Integrated modeling of hydrological and agricultural aspects of droughts in Odra river basin under a changing climate

PRELUDIUM BIS-1 project, UMO-2019/35/O/ST10/04392

PI: Dr. hab. Mikolaj Piniewski
PhD Student: Mohammad Reza Eini

Project overview

Drought is one of the most pressing environmental hazards in Central Europe, with severe consequences for ecosystems, agriculture, water management, and society. In Poland, recent droughts (2015, 2018, 2019, 2020) led to agricultural losses, municipal water shortages, and even wildfires (e.g., Biebrza National Park in 2020). While drought trend detection and monitoring have been widely studied, the accuracy of drought predictions remains limited due to challenges in hydrological modeling, sparse observational data, and the complexity of drought drivers.
This project aimed to enhance the accuracy of process-based and data-driven models for predicting drought by integrating multi-objective calibration techniques and satellite-based datasets into agro-hydrological applications. The research combined SWAT+, a process-based distributed model, with Artificial Neural Networks (ANNs), a data-driven machine learning method. By doing so, the project bridged mechanistic and statistical approaches to achieve more reliable predictions of drought, river discharge, and crop yields.

The main objectives were:

  • To improve the accuracy of agro-hydrological models (SWAT+ and ANN) for drought prediction.
  • To assess historical and projected changes in meteorological, agricultural, and hydrological droughts in Poland.
  • To evaluate the usefulness of satellite-based precipitation and soil moisture datasets in hydrological modeling.

More specifically, the project verified the following hypotheses:

  • H1: Multi-objective calibration using soil moisture and crop yields, in addition to river discharge, improves the accuracy of SWAT+ simulations.
  • H2: Drought indices such as SPI and SRI enhance the performance of ANNs in simulating discharge, crop yields, and hydrological drought.
  • H3: Satellite-based soil moisture datasets increase the reliability of process-based models, whereas precipitation products need careful evaluation before hydrological applications.

To achieve these aims, three main tasks were performed:

  1. Data collection and integration: assembling ground-based, regional, and satellite-based precipitation and soil moisture datasets, along with discharge and crop yield observations.
  2. Modeling and calibration: implementing SWAT+ for meso-scale and macro-scale basins in Poland (Wełna catchment, Odra River Basin, and Poland+), and training ANN models for discharge, drought, and crop yield prediction.
  3. Evaluation and attribution: assessing drought patterns across Poland, comparing modeling approaches, and quantifying the added value of satellite datasets and drought indicators.

The study was carried out at Warsaw University of Life Sciences (SGGW), with strong international collaboration through research stays at IRPI-CNR (Italy), PIK (Germany), and UFZ Leipzig (Germany).

 

Outputs

Scientific articles:

