A Fuzzy Inference–Based Framework for Drought Impact-Based Forecasting and Early Warning: Integrating Hydrological Forecasting with Multisectoral Risk Analysis

Climate change intensifies drought risks in Taiwan, particularly threatening the Zhuoshui River Basin—a region critical for agriculture, industry, and domestic water supply. Traditional drought monitoring systems focus primarily on meteorological and hydrological indicators but fail to capture cascading impacts across socio-economic and environmental systems. This study develops a Drought Impact-Based Forecasting (DIBF) framework that bridges hydrological predictions with multisectoral risk assessment, providing actionable early warnings for integrated drought management.
The framework integrates hydrological forecasting and risk-impact assessment through three components. First, Recurrent Nonlinear Autoregressive with Exogenous Inputs (R-NARX) models predict groundwater levels and river discharge 1–3 months ahead. The models achieve test R² of 0.84 for groundwater estimation and above 0.91 for discharge forecasting at major gauging stations (Jiji Weir, Zhangyun Bridge, and Xikou), demonstrating stable predictive capability across the basin's key monitoring locations. A Self-Organizing Map coupled R-NARX (SOM-R-NARX) model enhances spatial resolution by generating grid-based groundwater prediction maps (overall RMSE = 1.36 m, R² = 0.51), enabling spatially-explicit hazard assessment across the basin.
The core innovation lies in the DIBF module, which systematically integrates multisectoral drought risks through a Fuzzy Inference System (FIS). The system synthesizes: (1) Hazard factors from rainfall-based, groundwater-based, and streamflow-based drought indices validated for the basin; (2) Exposure factors quantifying industrial water demand, agricultural irrigation requirements (first-crop rice production areas), groundwater-dependent activities, and population reliance on surface water; and (3) Vulnerability factors assessing adaptive capacity across agricultural systems (crop sensitivity, irrigation infrastructure), industrial sectors (water storage, alternative sources), environmental dimensions (groundwater overdraft risks, ecological flows), and social aspects (water allocation conflicts, vulnerable populations). These heterogeneous risk factors—represented in both qualitative expert knowledge and quantitative measurements from interdisciplinary research—are transformed into interpretable impact scores through fuzzy rule-based reasoning.
A risk matrix combining forecast likelihood and impact severity delivers a four-level warning classification (green–yellow–orange–red) with sector-specific response recommendations: irrigation adjustments for agriculture, water allocation shifts for industry, groundwater pumping restrictions for environmental protection, and inter-sectoral coordination for social stability. The system provides 1–3 month lead-time forecasts with sub-basin spatial disaggregation.
Applied to Taiwan's most water-stressed basin, this framework operationalizes DIBF principles through transparent fuzzy inference, explicitly linking hydrological forecasts to multisectoral impacts and synthesizing cross-disciplinary risk knowledge into unified, actionable information. The approach provides a replicable template for drought early warning systems that support evidence-based decision-making balancing industrial, agricultural, environmental, and social priorities under climate change.

Keywords: Drought impact-based forecasting(DIBF);Hydrological forecasting;Groundwater-streamflow interactions;Fuzzy inference system