Ganges Research Articles

Composition of heavy metals in sediment, water, and fish of the Ganga and Yamuna Rivers in two major cities of India.

The rapid industrial development in the Indian capital region has led to significant waste generation, which, despite undergoing treatment prior to disposal, contributes substantially to water body contamination. Given the diverse nature of these wastes and their potential repercussions across the food chain, a study was conducted to evaluate heavy metal contamination levels in the Ganga and Yamuna Rivers of two major cities. Six heavy metals (Pb, Cd, Hg, Cu, Cr, and Zn) were analyzed in fish, water, and sediment samples by utilizing flame atomic absorption spectrophotometry (Avanta Σ) from March 2019 to February 2020. Results revealed distinct heavy metal distribution patterns, with Cr > Zn > Pb > Cu > Cd > Hg in the Ganga River and Zn > Cr > Pb > Cu > Cd > Hg in the Yamuna River for fish samples. Additionally, levels of Hg in Cyprinus carpio and Sperata oar from the Ganga River, and Pb, Cd, Hg, and Cr in Salmophasia bacaila and Mystus cavasius from the Yamuna River exceeded WHO/FAO permissible limits. In water samples, the predominant heavy metal sequences were Pb > Cu > Zn > Cr > Cd > Hg for the Ganga River and Cr > Zn > Pb > Cu > Cd > Hg for the Yamuna River, with Pb, Cr, Zn, and Cd surpassing WHO standards. Sediment analysis revealed varying heavy metal compositions, with Zn > Cr > Pb > Cu > Cd > Hg in the Ganga River and Cr > Zn > Pb > Cu > Cd > Hg in the Yamuna River. While drinking water and fish from the Ganga River were deemed safe for consumption, those from the Yamuna River were not. Given the toxic nature of heavy metals and their detrimental health impacts, regular monitoring and effective management strategies are imperative.

$$\lambda $$-possibility-center based MCDM technique on the control of Ganga river pollution under non-linear pentagonal fuzzy environment

$$\lambda $$-possibility-center based MCDM technique on the control of Ganga river pollution under non-linear pentagonal fuzzy environment

Bootstrap Reduced Major Axis (BRMA) to optimize the satellite‐derived discharge rating curves

AbstractWe estimate river discharge by utilizing limited in‐situ and multi‐satellite data through Bootstrap Reduced Major Axis (BRMA) optimization method (). We establish the BRMA by analyzing the functional relationships (H, W, H.W vs. Q) at four distinct locations along the Ganga (Shahzadpur and Azmabad) and the Narmada (Hoshangabad and Mandleshwer) rivers. To measure the channel width (W) we have used the Landsat 5, 7, and 8 images (2006–2019). We use the water level (H) data from satellite altimeter (Jason 2, Jason 3, Envisat, and Sentinel 3A). We have used BRMA to establish functional relationships between channel width (W), water level (H), and H.W to their corresponding discharge at each gauge stations in the study reach. Efficacy of the proposed approach is evaluated through a comparative analysis with the traditional ordinary least squares (OLS) regression method. We observed that the BRMA exhibits better performance as compared to the best fit curve obtained by using the OLS regression. We noticed that the functional relationship between the WH‐Q outperforms as compared to the other empirical curves at the both gauge stations of the Ganga River. The accuracy estimates of the Ganga River is in a range of (0.76–0.95), (1–23) and (0.25–0.44). In the Narmada River, the functional relationship between H‐Q outperforms. The accuracy of discharge at both the gauge stations of the Narmada River are found to be in a range (0.76–0.95), (1–23), to (0.25–0.44). This study is a step towards estimating discharge from satellites data.

Discovery of sulfonamide resistance genes in deep groundwater below Patna, India

Global usage of pharmaceuticals has led to the proliferation of bacteria that are resistant to antimicrobial treatments, creating a substantial public health challenge. Here, we investigate the emergence of sulfonamide resistance genes in groundwater and surface water in Patna, a rapidly developing city in Bihar, India. We report the first quantification of three sulfonamide resistance genes (sulI, sulII and sulIII) in groundwater (12–107 m in depth) in India. The mean relative abundance of gene copies was found to be sulI (2.4 × 10−2 copies/16S rRNA gene) > sulII (5.4 × 10−3 copies/16S rRNA gene) > sulIII (2.4 × 10−3 copies/16S rRNA gene) in groundwater (n = 15) and surface water (n = 3). A comparison between antimicrobial resistance (AMR) genes and wastewater indicators, particularly tryptophan:fulvic-like fluorescence, suggests that wastewater was associated with AMR gene prevalence. Urban drainage channels, containing hospital and domestic wastes, are likely a substantial source of antimicrobial resistance in groundwater and surface water, including the Ganges (Ganga) River. This study is a reference point for decision-makers in the fight against antimicrobial resistance because it quantifies and determines potential sources of AMR genes in Indian groundwater.

