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Evaluation of aquifer potential is essential, as the potable water demand has increased globally over the last few decades. The present study delineated different zones of groundwater potential and groundwater quality of the Kallada River basin (KRB) in southern India, using geo-environmental and hydrogeochemical parameters, respectively. Geo-environmental variables considered include relative relief, land use/land cover, drainage density, slope angle, geomorphology, and geology, while hydrogeochemical parameters include pH, electrical conductivity (EC), Cl - , Fe 3+ , and Al 3+ concentrations. Analytical hierarchy process (AHP) was used for categorizing groundwater potential and quality zones. Nearly 50% of KRB is categorized as very high and high groundwater potential zones, occupying the western and midland regions. The central and west-central parts of KRB are characterized by excellent groundwater quality zones, while the eastern and western parts are characterized by good and poor groundwater quality zones, respectively. By integrating the groundwater potential and groundwater quality, sustainable groundwater management is observed to be necessary at about 54% of the basin, where site-specific groundwater management structures such as percolation ponds, injection wells, and roof water harvesting have been proposed using a rule-based approach. This integrated groundwater potential-groundwater quality approach helps policymakers to implement the most suitable management strategies with maximum performance., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The present investigation evaluates three satellite precipitation products (SPPs), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Centre (GPCC), Climate Hazard Infrared Precipitation with Station Data (CHIRPS), and two reanalysis datasets, namely, the ERA5 atmosphere reanalysis dataset (ERA5) and Indian Monsoon Data Assimilation and Analysis (IMDAA), against the good quality gridded reference dataset (1991-2022) developed by the India Meteorological Department (IMD). The evaluation was carried out in terms of the rainfall detection ability and estimation accuracy of the products using metrics such as the false alarm ratio (FAR), probability of detection (POD), misses, root mean square error (RMSE), and percent bias (PBIAS). Among all the rainfall products, ERA5 had the best ability to capture rainfall events with a higher POD, followed by MSWEP. Both MSWEP and ERA5 had PODs of 70-100% in more than 90% of the grids and less than 35% of missing rainfall events in the entire Tamil Nadu. In the case of the rainfall estimation accuracy evaluation, the MSWEP exhibited superior performance, with lower RMSEs and biases ranging from - 25 to 25% at the annual and seasonal scales. In northeast monsoon (NEM), CHIRPS demonstrated a comparable performance to that of MSWEP in terms of the RMSE and PBIAS. These findings will help product users select the best reliable rainfall dataset for improved research, diversified applications in various sectors, and policy-making decisions., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Drought is one of the common natural disasters with a wide range of occurrences in terms of space and time, and with varying levels of severity, that may result in economic damage and health issues to humans. This study focuses on assessing drought severity in the Central Highlands of Vietnam based on ground meteorological stations and multispectral remote sensing data. A Modification of the Normalized Difference Drought Index (MNDDI) was developed to enhance the effectiveness of remote sensing indices in the drought assessment. Results indicate that MNDDI outperforms Normalized Difference Drought Index and other investigated indicators, such as Normalized Difference Vegetation Index, Normalized Difference Latent Heat Index, and Normalized Difference Water Index, in representing the Earth's surface response to drought events. Correlations ranging from 0.85 to 0.63 were identified between MNDDI and various time scales of the commonly used meteorological drought indicator, namely the Standardized Precipitation Index, during the drought year of 2015. This work also reveals the superiority of MNDDI in portraying the response of land cover types to drought situations. The finding of a severe drought phenomenon in critical agricultural zones is highly consistent with the report from the Ministry of Agriculture and Rural Development of Vietnam. This study contributes valuable insights to the preliminary assessment of drought through remote sensing data, offering a foundation for precise drought outlooks and effective risk management strategies., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This research provides a comprehensive analysis of groundwater pollution in the Lower Anayari Catchment (LAC) through δ 2 H and δ 18 O isotopic analysis, along with positive matrix factorization (PMF) and PCS-MLR receptor models. Forty groundwater samples were collected from hand-dug wells and equipped boreholes across the LAC. Flame photometry for Na + and K + , complexometric titration for Ca 2+ , ion chromatography for Cl - , F - , NO 3 - , SO 4 2- , and PO 4 3- , and atomic absorption spectrometry for Mg 2+ , Fe, Pb, Cd, As, and Ni were analytical techniques/instruments employed. In regard to cations, Na + has the highest average concentration of 63.0 mg/L, while Mg 2+ has the lowest at 2.58 mg/L. Concerning the anions and nutrients, Cl - has the highest mean concentration of 18.7 mg/L, and Fl - has the lowest at 0.50 mg/L. Metalloids were detected in trace amount with Fe displaying the highest mean concentration of 0.077 mg/L whereas Cd and As recorded lowest (0.001 mg/L). The average values for groundwater δ 18 O and δ 2 H were - 3.64‰ and - 20.7‰, respectively; the average values for rainwater isotopic composition were - 3.41‰ for δ 18 . This research contributes to the broader field of hydrological study and provides practical implications for managing and conserving water resources in similar contexts. The innovative combination of isotopic and statistical analyses sets a new standard for future studies in groundwater quality assessment, emphasising the need for comprehensive approaches that consider both geological characteristics and human impacts for sustainable water resource management.2 H. It is believed that natural geological features, particularly biotite granitoid and volcanic flow/subvolcanic rocks from the Birimian Supergroup, significantly influence groundwater mineralisation. Additionally, the impact of anthropogenic activities on water quality, with urban development and agricultural practices, may be attributed to increasing levels of certain contaminants such as Fe, Ni, NO 3 - , and PO 4 3- . This research contributes to the broader field of hydrological study and provides practical implications for managing and conserving water resources in similar contexts. The innovative combination of isotopic and statistical analyses sets a new standard for future studies in groundwater quality assessment, emphasising the need for comprehensive approaches that consider both geological characteristics and human impacts for sustainable water resource management., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Assessing and mapping flood risks are fundamental tools that significantly contribute to the enhancement of flood management strategies. Identifying areas that are susceptible to floods