Sadeghi et al. (2026) Watershed Health Dividend of Floodwater Spreading: Quantifying Benefits Through Counterfactual Scenarios

Identification

• Journal: Water Resources Management
• Year: 2026
• Date: 2026-01-01
• Authors: Seyed Hamidreza Sadeghi, Fariba Esmaeili, Elham Azizi, Reza Chamani, Masoumeh Havasi, Fatemeh Kateb, M Pakparvar, H Beigi, Negin Behnia
• DOI: 10.1007/s11269-025-04477-z

Research Groups

• Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, Iran
• Watershed Science and Engineering, Faculty of Natural Resources, University of Birjand, Birjand, Iran
• Soil Conservation and Watershed Management Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Shiraz, Iran

Short Summary

This study quantifies the causal impact of the Kowsar floodwater spreading (FWS) system on the Garbayegan-Fasa Watershed's health in Iran using a novel counterfactual analysis within a Pressure-State-Response (PSR) framework, demonstrating that FWS significantly improved and protected watershed health, preventing an 8.84% decline under dynamic climatic pressures.

Objective

• To quantitatively assess the causal impact of the Kowsar floodwater spreading (FWS) system on the health of the Garbayegan-Fasa Watershed in Iran, distinguishing its effects from concurrent environmental changes using a novel counterfactual scenario analysis within a Pressure-State-Response (PSR) framework.

Study Configuration

• Spatial Scale: Garbayegan-Fasa Watershed, Fars Province, Iran, covering an area of 580.80 km². The watershed is divided into four sub-watersheds (Kowsar, Gehrab, Bishezard, Chahqooch), with elevations ranging from 1,117 meters to 1,845 meters above mean sea level.
• Temporal Scale: The Kowsar FWS system was implemented between 1982 and 2003. Data for analysis spanned from 1995 (pre-FWS baseline) and 2000 (for some indicators) to 2023/2024 (current/post-FWS).

Methodology and Data

• Models used:
  • Pressure-State-Response (PSR) framework for watershed health assessment.
  • Thornthwaite method for potential evapotranspiration (PET).
  • Roose’s (1977) method for rainfall erosivity.
  • Kirpich method for concentration time.
  • Revised Universal Soil Loss Equation (RUSLE) model for water erosion potential.
  • Soil Conservation Service (SCS) method for peak-flow discharge.
  • Variance Inflation Factor (VIF) analysis for multicollinearity assessment.
  • Arithmetic mean for calculating Pressure, State, and Response indices.
  • Geometric mean for determining the final Watershed Health Index (WHI).
• Data sources:
  • Meteorological data from Kosar, Fasa, and Darab weather stations (wind speed, mean annual precipitation, maximum 24-hour precipitation, mean air temperature).
  • Google Earth Engine platform (Palmer Drought Severity Index (PDSI), Land Surface Temperature (LST), Salinity Index (SI), Bare Soil Index (BSI), Normalized Difference Vegetation Index (NDVI), Net Primary Productivity (NPP)).
  • Digital Elevation Model (DEM) with 12.5-meter resolution (drainage network length, slope gradient, elevation changes).
  • Google Earth Pro imagery for Land-Use/Cover (LULC) changes.
  • Field visits and expert opinions for identifying watershed challenges.
  • Official statistics (e.g., population data from amar.org.ir).

Main Results

• The Kowsar FWS system was identified as the primary driver of improved watershed health in the Garbayegan-Fasa Watershed.
• Under static environmental conditions (Scenario b), the FWS system alone led to a substantial increase in the health index by 104.76%.
• Even under observed dynamic climatic and hydrological pressures (Scenario c), watershed health showed a significant improvement of 87.29% from the pre-implementation baseline.
• A critical counterfactual scenario (d), simulating conditions without FWS but with dynamic changes, indicated that watershed health in the Kowsar sub-watershed would have declined by 8.84% (to an index of 0.32).
• The 17.47% difference in health improvement between static (Scenario b) and dynamic (Scenario c) FWS scenarios is attributed to the compounding pressures of climate change.
• The dominant driver of health in the Kowsar sub-watershed shifted from external pressure to management response, signifying that active human intervention became the primary factor in maintaining ecological stability.
• The benefits of FWS were spatially heterogeneous, with significant improvement primarily limited to the Kowsar sub-watershed where the infrastructure was implemented, while other sub-watersheds showed neutral or slightly negative health trends.

Contributions

• Introduces a novel counterfactual analysis approach within the Pressure-State-Response (PSR) framework to quantitatively isolate and attribute the causal impact of floodwater spreading (FWS) interventions, moving beyond correlational assessments.
• Provides robust, model-based empirical evidence quantifying the "watershed health dividend" of FWS, demonstrating its role not just as an improvement measure but as a vital intervention preventing environmental degradation in arid regions.
• Highlights the spatially heterogeneous nature of FWS benefits, underscoring that the success of nature-based solutions is highly dependent on local implementation and geophysical context.
• Offers practical implications for water resource managers and policymakers, advocating for continued investment in FWS as essential, cost-effective infrastructure for ecological nourishment and achieving Sustainable Development Goals (SDG 6 and SDG 15).

Funding

• Tarbiat Modares University

Citation

@article{Sadeghi2026Watershed,
  author = {Sadeghi, Seyed Hamidreza and Esmaeili, Fariba and Azizi, Elham and Chamani, Reza and Havasi, Masoumeh and Kateb, Fatemeh and Pakparvar, M and Beigi, H and Behnia, Negin},
  title = {Watershed Health Dividend of Floodwater Spreading: Quantifying Benefits Through Counterfactual Scenarios},
  journal = {Water Resources Management},
  year = {2026},
  doi = {10.1007/s11269-025-04477-z},
  url = {https://doi.org/10.1007/s11269-025-04477-z}
}