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Drought vulnerability assessment using morphometric features and extreme precipitation indicators to prioritize sub-basins: AI-based Fuzzy Logic approach
(2026-03-01)
Nigam, Utkarsh
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Patel, Vinodkumar M.
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Patel, Dhruvesh P.
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Jodhani, Keval H.
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Gupta, Nitesh
The identification of watersheds and extraction of drainage networks are essential for effective hydrological and geomorphological modelling. This study investigates the influence of morphometric factors and extreme precipitation events on the hydrological responses of the Sabarmati River Basin (SRB), India, to identify the drought-vulnerable sub-basins. Watershed prioritization was carried out using satellite remote sensing, GIS, and secondary data, including topographic sheets and ASTER DEM with a spatial resolution of 90 m. The SRB was divided into nine sub-watersheds, and 28 morphometric parameters were evaluated, comprising 07 linear, 15 areal, and 06 relief parameters. A compound factor (CF) was derived using multi-criteria decision-making techniques such as Weighted Sum Analysis (WSA), Principal Component Analysis (PCA), Analytic Hierarchy Process (AHP), Fuzzy-AHP (FAHP), and TOPSIS. Sub-watersheds were ranked based on CF value, where a lower CF indicated higher priority for runoff management strategies. Additionally, 42 years of precipitation data were analysed using the Standardized Precipitation Index (SPI) at timescales ranging from 3 to 24 months to assess trends in drought and extreme precipitation events. The analysis indicate decline in runoff potential in several sub-basins, however others (e.g., SB2, SB3, SB4, SB5) exhibit positive precipitation trends, making them suitable for runoff enhancement. This integrated methodology offers a comprehensive framework for managing sub-basins, optimizing runoff potential, and supporting sustainable water conservation. The results provide actionable insights for policymakers and planners to better utilize the SRB water resources based on its geomorphological and climatic characteristics.
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Multi-temporal scale of urban rainfall in the Eastern Northeast based on observed data and gridded products: physiographic factors and changes in land use and occupation
(2026-02-01)
da Silva, Luis Felipe Francisco Ferreira
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de Oliveira Romão, William Max
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Lyra, Gustavo Bastos
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da Silva, Elania Barros
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da Silva Costa, Micejane
Rainfall is one of the most important meteorological variables in the daily lives of urban populations. The city of Maceió, the capital of Alagoas, located in the eastern part of the Northeast of Brazil (ENEB), has 50 neighborhoods and a population of approximately one million people, with few studies on the subject. The objectives were: (i) to validate the CHIRPS product; (ii) to identify the preferential rainfall periods in Maceió via GIS; (iii) to map areas for the installation of in situ stations in the city with the aim of supporting the prevention of hydrometeorological disasters; and (iv) creation of a theoretical-conceptual rainfall model. The statistical indicators (R², ρ, BIAS, MAPE and RMSE) were used to validate the gridded precipitation product CHIRPS from 11 CEMADEN rain gauge stations. Monthly rain occurrence maps via Spline tension were developed by QGIS (Quantum GIS) software. The HAND model was applied at neighborhood level for the assessment of urban floods. Waterborne disease data were obtained from SINAN, Natural Disaster data via S2iD from the period 2000 to 2023, and the NDVI and EVI indices in the years 2015 and 2022 were evaluated in the study. All stations were monotonically positive (ρ > 0.65) and significant (p-value < 0.001), indicating that CHIRPS is able to capture rainfall variability despite the influences of the coast, Lagoa Mundaú, and topography. Most stations showed underestimation (negative BIAS) and lower errors (MAE and RMSE). Spatially, the increase in rainfall on the coastal plateau is due to the interaction of the wind regime with the relief, driven by the circulation of breezes and the influence of trade winds. The preferential rainfall period occurs between 04:00 am and 07:00 am. The HAND model identified very high and high susceptibility, mainly on the coast, in areas adjacent to Lagoa Mundaú, and in neighborhoods crossed by rivers and urban canals, and low susceptibility in densely populated neighborhoods. Waterborne diseases together with transformations via NDVI and EVI indicated that rainfall amplifies risk scenarios for the most vulnerable and densely populated populations. In light of this, it is perceived that the rainfall patterns in Maceió are due to the interaction of physiographic and/or anthropogenic factors and meteorological systems – theoretical-conceptual model – which requires improvements in infrastructure and an active monitoring system.
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Sustainable groundwater management through water quality index and geochemical insights in Valsad India
(2025-12-01)
Jodhani, Keval H.