and devising strategies to reduce the risk of waterlogging is of utmost importance. In the present study, an integrated approach, combining advanced remote sensing technologies, Geographic Information Systems (GIS), and analytic hierarchy process (AHP), was adopted in the Patan district of Gujarat, India, with a coastline spanning over 1600 km, to evaluate the numerous variables that contribute to the risk of flooding and waterlogging. After evaluating the flood conditioning factors and their respective weights using the analytic hierarchy process (AHP), the results were processed in GIS to accurately delineate areas that are prone to flooding. The results highlighted exceptional precision in identifying vulnerable areas, allowing for a thorough evaluation of the impact severity. The integrated approach yields valuable insights for multi-criteria assessments. The findings indicate that a significant portion of the district's land, precisely 8.94%, was susceptible to very high- risk of flooding, while 27.76% were classified as high-risk areas. Notably, 35.17% of the region was identified as having a moderate level of risk. Additionally, 20.96% and 7.15% were categorized as low-risk and very low-risk areas, respectively. Overall, the study highlights the need for proactive measures to mitigate the impact of floods on vulnerable communities. The research findings were verified by conducting ground truth and visual assessments using microwave satellite imagery (Sentinel-1). The aim of this validation was to test the accuracy of the study in identifying waterlogged agricultural areas and their extent based on AHP analysis. The ground verification and analysis of satellite images confirmed that the model accurately identified approximately 74% of the area categorized under high and very high flood vulnerability to be waterlogged and flooded. This research can provide valuable assistance to policymakers and authorities responsible for flood management by gathering necessary information about floods, including their intensity and the regions that are most susceptible to their impact. Additionally, it is crucial to implement corrective measures to improve soil drainage in vulnerable areas during heavy rainfall events. Prioritizing the adoption of sustainable agricultural practices and improving land use are also crucial for environmental conservation., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Saltwater intrusion has become one of the most concerning issues in the Vietnamese Mekong Delta (VMD) due to its increasing impacts on agriculture and food security of Vietnam. Reliable estimation of salinity plays a crucial role to mitigate the impacts of saltwater intrusion. This study developed a hybrid technique that merges satellite imagery with numerical simulations to improve the estimation of salinity in the VMD. The salinity derived from Landsat images and by numerical simulations was fused using the Bayesian inference technique. The results indicate that our technique significantly reduces the uncertainties and improves the accuracy of salinity estimates. The Nash-Sutcliffe coefficient is 0.74, which is much higher than that of numerical simulation (0.63) and Landsat estimation (0.6). The correlation coefficient between the ensemble and measured salinity is relatively high (0.88). The variance of the ensemble salinity errors (5.0 ppt 2 ) is lower than that of Landsat estimation (10.4 ppt 2 ) and numerical simulations (9.6 ppt 2 ). The proposed approach shows a great potential to combine multiple data sources of a variable of interest to improve its accuracy and reliability wherever these data are available., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The forest ecosystem of Indian Himalayan Region offers various ecosystem services (ESs) that are crucial for the sustenance of human beings. However, the rapid expansion of human activities (HA) poses a significant threat to the provision of the forest ecosystem services (FES). For simple and definitive assessments of FES and HA, the use of indicators has become an indispensable approach. In the present study, we performed: (i) indicator-based mapping of FES and HA, and (ii) evaluated the impact of HA on FES with the aid of geospatial techniques. Village-level analysis was conducted for FES and HA in the Aglar watershed of Uttarakhand, India for 2015. Four dominant forest types in the watershed-Quercus mixed, Pinus roxburghii, Cedrus deodara, and mixed forest were considered. For spatial characterization of FES, indicators such as forest carbon stock, net primary productivity, total water retention, and sediment yield were assessed, whereas human activity index (HAI) was evaluated using indicators of HAs, namely population density, road density, farmland, and habitation ratio. The integration of normalized values of FES indicators generated multiple ecosystem services indicator (MESI), and HAI was contructed using analytical hierarchical process based assignment of weights to HA indicators. Spatial analysis techniques such as ordinary least-square regression (OLS) and geographically weighted regression (GWR) models were used to derive the spatial relationship between them. The adjusted R 2 and AIC were utilized to evaluate the effectiveness of the model. The GWR model had a better fit with an adjusted R 2 of 0.68 and a lower AIC of 42.940, compared to the OLS model with an adjusted R 2 of 0.21 and an AIC of 60.52. The statistics showed that GWR performed better than OLS and ably captured the heteroscedasticity of the phenomena. An inverse relation was observed between MESI and HAI. The findings of the study highlight the close link between the supply of FES and the impact of human-induced disturbances over the provision of FES, which has the potential to increase over time. The study provides a scientific basis for structuring policy dialogues to coordinate the long-term regional sustainability of FES provisioned from the Himalayan regions., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Land-use and land-cover (LULC) is an important component for sustainable natural resource management, and there are considerable impacts of the rapid anthropogenic LULC changes on environment, ecosystem services, and land surface processes. One of the significant adverse implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalaya, India to understand the linkages between LULC dynamics and LST, derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relationship between LST, urban heat island (UHI), and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), was also analysed. LULC change detection analysis from 1992 to 2020 revealed that the built-up area has increased significantly from 12% in 1992 to 40% in 2020, while the extent of water bodies has decreased from 6% in 1992 to 4% in 2020. The area under plantations has decreased from 26% in 1992 to 17% in 2020, and forests have decreased from 4 to 2% during the same period. Urban sprawl of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past four decades. The study revealed that the maximum LST in the city has increased by 11°C between 1992 and 2020. During the same period of time, the minimum LST in the city has increased by 5°C, indicating the impact of urbanization on the city environment, which is reflected by the observed changes in various environmental indices. UHI impact in the city is quite evident with the maximum LST at the city centre having increased from 13.03°C in 1992 to 22.01°C in 2020. The findings