Scholarlink

<p>Extreme hydrological events, including floods and droughts, pose major challenges across the Narmada Basin in Central India, affecting ecosystems, agriculture, and livelihoods. This study analyzes historical and projected changes in rain spell characteristics using high-resolution IMD rainfall data and bias-corrected CMIP6 simulations across four Shared Socioeconomic Pathways (SSPs). We identify three key rain spell metrics: maximum volume (V<sub>max</sub>), maximum intensity (R<sub>max</sub>), and maximum duration (D<sub>max</sub>), and apply Cumulative Distribution Function (CDF) matching to reduce systematic biases in model outputs. Stationarity was evaluated using the nonparametric Wald–Wolfowitz (WW) test, which revealed widespread nonstationary behaviour under higher warming scenarios, particularly for SSP370 and SSP585. Stationary and nonstationary Generalized Extreme Value (GEV) models were subsequently employed to estimate historical and future return levels for 2-, 10-, and 100-year extremes. The analysis indicates basin-wide increases of ~25–75% for frequent events and over 100–250% for rare (100-year) extremes by late century, with strong zonal variability (Zones 1 to 5). Downstream regions (Zones 1–2) are projected to experience longer and more voluminous rain spells, amplifying flood risks and infrastructure pressures, while upstream zones (4–5) may face shorter but more intense bursts, heightening flash-flood potential. These findings highlight the increasing hydrological hazards associated with warming and emphasize the need for robust risk assessment, flood management, and climate-adaptive water resource strategies across the basin.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Understanding climate impacts on multivariate characteristics of rain spells in central India’s Narmada Basin

  • 期刊论文

  • 发表日期：

    2026年3月26日

查看全文
  • Arun Dev Singh,
  • Somil Swarnkar,
  • Amit Kumar Maurya

摘要

Extreme hydrological events, including floods and droughts, pose major challenges across the Narmada Basin in Central India, affecting ecosystems, agriculture, and livelihoods. This study analyzes historical and projected changes in rain spell characteristics using high-resolution IMD rainfall data and bias-corrected CMIP6 simulations across four Shared Socioeconomic Pathways (SSPs). We identify three key rain spell metrics: maximum volume (Vmax), maximum intensity (Rmax), and maximum duration (Dmax), and apply Cumulative Distribution Function (CDF) matching to reduce systematic biases in model outputs. Stationarity was evaluated using the nonparametric Wald–Wolfowitz (WW) test, which revealed widespread nonstationary behaviour under higher warming scenarios, particularly for SSP370 and SSP585. Stationary and nonstationary Generalized Extreme Value (GEV) models were subsequently employed to estimate historical and future return levels for 2-, 10-, and 100-year extremes. The analysis indicates basin-wide increases of ~25–75% for frequent events and over 100–250% for rare (100-year) extremes by late century, with strong zonal variability (Zones 1 to 5). Downstream regions (Zones 1–2) are projected to experience longer and more voluminous rain spells, amplifying flood risks and infrastructure pressures, while upstream zones (4–5) may face shorter but more intense bursts, heightening flash-flood potential. These findings highlight the increasing hydrological hazards associated with warming and emphasize the need for robust risk assessment, flood management, and climate-adaptive water resource strategies across the basin.