Surface Water Changes Research Articles

Changes in extent of open-surface water bodies in China's Yellow River Basin (2000–2020) using Google Earth Engine cloud platform

Dynamics of open surface water is of great significance to understand the impacts of global changes and human activities on water resources. However, long-term changes of open-surface water bodies in the Yellow River Basin have remained poorly characterized. In the study, 26,681 high-quality Landsat images were processed using the Google Earth Engine (GEE) cloud platform to study the long-term changes of the open water bodies in the Yellow River Basin from 2000 to 2020. A percentile-based image synthesis method was used to collect training samples. The multi-index water body extraction rule (MIWER) was proposed to rapidly extract surface water bodies. The results show that (1) The MIWER is suitable for long-term and large-scale surface water mapping based on Landsat images; (2) The average area of permanent and seasonal water in the last 20 years is 9062.59 km 2 and 6918.83 km 2 , respectively, accounting for 56.7% and 43.3% of the total surface water in the Yellow River Basin, respectively; (3) From 2000–2020, both permanent and seasonal water in the whole Yellow River Basin and sub-basins have increased. In general, the total open surface water shows an increasing trend with obvious spatial heterogeneity; (4) The changes of surface water in the Yellow River Basin are related to climate change and intensive human activities, but the influencing factors vary in different regions. The findings of this study demonstrate a novel robust, low-cost water extraction method that satisfies the requirements of terrestrial water inundation mapping and management across the Yellow River Basin. The methodology of this study is expected to be extended to global surface waters, providing valuable information for global surface water resource planning and management.

Revising precipitation – water storages – vegetation signatures with GRACE-based data assimilation

• Assessing modeled water – vegetation dynamics by observation-based data sets. • Observation-based data derived from GRACE assimilation and remote sensing. • Duration for precipitation refilling water storages is shorter modeled than observed. • Model overestimates water storage amount contributing to vegetation growth. • Insights will help modelers improve model structures; e.g. climate events like ENSO. The availability of freshwater is highly influenced by climate change, extreme climate events and by anthropogenic use. Countries where a large part of the population depends on the agricultural sector, such as South Africa, are strongly affected by changes in climate, which emphasizes that water is an essential source for food production and drinking water. To analyze changes in surface and subsurface water, model simulations and in situ data are commonly used. However, both have limitations, for example, the models rely on potentially erroneous forcing data and insufficient process representations and the in situ data do not represent the larger-scale weather and climate due to spatial and temporal heterogeneity. This can be mitigated by assimilating remote-sensed satellite data into models. In this research, we build a realistic picture of the water and its propagation (measured between peak times) from fluxes as precipitation, to its way through the storages and its impact on vegetation at the 50 km scale by using observation-based data. The observations are derived from MODIS remote-sensing and integrating GRACE total water storage anomaly (TWSA) observations into a hydrological model via data assimilation. Our objective is to identify shortcomings in model simulations by confronting them with the (synthesized) observations. Moreover, we demonstrate the importance of integrating observations into the models. We base these comparisons on signatures or sub-signals (e.g., temporal lags and annual amplitudes) that we derive via regression analysis, principal component analysis, sensitivity analysis and correlation analysis from the synthetic data and the model output. Our main results show that correlations and signatures in real observations are found weaker as compared to what is simulated in the model, e.g. for the contribution of precipitation to groundwater. Lag times between precipitation and surface and groundwater storage peaks are observed to be longer than in the model. The observed propagation of soil water from storages to vegetation is often shorter than in the model, while for groundwater it is longer. We believe our findings will be highly relevant for modelers; the gained knowledge can be used to improve models. In addition, we feel our study underlines the potential of GRACE assimilation into hydrological models.

Determination and Estimation of Water Surface Change With Landsat Data and Machine Learning Algorithms; A Case Study in Lake Marmara

Water resources play an important role in the continuity of life. Therefore, it is necessary to map water resources and monitor changes. Remote sensing technologies provide important data in the monitoring, control, and protection studies of water resources. These data are important for planners in studies related to water bodies. In this study, the change of the water surface of Marmara Lake, located in Gölmarmara district, 70 km from Manisa, was determined. In addition, an estimation study of the future spatial change of Marmara Lake was carried out. In this direction, the surface areas were obtained as a result of the analysis of the Landsat 7 images of the study area for the years 2002-2021 with the unsupervised classification method. In addition, precipitation, temperature, and LST data of the area were obtained with the help of Google Earth Engine. RBF Regressor, Linear Regression, Additive Regression, and MultiLayer PerceptronCS methods were used to make the most accurate estimation using the data obtained. Using the data between 2002 and 2012, the change between 2013 and 2021 was determined. When the results were examined, it was observed that the best estimation was obtained with MultiLayer Perceptron CS with R2= 0.91. As a result of the estimation study carried out for the years 2022 and 2026 with this method, it is predicted that the lake will shrink much more and reach 1.56 km2.