shall serve as a vital source of knowledge for urban planners and decision-makers in developing sustainable urban environmental management strategies for Srinagar city., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Groundwater vulnerability can partially reflect the possibility of groundwater contamination, which is crucial for ensuring human health and a good ecological environment. The current study seeks to assess the groundwater vulnerability of Zhengzhou City by adopting an amended version of the traditional DRASTIC model, i.e., the DRASTICL model, which incorporates land use type indicators. More specifically, the AHP-DRASTICL, entropy-DRASTICL, and AE-DRASTICL models were established by optimizing weights using the analytic hierarchy process (AHP) and entropy weight method. The evaluation results for these five models were divided into five levels: very low, low, medium, high, and very high. Using Spearman's rank correlation coefficient, the nitrate concentration was used to verify the groundwater vulnerability assessment results. The AE-DRASTICL model was found to perform the best, with a Spearman correlation coefficient of 0.78. However, the AHP and entropy weight method effectively improved the accuracy of vulnerability assessment results, making it more suitable for the study area. This study provides important insights to inform the design of strategies to protect groundwater in Zhengzhou., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The consequences of climate change on agriculture water demand are among the current and prospective challenges. The amount of water needed by crops is significantly affected by the regional climate. The influence of climate change on irrigation water demand and reservoir water balance components were examined. The results of seven regional climate models were compared, and the top-performing model was chosen for the study area. After model calibration and validation, the HEC-HMS model was used to forecast future water availability in the reservoir. The results show that under the RCP 4.5 and RCP 8.5 emission scenarios, the reservoir's water availability in the 2050s will decline by approximately 7% and 9%, respectively. The CROPWAT results showed that the required irrigation water might rise by 26 to 39% in the future. However, the water supply for irrigation may be drastically reduced due to the drop in reservoir water storage. As a result, the irrigation command area could drop up to 21% (2878.4 ha) to 33% (4502 ha) in future climatic conditions. Therefore, we recommend alternative watershed management techniques and climate change adaptation measures to endure upcoming water shortages in the area., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Evidence from increasing mineralization, micropollutant concentrations, waterborne epidemics, an algal boom, and dissolved organic matter has provided substantial evidence that climate change impacts water quality. While the impact of the extreme hydrological event (EHE) on water quality (WQ) has aroused considerable research interest, research uncertainty has been premised on WQ data scarcity, a short time frame, data non-linearity, data structure, and environmental biases on WQ. This study conceptualized a categorical and periodic correlation using confusion matrices and wavelet coherence for varying standard hydrological drought index (SHDI; 1971-2010) and daily WQ series (1977-2011) of four spatially distinct basins. By condensing the WQ variables using chemometric analyses, confusion matrices were assessed by cascading the SHDI series into 2-, 3-, and 5-phase scenarios. 2-phase revealed an overall accuracy (0.43-0.73), sensitivity analysis (0.52-1.00), and Kappa coefficient (- 0.13 to 0.14), which declines substantially with the phase increase, suggesting the disruptive impact of EHE on WQ. Wavelet coherence depicted the substantial ([Formula: see text]) mid- and long-term (8-32 days; 6-128 days) co-movement of streamflow over WQ, confirming the varying sensitivity of WQ variables. Land use/land cover mapping and the Gibbs diagram corroborate the eventful WQ evolution by EHE and their spatial variability concerning landscape transformation. Overall, the study deduced that hydrologic extreme triggers substantial WQ disruption with dissimilar WQ sensitivity. Consequently, suitable chemometric indicators of EHE impacts such as WQ index, nitrate-nitrogen, and Larson index at designated landscapes were identified for extreme chemodynamics impact assessment. This study proffers a recommendation for monitoring and managing the impact of climate change, floods, and drought on water quality., (© 2023. The Author(s).)

Pristine tropical river systems are coming under increasing pressure from the development of economic resources such as forestry and mining for valuable elements. The Lebir catchment, north eastern Malaysia, is now under development as a result of unregulated tree felling and mining for essential and rare metals. Two sediment cores, one in the upstream reaches and the other from the downstream reaches, were taken from flood prone area of the Lebir River, Malaysia, and analysed for their elemental composition by XRF, specifically Al, Si, Fe, Ca, K, Mg, Mn, V, Cu, Ni, Pb, Cr, Zn, As, Th and U. Activities of fallout radionuclides, 137 Cs and 210 Pb were also determined to from a geochronological context. The elemental concentrations in the soils were assessed in terms of their enrichment factor and Si, Ca, K, Mg, Mn, V, Cu, Ni and Zn were found not to be enriched, whereas As, Th and U had elevated enrichment factors. The Th and U were particularly enriched in the downstream core indicating inputs from a tributary that drains a catchment with known deposits of Th and possibly U. The results suggest that the growth in economic development is fostering the transport of contaminants by the major rivers which, in turn, is contaminating the riverine floodplains. This points to the need for a more integrated and holistic approach to river basin management to maintain the environmental quality of these fragile aquatic systems., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Under the global warming, it is particularly important to explore the response of extreme climate to global climate change over the arid regions. Based on daily temperature (maximum, minimum, and average) and precipitation data from meteorological stations in Xinjiang, China, we analyzed the spatiotemporal characteristics of extreme temperature and extreme precipitation events via combining thin plate smoothing spline function interpolation, Sen's slope, and Mann-Kendall test. Our results showed that during 1960-2019, the extreme low temperature index of frost days (FD), icing days (ID), cold days (TX10p), cold nights (TN10p), and cold speel duration index (CSDI) all showed the downward trend to varying degrees, and the extreme high temperature index of summer days (SD25), warm days (TX90p), warm night (TN90p), and warm speel duration index (WSDI) all showed an upward trend to varying degrees, and the extreme low temperature index of high altitude mountains decreases more than that of the basin and plains. In addition, all the extreme temperature indices are closely related to the annual average temperature in Xinjiang (R > 0.6). Among the extreme precipitation indices, except for the consecutive dry days (CDD), the other extreme precipitation indices showed increasing trends to different degrees, but the changes in extreme precipitation