Chemical Nature of Hooghly (Ganga) River Water and an Assessment of their Impact on the Eco-Pedon System of Indian Botanic Garden, Howrah

The eco-pedon system of the protected premises of the Indian Botanic Garden, located along the river Hooghly in the lower gangetic plain in the district of Howrah, has been studied with a conservational point of view to understand the indiscernible phenomenon of the impact of the river water on the garden eco-pedo-chemical characteristics often submerged due to occasional tidal inundations of the river during the monsoons.It is experimentally revealed that a chemically dilute river water siphons off the mineral riches of the submerged soil system during flash floods. The intensity of mineral erosion is noted to be directly proportional to the periodic length of submergence of the soil system notwithstanding its natural revamping capacity.

UAV and Deep Learning: Detection of selected riparian species along the Ganga River

Abstract. Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and well-being. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 × 3200 resolution. Additionally, our model demonstrates real-time species detection capability through innovative frame sampling techniques with UAVs. This methodology provides promising technique to collect scientific data on IUCN red listed turtles and critically endangered gharials, allowing detection, monitoring, and real time counting with minimal intrusion. In conclusion, the fusion of UAVs and deep learning promises to revolutionize habitat monitoring, aiding conservation decision-making.

Quantifying soil erosion and soil organic carbon conservation services in indian forests: A RUSLE-SDR and GIS-based assessment

Indian forests, having sixteen types of vegetation, provide various ecosystem services. Forests reduce water erosion of soil, reduce floods and conserve low river flow during dry seasons. Present study estimated annual sedimentation, texture, soil organic carbon control service of forests of India using the Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Ratio (SDR) approach through GIS technique along with identifying important drivers responsible for erosion in forests. The soil erosion control service was based on five factors viz., rainfall erosivity, soil erodibility, slope, crop management, conservation practice factor and SDR across the various forest dominated river basins. Geo-detector modelling was performed to evaluate environmental drivers responsible for soil erosion. Valuation of soil control service of the India’s forests was estimated based on damage and replacement cost method. Annual soil loss rate was maximum in Tropical Thorn Forest and in Ganga River basin. Maximum sediment control and SOC conservation against the soil erosion control service by India’ forests were due to Tropical Dry Deciduous Forest with a total annual sedimentation control and SOC conservation of 33,056,560 tonnes and 3,31,115.06 tonnes, respectively. The conservative estimate of the economic value of soil conservation service by the forests of India was US$ 535.6 M with an average of 4.40 US$ ha−1 and a maximum share of value for Tropical Dry Deciduous Forest i.e. 294.18 million US$ and average maximum value for Littoral and Swamp Forest (28.91 US$ ha−1). The soil erosion in Tropical and Sub-tropical Forest was influenced by the interactive effects between NDVI and Bulk Density. In contrast, soil erosion in the temperate and alpine area was influenced by anthropogenic activity in combination with climatic factors. Present study accounts the role of India’s forests for the soil conservation service and may provide inputs to implement forest and reservoir management strategies besides providing information for Integrated Watershed management and Catchment Treatment Plans.

Multi-metric biomonitoring of a seasonally flooded Baraila wetland using benthic macroinvertebrates as indicator organisms in the middle Ganga River Basin (Vaishali), India

ABSTRACT Human encroachment, urban and agricultural expansion, frequent droughts, eutrophication, infestation of weeds, and overexploitation of resources has predominantly impacted the water quality and biodiversity of the Baraila wetland. The present seasonal study has aimed to explore an under-researched subject of analyzing the water quality of the wetland through multi-metric biomonitoring approaches in 2022. A total of 24 water and benthic samples were collected from the four sampling locations. All the water parameters were within a suitable range to support a diverse range of flora and fauna, except total suspended solids (TSS) and nutrients, indicating toward the impact of agricultural expansion in the wetland's area. Of the 13 identified families of benthic macroinvertebrates, the semi-tolerant families of Viviparidae and Bithyniidae have dominated the wetland, with a relative abundance of 27.04 and 37.77%, respectively, in the comparative seasons. Moreover, the indices used in the study have categorized the wetland under moderately polluted condition. The Hierarchical Cluster Analysis (HCA) has revealed the sensitivity toward organic pollution and habitat preferences has played a major role in the species-specific assemblage of benthic community. The Mantel's correlogram further validates that the moderately polluted status of wetland has favored semi-tolerant to tolerant fauna in the wetland.

