Search Results (15)

Today, open-source Cloud Computing platforms are valuable for geospatial image analysis while the combination of the Google Earth Engine (GEE) platform and new satellite launches greatly facilitate the monitoring of national-scale lake Water Quality (WQ). The main aim of this research is to assess the transferability and performance of published general, natural-only and artificial-only lake WQ models (Chl-a, Secchi Disk Depth-SDD- and Total Phosphorus-TP) across Greece’s WFD (Water Framework Directive) lake sampling network. We utilized Landsat (7 ETM +/8 OLI) and Sentinel 2 surface reflectance (SR) data embedded in GEE, while subjected to different atmospheric correction (AC) methods. Subsequently, Carlson’s Trophic State Index (TSI) was calculated based on both in situ and modelled WQ values. Initially, WQ models employed both DOS1-corrected (Dark Object Subtraction 1; manually applied) and GEE-retrieved respective SR data from the year 2018. Double WQ values per lake station were inserted in a linear regression analysis to harmonize the AC differences, separately for Landsat and Sentinel 2 data. Yielded linear equations were accompanied by strong associations (R² ranging from 0.68 to 0.98) while modelled and GEE-modelled TSI values were further validated based on reference in situ WQ datasets from the years 2019 and 2020. The values of the basic statistical error metrics indicated firstly the increased assessment’s accuracy of GEE-modelled over modelled TSIs and then the superiority of Landsat over Sentinel 2 data. In this way, the hereby adopted methodology was evolved into an efficient lake management tool by providing managers the means for integrated sustainable water resources management while contributing to saving valuable image pre-processing time. Full article

Understanding the current water quality dynamics is necessary to ensure that ecological and sociocultural services are provided to the population and the natural environment. Water quality monitoring of lakes is usually performed with in situ measurements; however, these are costly, time consuming, laborious, and can have limited spatial coverage. Nowadays, remote sensing offers an alternative source of data to be used in water quality monitoring; by applying appropriate algorithms to satellite imagery, it is possible to retrieve water quality parameters. The use of global remote sensing water quality products increased in the last decade, and there are a multitude of products available from various databases. However, in Latin America, studies on the inter-comparison of the applicability of these products for water quality monitoring is rather scarce. Therefore, in this study, global remote sensing products estimating various water quality parameters were explored on Lake Titicaca and compared with each other and sources of data. Two products, the Copernicus Global Land Service (CGLS) and the European Space Agency Lakes Climate Change Initiative (ESA-CCI), were evaluated through a comparison with in situ measurements and with each other for analysis of the spatiotemporal variability of lake surface water temperature (LSWT), turbidity, and chlorophyll-a. The results of this study showed that the two products had limited accuracy when compared to in situ data; however, remarkable performance was observed in terms of exhibiting spatiotemporal variability of the WQ parameters. The ESA-CCI LSWT product performed better than the CGLS product in estimating LSWT, while the two products were on par with each other in terms of demonstrating the spatiotemporal patterns of the WQ parameters. Overall, these two global remote sensing water quality products can be used to monitor Lake Titicaca, currently with limited accuracy, but they can be improved with precise pixel identification, accurate optical water type definition, and better algorithms for atmospheric correction and retrieval. This highlights the need for the improvement of global WQ products to fit local conditions and make the products more useful for decision-making at the appropriate scale. Full article

Water quality indicator algorithms often separate marine and freshwater systems, introducing artificial boundaries and artifacts in the freshwater to ocean continuum. Building upon the Ocean Colour- (OC) and Lakes Climate Change Initiative (CCI) projects, we propose an improved tool to assess the interactions across river–sea transition zones. Fuzzy clustering methods are used to generate optical water types (OWT) representing spectrally distinct water reflectance classes, occurring within a given region and period (here 2016–2021), which are then utilized to assign membership values to every OWT class for each pixel and seamlessly blend optimal in-water algorithms across the region. This allows a more flexible representation of water provinces across transition zones than classic hard clustering techniques. Improvements deal with expanded sensor spectral band-sets, such as Sentinel-3 OLCI, and increased spatial resolution with Sentinel-2 MSI high-resolution data. Regional clustering was found to be necessary to capture site-specific characteristics, and a method was developed to compare and merge regional cluster sets into a pan-regional representative OWT set. Fuzzy clustering OWT timeseries data allow unique insights into optical regime changes within a lagoon, estuary, or delta system, and can be used as a basis to improve WQ algorithm performance. Full article

