Agriculture and fisheries play vital roles in sustaining livelihoods, whereas they are frequently suffered by recurring floods in the Wainganga basin of the Indian Peninsula. The climate change and unsustainable practices have significantly affected these livelihoods. The present study examines changing climate impacts on hydrological mechanisms in the Wainganga basin, precisely Kumhari, Ramakona, and Ashti using Soil and Water Assessment Tool (SWAT). Before applying the SWAT model, it was calibrated (1996–2008) and validated (2009–2013) with warm-up period of 3 years (i.e., from 1993 to 1995) using observed data, projecting future streamflow with NEX-GDDP RCP 4.5 and RCP 8.5 scenarios. The outcomes reveal complex dynamic scenarios. It is projected that precipitation (3%–9%), surface runoff (23%–31%), and lateral flow (18%–29%) will increase in future, however, both evapotranspiration (ET) (16%–17%) and potential evapotranspiration (PET) (17%–18%) are expected to decline in compare to baseline period. This hydrological shift alters the localized water equilibrium and is modulated primarily by changes in relative humidity and wind velocity, while temperature and precipitation show upward trends. The findings of the present study contribute to the understanding of future water availability and management strategies in the face of climate variability and change.
A. Thakur, A. Nema, Prabhash K. Mishra et al.· Frontiers in Environmental S...· 0 citations
The accelerating impact of climate change on meteorological dynamics and air quality across India poses a pressing challenge for urban sustainability and public health resilience. In this study, we present a data-driven AI framework based on Bidirectional Long Short-Term Memory (BiLSTM) networks to forecast PM₂.₅ concentrations using multivariate environmental data, including temperature, humidity, wind speed, UV index, and particulate matter levels. Comparative analysis with both conventional deep learning models (LSTM, GRU) and statistical baselines (ARIMAX, MLR) demonstrates the BiLSTM’s superior capacity in learning long-range temporal dependencies. Among the evaluated models, the proposed BiLSTM framework achieved the highest predictive performance with an
R
2
of 0.8113, MAE of 0.2988, and RMSE of 0.4359. Consequently, the model supports not only enhanced predictive accuracy but also operational daily, localized decision-making for air quality management, offering actionable insights for climate-aware urban planning and smart city governance. By integrating advanced AI techniques with high-dimensional environmental datasets, this work underscores the transformative role of computational intelligence in shaping adaptive, evidence-based sustainability strategies for future-ready cities.
Vinayak Gupta, Yajnaseni Dash, Ankush Goyal et al.· Frontiers in Climate· 0 citations
