EGU23-587, updated on 27 Mar 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Multivariate regression tree approach to evaluate relationship between hydroclimatic characteristics and agricultural and hydrological droughts

ana paez1,2, Gerald Corzo1, and Dimitri Solomatine1,2
ana paez et al.
  • 1IHE Delft Institute for Water Education, Hydroinformatics and Socio-Technical Innovation, Netherlands (
  • 2Delft University of Technology, Water Resources Section, Netherlands (

Projections indicate that agricultural and hydrological droughts' frequency, severity, and duration are expected to increase globally in the twenty-one century. A better understanding of droughts drivers is key to creating preparedness and resilience to projected events. Typically, droughts are caused by lower precipitation and/or higher evaporation than normal in a region. The region's characteristics and anthropogenic influences may enhance or alleviate the drought events. Evaluating the multiple factors influencing droughts is complex and requires innovative approaches. To address this complexity, this study applies a multivariate approach to evaluate the relationship between ten hydroclimatic characteristics and the severity of agricultural and hydrological droughts. A process-based model (Soil Water Assessment Tool) is used for hydrological modeling. The model outputs (soil moisture and streamflow) are used to calculate the indicators for the drought's analysis: Soil Moisture Deficit Index for agricultural droughts and the Standardized Streamflow Index for hydrological droughts. Then, the Multivariate decision tree approach is applied to evaluate the relevance and relationship between the hydroclimatic characteristics and the agricultural and hydrological drought severity at each subbasin. The approach is applied in the Cesar River basin (Colombia, South America), an area of ecological interest declared RAMSAR site.

Study outcomes indicate that evapotranspiration, precipitation, and percolation are the primary drivers of agricultural droughts. Other hydroclimatic parameters such as the curve number, water yield, solid yield, and slope play a relevant role in the subbasin's exposure to agricultural droughts. Subbasins with precipitation lower than 1318 mm, evapotranspiration higher than 1191 mm, percolation higher than 648 mm, and soil yield higher than 101 mm experienced more severe agricultural drought conditions during the period of analysis Regarding hydrological droughts; findings show that evapotranspiration and water yield are principal drivers. Results indicate that precipitation, percolation, and surface runoff also influence the severity of hydrological droughts. Most severe drought conditions during the evaluation period are observed in subbasins with evapotranspiration higher than 826 mm, water yield higher than 9 mm, and precipitation higher than 1398 mm. The outcomes of our analysis indicate that seven out of ten hydroclimatic characteristics evaluated influence the severity of agricultural and hydrological droughts. In addition, the results demonstrate that capturing the non-linear relationships between drivers of droughts and severity allows examining the hydroclimatic characteristics that influence droughts in a region.

How to cite: paez, A., Corzo, G., and Solomatine, D.: Multivariate regression tree approach to evaluate relationship between hydroclimatic characteristics and agricultural and hydrological droughts, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-587,, 2023.