in Xinjiang were mainly manifested by the increase of heavy precipitation in a short period (the increase of heavy precipitation and extreme heavy precipitation was the largest, 44.8 mm/10a and 17.6 mm/10a, respectively) and spatially concentrated in the Ili River and Altai Mountains in northern Xinjiang. Meanwhile, annual precipitation was positively correlated with the extreme precipitation index (R > 0.4), except for the CDD. This study provides theoretical support for the prevention and control of natural disasters in the dry zone., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Hydrogeochemical processes that govern selected inorganic substances distribution in a semi-confined aquifer were characterized using traditional hydrogeochemical approaches and natural background levels (NBLs). Saturation indices and bivariate plots were used to investigate the effects of water-rock interactions on natural evolution of the groundwater chemistry, whereas Q-mode hierarchical cluster analysis and one-way analysis of variance classified the groundwater samples into three distinct groups. To highlight the groundwater status, NBLs and threshold values (TVs) of the substances were calculated using pre-selection method. Piper's diagram showed that the Ca-Mg-HCO 3 water type was the only hydrochemical facies of the groundwaters. Although all samples, except a borewell with a high NO 3 - concentration, had major ion and transition metal concentrations within the World Health Organization's recommended guideline values for drinking water, Cl - , NO 3 - and PO 4 3- exhibited scattered distribution patterns, reflecting their nonpoint anthropogenic sources in the groundwater system. The bivariate and saturation indices revealed that silicate weathering and possible gypsum and anhydrite dissolution contributed to the groundwater chemistry. In contrast, NH 4 + , Fe T and Mn abundance appeared to be influenced by redox conditions. Strong positive spatial correlations between pH, Fe T , Mn and Zn suggested that mobility of these metals was controlled by pH. The relative high F - concentrations in lowland areas may imply the impact of evaporation on this ion's abundance. Contrary to TVs of HCO 3 - , those of Cl - , NO 3 - , SO 4 2- , F - and NH 4 + were below the guideline values, confirming the influence of chemical weathering on the groundwater chemistry. Based on the present findings, further studies that take into account more inorganic substances are required for NBLs and TVs determination in the area, thereby setting up a robust sustainable management plan for the regional groundwater resources., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The present research underlines the need to expand far outside bundling or hierarchical providing strategy that often focuses on a specific habitat or ecosystem and creates a location-based strategy that considers how dependency in other parts of the region with ecosystem functions and processes leads to complements and resources' trade-offs. Thus, for assessment of spatial heterogeneity based on willingness to pay (WTP) for upgrading environmental attributes across Heihe River Basin (HRB), a choice experiment survey was carried out in the entire river basin. The HRB is one of the big inland river in the Northwestern region of China and is selected on basis of its geomorphological and geographical significance. A sum of 1679 individuals were interviewed through choice experiment technique from whole river basin consisting of five main cities and 33 adjoining rural areas. The Random Parameter logit model, Krinsky-Robb technique as well as delta method were applied for the evaluation of spatial heterogeneity and estimation of individual specific WTP, respectively. Spatial heterogeneity is verified among sampled individuals' preferences about upgradation of environmental attributes, such as, observed preferences of individuals' and their varying corresponding WTP amounts for per unit's upgradation in agriculture product quality, greenhouse gases reduction, farmland landscape, and biodiversity, which reflects heterogeneous tastes and preferences of the selected individuals. In addition, the assessed outcomes for identifying the impacts of distance decay through random parameter logit model depicted the vital role of distance influence on respondents' WTP for restoring the degraded environmental attributes, such that among 3 ad hoc distance bands, WTP of those sampled individuals who are in proximity of ≤ 10 km to HRB is more than the rest of the individuals, i.e., individuals living in the range of ≤ 20 km and > 20 km. For instance, WTP for agriculture product quality is 119.147 CNY/year in ≤ 10 km and is higher than the remainders., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

A vulnerability assessment of the aquifers in the agricultural area of Zacharo in SW, Peloponnese, Greece, was conducted using the DRASTIC index and the susceptibility index (SI). Sensitivity analysis was conducted and thematic maps for each parameter were generated to analyse the impact of individual parameter on the collective groundwater vulnerability. Results derived from the DRASTIC and SI maps revealed that the extremely highly vulnerable zones are concentrated at three coastal sites in the western part of the study area. Data from these maps also indicate low vulnerability areas throughout the eastern part of the region. The distribution of nitrate concentrations in groundwater is better correlated with the DRASTIC (79.2%) compared to SI (60.2%). Neither method takes into consideration the impact of dilution and nitrate to ammonium reduction, on the nitrate content of groundwater, thus overestimating the vulnerability index. Moreover, the SI method overestimates the impact of olive groves' land use type on the susceptibility index, thus resulting to a lower correlation with the observed nitrate concentrations., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Groundwater vulnerability assessment using the fuzzy logic technique is attempted in this study. A hierarchical fuzzy inference system is created to serve the selected objective. The parameters considered in this study are similar to the seven parameters used in conventional DRASTIC methods; however, the effect of land use and land cover is studied by including it as an additional parameter in a model. A hierarchy is created by comparing two input parameters, say (D and R), and the output of the same is paired as an input with the third parameter (A) and so on using the fuzzy toolbox in MATLAB. Thus, the final output of fuzzy inference systems six and seven (FI6 and FI7) is defuzzified and mapped using ArcGIS to obtain the groundwater vulnerability zones by fuzzy DRASTIC and fuzzy DRASTIC-L. Each map is grouped into five vulnerability classes: very high, high, moderate, low, and very low. Further, the results were validated using the observed nitrate concentration from 51 groundwater sampling points. The receiver operating curve (ROC) technique is adopted to determine the best suitable model for the selected study. From this, area under the curve is estimated and found to be 0.83 for fuzzy DRASTIC and 0.90 for fuzzy DRASTIC-L; the study concludes that fuzzy DRASTIC-L has a better value of AUC suits best for assessing the groundwater vulnerability in Thoothukudi District., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The groundwater is very precious in the world. Rapid urbanization and industrialization create tremendous stress on groundwater