Exploring freshwater microalgal strains for Omega-3 polyunsaturated fatty acids and carotenoids from the Ganges River in India

Microalgae, increasingly recognized as a cost-effective reservoir of natural compounds. Their extensive application in nutraceuticals, and pharmaceuticals industry are making them mercantile worthy. This study delves into the realm of bioprospecting freshwater microalgae with focus on extracting high-value products, specifically omega-3 polyunsaturated fatty acids and carotenoids. Freshwater-derived microalgal strains were isolated from Ganges River and refined, and grown in BG-11 culture medium. Microscopic examination was used to screen these microalgae based on morphology. Subsequently, the biochemical properties of the selected microalgal strains were investigated. Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), two crucial omega-3 polyunsaturated fatty acids, were identified in significant quantities in Chlorella variabilis (3.6±1.32%; 2.7±0.13%) and Monoraphidium contortum (2.2±0.36%; 3.2±0.2%). Moreover, the investigation also focused on carotenoids; lutein and astaxanthin. The findings indicated a high concentration of lutein in Chlorella variabilis (4.5 mg g−1), while Monoraphidium contortum exhibited abundant levels of astaxanthin (5.9 mg g−1). These research findings demonstrate the promising capacity of the isolated microalgal strains to yield valuable bioactive compounds.

Applications of IoT technology for climate change adaptation in the Mekong Delta

The Mekong Delta, with an area of over 40,000 square kilometers, is the largest agricultural and fishery production center in Viet Nam. However, along with the Ganges River Delta and the Nile River Delta, the Mekong Delta is one of the three delta regions most severely impacted by climate change. In recent years, this region has faced drought, saltwater intrusion, environmental degradation, and land subsidence mainly caused by climate change. The Internet of Things (IoT) technology has been envisioned as a powerful tool for combating climate change. This paper presents some research projects that leverage the potential of IoT technology and applications to improve the effectiveness of rice cultivation and aquaculture; and support the development of riverbank landslide early warning systems for the region. Through these applications, IoT technology has provided practical answers to address climate change and save the environment.

Exploring the reproductive features of Clupisoma garua from Ganges River, Bangladesh: insights into eco-climatic factors for sustainable management and advancements in aquaculture practices.

The Garua bacha, Clupisoma garua, holds considerable commercial and nutritional significance as a fish species. This study investigates the relationships between GSI (gonadosomatic index) and eco-climatic conditions, and provides comprehensive insights into several facets of reproduction, such as size at first sexual maturity (Lm), spawning season, peak spawning period, and proposes sustainable management strategies for C. garua in the Ganges River, northwestern, Bangladesh. Since January to December 2017, 570 female individuals have been gathered from the Ganges River using cast nets, gill nets, and square lift nets on a monthly basis. The total length (TL) of the specimens varied from 3.90 to 26.30cm. Lm was estimated as 14.30cm based on the results of the TL vs. GSI, MGSI (modified gonadosomatic index), DI (Dobriyal index), 14.00cm through TL-FL regressions, and 15.18cm depending on Lmax for this species. Furthermore, the spawning period was observed from May to August, peaking in June and July, based on greater GSI, MGSI, and DI values. Throughout the peak spawning season, there was not a significant differ from a value of 100 in the relative weight (WR) of the female species. During the spawning season, a substantial association between temperature and GSI (p = 0.0038); rainfall and GSI (p = 0.0043); DO and GSI (p = 0.0043); pH and GSI (p = 0.0002); and alkalinity and GSI (p = 0.0001) was detected. Analyzing a 55-year data series (1964-2018), it became clear that an increase in average air temperature of 0.0289 °C and a decrease in rainfall of 2.988mm per year might possibly delay the spawning season of this species. As a consequence, the results of this study provide crucial information for developing management strategies to safeguard C. garua populations in the Ganges River and its adjacent ecosystems.