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Gupta, Nitesh
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Dadia, Sanidhya
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Patel, Harsh
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Patel, Dhruvesh
Groundwater quality assessment is crucial for sustainable water resource management and public health protection. This study evaluated the Water Quality Index (WQI) of groundwater in the southern part of Gujarat focusing on the Valsad District. Groundwater in this region occurs in porous, unconsolidated formations and fracture formations, both under groundwater table conditions and confined aquifers. Various parameters including Nitrate (NO<inf>3</inf>¯), pH, Calcium (Ca2+), Electrical Conductivity (EC), Total Hardness (TH), Magnesium (Mg2+), Total Dissolved Solids (TDS), Potassium (K+), Sodium (Na+), Sulphate (SO<inf>4</inf>2−), Chloride (Cl¯), Bicarbonate (HCO<inf>3</inf>¯), Silicate (SiO<inf>4</inf>4−), and Fluoride (F¯) were analyzed to assess groundwater quality. Results indicate that most of the parameters fell within acceptable permissible limits for drinking water, except for Muli and Nanaponda villages with the parameters Cl¯, EC, and TDS exceeding the permissible limit. The WQI analysis revealed that 31.25% of water samples from different villages were found in the excellent category (WQI < 25). About 68.75% of samples from 16 villages were classified as good quality category (WQI ∼ 25–50). Overall, the WQI ranged from 14.20 to 41.98, suggesting that groundwater in the Valsad district is suitable for drinking. The Piper diagram analysis of water samples collected from the field indicated unique geochemical compositions and good water. The diagram revealed that the Ca2+ was the predominant cation, followed by K+, Na+, and Mg2+. Among the anions, the HCO<inf>3</inf>− showed the highest concentrations, followed by SO<inf>4</inf>2−, NO<inf>3</inf>−, and Cl−. This dominance pattern demonstrated that the weathering of minerals significantly influenced the groundwater. This study recommends remediation for areas with reduced water quality to address geogenic and anthropogenic contamination.
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Rainfall variability, land use and land cover dynamics, and implications for climate risk through a theoretical-conceptual model for the Eastern Northeast of Brazil
(2025-06-01)
da Silva Costa, Micejane
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Francisco de Oliveira Júnior, José
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Godoy de Barros, Bárbara
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Francisco Ferreira da Silva, Luís Felipe
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Alencar Cardoso, Kelvy Rosalvo
The study assessed the variability of rainfall, land use and land cover (LULC), the influence of meteorological systems and their interactions, followed by the increase in climate risks in Maceió – Alagoas – ENEB. Monthly rainfall data were obtained from the 64-year historical series (1958–2022) of the TerraClimate platform. The TerraClimate precipitation data were validated ANA and SEMARH data, resulting in R2 = 0.72 and r = 0.85. Spatially, the highest rainfall accumulations occurred from April to July (rainy season), with May and June being the wettest months (>800 mm). In contrast, the months from October to February (dry season) and March, August, and September (transition season) recorded lower rainfall, with November and December being the driest months (150–201 mm). The seasonal occurrence of rainfall and the relief in Maceió were associated with areas of climate risk. Maceió is a region vulnerable to extreme rainfall events due to its geographical location, the influence of the Atlantic Ocean and the Mundaú/Manguaba Lagoons, and the interaction with multi-scale meteorological systems. With LULC, there was an expansion of forest areas in rural areas and a reduction of agricultural areas, followed by increased urbanization in neighborhoods in the western part of the city (coastal plateau). The theoretical-conceptual model of climate risk provides essential information for planning and supports decision-making for public policies. The ability to assess the impacts of extreme rainfall underscores the importance of adaptive responses by local communities through the management of public policies and strategies for adapting to climate risk.
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Impact of the El Niño on Fire Dynamics on the African Continent
(2024-01-01)
de Oliveira-Júnior, José Francisco
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Mendes, David
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Szabo, Szilard
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Singh, Sudhir Kumar
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Jamjareegulgarn, Punyawi
Several studies investigated the occurrence of fires in Africa with numerical modeling or applied statistics; however, only a few studies focused on the influence of El Niño on the fire risk using a coupled model. The study aimed to assess the influence of El Niño on wildfire dynamics in Africa using the SPEEDY-HYCOM model. El Niño events in the Eastern Tropical Pacific were classified via sea surface temperature (SST) anomaly based on a predefined climatology between 1961 and 2020 for the entire time series of SST, obtaining linear anomalies. The time series of the SST anomalies was created for the region between 5° N and 5° S and 110° W and 170° W. The events were defined in three consecutive 3-month periods as weak, moderate, and strong El Niño conditions. The Meteorological Fire Danger Index (MFDI) was applied to detect fire hazards. The MFDI simulated by the SPEEDY-HYCOM model for three El Niño categories across different lagged months revealed relevant distinctions among the categories. In the case of ‘Weak’, the maximum variability of fire risk observed at time lags (0, -3, -6, and -9 months) was primarily in Congo, Gabon, and Madagascar. The ‘Moderate’ pattern had similar characteristics to ‘Weak’ except for the lag-6 months and its occurrence in the equatorial zone of Africa. ‘Strong’ showed a remarkable impact in East Africa, resulting in high fire risk, regardless of time lags. Precipitation and evaporation simulations (SPEEDY-HYCOM) indicated that El Niño categories in Africa need particular attention in the central, southern, and southeastern regions emphasizing the significance of lag-0 and lag-6 (evaporation) as well as lag-0, lag-6, and lag-9 (precipitation). The SPEEDY-HYCOM coupled model in conjunction with the MFDI was efficient in assessing climate variabilities in Africa during El Niño events. This model allows the analysis and prediction of wildfire risks based on El Niño events, providing crucial information for wildfire management and prevention. Its simulations uncover significant variations in risks among different El Niño categories and lagged months, contributing to the understanding and mitigation of this environmental challenge.

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