertainty in the rainfall erosivity estimates. Moreover, the topic is also very relevant from the climate change perspective because rainfall erosivity is projected to change in the future (e.g., 18% increase for the EU until 2050). Thus, rainfall simulators could also be used to create rainfall data with different characteristics to simulate changing climate conditions. Furthermore, comparison of different rainfall models (e.g., ARP, disaggregation, nonparametric) has to be carried out in order to determine the most suitable model for rainfall erosivity estimations.
In the proposed project we will combine the expertise of the University of Hannover that is mainly related to the stochastic rainfall models and the knowledge from the University of Ljubljana that is oriented to the rainfall erosivity and soil erosion modelling in general. Thus, we will be able to merge two topics that are in the most cases not considered as part of one study.
The main objective of the proposed bilateral project is to improve the knowledge about rainfall erosivity and soil erosion using a combination of stochastic rainfall models and past measured rainfall data. Researchers from both countries will be involved in all parts of the project, which will lead to knowledge exchange and improve further cooperation between both universities and researchers.
Project work packages
WP1: Determination of the suitable catchments and data collection
WP2: Analysis and selection of different rainfall models
WP3: Fitting the models with the data, validation
WP4: Update improve models iteratively according to validation results
WP5: Include non-stationarity into the best performing model
WP6: Apply model for historic and future climate scenarios