The late Neogene to Quaternary surface water changes as responses from planktonic foraminifera at the transitional Subantarctic Zone

A well preserved and continuous fossil record of planktonic foraminifera could provide insights into global paleoceanographic and surface water changes. We utilize a ~10-million-year record of planktonic foraminifera along with biogenic proxies and stable isotopes from the Ocean Drilling Program (ODP) Site 1088 Hole B, to understand the late Neogene to Quaternary faunal evolution and surface water changes happening in the Subantarctic Zone. Organic carbon and CaCO3 proxy records suggest high productive surface waters linked to the biogenic productivity high in the Indian Ocean between ~9.4 and 8.1 Ma. The planktonic foraminiferal fossil record between ~7.1 and 6.2 Ma shows high relative abundance of surface-dwelling faunas Globigerina bulloides, Globigerinita glutinata and Neogloboquadrina pachyderma simultaneously with high CaCO3 content. The evolution of deep dwelling faunas especially the globoconellids can be tracked between the late Miocene to middle Pliocene as a consequence of deeper export in nutrients and organic matter. At site 1088 the evolution within the Globoconella inflata lineage coincides with changes in hydrographic conditions. The Miocene – Pliocene transition (~6 to 4.5 Ma) marks the appearance of Globorotalia crassaformis and Neogloboquadrina dutertrei at the site. The warmer middle Pliocene assemblage between 4.6 and 3 Ma shows higher abundance of Globoconella inflata and Globoconella puncticulata. An inverse relationship between the polar fauna Neogloboquadrina pachyderma and Globoconella inflata, typical of low to mid lattitudes, suggest a southward position of the subtropical and subantarctic fronts during the middle Pliocene. Enriched organic carbon and ẟ13C values at Site 1088 indicate the presence of nutrient rich surface waters between 4.2 and 4.0 Ma and 3.4 to 3.1 Ma. Transition to a much dynamic Pleistocene saw glacial interglacial variability among the dominant planktonic foraminifera at the site indicating varying surface water conditions controlled by the movement of hydrographic fronts. Our study shows how the large changes in global climate, ocean circulation, nutrient enrichment and frontal movements influenced the abundance, occurrence and evolution of surface and deep dwelling planktonic foraminifera over the last 10 Ma.

Dynamic Analysis in Surface Water Area and Its Driving Factors in Northeast China from 1988 to 2020

The spatiotemporal changes in surface water area (SWA) in the basins of Northeast China have far-reaching impacts on their economic, agricultural, and social development and ecological sustainability. However, the long-term variation characteristics of water bodies in the Northeast basin and its main driving factors are still unclear. Based on the global surface water dataset, combined with the Meteorological and Vegetation Normalized Index (NDVI) datasets, this study used linear regression and correlation analysis to investigate the temporal and spatial variation characteristics of surface water in Northeast China and its driving factors from 1988 to 2020. The results show that (1) the seasonal surface water area (SSWA) and permanent surface water area (PSWA) in Northeast China increased at the rates of 58.408 km2/ year and 169.897 km2/ year, respectively, from 1988 to 2020. Taking 2000 as the node, PSWA and SSWA showed a trend of first decreasing and then increasing. (2) Changes in surface water types in each basin have significant space–time differences, and the transition between water bodies is dominated by the addition and reduction of seasonal water bodies. PSWA decreased significantly in western basins such as the Ulagai River Basin, the Otindag Desert, and the Liao River Basin, but increased significantly in the Songhua River Basin. (3) The driving forces of surface water change in different basins are different. Temperature and NDVI play a leading role in the change of SWA in the western arid region; permafrost degradation under the condition of air temperature rise is an indispensable factor affecting SWA change in the Argun River Basin; the eastern basin with a larger surface water area responded more strongly to changes in precipitation and evapotranspiration. Land-use conversion and water conservancy project construction were the main reasons for the increase of SWA in the Songhua River Basin under reduced precipitation. This research provides a reference for the in-depth study of the characteristics of surface water resources in Northeast China and has important practical significance for the scientific management of water resources in the basin.

Spatio-temporal characterization of surface water dynamics with Landsat in endorheic Cuvelai-Etosha Basin (1990–2021)