Remote sensing has emerged as a promising tool for monitoring water quality (WQ) in aquatic ecosystems. This study evaluates the effectiveness of remote sensing in assessing WQ parameters in Cajititlán and Zapotlán lakes in the state of Jalisco, Mexico. Over time, these lakes have witnessed a significant decline in WQ, necessitating the adoption of advanced monitoring techniques. In this research, satellite-based remote sensing data were combined with ground-based measurements from the National Water Quality Monitoring Network of Mexico (RNMCA). These data sources were harnessed to train and evaluate the performance of six distinct categories of machine learning (ML) algorithms aimed at estimating WQ parameters with active spectral signals, including chlorophyll-a (Chl-a), turbidity, and total suspended solids (TSS). Various limitations were encountered during the study, primarily due to atmospheric conditions and cloud cover. These challenges affected both the quality and quantity of the data. However, these limitations were overcome through rigorous data preprocessing, the application of ML techniques designed for data-scarce scenarios, and extensive hyperparameter tuning. The superlearner algorithm (SLA), which leverages a combination of individual algorithms, and the multilayer perceptron (MLP), capable of handling complex and non-linear problems, outperformed others in terms of predictive accuracy. Notably, in Lake Cajititlán, these models provided the most accurate predictions for turbidity (r² = 0.82, RMSE = 9.93 NTU, MAE = 7.69 NTU), Chl-a (r² = 0.60, RMSE = 48.06 mg/m³, MAE = 37.98 mg/m³), and TSS (r² = 0.68, RMSE = 13.42 mg/L, MAE = 10.36 mg/L) when using radiometric data from Landsat-8. In Lake Zapotlán, better predictive performance was observed for turbidity (r² = 0.75, RMSE = 2.05 NTU, MAE = 1.10 NTU) and Chl-a (r² = 0.71, RMSE = 6.16 mg/m³, MAE = 4.97 mg/m³) with Landsat-8 radiometric data, while TSS (r² = 0.72, RMSE = 2.71 mg/L, MAE = 2.12 mg/L) improved when Sentinel-2 data were employed. While r² values indicate that the models do not exhibit a perfect fit, those approaching unity suggest that the predictor variables offer valuable insights into the corresponding responses. Moreover, the model’s robustness could be enhanced by increasing the quantity and quality of input variables. Consequently, remote sensing emerges as a valuable tool to support the objectives of WQ monitoring systems. Full article

Remote sensing (RS) data have allowed prospective zones of water accumulation (PZWA) that have been harvested during rainstorms to be revealed. Climatic, hydrologic, and geological data have been combined with radar and optical remote sensing data. A wide array of remote sensing data, including SRTM, Sentinel-1&2, Landsat-8, TRMM, and ALOS/PALSAR data, were processed to reveal the topographical characteristics of catchments (elevation, slope, curvature, and TRI) and geological (lineaments, lithology, and radar intensity), hydrological (Dd, TWI, and SPI), ecological (NDVI, InSAR CCD), and rainfall zones in Wadi Queih (WQ), which is an important drainage system that drains into the Red Sea. Radar data improved the structural elements and showed that the downstream area is shaped by the northeast–southwest (NE-SW) fault trend. After giving each evidential GIS layer a weight by utilizing a GIS-based, knowledge-driven methodology, the 13 GIS layers were integrated and combined. According to the findings, the studied basin can be classified into six zones based on how water resources are held and captured, which are very low, low, moderate, high, very high, and excellent. These zones correspond to 6.20, 14.01, 21.26, 36.57, 17.35, and 4.59% of the entire area. The results suggested a specific location for a lake that can be used to store rainwater, with a capacity of ~240 million m³ in the case of increasing rainfall yield. Such a lake complements the present lake at the end of WQ, which can hold about 1 million m³. InSAR coherence change detection (CCD) derived from Sentinel-1 data revealed noticeable changes in land use/land cover (LU/LC) areas. Areas that displayed changes in surface water signatures and agricultural and human activities were consistent with the predicted very high and excellent zones. Thus, the predicted model is an important approach that can aid planners and governments. Overall, the integration of optical and radar microwaves in RS and GIS techniques can reveal promising areas of rainwater and water accumulation. Full article