Spatial distribution of polycyclic aromatic hydrocarbons in water and sediment in the Ganga River: source diagnostics and health risk assessment on dietary exposure through a common carp fish Labeo rohita.

We evaluated spatial distribution and source apportionment of polycyclic aromatic hydrocarbons (PAHs) in water and sediments at four selected sites of the Ganga River. Also, we measured PAHs in muscle tissues of Rohu (Labeo rohita), the most common edible carp fish of the Ganga River and potential human health risk was addressed. Total concentration of PAHs (∑PAHs) in water was highest at Manika Site (1470.5ng/L) followed by Knuj (630.0ng/L) and lowest at Adpr (219.0ng/L). A similar trend was observed for sediments with highest concentration of ∑PAHs at Manika (461.8ng/g) and lowest at Adpr Site (94.59ng/g). Among PAHs, phenanthrene (Phe) showed highest concentration in both water and sediment. Of the eight major carcinogenic contributors (∑PAH8C), Indeno (1,2,3-C,D) pyrene (InP) did appear the most dominant component accounting for 42% to this group at Manika Site. Isomer ratios indicated vehicular emission and biomass combustion as major sources of PAHs. The ∑PAHs concentrations in fish tissue ranged from 117.8 to 758.0ng/g (fresh weight basis) where low molecular weight PAHs assumed predominance (above 80%). The risk level in fish tissues appeared highest at Manika Site and site-wise differences were statistically significant (p < 0.05). The ILCR (> 10-4) indicated carcinogenic risk in adults and children associated with BaP and DBahA at Manika Site and with BaP at Knuj Site. Overall, the concentrations exceeding permissible limit, carcinogenic potential and BaP equivalent all indicated carcinogenic risks associated with some individual PAHs. This merits attention because the Ganga River is a reservoir of fisheries.

An analytical review on the integrated management of river resources throughNamami Gange

ABSTRACTThe Ganga basin is witnessing accelerated deterioration of agricultural fields, with severe soil erosion leading to nutrients leaching into the river, wreaking havoc on the river's ecosystems. Many Ganga River management plans have been implemented in the past, but a more coordinated and integrated effort that tries to implement basin management strategies with technology preparedness considered critical for the preservation of agricultural regions had been lacking. The Namami Gange Program (NGP) is a first of its kind integrated conservation mission pillared on sustainable water management policies and practices, launched in 2014. Unfortunately, scientific literature on its overall functioning and progress is almost non-existent. This review presents its overall functioning, critically analyses its associated components, and presents case studies, pilot studies, along with analytical critiques. An objective analytical evaluation of the NGP has been provided concluding with sectoral interventions. We have concluded that the ambitions for boosting energy production, irrigation, manufacturing, and expanding the river as an inland waterway interfere with the Ganga cleanup because they are both part of the growth strategy pushed by the administration.

Assessment of hydrochemical characteristics and trace metals of Punpun River water – a tributary of the Ganga River

ABSTRACT This manuscript presents the first inventory of dissolved ions (cations and anions) and trace metals (Pb, Mn, Cr, Co, Ni, Cu and Zn) in the Punpun River water to assess its irrigational suitability, pollution sources and hydrogeochemical processes. For this, the physicochemical parameters and trace metals in river water were analysed in the pre- and post-monsoon seasons. The results showed that the concentrations of cations and anions were Ca2+ &amp;gt; Na+ &amp;gt; Mg2+ &amp;gt; K+ and HCO3− &amp;gt; SO42- &amp;gt; Cl− &amp;gt; NO3− &amp;gt; PO43-, respectively, in both the pre- and post-monsoon seasons. However, trace metal concentration in pre- and post-monsoon followed the trend of Pb &amp;gt; Mn &amp;gt; Cr &amp;gt; Ni &amp;gt; Cu &amp;gt; Zn &amp;gt; Co and Pb &amp;gt; Ni &amp;gt; Mn &amp;gt; Cr &amp;gt; Zn &amp;gt; Co &amp;gt; Cu, respectively. The results reveal that combined effects of rock weathering and evaporation-concentration influence hydrochemistry, along with anthropogenic factors like industrial effluents, urban domestic sewage and agricultural runoff. Piper and Durov plots suggest that the river water is dominated by Ca–Mg–HCO3− type, and the processes were governed by simple ion dissolution and mixing. However, the Gibbs plot shows that silicate and carbonate weathering govern the hydrochemical properties. Irrigational indices like sodium adsorption ratio, %Na, residual sodium carbonate, Kelly's ratio, magnesium hazard, and trace metals indicate that most of the water samples are suitable for irrigation. Comprehensive pollution index values suggest that the river water quality ranges from sub-clean to slightly to moderately polluted.