  1. International Journal of Climatology
    Tomczyk, A. M., Piniewski, M., Eini, M. R., & Bednorz, E. (2022). Projections of changes in maximum air temperature and hot days in Poland. 42(10), 5242–5254. Wiley. DOI:
    https://doi.org/10.1002/joc.7530
  2. Earth-Science Reviews
    Piniewski, M., Eini, M. R., Chattopadhyay, S., Okruszko, T., & Kundzewicz, Z. W. (2022). Is there a coherence between observed and projected indices of low flows in Central Europe? 233, 104187. Elsevier. DOI:
    https://doi.org/10.1016/j.earscirev.2022.104187
  3. Science of The Total Environment
    Delavar, M., Eini, M. R., Shokri, V., Zaghiyan, M. R., Shahbazi, A., Noormohammadi, F., & Motamedi, A. (2022). Model-based water accounting for integrated assessment of water resources systems at the basin scale. 830, 154810. Elsevier. DOI:
    https://doi.org/10.1016/j.scitotenv.2022.154810
  4. Journal of Hydrology: Regional Studies
    Eini, M. R., Rahmati, A., & Piniewski, M. (2022). Hydrological application and accuracy evaluation of PERSIANN satellite-based precipitation estimates over a humid continental climate catchment. 41, 101109. Elsevier. DOI:
    https://doi.org/10.1016/j.ejrh.2022.101109
  5. Science of The Total Environment
    Eini, M. R., Rahmati, A., Salmani, H., Brocca, L., & Piniewski, M. (2022). Detecting characteristics of extreme precipitation events using regional and satellite-based precipitation gridded datasets in Central Europe. 852, 158497. Elsevier. DOI:
    https://doi.org/10.1016/j.scitotenv.2022.158497
  6. Agricultural Water Management
    Eini, M. R., Salmani, H., & Piniewski, M. (2023). Comparison of process-based and statistical approaches for simulation and projections of rainfed crop yields. 277, 108107. Elsevier. DOI:
    https://doi.org/10.1016/j.agwat.2022.108107
  7. Journal of Arid Environments
    Eini, M. R., Motehayeri, S. M. S., Rahmati, A., & Piniewski, M. (2023). Evaluation of the accuracy of satellite-based rainfed wheat yield dataset in a complex geography. 212, 104963. Elsevier. DOI:
    https://doi.org/10.1016/j.jaridenv.2023.104963
  8. Science of The Total Environment
    Eini, M. R., Massari, C., & Piniewski, M. (2023). Satellite-based soil moisture enhances the reliability of agro-hydrological modeling in large transboundary river basins. 873, 162396. Elsevier. DOI:
    https://doi.org/10.1016/j.scitotenv.2023.162396
  9. Agricultural and Forest Meteorology
    Eini, M. R., Rahmati, A., Salmani, H., Mujahid, S., & Piniewski, M. (2023). Detecting drought events over Central Europe using regional and satellite-based precipitation datasets. 342, 109733. Elsevier. DOI:
    https://doi.org/10.1016/j.agrformet.2023.109733
  10. Journal of Irrigation and Drainage Engineering
    Delavar, M., Raeisi, L., Eini, M. R., Morid, S., Mohammadi, H., & Abbasi, H. (2024). Assessing the effectiveness of water-saving plans at the farm and basin level using agro-hydrological modeling and water accounting. 154(4), 04024009. ASCE. DOI:
    https://doi.org/10.1061/JIDEDH.IRENG-10110
  11. Journal of Water and Climate Change
    Khaledi-Alamdari, M., Fakheri-Fard, A., Majnooni-Heris, A., & Eini, M. R. (2024). Analyzing the impact of climatic conditions on rainfed wheat yield in northwest Iran: a parametric and nonparametric approach. 15(12), 5957–5972. IWA. DOI:
    https://doi.org/10.2166/wcc.2024.519
  12. Journal of Hydrology
    Kalaki, M. F., Delavar, M., Farokhnia, A., Morid, S., Kuchak, V. S., Hajihosseini, H., Shahbazi, A., Nourmohammadi, F., Motamedi, A., & Eini, M. R. (2025). An ensemble multi-model approach for long-term river flow forecasting in managed basins of the Middle East: Insights from the Karkheh River Basin. 654, 132846. Elsevier. DOI:
    https://doi.org/10.1016/j.jhydrol.2025.132846
  13. European Journal of Agronomy
    Eini, M. R., Conradt, T., & Piniewski, M. (2024, submitted). Sequential hybridization enhances the reliability of a statistical crop yield model – exemplified by wheat and sugar beet yields in Poland. Elsevier.

 

Events/News

The main project outputs were presented at major international scientific events, including:

  • The EGU General Assembly (Vienna, Austria, 2023 and 2024)
  • The International SWAT Conferences (2022, 2023, 2024)
  • The IUGG General Assembly (Berlin, Germany, 2023)
  • Local and regional conferences in Poland

 

External collaborators

  • Research Institute for Geo-Hydrological Protection (IRPI-CNR), Italy
    Dr. Luca Brocca, Dr. Christian Massari
  • Potsdam Institute for Climate Impact Research (PIK), Germany
    Dr. Tobias Conradt
  • Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
    Dr. Martin Volk