Climate and land use changes impact catchment hydrology and water quality (WQ), yet few studies have investigated the amount of land use changes required to meet specific WQ targets under future climate projections. The aim of this study was to determine streamflow and nutrient load responses to future land use change (LUC) and climate change scenarios. We hypothesized that (1) increasing forest coverage would decrease nutrient loads, (2) climate change, with higher temperatures and more intense storms, would lead to increased flow and nutrient loads, and (3) LUC could moderate potential nutrient load increases associated with climate change. We tested these hypotheses with the Soil and Water Assessment Tool (SWAT), which was applied to a lake catchment in New Zealand, where LUC strategies with afforestation are employed to address lake WQ objectives. The model was calibrated from 2002 to 2005 and validated from 2006 to 2010 using measured streamflow (Q) and total nitrogen (TN), total phosphorus (TP), nitrate (NO₃-N), and ammonium (NH₄-N) concentrations of three streams in the catchment. The model performance across the monitored streams was evaluated using coefficient of determination (R²) and Nash–Sutcliffe efficiency (NSE) metrics to provide a basis for model projections. Future scenarios incorporated LUC and climate change (CC) based on the Representative Concentration Pathway 8.5 and were compared to the baseline streamflow and WQ indicators. Consistent with our hypotheses, Q, TN, and TP loads were predicted to decrease with afforestation. Specifically, afforestation of 1.32 km² in one of the monitored stream sub-catchments (subbasin 3), or 8.8% of the total lake catchment area, would result in reductions of 11.9, 26.2, and 17.7% in modeled annual mean Q, TN, and TP loads, respectively. Furthermore, when comparing simulations based on baseline and projected climate, reductions of 13.6, 22.8, and 19.5% were observed for Q, TN, and TP loads, respectively. Notably, the combined implementation of LUC and CC further decreased Q, TN, and TP loads by 20.2, 36.7, and 28.5%, respectively. This study provides valuable insights into the utilization of LUC strategies to mitigate nutrient loads in lakes facing water quality challenges, and our findings could serve as a prototype for other lake catchments undergoing LUC. Contrary to our initial hypotheses, we found that higher precipitation and temperatures did not result in increased flow and nutrient loading. Full article

As natural selenium (Se)-rich soil in China is generally characterized by a high geological background of cadmium (Cd), the safe utilization of such seleniferous soil remains a challenge. The accumulating evidence shows that the threshold value of the Se:Cd ratio is a determinant of regulating Cd accumulation in plants. However, the factors modulating the soil’s Se:Cd ratio in selenium-enriched regions are not well understood. Here, a comprehensive study aimed at quantitatively analyzing the effects of land-use types, parent-material types, and soil properties on the distribution and influencing factors of Se, Cd, and the Se:Cd ratios. According to land use and parent-material types, 77 soil samples were collected in Yuanzhou District, a typical naturally seleniferous area in the subtropical hilly area. The results suggested that, compared with quaternary red clays (qrc), the Se content of soils derived from river and lake sediments (rls) and weathered acidic crystalline rocks (wacr) decreased by 5.81%–19.75%, while the weathered quartzite (wq)-derived soils was increased significantly. The soil Cd content in an orchard was significantly reduced compared with that in a paddy field. A redundancy analysis (RDA) revealed that SOM, Total K, and Total P significantly affected the changes in Se and Cd contents. In addition, the land-use type had the most significant effect on the Se:Cd ratio, with a regression coefficient of −0.6999 analyzed by the binary logistic regression model (p < 0.05). Furthermore, pH and Total K were the critical soil properties in controlling the Se:Cd ratio. The study indicated that the Se:Cd ratio in natural selenium-rich soil was mainly regulated by land-use types. Therefore, it is a feasible measure to regulate the Se:Cd ratio by using agronomic practices, mainly regulating soil pH, for the safe utilization of selenium-rich soil with a high Cd background. Full article

