Abeysingha et al. (2025) Future hydro-climate extremes in the cypress creek watershed in Texas under different CMIP6 scenarios

Identification

• Journal: Sustainable Water Resources Management
• Year: 2025
• Date: 2025-09-10
• Authors: N. S. Abeysingha, Ram L. Ray, Temesgen Gashaw Tarkegn
• DOI: 10.1007/s40899-025-01285-6

Research Groups

• College of Agriculture, Food, and Natural Resources, Prairie View A&M University, Prairie View, TX, USA
• Department of Agricultural Engineering and Soil Science, Faculty of Agriculture, Rajarata University of Sri Lanka, Anuradhapura, Sri Lanka

Short Summary

This study quantifies future hydro-climate extremes in the Cypress Creek watershed, Texas, using the SWAT+ model and CMIP6 projections, revealing increased drought likelihood due to rising temperatures and declining precipitation, alongside a general decrease in flood severity but high hydroclimatic variability.

Objective

• To investigate future hydro-climate extremes (droughts and floods) under changing climate and land cover conditions in the Cypress Creek watershed in Texas for the period 2025–2065.
• To configure, calibrate, and validate the SWAT+ model for the Cypress Creek watershed.
• To evaluate projected changes in future extreme events using an ensemble of four CMIP6 GCMs under SSP2-4.5 and SSP5-8.5 scenarios.

Study Configuration

• Spatial Scale: Cypress Creek watershed, northwest Harris County, Texas, USA, covering an area of 749 square kilometers.
• Temporal Scale: Baseline period: 1985–2014; Future projection period: 2025–2065.

Methodology and Data

• Models used:
  • SWAT+ (Soil and Water Assessment Tool Plus) for hydrological simulations.
  • Land Change Modeler (LCM) module within TerrSet software for future land use/land cover (LULC) projections.
• Data sources:
  • Climate Projections: NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDP) dataset (NEX-GDDP-CMIP6) from four CMIP6 GCMs (EC-Earth3, CNRM-CM6, GFDL-ESM4, ACCESS-CM2) under SSP2-4.5 and SSP5-8.5 scenarios.
  • Baseline Climate Data: Daymet gridded dataset (1982–2014) for temperature and solar radiation; Global Meteorological Forcing Dataset for Land Surface Modeling (GMFD) for daily precipitation.
  • Topographical Data: Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) at 30 meter resolution.
  • Land Cover Data: National Land Cover Database (NLCD) 2010 at 30 meter resolution (baseline); predicted 2040 LULC map.
  • Soil Data: Gridded Soil Survey Geographic (gSSURGO) dataset at 30 meter resolution.
  • Streamflow Data: Daily streamflow data from USGS discharge gauge stations for model calibration and validation.
  • Extreme Event Indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Streamflow Drought Index (SDI), Interquartile Range (IQR) method for flood peak assessment, and Severity-Duration-Frequency (SDF) curves.
  • Trend Analysis: Mann–Kendall (MK) test and Sen’s slope estimator.

Main Results

• Temperature and Precipitation Changes:
  • Average annual temperature is projected to increase by 2.32 °C to 2.68 °C. Maximum temperature is projected to increase by 2.45 °C to 2.78 °C, and minimum temperature by 2.18 °C to 2.58 °C under SSP2-4.5 and SSP5-8.5 scenarios, respectively.
  • Precipitation is expected to decline by 6.12% under SSP2-4.5 and by 4.72% under SSP5-8.5 during 2025–2065.
• Drought Conditions:
  • Both SPI and SPEI show statistically significant declining trends across both scenarios and time scales, indicating an increased likelihood of drought.
  • The probability of drought occurrences increases, particularly on the 12-month timescale (1.4% increase under SSP2-4.5).
  • Low-flow conditions (5th percentile of daily streamflow) show a significant decrease (p < 0.05) under the SSP5-8.5 scenario, suggesting increasing drought severity.
• Flood Conditions:
  • Flood severity generally declines under future scenarios, with SSP5-8.5 producing the lowest flows across all durations and return periods, primarily due to enhanced temperature-induced evapotranspiration outweighing minor precipitation advantages.
  • High-flow conditions (95th percentile of daily streamflow) show a statistically significant increase (p < 0.05) under the SSP2-4.5 scenario.
  • The IQR method indicates a higher number of potential flood event counts under SSP5-8.5 compared to SSP2-4.5, but potential flood peak values are higher under SSP2-4.5.
  • Wetting/flood frequencies increase during winter periods and at the 6-month time scale relative to baseline under both scenarios.
• Hydroclimatic Variability: Findings reveal considerable hydroclimatic variability in the future of the Cypress Creek watershed, triggering drought but also contributing to floods, with a potential shift from extreme wet events toward more persistent moderate-to-severe droughts in the more extreme SSP5-8.5 scenario.

Contributions

• This study is the first to assess the future hydroclimatic status of the Cypress Creek watershed, addressing a significant gap in existing literature despite the watershed's importance.
• It provides a scientific basis for disaster forecasting and mitigation, offering critical support to local stakeholders and governing bodies responsible for watershed management.
• The research highlights the dominant role of temperature-driven evapotranspiration in shaping future streamflow, even with minor precipitation differences between scenarios, which is crucial for water resource planning.
• The study employs a robust methodology, including a calibrated and validated SWAT+ model, ensemble CMIP6 GCMs, and multiple drought/flood indices, enhancing the reliability of future projections for the region.

Funding

• Natural Resources and Conservation Services (NRCR), United States Department of Agriculture (USDA) [grant number: NR237442XXXXC023]

Citation

@article{Abeysingha2025Future,
  author = {Abeysingha, N. S. and Ray, Ram L. and Tarkegn, Temesgen Gashaw},
  title = {Future hydro-climate extremes in the cypress creek watershed in Texas under different CMIP6 scenarios},
  journal = {Sustainable Water Resources Management},
  year = {2025},
  doi = {10.1007/s40899-025-01285-6},
  url = {https://doi.org/10.1007/s40899-025-01285-6}
}