quality and quantity. Unscientific groundwater extraction and waste disposal methods impact the groundwater aquifer's susceptibility in the coastal area. This research examines how industrial waste, seawater intrusion, and solid waste dumping affect the Thoothukudi District, located on the southwest coast of Tamil Nadu, India. The groundwater vulnerability potential is determined using the DRASTIC and analytical hierarchy process (AHP)-based DRASTIC model. DRASTIC-AHP method's weights and ranks are determined using multi-criteria decision analysis (MCDA)-based pairwise comparison method. Remote sensing (RS) and geographical information system (GIS) are implemented to prepare the input layers for DRASTIC and DRASTIC-AHP. The findings reveal a very high category of vulnerability along the coastline that is covered in sand and loose sediments from an aquifer. Similar conditions exist on the southeast side, which is covered with gravel, sand, and sandstone with shale and has relatively low-slope topography. This enables higher contaminant percolation into the groundwater and raises the possibility for pollution. The DRASTIC-AHP method's results reveal that the southeast side has a significant possibility of contamination. The water table, net recharge, vadose zone, and conductivity greatly impacted the DRASTIC vulnerability assessment due to their stronger weight than theoretical weight. It may be stated that the DRASTIC technique is more cost-effective and time-efficient in analyzing a wide range of regional groundwater risks while avoiding sloppy, uncontrolled land development and other unwanted activities., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The groundwater vulnerability assessment is known as a useful tool for predicting and prevention of groundwater pollution. This study targets the DRASTIC, evidential belief function (EBF), and logistic regression (LR) models to assess vulnerability in Kabul aquifers, Afghanistan Country. The growth of urban sprawl, groundwater overexploitation, and lack of suitable municipal sewage systems as anthropogenic sources have been the main potential to increase groundwater contaminants such as nitrate in the study area. The vulnerability map has been developed based on various effective factors including altitude, slope (percentage rise), aspect, curvature, land-use type, drainage density, distance from river, annual mean precipitation, net recharge, geology/lithology units, the impact of the vadose zone, aquifer media, depth to water (unsaturated zone), saturated zone, drawdown, and hydraulic conductivity. To identify groundwater pollution, the spatial variation of nitrate concentration data in 2018 was considered indication of groundwater pollution. Based on descriptive statistics, the value of 2.65 mg/l (the median of the pixel values of nitrate map) was selected as a threshold to differentiate the occurrence and non-occurrence of pollution. The groundwater quality data were selected and randomly divided into two datasets for training and validation, including 70% and 30%, respectively. The success-rate and prediction-rate curves were computed based on the receiver operating characteristic (ROC) curve and the area under the curve (AUC) to estimate the efficiency of models. The ROC-AUC of success rates for EBF, LR, and DRASTIC models were estimated to be 67%, 66%, and 52%, respectively. Moreover, the ROC-AUC of the prediction rates of the EBF, LR, and DRASTIC models were obtained 61%, 63%, and 55%, respectively. Based on correlation between mean nitrate concentration and the mean vulnerability indexes in each model, the EBF model is the most compatible with the current developed vulnerability zones as the role of mankind in changing the environment in real conditions in comparison to LR and DRASTIC models., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Floods in urban areas result in a detrimental impact on the natural environment and human health and pose major risks to assets and communication systems. In cities with high population density, the magnitude of flood damage largely depends upon flood inundation as well as floodwater depths. The present study compared recent flood inundation extent, damages caused, and possible floodwater depth in two highly developed metropolises of China and Japan, i.e., Wuhan and Kumamoto cities, for the year 2020. Sentinel-1 satellite data-driven change detection algorithm in Google Earth Engine (GEE) was applied to identify potentially flooded regions. Major land use land cover classes such as urban areas and croplands affected by the flood were mapped in conjunction with the exposed population. ALOS PALSAR digital elevation model (DEM) was used to study the inundation depth. The study revealed that 322 km 2 of the area has been inundated by floodwater in Wuhan city with 230 km 2 and 140 km 2 areas under damaged croplands and urban regions. Around 817,095 people were exposed to this natural catastrophe in Wuhan. The city Kumamoto has witnessed an inundation area of about 505 km 2 with damaged cropland of 350 km 2 and an urban area of 83 km 2 ., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The pressure and dependence on coastal aquifers are on the rise in many parts of the globe. These lead to overexploitation, aggravated levels of groundwater pollution, and seawater intrusion. Integrated analyses can create holistic insights into the quality and the vulnerability of aquifers to seawater intrusion. In this study, Mombasa North coast's coastal aquifer was characterized by integrating multiple approaches-GALDIT overlay index, seawater intrusion groundwater quality index GQI SWI , total hardness, water quality index (WQI)-and the results were further explored and interpreted with geospatial analysis techniques. The study suggests that the predominant water type in areas under moderate or high vulnerabilities to seawater intrusion is the Na-Cl water type. However, similar Na-Cl water types can produce a range of total hardness from soft to hard. GQI SWI classification can be used to narrow down the observations from Stuyfzand's TH-based classification system. In the aquifer studied, the results of the GALDIT overlay index, a weighted aggregation of intrinsic parameters contributing to seawater intrusion, show that 29%, 59%, and 12% of the aquifer have low, moderate, and high vulnerabilities, respectively. The GQI SWI analysis indicates that the groundwater is largely brackish (68%) but saline towards the southern end of the aquifer at 32%. Total hardness values indicate that 67% of the aquifer's coverage falls under the "moderately hard" category. The geodatabase creation introduced in the study provides a template for similar studies and a baseline for future WQI and water quality monitoring. However, temporal studies on chronological timescales are recommended for sustainable management of the aquifer., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In this paper, a novel ANN flood forecasting model is proposed. The ANN model is combined with traditional hydrological concepts and methods, taking the initial Antecedent Precipitation Index (API), rainfall, upstream inflow and initial flow at the forecast river section as input of model, and flood flow forecast of the next time steps as output of the model. The distributed rainfall is realized as the input of the model. The simulation is processed by dividing the watershed into several rainfall-runoff processing units. Two hidden layers are used in the ANN, and the