Assessing heavy metal and physiochemical pollution load of Danro River and its management using floating bed remediation

River Danro in Garhwa (India) plays a vital role as a significant source of surface water and a crucial tributary of the North Koel River, ultimately joining the Ganga River Basin. Serving both urban-industrial and rural areas, the region faces challenges, including sand mining near Belchampa Ghat. This study aimed to assess physicochemical and heavy metals pollution at nine sampling locations, utilizing the Overall Index of Pollution (OIP), Nemerow Pollution Index (NPI), and Heavy Metal Pollution Index (HPI). OIP values indicated excellent surface water quality (0.71) in non-monsoon and slight pollution (6.28) in monsoon. NPI ranged from 0.10 to 1.74 in non-monsoon and from 0.22 (clean) to 27.15 (heavily polluted) in monsoon. HPI results suggested groundwater contamination, particularly by lead. Principal component analysis (PCA) and geospatial mapping showed similar outcomes, highlighting the influence of adjacent land use on water quality. Recognizing the significance of the Danro River in sustaining life, livelihoods, and economic growth, the study recommends implementing measures like floating bed remediation and regulatory actions for effective river management. The study acknowledges weaknesses in the current practical assessment methods for water contamination. These weaknesses make it difficult to put plans for cleaning up and controlling contamination into action. Because of this, future research on developing new in-place remediation techniques should focus on creating better ways to measure how effective the cleanup is.

The influence of moisture transport processes on the stable isotopic compositions in precipitation on the South slope of the Himalayas

Understanding spatiotemporal variations in stable isotopes in precipitation (ẟ18O and ẟD) on the south slope of the Himalayas is crucial for comprehending moisture variations reaching the Tibetan Plateau (TP). Here, we analyze these variations at seven stations in Nepal spanning altitudes from 102 m to 5050 m above sea level in 2017. Our findings show no significant temperature or precipitation amount effect on isotopes, with synoptic precipitation exerting a strong influence. Non-monsoon precipitation primarily originates from the vicinity of the Koshi River basin and the Ganges River, while during the monsoon, the Bay of Bengal, the Koshi River basin, and the Ganges River contribute, with minimal input from the Arabian Sea. Convective activities significantly regulate inner-seasonal precipitation δ18O, with strong convection associated with low isotopic values. Increasing altitude leads to substantial differences in δ18O values, indicating modulation by higher condensation levels and lower temperatures. These findings underscore the impact of regional moisture transport on precipitation isotopic variability and offer insights into interpreting paleo-isotope records in the Indian Summer Monsoon region.

Farmer adoption-based prompt networking and modeling for targeting optimal agro-conservation practices

Despite the availability of robust agricultural models, the successful on-ground implementation of all Best Management Practices (BMPs) remains elusive due to the lack of consideration for farmers' preferences. This study develops a unique network-based optimization technique coupled with Soil and Water Assessment Tool (SWAT) which delineates combined set of cost-effective BMPs at field-scale based on farmer's conservation identity in Ganges River Basin, India. SWAT was calibrated and validated using 10-year monthly discharge and nitrate data, and the selected BMPs were simulated using sequential uncertainty fitting framework. Farmers belonging to the Shahpur subdistrict were found to have the maximum normalized conservation identity of 90%, whereas the northeastern portion of the watershed contributed a maximum nitrate load of 7.5–9.92 kg/acre to the watershed outlet. An optimized combination of BMPs (riparian buffer-conservation tillage) with an efficiency of 0.28 was obtained through the modified network technique that targeted subbasins of the Morna subdistrict using farmer conservation identities. The results provide valuable guidance for watershed modelers, conservation planners, and researchers in pinpointing regions for targeted farmer training, subsidies, and other initiatives aimed at facilitating prompt field-scale BMP adoption. A flexible feature of the approach is its ability to modify the BMP action plans by replacing even technically feasible, cost-effective combinations based on farmers' preferences.