Systematic evaluation of water environment carrying capacity (WECC) is a prerequisite for achieving sustainable development, which reflects the water environment comprehensive condition of lake basin under the current economic development scenario. Therefore, taking the Taihu Basin as a case study, a scientific comprehensive evaluation index system of WECC was established based on the Pressure-State-Response (PSR) assessment framework, which included water resources (WR), pollution emission (PE), water quality (WQ), water ecology (WE), and land use (LU) sub-systems. An expert group was invited to determine the weights of each indicator using the group analytic hierarchy process (G-AHP) method, and the indicators in the WR, PE, WQ, WE, and LU sub-systems were 6.5%, 21.8%, 27.9%, 11.1%, and 32.9%, respectively. According to the evaluation results, the WECC index of Taihu Basin increased by 51.4% from 2007 to 2019, but it still slightly exceeded the carrying capacity of the water environment; the water quality and pollution discharge indices had the most significant improvement. Algal blooms are a major factor challenging WECC in the Taihu Basin. Therefore, the overall restoration of the water eco-system must receive more attention in the future. Full article

Lake Xingyun is a hypertrophic shallow lake on the Yunnan Plateau of China. Its water quality (WQ) has degraded severely during the past three decades with catchment development. To better understand the external nutrient loading impacts on WQ, we measured nutrient concentrations in the main tributaries during January 2010–April 2018 and modelled the monthly volume of all the tributaries for the same period. The results show annual inputs of total nitrogen (TN) had higher variability than total phosphorus (TP). The multi-year average load was 183.8 t/year for TN and 23.3 t/year for TP during 2010–2017. The average TN and TP loads for 2010–2017 were 36.6% higher and 63.8% lower, respectively, compared with observations in 1999. The seasonal patterns of TN and TP external loading showed some similarity, with the highest loading during the wet season and the lowest during the dry season. Loads in spring, summer, autumn, winter, and the wet season (May–October) accounted for 14.2%, 48.8%, 30.3%, 6.7%, and 84.9% of the annual TN load and 14.1%, 49.8%, 28.1%, 8%, and 84.0% of the annual TP load during 2010–2017. In-lake TN and TP concentrations followed a pattern similar to the external loading. The poor correlation between in-lake nutrient concentrations and tributary nutrient inputs at monthly and annual time scales suggests both external loading and internal loading were contributing to the lake eutrophication. Although effective lake restoration will require reducing nutrient losses from catchment agriculture, there may be a need to address a reduction of internal loads through sediment dredging or capping, geochemical engineering, or other effective measures. In addition, the method of producing monthly tributary inflows based on rainfall data in this paper might be useful for estimating runoff at other lakes. Full article

To assess water quality (WQ) online for assuring the safety of drinking water, a novel early warning system integrating a high-frequency monitoring system (HFMS) and data quality control (QC) was developed at Lake Qiandao. The HFMS was designed for monitoring water quality, nutrient inputs by main tributaries, water currents and meteorology at different sites at Lake Qiandao. The EWS focused on data availability, a QC method, a statistical analysis method and data applications instead of technological aspects for sondes, wireless data transfer and interface software development. QC was implemented before use to delete the abnormal values of outliers, to detect change points, to analyse the change trend, to interpolate discrete missing measurements, and find continuous missing or wrong observations caused by technical problems with the sonde. For demonstrating advantages and data availability, surface and profiling measurements at two sites were plotted. The plots show obvious seasonal and diel variations, demonstrating the success of integration of the system with advanced automated technology and good QC. This successfully developed system is now not only giving early warning signals, but also providing critical WQ information for the security of drinking water diverted to Hangzhou city through a tunnel of 110 km length. The automatic monitoring data with QC is also being used to produce initial conditions for WQ prediction based on a three dimensional hydrodynamic-ecosystem model. Full article

