Zhao et al. (2025) Multidimensional Copula-Based Assessment, Propagation, and Prediction of Drought in the Lower Songhua River Basin

Identification

• Journal: Hydrology
• Year: 2025
• Date: 2025-10-31
• Authors: Yan Zhao, Tao Liu, Zijun Wang, Xiang Huang, Yingna Sun, Changlei Dai
• DOI: 10.3390/hydrology12110287

Research Groups

• School of Hydraulic and Electric Power, Heilongjiang University, Harbin 150080, China
• Institute of Groundwater Cold Region, Heilongjiang University, Harbin 150080, China
• International Joint Laboratory of Hydrology and Hydraulic Engineering in Cold Regions of Heilongjiang Province, Harbin 150080, China

Short Summary

This study assesses, propagates, and predicts multidimensional drought (meteorological, hydrological, agricultural) in the lower Songhua River basin under future climate change scenarios using a coupled modeling framework. It reveals a significant increase in multidimensional drought risk, with varying propagation patterns and thresholds across different climate scenarios.

Objective

• To analyze meteorological, hydrological, and agricultural droughts in the lower Songhua River basin using multidimensional copula-based assessment, propagation, and prediction.
• To investigate the temporal evolution patterns, joint occurrence probabilities, recurrence intervals, and propagation mechanisms (including transmission times and thresholds) of different drought types under future climate change scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5).

Study Configuration

• Spatial Scale: Lower Songhua River Basin (from Tangyuan to Tongjiang), Heilongjiang Province, China, covering an area of 27,968.64 km².
• Temporal Scale:
  • Observed data: 1970–2014 (meteorological), 2000–2018 (runoff).
  • Simulated/Projected data: 2015–2100 (future runoff, drought indices), with analyses for short-term (2020–2030), medium-term (2020–2050), long-term (2020–2070), and specific periods 2020–2060 and 2061–2100.

Methodology and Data

• Models used:
  • SWAT (Soil and Water Assessment Tool) hydrological model
  • PLUS (Patch-generating Land Use Simulation) model
  • CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate models (MIROC-ES2L, ACCESS-ESM1-5, MPI-ESM1-2-HR, NorESM2-MM, INM-CM4-8 selected for ensemble averaging)
  • Copula-based joint distribution model (C-Vine Copula)
  • Bayesian conditional probability network
  • Mann–Kendall (M-K) trend analysis
  • Pettitt abrupt change analysis
  • Spearman rank correlation coefficient (for lag correlation)
• Data sources:
  • Digital Elevation Model (DEM) (90 m resolution)
  • Land use data (2000, 2010, 2020, 1 km resolution)
  • Soil data (1 km resolution, HWSD Soil Database)
  • Daily meteorological data (1970–2014) from 8 stations (maximum/minimum temperatures, precipitation, relative humidity, wind speed)
  • Monthly runoff measurements (2000–2018) from Jiamusi hydrological station
  • CMIP6 climate model data (SSP1-2.6, SSP2-4.5, SSP5-8.5 scenarios)
  • Drought indices: SPAEI (Standardized Precipitation–Actual Evapotranspiration Index), SRI (Standardized Runoff Index), SSMI (Standardized Soil Moisture Index)

Main Results

• Multidimensional drought risk is projected to increase significantly across future climate scenarios.
• The joint occurrence probability of droughts generally decreases with increasing drought intensity, with the largest decline observed at moderate drought intensity (cumulative frequency of 50%), indicating its high sensitivity to climate change.
• Under SSP1-2.6 and SSP5-8.5, joint probabilities are higher for medium to high cumulative frequencies (50% and 75%), while SSP5-8.5 shows lower joint probability for extreme droughts, suggesting more extreme but less multidimensionally linked droughts. SSP2-4.5 shows higher joint probabilities at low frequency (25%), reflecting a more complex drought pattern.
• Meteorological drought propagates to hydrological drought within approximately 6.00 months and to agricultural drought within approximately 3.67 months. Severity amplifies this effect, and propagation thresholds between drought types decrease with increasing intensity. Under SSP5-8.5, propagation times are notably reduced (e.g., meteorological to hydrological drought decreases from 6 months to 2 months at monthly scale).
• The occurrence risk of agricultural and hydrological drought increases nonlinearly with meteorological drought severity. Under SSP5-8.5, the probability of extreme hydrological drought reaches 0.41 under extreme meteorological drought.
• Drought transmission thresholds are lower under SSP5-8.5 (e.g., mild agricultural drought at -0.6 SPAEI), indicating faster drought propagation, while higher under SSP1-2.6 (e.g., mild agricultural drought at -1.9 SPAEI), suggesting a buffering effect.
• Bivariate and trivariate return period analyses show that "AND" conditions consistently yield higher return periods than "OR" conditions. The most significant change in return periods occurred at the 25% cumulative frequency (severe drought scenarios), indicating high sensitivity to future climate change.
• Mann–Kendall and Pettitt tests reveal distinct temporal evolution patterns: SSP1-2.6 shows early improvements in SRI and SSMI (2030–2045), SSP2-4.5 a gradual transition, and SSP5-8.5 concentrated abrupt changes with the highest intensity (SRI's UT increasing by >240%). SSMI is the most sensitive index, while SPEI is the most conservative.
• Model validation: PLUS model (Kappa = 0.83, OA = 0.9285%, QD = 5.70%, AD = 1.44%); SWAT model (Calibration R² = 0.75, NSE = 0.75, PBIAS = -2.4; Validation R² = 0.72, NSE = 0.71, PBIAS = -3.5).

Contributions

• Establishes a three-dimensional meteorological–hydrological–agricultural drought monitoring system using SPAEI, SRI, and SSMI, addressing limitations of previous Chinese drought studies.
• Integrates the latest CMIP6 climate scenarios with dynamic land-use changes projected by the PLUS model, offering a more advanced approach than most existing regional studies.
• Quantifies joint occurrence probability and propagation thresholds using an integrated framework of Copula functions and Bayesian networks, combined with CMIP6 multi-model ensembles and machine learning, providing a novel and comprehensive risk analysis.
• Applies advanced methods for drought trigger threshold identification and characteristic variable extraction to a high-latitude semi-humid climatic zone, with adaptive modifications.
• Provides a comprehensive methodological framework for drought identification, propagation mechanisms, and risk assessment, demonstrating high simulation accuracy and predictive capability for climate change-sensitive areas.

Funding

This research received no external funding.

Citation

@article{Zhao2025Multidimensional,
  author = {Zhao, Yan and Liu, Tao and Wang, Zijun and Huang, Xiang and Sun, Yingna and Dai, Changlei},
  title = {Multidimensional Copula-Based Assessment, Propagation, and Prediction of Drought in the Lower Songhua River Basin},
  journal = {Hydrology},
  year = {2025},
  doi = {10.3390/hydrology12110287},
  url = {https://doi.org/10.3390/hydrology12110287}
}

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