Daneshi et al. (2026) Seasonal agreement of GLDAS and FLDAS soil-moisture products with precipitation in the Lake Urmia Basin, northwestern Iran

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2026
• Date: 2026-09-21
• Authors: Alireza Daneshi, Tahereh Mohammadi, Soraya Yaghobi, Gholamreza Khosravi, Iman Islami, Hassan Fathizad, Hossein Omrani, Lichang Yin, Zhenlei Yang, Weili Duan, Hossein Azadi
• DOI: 10.1016/j.ejrh.2026.103887

Research Groups

• State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
• Department of Watershed Management Sciences and Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
• Department of Rangeland Management, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran
• Department of Arid and Desert Regions Management, College of Natural Resources and Desert Studies, Yazd University, Yazd, Iran
• Department of Remote Sensing and GIS, Tabriz University, Tabriz, Iran
• Department of Geography, Ghent University, Ghent, Belgium

Short Summary

This study examined the seasonal agreement between GLDAS-Noah and FLDAS-Noah near-surface soil moisture products and GPCC precipitation in the Lake Urmia Basin from 2000 to 2020. The agreement varied significantly by month and location, indicating that the utility of these products for regional drought screening is conditional on season and physiography in data-scarce semi-arid regions.

Objective

• To quantify product-to-product and precipitation–soil-moisture associations for four representative mid-season months (January, April, July, October).
• To identify months and locations within the Lake Urmia Basin with stronger or weaker hydroclimatic coherence between the datasets.
• To evaluate the practical limitations of using GLDAS and FLDAS soil moisture products for regional drought assessment when direct, basin-wide soil-moisture validation is unavailable.

Study Configuration

• Spatial Scale: Lake Urmia Basin, an endorheic watershed of approximately 51,800 square kilometers in northwestern Iran. Gridded data were harmonized to a common basin overlay.
• Temporal Scale: 21-year period from 2000 to 2020. Monthly time steps were used, with a focus on four representative mid-season months: January (winter), April (spring), July (summer), and October (autumn).

Methodology and Data

• Models used:
  • GLDAS-Noah v2.1: Global Land Data Assimilation System, Noah land surface model, providing 0–10 cm soil moisture.
  • FLDAS-Noah V001: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System, Noah land surface model, providing 0–10 cm soil moisture.
• Data sources:
  • Precipitation:
    • GPCC Full Data Monthly Product Version 2022 (Global Precipitation Climatology Centre) at 0.5° x 0.5° spatial resolution.
    • In-situ precipitation records from 24 meteorological stations in the Lake Urmia Basin (Iran Meteorological Organization).
  • Soil Moisture:
    • GLDAS-Noah v2.1: 0.25° x 0.25° spatial resolution, 3-hourly data averaged to monthly means.
    • FLDAS-Noah V001: Approximately 0.1° spatial resolution, monthly product.
  • Model Forcing: GLDAS uses observation-based and reanalysis forcing; FLDAS is driven by MERRA-2 meteorology and CHIRPS precipitation.
• Analysis: Pearson, Spearman, and Kendall correlation coefficients were used to assess linear and rank-based associations. Error metrics (Bias, Mean Absolute Error, Root Mean Square Error) were calculated for the GPCC-station comparison. All gridded products were spatially harmonized to a common basin overlay for consistent comparison.

Main Results

• The GPCC precipitation product showed small mean biases when compared to in-situ station data in January (1.14 mm/month) and July (-0.70 mm/month), but larger positive biases in April (4.30 mm/month) and October (9.78 mm/month). Point-grid correlations between GPCC and station data were modest (Pearson r ranging from 0.08 to 0.19).
• Spatial patterns of GPCC precipitation indicated highest values in April (up to 88.7 mm/month) and January (up to 72.7 mm/month) in the southern and southwestern basin, with July being the driest month (maximum 5.1 mm/month).
• GLDAS and FLDAS soil moisture fields exhibited broadly similar seasonal wet-dry patterns, with peak values of 0.46 cubic meters per cubic meter (m³/m³) in April and January.
• Basin-wide spatial correlation analysis revealed that the strongest agreement varied seasonally:
  • January: GPCC-GLDAS showed the strongest association (Pearson r = 0.57).
  • April: GPCC-FLDAS showed the strongest association (Pearson r = 0.54).
  • July and October: FLDAS-GLDAS showed the highest cross-product agreement (Pearson r = 0.43 and 0.47, respectively), while GPCC-soil-moisture associations were weaker.
• Local temporal correlations between soil moisture and precipitation varied spatially, with stronger positive correlations observed in specific regions (e.g., western, southern, central for FLDAS-GPCC; northeastern, eastern, southern, southwestern for GLDAS-GPCC) and weaker correlations during summer months, consistent with localized convective rainfall and rapid evaporative losses.

Contributions

• This study provides a transparent screening framework for evaluating the hydroclimatic coherence of gridded soil moisture products in data-scarce, complex dryland basins like Lake Urmia, where direct, long-term in-situ soil moisture observations are unavailable.
• It offers novel insights by combining two Noah-based land data assimilation products (GLDAS and FLDAS) with a gauge-checked precipitation dataset (GPCC) and employing multiple correlation measures and spatial mapping.
• The research highlights that the agreement and utility of these products for drought assessment are conditional on the specific season, physiographic setting, and the inherent limitations of model structure and forcing data, providing practical guidance for their application in regional drought monitoring.

Funding

• CAS Pioneer Hundred Talents Program (Project No. BRA-E5250101)

Citation

@article{Daneshi2026Seasonal,
  author = {Daneshi, Alireza and Mohammadi, Tahereh and Yaghobi, Soraya and Khosravi, Gholamreza and Islami, Iman and Fathizad, Hassan and Omrani, Hossein and Yin, Lichang and Yang, Zhenlei and Duan, Weili and Azadi, Hossein},
  title = {Seasonal agreement of GLDAS and FLDAS soil-moisture products with precipitation in the Lake Urmia Basin, northwestern Iran},
  journal = {Journal of Hydrology Regional Studies},
  year = {2026},
  doi = {10.1016/j.ejrh.2026.103887},
  url = {https://doi.org/10.1016/j.ejrh.2026.103887}
}