topology of ANN is optimized by connecting the hidden layer neurons only with the input which has physical conceptual causes. The topological structure of the proposed ANN model and its information transmission process are more consistent with the physical conception of rainfall-runoff, and the weight parameters of the model are reduced. The arithmetic moving-average algorithm is added to the output of the model to simulate the pondage action of the watershed. Satisfactory results have been achieved in the Mozitan and Xianghongdian reservoirs in the upper reaches of Pi river in Huaihe Basin, and the Fengman reservoir in the upper reach of Second Songhua river in Songhua basin in China., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Available quality groundwater resources are declining globally due to population growth, agricultural activities, industrialization, and urbanization. Wells in Bahabad Plain, located in Yazd Province, Iran, are the primary source of water demanded by humans for drinking and agricultural activities. Hence, it is pivotal to examine the quality of groundwater resources, which is the main objective of this study. To this end, seventeen water samples collected in the region were using standard sampling methods, and ten parameters were examined from the samples, namely pH, TH, TDS, SO 4 , HCO 3 , Cl - , K + , Ca + , Mg + , and Na + . Water quality index (WQI) was used to determine water quality, and SI, GODS, and DRASTIC methods were used to examine aquifer vulnerability to contamination. Schoeller's diagram indicated that 11% of the samples were at an excellent level; 33% were at a drinkable level; 20% were at a not-suitable level for drinking; 26% were terrible for drinking; and 10% were at an almost non-drinkable level. According to WQI results, 23.53% of the samples had no limitation for drinking, 35.29 were of low quality, 17.65% were of inferior quality, and 23.53% were non-drinkable. The GODS method results indicated that the eastern, central, western, and small parts sporadically scattered in northern regions of the study area (39.93%) had a moderate vulnerability. Lastly, a significant portion of the northern and southern districts (60.07%) was found to have a low vulnerability to contamination, which was in line with WQI results., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Rainfall is generally partitioned into throughfall, stemflow, and interception in ecosystems. Stemflow variability can affect the hydrology, ecology, and soil chemistry patterns. However, the influence of canopy structure and rainfall characteristics on stemflow production in sugarcane plantations which are important for renewable energy production remain poorly understood. By using funnels attached to the sugarcane stems, the present study determined the stemflow amount during the period of sugarcane growth and its relationship with plant development. Approximately, 14% of gross rainfall reached the soil as stemflow, and the funneling ratios was 60. In general, it was observed a positive relationship between stemflow rates with both leaf area index and plant height. This was attributed to an increasing number of acute branching angles of the sugarcane leaves as well as high stem tillering and density. However, at the end of growth cycle, stemflow rate was lower than in previous periods which can be attributed to changes in sugarcane canopy such as stems inclination and lodging, reducing the effectiveness of water conveyance along the stem. Our study showed the need to include stemflow to better understand the hydrology of sugarcane plantations., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Groundwater has never been heavily relied on as a water source in Northern Iraq as it has been in the last two decades due to the rapid and often unplanned urbanization, industrial and agricultural projects. This paper attempts to present a concise groundwater vulnerability assessment of Rania basin to the local and regional planning authorities to ensure a more sustainable development in the area. The focus of the study is the Rania basin, which is a part of Dokan subbasin in northeast Iraq. The initial groundwater vulnerability assessment is mapped with standard DRASTIC model. It is then modified by adding "lineament density index" to the original seven DRASTIC parameters due to the previously established close relationship between flow and yield of groundwater with lineament. The area is categorized into five vulnerability index zones of very low (26%), low (32%), medium (31%), high (11%), and very high (0.012%). The modified model offers a slightly different vulnerability classification of very low (16.61%), low (35.45%), medium (30.32), high (17.57), and very high (0.05%). Measured nitrate concentration is used to validate the assessment results. A progressive increase in nitrate level somehow reflects the different vulnerability zones identified by both DRASTIC models. Average nitrate values of samples taken in wet season show 14.99 mg/l, 17.94 mg/l, and 18.63 for very low vulnerability, low vulnerability, and medium vulnerability zones in the standard DRASTIC model. However, when the area is mapped by the modified DRASTIC model, the vulnerability classes of very low and medium show slightly higher nitrate values of 15.96 mg/l and 20.1 mg/l and a lower value of 17.68 mg/l for the low vulnerability class. This is despite a noticeable increase in nitrate level between wet and dry seasons due to the rise in water table in the wet season., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The objective of the current study is groundwater vulnerability assessment using DRASTIC, modified DRASTIC, and three statistical bivariate models (frequency ratio (FR), evidential belief function (EBF), and weights-of-evidence (WOE)) for Sari-Behshahr plain, Iran. A total of 218 wells were sampled for nitrate concentration measurement in 2015. Datasets were generated using results from 109 wells having nitrate concentrations greater than 50 mg/L. The nitrate data were divided into two groups of 70% (76 locations as training dataset) for modeling and 30% (33 locations as a testing dataset) for model validation. Finally, five groundwater potential pollution (GPP) maps were produced by the training dataset and then evaluated using the testing dataset and receiver operating characteristic (ROC) method. Results of the ROC method showed that the WOE model had the highest predictive power, followed by EBF, FR, modified DRASTIC, and DRASTIC models. Results of the maps obtained revealed that high and very high pollution potential covered the southern part of the study areas, where big cities are located. Results of the present study can be replicated in other locations for identifying groundwater contaminant prone areas., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The urban watershed of Guwahati situated on the bank of the Brahmaputra River is one of the fastest growing cities of India. During the last two decades, water security concerns due to climatic variabilities have become a pronounced issue in the urban watershed of Guwahati. Thus, the study aims to calculate the long-term temporal trends of temperature, precipitation, extreme climate indices, and river discharge to assess the variations and patterns of hydro-climatic variations in the urban watershed of Guwahati from 1991 to 2019. Furthermore, the current study also tries to correlate these extreme climatic indices to river discharge to determine the degree of