Given the great importance of lakes in Earth’s environment and human life, continuous water quality (WQ) monitoring within the frame of the Water Framework Directive (WFD) is the most crucial aspect for lake management. In this study, Earth Observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM⁺) and Landsat 8 Operational Land Imager (OLI) sensors have been combined with co-orbital in situ measurements from 50 lakes located in Greece with the main objective of delivering robust WQ assessment models. Correlation analysis among in situ co-orbital WQ data (Chlorophylla, Secchi depths, Total phosphorus-TP-) contributed to distinguishing their inter-relationships and improving the WQ models’ accuracy. Subsequently, stepwise multiple regression analysis (MLR) of the available TP and Secchi depth datasets was implemented to explore the potential to establish optimal quantitative models regardless of lake characteristics. Then, further MLR analysis concerning whether the lakes are natural or artificial was conducted with the basic aim of generating different remote sensing derived models for different types of lakes, while their combination was further utilized to assess their trophic status. Correlation matrix results showed a high and positive relationship between TP and Chlorophyll-a (0.85), whereas high negative relationships were found between Secchi depth with TP (−0.84) and Chlorophyll-a (−0.83). MLRs among Landsat data and Secchi depths resulted in 3 optimal models concerning the assessment of Secchi depth of all lakes (Secchi_general; R = 0.78; RMSE = 0.24 m), natural (Secchi_natural; R = 0.95; RMSE = 0.14 m) and artificial (Secchi_artificial; R = 0.62; RMSE = 0.1 m), with reliable accuracy. Study findings showed that TP-related MLR analyses failed to deliver a statistically acceptable model for the reservoirs; nevertheless, they delivered a robust TP_general (R = 0.71; RMSE = 1.41 mg/L) and TP_natural model (R = 0.93; RMSE = 1.43 mg/L). Subsequently, trophic status classification was conducted herein, calculating Carlson’s Trophic State Index (TSI) initially throughout all lakes and then oriented toward natural-only and artificial-only lakes. Those three types of TSI (general, natural, artificial) were calculated based on previously published satellite-derived Chlorophyll-a (Chl-a) assessment models and the hereby specially designed WQ models (Secchi depth, TP). The higher deviation of satellite-derived TSI values in relation to in situ ones was detected in reservoirs and shallower lakes (mean depth < 5 m), indicating noticeable divergences among natural and artificial lakes. All in all, the study findings provide important support toward the perpetual WQ monitoring and trophic status prediction of Greek lakes and, by extension, their sustainable management, particularly in cases when ground truth data is limited. Full article

The interaction between land use and water quality is of great importance worldwide as agriculture has been proven to exert a huge pressure on the quality of groundwater and surface waters due to excess losses of nutrients (nitrogen and phosphorous) through leaching and erosion processes. These losses result in, inter alia, high nitrate concentrations in groundwater and eutrophication of rivers, lakes and coastal waters. Combatting especially non-point losses of nutrients has been a hot topic for river basin managers worldwide, and new important mitigation measures to reduce the input of nutrients into groundwater and surface waters at the pollution source have been developed and implemented in many countries. This Special Issue of the Land use and Water Quality conference series (LuWQ) includes a total of 11 papers covering topics such as: (i) nitrogen surplus; (ii) protection of groundwater from pollution; (iii) nutrient sources of pollution and dynamics in catchments and (iv) new technologies for monitoring, mapping and analysing water quality. Full article

