Maftei et al. (2026) Ecohydrology in the Context of Climate Change: Strategies for Management, Monitoring, and Modeling

Identification

Journal: Water
Year: 2026
Date: 2026-03-08
Authors: Carmen Maftei, Ashok Vaseashta
DOI: 10.3390/w18050643

Research Groups

Civil Engineering Faculty, Transilvania University of Brasov, Romania
Strategic Research, International Clean Water Institute, Manassas, VA, USA
Research Institute, University of Bucharest, Romania

Short Summary

This editorial synthesizes research from a Special Issue on ecohydrology, focusing on strategies for management, monitoring, and modeling in the context of climate change, highlighting advancements in understanding hydroecological coupling and adaptive resource governance through technological convergence and analytical innovation.

Objective

To bridge knowledge gaps in ecohydrological processes, water balance, climate change impacts on ecosystems, carbon storage, nutrient transfer, and sustainable solutions.
To integrate data-driven hydrological modeling, isotope tracing, remote sensing assimilation, and process-based carbon–nitrogen coupling frameworks to resolve scale-dependent ecohydrological dynamics under climate change scenarios.

Study Configuration

Spatial Scale: Ranging from specific catchments (e.g., Jinjiang Basin, Voghji River, Aconcagua, Duqueco, Yellow River Basin, Lake Sevan Basin, Danube Delta Biosphere Reserve) and regions (e.g., Dobrogea, Western Kazakhstan, Marrakech, Yangtze River Basin) to national (China, Romania, Armenia, Poland) and global (CMIP6 models) scales.
Temporal Scale: Spanning from a complete hydrologic year (June 2022 to June 2023), daily data (2012–2020), long-term records (1965–2019), and multi-decadal analyses (2001–2021, past three decades, 2002–2024) to future projections for the 21st century (2031–2090).

Methodology and Data

Models used: Biome-BGC Model (with Differential Evolution algorithm), Snowmelt-Runoff Model (SRM), Génie Rural à X Paramètres Journalier (GRxJ) model family (with CemaNeige snow module), Variational Quantum Regression (VQR), Quantum Neural Network (QNN), Soil and Water Assessment Tool (SWAT) model, Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble, Composite Drought Index (CDI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Copula-based standardized compound dry–hot index (SCDHI), Change Point Detection (CPD), stationarity tests, trend analysis, series decomposition (STL, EEMD), Gibbs diagram, and various irrigation water quality indices (e.g., Kelly index, sodium adsorption ratio).
Data sources: Stable carbon and nitrogen isotopes of organic matter (plants and sediments), stable isotope composition (δ2H, δ18O) of water, precipitation, temperature, evapotranspiration, snow cover (MODIS), streamflow, river discharge, natural environmental indicators (topography, vegetation, meteorology, hydrology), GPM IMERG satellite product, ERA5-Land reanalysis data, MODIS Net Primary Productivity (NPP), and hydrochemical parameters.

Main Results

Macrophytes are confirmed as a significant source of organic matter in lake sediments, with sediment 15N-enrichment attributed to decomposition.
Subsurface drip irrigation significantly improves drought resilience and water-use efficiency in semi-arid olive cultivation.
Future climate change projections indicate seasonally contrasting sensitivities of minimal low-water runoff in Western Kazakhstan, necessitating differentiated management strategies.
The Biome-BGC model, optimized with a differential evolution algorithm, accurately simulates vegetation gross primary productivity (GPP) at large scales.
The Snowmelt-Runoff Model (SRM) generally outperforms GRxJ models for daily streamflow simulation in snow- and rain-dominated basins, though GRxJ improves in mixed regimes.
Variational Quantum Regression (VQR) demonstrates superior accuracy in modeling monthly river discharge compared to classical machine learning methods.
Distributed hydrological modeling (SWAT) facilitates comprehensive watershed eco-functional zoning, integrating natural and anthropogenic factors.
A high-resolution Composite Drought Index (CDI) dataset effectively assesses drought severity in Dobrogea, Romania, supported by satellite precipitation data.
Lake Sevan Basin exhibits a persistent warming trend, with projected mean annual temperature increases of up to 6 °C by the end of the century under high-emission scenarios.
Compound dry–hot events significantly impact vegetation NPP, with varying resistance and resilience across vegetation types in the Yangtze River Basin, highlighting risks for cultivated areas and grasslands.
Precipitation patterns near the Danube Delta Biosphere Reserve show high variability and distinct seasonal shifts, with pronounced seasonality in June and July.
Surface waters in the Voghji River catchment are largely suitable for irrigation, but some areas show moderate salinity requiring targeted water management.

Contributions

Provides a rigorous, integrative, and forward-looking reference for researchers, practitioners, and policymakers in climate dynamics and water-ecosystems science.
Advances contemporary understanding of hydroecological coupling, climate perturbation diagnostics, and adaptive resource governance through conceptual refinement and methodological innovation.
Emphasizes technological convergence, integrating satellite-based Earth observation, advanced remote sensing analytics, hybrid process-based–numerical simulations, machine learning-enabled data assimilation, and optimization frameworks for ecohydrological applications.
Enhances the quantification of water-balance components, evapotranspiration flux partitioning, vegetation–soil–atmosphere feedback, carbon–nutrient transport, and groundwater–surface-water interactions under non-stationary climatic regimes.
Introduces advanced algorithmic architectures to improve the robustness of hydroclimatic risk assessment and decision-support systems, particularly for diagnosing compound extremes and safeguarding water and ecosystem sustainability.
Offers quantitative tools for evaluating climate-change impacts and designing adaptive management strategies, bridging monitoring infrastructures with scalable modeling frameworks and governance-relevant metrics.

Funding

This research received no external funding.

Citation

@article{Maftei2026Ecohydrology,
  author = {Maftei, Carmen and Vaseashta, Ashok},
  title = {Ecohydrology in the Context of Climate Change: Strategies for Management, Monitoring, and Modeling},
  journal = {Water},
  year = {2026},
  doi = {10.3390/w18050643},
  url = {https://doi.org/10.3390/w18050643}
}

Original Source: https://doi.org/10.3390/w18050643