hydro-climatic variations. The Mann-Kendall statistical techniques and Sen's estimator were used to calculate the statistical significance, stability, and averaged magnitude of trends in the hydro-meteorological data. The result shows that the wetness indices, R20 and RX5Day, reported a decline in Guwahati's urban watershed from 1991 to 2019, resulting in a reduction in intensity and duration of heavy rainfalls while the dry spell (CDD) has been more distinct in the study area with a rise in the average temperature by 0.023 °C/year. Similarly, the most significant statistical trend was found in the monsoonal discharge of the Brahmaputra with a negative trend of - 204.16 m 3 /s/year. The results also show that fluctuations in rainfall patterns have a direct impact on the discharge of the Brahmaputra. These phenomena can affect the quantity of river water resulting in a severe impact on water security in the study area., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Groundwater pollution is a serious problem in north China. However, the study on the vulnerability of karst groundwater is mainly in south China, and there are few studies in north China. To study the applicability of different models of karst areas in north China, this paper chose a special study area-Hebi City, where the exposed karst area is widely developed in the hilly area, but the covered karst area is in the eastern part of the study area. The DRASTIC model, the AHP-DRASTIC model, and the improved COPK model were adopted to evaluate the vulnerability of shallow karst groundwater in Hebi City. Cl - , SO 4 2- , NO 3 - , and TDS were selected to verify the rationality of the evaluation results. It shows that the improved COPK model is more suitable for the shallow karst groundwater vulnerability evaluation in the karst areas in northern China represented by the study area than the other two. The study area was divided into 4 classes by the improved COPK model: highest (14.07%), high (53.05%), low (21.37%), and lowest (11.51%). Then, the analytic hierarchy process and comprehensive index model were used to evaluate the groundwater pollution load intensity, and the study area was divided into 3 classes: high (23.33%), moderate (64.66%), and low (12.01%). According to the analysis of the relationship between groundwater pollution load intensity and groundwater quality, it can be found that human activities have an obvious influence on groundwater quality in the study area. Finally, combined with human activities, the study area was divided into 3 remediation areas, 1 control area, and 1 protected area. This paper can provide a scientific basis for rational exploitation and utilization of groundwater resources. It can also provide a reference for future generations to evaluate the groundwater vulnerability in the northern China karst areas.

Groundwater contamination risk assessment is a useful tool for groundwater pollution prevention and control. Previous study has evaluated groundwater contamination risk at Yinchuan Plain, China, according to aquifer vulnerability. The present study enriches the assessment of contamination risk by adding pollution loading, which represents the hazard from human activities, and groundwater value, which represents the economic loss as a result of groundwater pollution. An approach that combines toxicity, release possibility, and the potential release quantity of pollutants on the ground surface was used to estimate the pollution loading. An integrated approach that considered both the in situ and extractive values was used to estimate groundwater value. In addition, a basic risk map was constructed by overlying the vulnerability and pollution loading maps showing the potential probability of pollution, while a value-weighted risk map was produced by overlying the basic risk and value map indicating the urgency of protection. The validation by specific contaminants shows the reliability of the basic risk assessment. Both the basic and value-weighted risk maps indicate a very high groundwater contamination risk in Yinchuan City, and the southern part of Yinchuan Plain exhibited a relatively high contamination risk. As a result of not only high vulnerability but also very high pollution loading and groundwater value, Yinchuan City is the most urgent area requiring groundwater protection. The produced maps provide effective information for decision-making regarding the optimization of monitoring network, preferential treatment, and allocating future potentially hazardous.

Providing safe drinking water for the entire world's population is essential for ensuring sustainable development. The presence of harmful compounds in aquifers, majorly toxic elements, is a serious environmental concern around the globe. This research aimed to quantify for the initial period the amounts of toxic elements in freshwater in the Dehradun Industrial Region of Uttrakhand, India, as well as the associated health risks. The PTEs (potentially toxic elements) Fe, Cd, Mn, Cu, Cr and Pb, Zn, Ni is measured by AAS and compared to BIS and WHO requirements for drinking safety. The order of mean trace element values in all groundwater samples were determined as Fe > Zn > Cu > Ni > Co > Cd > Pb. HPI was discovered to be higher than high class during the research period (HPI > 30), but under the severe contamination criterion of 100. Iron's MI and PI values were consistently over the threshold limit during the research period, and certain toxic elements were discovered exceptionally near the threshold limit, indicating a severe future influence on groundwater quality. According to PCA (principal component analysis), CM (correlation matrix), and potential health hazard, maximum levels of toxic elements in groundwater in the Dehradun region are attributed to land use patterns, anthropogenic activity, industrial activity, fertilizer and pesticide leaching, and residential waste into the aquifer system. The findings of this study could aid local planners and policymakers in preventing health risks from contaminated aquifers through the deployment of suitable monitoring and mitigation measures., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Water resources variability and availability in a basin affect river flows and sustain river ecosystems. Climate change and human activities disrupt runoff sequences, causing water environmental issues like river channel interruptions. Therefore, determining ecological flow in changing environments is challenging in hydrological research. Based on an analysis of long-term changes in hydrological and meteorological variables and interruption conditions in the semi-arid Liu River Basin (LRB), this study summarizes the controlling factors of river interruption at different temporal and spatial scales and proposes a framework to determine ecological flow under changing environments. Hydrological model and the monthly optimal probability distribution were used to determine the optimal ecological runoff of LRB. The results showed that from 1956 to 2017, precipitation and potential evapotranspiration in the basin showed no significant decreasing trend, but the streamflow significantly decreased, and the downstream interruption worsened, with an average annual interruption duration of 194 days at Xinmin Station from 1988 to 2017. The controlling factors of river interruption are as follows: (1) soil and water conservation measures in the upstream significantly reduce the runoff capacity; (2) the operation mode of the controlling