Zhang et al. (2025) Hydrological drivers of maize productivity: A new analytical framework

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2025
• Date: 2025-11-28
• Authors: Qicheng Zhang, Xiaofang Shen, Weihong Dong, Xiaosi Su, Yuyu Wan, Hang Lyu, Tiejun Song
• DOI: 10.1016/j.ejrh.2025.102976

Research Groups

• Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University
• College of Construction Engineering, Jilin University
• College of New Energy and Environment, Jilin University
• Institute of Water Resources and Environment, Jilin University

Short Summary

This study developed the Crop-Oriented Hydrological Variables Zoning and Quantification Framework (COHV-ZQ), integrating Multiscale Geographically Weighted Regression (MGWR) and clustering analysis, to spatially delineate hydrological-crop zones and quantify the spatiotemporal relationships between hydrological variables and maize net primary productivity (NPP) in Songyuan City, Northeast China, finding evapotranspiration to be the dominant direct driver of maize NPP.

Objective

• To develop the Crop-Oriented Hydrological Variables Zoning and Quantification Framework (COHV-ZQ) for spatially delineating hydrological-crop zones.
• To identify and quantify the spatiotemporal relationships between multiple hydrological variables (precipitation, evapotranspiration, groundwater storage change, and root zone soil moisture change) and maize net primary productivity (NPP) across phenological stages.
• To establish a replicable analytical framework for optimizing water allocation and improving climate resilience in grain-producing regions.

Study Configuration

• Spatial Scale: Songyuan City, Northeast China (approximately 22,000 km²). Data were processed on a 500 m × 500 m grid, and resampled to a 5 km × 5 km grid for modeling.
• Temporal Scale: Maize growing seasons (May to September) from 2003 to 2022 (20 years), with monthly resolution.

Methodology and Data

• Models used:
  • Crop-Oriented Hydrological Variables Zoning and Quantification Framework (COHV-ZQ)
  • Multiscale Geographically Weighted Regression (MGWR)
  • K-means clustering
  • Linear regression models
  • Merged Precipitation Dataset (MPD) method (for multi-source precipitation fusion)
• Data sources:
  • Precipitation: Ground-based measurements (China Surface Climate Data Daily Values Dataset V3.0), Satellite/Reanalysis (CRU TS4.07, ERA5-Land, GPM IMERG Final Precipitation L3).
  • Evapotranspiration: MOD16A2GF v061 remote sensing product.
  • Groundwater storage change & Root zone soil moisture change: GLDASGLSM025DA1D2.2 version (GLDAS-2.2 system).
  • Net Primary Productivity (NPP) / Grain yield proxy: MOD17A3HGF dataset, combined with monthly Gross Primary Productivity (GPP) data.
  • Land use change: MCD12Q1 dataset (MODIS Terra and Aqua satellite observations).

Main Results

• The COHV-ZQ framework successfully delineated 5-6 distinct monthly sensitivity zones (Z1–Z6) within the study area, each characterized by unique hydrological–NPP relationships, exhibiting strong spatial clustering (Moran’s I > 0.87, p < 0.05).
• Evapotranspiration (ET) was identified as the dominant direct driver of maize NPP, consistently showing a stable, positive quantitative relationship across most zones and months.
• A 1 mm increase in monthly ET from May to September resulted in an enhancement of NPP by 15, 5, 2.7, 2.1, and 4.6 g C m⁻², respectively, in the study area.
• Precipitation (P) had the weakest direct influence on NPP, often showing sporadic or indirect effects through soil water replenishment and groundwater recharge (R² > 0.7, p < 0.05).
• Groundwater storage change (ΔGW) and root zone soil moisture change (ΔRZ) exhibited complex, spatiotemporally heterogeneous, and often antagonistic effects on maize NPP, serving as secondary drivers.
  • In water stress zones (prevalent in May and September), ΔRZ increases positively influenced NPP, while ΔGW increases showed inhibitory effects.
  • In waterlogging stress zones (prevalent in June to August), rising groundwater levels and increased root-zone moisture negatively impacted NPP.
• The newly developed Merged Precipitation Dataset (MPD) demonstrated superior R² (0.8804) compared to individual satellite products (CRU: 0.4604, ERA5: 0.6830, GPM: 0.8160) and better spatial variability than meteorological station interpolation.
• Multiscale Geographically Weighted Regression (MGWR) outperformed Global Regression (GLR) and Geographically Weighted Regression (GWR) models, achieving the lowest AICc (466), highest R² (0.902), and Adjusted R² (0.871), and smallest RSS (38), indicating its superior ability to capture multiscale spatial heterogeneity.

Contributions

• Development of the novel Crop-Oriented Hydrological Variables Zoning and Quantification Framework (COHV-ZQ) that integrates MGWR and clustering analysis to delineate hydrological-crop functional units.
• Quantification of the spatiotemporally heterogeneous impacts of multiple hydrological variables (precipitation, evapotranspiration, groundwater storage change, and root zone soil moisture change) on maize net primary productivity (NPP) across critical phenological stages.
• Establishment of evapotranspiration (ET) as the dominant direct hydrological driver of maize NPP in a semi-arid, supplementally irrigated rainfed agroecosystem, contrasting with previous findings for purely rainfed systems.
• Provision of a replicable analytical framework for identifying spatially heterogeneous hydrological drivers and informing localized, temporally adaptive water management strategies.
• Integration of multi-source remote sensing and ground-based data to overcome data limitations and accurately capture spatiotemporal distributions of hydrological variables and crops.

Funding

• Joint Funds of the National Natural Science Foundation of China (U23A2024)

Citation

@article{Zhang2025Hydrological,
  author = {Zhang, Qicheng and Shen, Xiaofang and Dong, Weihong and Su, Xiaosi and Wan, Yuyu and Lyu, Hang and Song, Tiejun},
  title = {Hydrological drivers of maize productivity: A new analytical framework},
  journal = {Journal of Hydrology Regional Studies},
  year = {2025},
  doi = {10.1016/j.ejrh.2025.102976},
  url = {https://doi.org/10.1016/j.ejrh.2025.102976}
}