Pen aquaculture is the main form of aquaculture in some shallow lakes in eastern China. It is valuable to map the spatiotemporal changes of pen aquaculture in eutrophic lakes to assess its effect on water quality, thereby helping the relevant decision-making agencies to manage the water quality (WQ) of lakes. In this study, an automatic approach for extracting the pen aquaculture area was developed based on Landsat data. The approach integrates five algorithms, including grey transformation, discrete wavelet transform, fast Fourier transform, singular value decomposition and k-nearest neighbor classification. It was successfully applied in the automatic mapping of the pen aquaculture areas in Lake Yangcheng from 1990 to 2016. The overall accuracies were greater than 92%. The result indicted that the practice of pen aquaculture experienced five stages, with the general area increasing in the beginning and decreasing by the end of the last stage. Meanwhile, the changes of nine WQ parameters observed from 2000 to 2016, such as ammonia (NH₃-N), pH, total nitrogen (TN), total phosphorus (TP), chlorophyll a, biochemical oxygen demand (BOD), chemiluminescence detection of permanganate index (CODMn), Secchi disk depth (SDD) and dissolved oxygen (DO), were analyzed in the lake sectors of Lake Yangcheng, and then their relationships were explored with the percentage of pen aquaculture area. The result suggested that the percentage of pen aquaculture area exhibits significantly positive correlations with NH₃-N, TN, TP, chlorophyll a, BOD and CODMn, but significantly negative correlations with SDD and DO. The experimental results may offer an important implication for managing similar shallow lakes with pen aquaculture expansion and water pollution problems. Full article

Non-point sources (NPS) of agricultural chemical pollution are one major reason for the water quality degradation of the Great Lakes, which impacts millions of residents in the states and provinces that are bordering them. Future climate change will further impact water quality in both direct and indirect ways by influencing the hydrological cycle and processes of nutrient transportation and transformation, but studies are still rare. This study focuses on quantifying the impacts of climate change on nutrient (Nitrogen and Phosphorus) losses from the two small watersheds (Walworth watershed and Green Lake watershed) within the Great Lakes region. Analysis focused on changes through this century (comparing the nutrient loss prediction of three future periods from 2015 to 2099 with 30 years for each period against the historical nutrient estimation data from 1985 to 2008). The effects on total phosphorus and nitrate-nitrogen losses due to changes in precipitation quantity, intensity, and frequency, as well as air temperature, are evaluated for the two small watersheds, under three special report emission scenarios (SRES A2, A1B, B1). The newly developed Water Erosion Prediction Project-Water Quality (WEPP-WQ) model is utilized to simulate nutrient losses with downscaled and bias corrected future climate forcing from two General Circulation Models (GFDL, HadCM3). For each watershed, the observed runoff and nutrient loads are used to calibrate and validate the model before the application of the WEPP-WQ model to examine potential impacts from future climate change. Total phosphorus loss is projected to increase by 28% to 89% for the Green Lake watershed and 25% to 108% for the Walworth watershed mainly due to the combined effects of increase of precipitation quantity, extreme storm events in intensity and frequency, and air temperature. Nitrate-nitrogen losses are projected to increase by 1.1% to 38% for the Green Lake watershed and 8% to 95% for the Walworth watershed with the different major influencing factors in each future periods. Full article

In the last decade, Lake Erie, one of the great lakes bordering Canada and the USA has been under serious threat due to increased phosphorus levels originating from agricultural fields. Large scale watersheds contributing to Lake Erie from the USA side are being simulated using hydrological and water quality (H/WQ) models such as the Soil and Water Assessment Tool (SWAT) and the results from the model are being used by policy and decision makers to implement better management decisions to solve emerging phosphorus issues. On the Canadian side, modeling applications are limited to either small watersheds or one major watershed contributing to Lake Erie. To the best of our knowledge, no efforts have been made to model the entire contributing watersheds to Lake Erie from Canada. This study applied the SWAT model for Northern Lake Erie Basin (NLEB; entire contributing basin to Lake Erie). Various provincial, national and global inputs of weather, land use and soil at various resolutions was assessed to evaluate the effects of input data types on the simulation of hydrological processes and streamflows. Twelve scenarios were developed using the input combinations and selected scenarios were evaluated at selected locations along the Grand and Thames Rivers using model performance statistics, and graphical comparisons of time variable plots and flow duration curves (FDCs). In addition, various hydrological components such as surface runoff, water yield, and evapotranspiration were also evaluated. Global level coarse resolution weather and soil did not perform better compared to fine resolution national data. Interestingly, in the case of land use, global and national/provincial land use were close, however, fine resolution provincial data performed slightly better. This study found that interpolated weather data from Environment Canada climate station observations performed slightly better compared to the measured data and therefore could be a good choice to use for large-scale H/WQ modeling studies. Full article