reservoir in the middle reaches changes from "all-year discharge" to "winter storage and spring release" to "combined storage and supply," severing the hydraulic connection between upstream and downstream; and (3) siltation in the downstream river channel coupled with over-extraction of groundwater increases the seepage capacity of the river. The monthly ecological flow of Naodehai Reservoir was determined by considering the monthly seepage losses after reconstructing the natural runoff using the SWAT model and determining the optimal probability distribution function for monthly runoff. The findings are important for downstream LRB ecological restoration and for determining the ecological flow of other river basins in changing environments., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The availability of water for agricultural use in the savannah plateau of Adamawa, Cameroon, is addressed in this paper. Specific field tests coupled with climatic data analysis have helped to characterize the hydrological and hydrogeological conditions of water resources in a small catchment. An agricultural suitability map, based on the water availability during the dry season, is produced. Measured saturated hydraulic conductivities indicated an acceptable disposition of the soils for agriculture. The transmissivity values indicated moderate groundwater potential with mean annual recharge of 96 mm. The agricultural suitability map shows that only 8.8% of the basin area is very favorable for agriculture and that 51% of the basin is not suitable for agriculture, due to water scarcity during the dry season. Due to the considerable depth to the water table, pumping for irrigation purposes is not affordable for the local low-income farmers. Therefore, there is a need to implement adapted solutions for irrigation to support farmers' endeavors, such as the development of water retention basins or the construction of boreholes equipped with electric pumps supplied by solar energy for irrigation., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Groundwater pollution of the watershed is mainly influenced by the multifaceted interactions of natural and anthropogenic process. To analyse the spatial-temporal variation and pollution source identification and apportionment, the dataset was subjected to a globally acknowledged coherent technique using water quality indices and chemometric techniques (principal component analysis (PCA) and cluster analysis. The bulk of the samples tested were below the BIS's permissible levels. Groundwater samples from the pre- and post-monsoon seasons mostly contained the anions HCO - 3  > Cl -  > SO 2- 4  > NO - 3 , while the primary cations were Ca 2+  > Mg 2+  > Na +  > K + . Groundwater was alkaline and hard at most of the sites. According to hydro-geochemical facies and relationships, Piper diagrams, and principal component analysis, weathering, dissolution, leaching, ion exchange, and evaporation were the key mechanisms influencing groundwater quality. The hydrochemical facies classified the groundwater samples into the Ca-Mg-HCO 3 type. For all the sampling locations, PIG was determined to be 0.43, 0.52, 0.47, 0.48, 1.00, and 0.70; respectively. The majority of the test locations fell into the low to medium contamination zone, as determined by the groundwater pollution index (PIG) and contamination index. Three principal components, which together account for 93.8% of the total variance, were identified via PCA. The study's findings confirm the value of these statistical techniques in interpreting and understanding large datasets and offering reliable information to reduce the time and expense of programmes for monitoring and evaluating water quality., Competing Interests: Conflict of interestInapplicable., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Understanding of flood dynamics forms the basis for the leading water resource management and flood risk mitigation practices. In particular, accurate prediction of river flow during massive flood events and capturing the hysteretic behavior of river stage-discharge are among the key interests in hydrological research. The literature demonstrates that data-driven models are significant in identifying complex and hidden relationships among dependent variables, without considering explicit physical schemes. In this regard, we aim to discover the extent to which data-driven models can recognize the hidden relationships among different hydrological variables, in order to generate accurate predictions of the river flow. A secondary aim involves the detection of whether data-driven models can digest the internal features of training inputs to extrapolate severe flood records beyond the training domain. To achieve these aims, we developed a recurrent neural network (RNN) model of two hidden layers to capture the hidden relationships among the inputs, and investigated the model's predictive capability using quantitative and qualitative analyses. The quantitative analysis comprised of a comparison between model predictions, and another set of precise independent records obtained through an advanced hydroacoustic system for reference. A qualitative approach was adopted to visualize the hysteretic behavior of the stage-discharge relations of the model records, with the high-resolution records of the hydroacoustic system. The findings display the potential of data-driven models for accurately predicting river flow. Consequently, the qualitative analysis revealed moderate correlations of stage-discharge loops as compared to the reference records. Additionally, the model was tested against severe destructive flood records generated from the East Asian monsoon and tropical cyclones. Its findings suggest that data-driven models cannot extrapolate new features beyond their training dataset. Overall, this study discusses the competence of RNNs in providing reliable and accurate river flow predictions during floods., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Fixed station sampling is the conventional method used to obtain data on the median water quality of reservoirs. A major source of uncertainty associated with this technique is that water quality at the fixed stations may not be representative of the ambient water quality in the reservoir at the time of sampling. This problem is particularly relevant for water quality variables such as chlorophyll, which have a markedly patchy spatial distribution. The use of Landsat reflectance data to estimate median chlorophyll concentrations in Roodeplaat Dam was investigated. A linear polynomial regression model for estimating chlorophyll concentrations from Landsat reflectance data, was firstly calibrated with chlorophyll concentration data obtained by sampling seven fixed stations on the reservoir at the time of the satellite overflight to produce an individual calibration. Secondly, the model was calibrated with a pooled set of sampled data obtained from five separate overflights, to obtain a generalised calibration.It was found that median chlorophyll concentrations determined from Landsat-derived data were similar to median chlorophyll concentrations estimated from fixed station data. However, the range of chlorophyll concentrations in the reservoir estimated from Landsat data was considerably larger than that estimated from fixed station data. Landsat derived estimates of chlorophyll concentrations have the added advantage of providing information on the spatial distribution of chlorophyll in the reservoir.