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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.)

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.)

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.)

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.)

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.)

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.)

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.)

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.)

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.)

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.