Exploring the dynamic microbial tapestry of South Asian rivers: insights from the Ganges and Yamuna ecosystems

AbstractThis review meticulously examines the dynamics of river microbiomes, with an emphasis on the Ganges and Yamuna rivers of South Asia. These rivers are vital for both ecological and cultural landscapes and offer to understand the interaction between ecological and anthropogenic factors and their impact on microbial communities and activities. Ecological and hydrological factors such as seasonal changes, water flow and physico‐chemical properties of rivers influence microbial diversity and abundance. The effect of heavy metals from industrial and agricultural sources on the river microbiome and how these pollutants modify microbial community structures and ecosystem health are not understood well yet. This underscores the need for sustainable water treatment and remediations for practical engineering solutions. The study reveals how these interactions, whether symbiotic or competitive, affect the composition and functionality of riverine microbial communities. An innovative aspect of our research is the potential of river microbiomes as indicators of urban sewage contamination. We demonstrate how microbial patterns can signal pollution levels, proving valuable for environmental monitoring, management and mitigation. A special attention to the role of microbes in river ecosystems' biogeochemical cycles has been paid to how these microbes contribute to nutrient recycling, organic matter decomposition and overall ecosystem productivity, underlining their crucial role in maintaining the aesthetic value of the river. Additionally, study evaluates the latest methodologies for analysing microbiome metagenomic data, including functional annotation and microbial community analysis techniques. Findings highlight the key importance of understanding river microbiomes for hydrology, ecology and microbiology researchers.

A data-driven global flood forecasting system for medium to large rivers.

Losses from catastrophic floods are driving intense efforts to increase preparedness and improve response to disastrous flood events by providing early warnings. Yet accurate flood forecasting remains a challenge due to uncertainty in modeling, calibrating, and validating a useful early warning system. This paper presents the Requisitely Simple (ReqSim) flood forecasting system that includes key variables and processes of basin hydrology and atmospheric forcing in a data-driven modeling framework. The simplicity of the modeling structure and data requirements of the system allows for customization and implementation in any medium to large rain-fed river basin globally, provided there are water level or discharge measurements at the forecast locations. The proposed system's efficacy is demonstrated in this paper through providing useful forecasts for various river basins around the world. This include 3-10-day forecasts for the Ganges and Brahmaputra rivers in South Asia, 2-3-day forecast for the Amur and Yangtze rivers in East Asia, 5-10-day forecasts for the Niger, Congo and Zambezi rivers in West and Central Africa, 6-8-day forecasts for the Danube River in Europe, 2-5-day forecasts for the Parana River in South America, and 2-7-day forecasts for the Mississippi, Missouri, Ohio, and Arkansas rivers in the USA. The study also quantifies the effect of basin size, topography, hydrometeorology, and river flow controls on forecast accuracy and lead times. Results indicate that ReqSim's forecasts perform better in river systems with moderate slopes, high flow persistence, and less flow controls. The simple structure, minimal data requirements, ease of operation, and useful operational accuracy make ReqSim an attractive option for effective real-time flood forecasting in medium and large river basins worldwide.

Unveiling climatic niches for deeper insights into invasion potential and enhanced distribution models of freshwater fishes

AbstractIntroduced species may exhibit variations in their preferred climatic niches between their native and introduced ranges, which can have important implications for the transferability of distribution models. In the Himalayan ecoregion, little is known about the geographic distribution and climatic niche overlap between native and introduced cold‐water species. Here, we used the COUE (centroid shift, overlap, unfilling, and expansion) framework to explore the invasive potential of rainbow (Oncorhynchus mykiss) and brown (Salmo trutta fario) trout and corresponding climatic niche overlap with native snow trout (Schizothorax plagiostomus and Schizothorax richardsonii) in the Indus and Ganges River basins. Although we found more stability in the climatic niche for O. mykiss (93%) than S. trutta (58%), both species do not conserve their climatic niches in this region (p &gt; 0.05). S. trutta has expanded more toward new environmental conditions (42%) compared to O. mykiss (7%). However, there are still available environmental gaps that O. mykiss and S. trutta can potentially occupy in the future. There was a higher overlap in climatic niches between S. plagiostomus and O. mykiss and between S. richardsonii and S. trutta. Observed shifts in climatic niches of these introduced species can negatively affect the transferability of distribution models, which may underestimate the assessments of habitat suitability for introduced trout in the Himalayas. Our study demonstrates that the information on climatic niche dynamics can inform the model‐building process and improve the transferability and predictive performance to better assess vulnerability of sensitive habitats to introduced species in the Himalayas and elsewhere.