Socio-economic damages caused by extreme floods have been increasing rapidly in recent years, mainly driven by changes in the climate and modulated by increasing human population in deltic areas and floodplains. The Cuvelai-Etosha Basin (CEB) in southern Africa, covering southern Angola and northern Namibia, experiences socially and economically devastating extreme floods. Yet, accurate information on past and current surface water changes and dynamics is lacking. Here, we estimate and map the surface water extents in the CEB and its surroundings (CEB + S) for 32 years (1990–2021) from Landsat data using random forest models to provide long-term baseline information on surface water changes and dynamics. Based on the reference data, a total of 15,677 ± 1080 km2 have been inundated by surface water in the CEB + S during 1990–2021. This extent was accurately mapped by our local water extent product (mapped area = 16,273 km2, user’s accuracy = 91.5 ± 2.5%, producer’s accuracy = 91.1 ± 6%). With user’s and producer’s accuracy of 91%, our overall water extent provides the first most accurate long-term baseline information on surface water inundation in CEB + S necessary for local spatial planning processes to minimise future negative impacts of floods in the basin. Interannual variability of surface water extent is, however, high, with water extent ranging from 520.8 ± 375.7 km2 to 12372.3 ± 1154.7 km2 during the 1990–2021 period. The largest annual water extents (>10,000 km2) were recorded in 2006, 2008, 2009, 2011, and 2017, whereas the smallest extents (<1000 km2) were recorded in 1992 and 2019. We found that over 40% of the area inundated in the CEB + S during 1990–2021 was inundated less than 9 times. With human population increasing rapidly in the CEB + S, rarely inundated areas with short water residence could become a prime target for human settlements, which may lead to huge socio-economic damages during extreme floods if no preventive measures are put in place. Globally available surface water maps from the Global Land Analysis and Discovery (GLAD) and European Commission’s Joint Research Centre (JRC) did not provide realistic surface water extent for CEB + S, especially during years with extreme floods. Therefore, locally adopted product for operational monitoring of surface water in the CEB + S is needed to provide accurate information for informing spatial planning processes and surface water resource management strategies in this endorheic basin and help minimise future negative impacts of floods.

A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine

Water resources are vital to the survival of living organisms and contribute substantially to the development of various sectors. Climatic diversity, topographic conditions, and uneven distribution of surface water flows have made reservoirs one of the primary water supply resources in Iran. This study used Landsat 5, 7, and 8 data in Google Earth Engine (GEE) for supervised monitoring of surface water dynamics in the reservoir of eight Iranian dams (Karkheh, Karun-1, Karun-3, Karun-4, Dez, UpperGotvand, Zayanderud, and Golpayegan). A novel automated method was proposed for providing training samples based on an iterative K-means refinement procedure. The proposed method used the Function of the Mask (Fmask) initial water map to generate final training samples. Then, Support Vector Machines (SVM) and Random Forest (RF) models were trained with the generated samples and used for water mapping. Results demonstrated the satisfactory performance of the trained RF model with the samples of the proposed refinement procedure (with overall accuracies of 95.13%) in comparison to the trained RF with direct samples of Fmask initial water map (with overall accuracies of 78.91%), indicating the proposed approach’s success in producing training samples. The performance of three feature sets was also evaluated. Tasseled-Cap (TC) achieved higher overall accuracies than Spectral Indices (SI) and Principal Component Transformation of Image Bands (PCA). However, simultaneous use of all features (TC, SI, and PCA) boosted classification overall accuracy. Moreover, long-term surface water changes showed a downward trend in five study sites. Comparing the latest year’s water surface area (2021) with the maximum long-term extent showed that all study sites experienced a significant reduction (16–62%). Analysis of climate factors’ impacts also revealed that precipitation (0.51 ≤ R2 ≤ 0.79) was more correlated than the temperature (0.22 ≤ R2 ≤ 0.39) with water surface area changes.

The Outburst of a Lake and Its Impacts on Redistribution of Surface Water Bodies in High-Altitude Permafrost Region

The lakes distributed in permafrost areas on the Tibetan Plateau (TP) have been experiencing significant changes during the past few decades as a result of the climate warming and regional wetting. In September 2011, an outburst occurred on an endorheic lake (Zonag Lake) in the interior of the TP, which caused the spatial expansion of three downstream lakes (Kusai Lake, Haidingnor Lake and Salt Lake) and modified the four independent lake catchments to one basin. In this study, we investigate the changes in surficial areas and water volumes of the outburst lake and related downstream water bodies 10 years after the outburst. Based on the meteorological and satellite data, the reasons for the expansion of downstream lakes were analyzed. Additionally, the importance of the permafrost layer in determining hydrological process on the TP and the influence of from lake expansion on engineering infrastructures were discussed. The results in this study showed the downstream lakes increased both in area and volume after the outburst of the headwater. Meanwhile, we hope to provide a reference about surface water changes and permafrost degradation for the management of lake overflow and flood on the TP in the background of climate warming and wetting.

Tracing surface water change from 1990 to 2020 in China's Shandong Province using Landsat series images

Accurate and long-term series surface water extraction is of great significance for development of agro-ecosystem and sustainable management of water resources. Data and knowledge of the spatial–temporal dynamics of surface water are still very limited in Shandong Province, China. This study provides a rapid and robust method for open-surface water detecting based on the Google Earth Engine (GEE) platform. The 30-m annual maps of surface water frequency are generated to analyze the distribution and variation of surface water in Shandong Province during 1990–2020. In addition, the effects of climatic and anthropogenic drivers are investigated on the temporal and spatial dynamics of surface water. A previously published global surface water dataset is used to valid our results. The results show that the correlation coefficient (R) of permanent surface water area (SWA) is 0.95 from 1990 to 2020. And in this study, the surface water area is well extracted in 1994 which is underestimated by Joint Research Centre (JRC). Surface water is widely distributed in Shandong Province; however, the interannual variation of SWA is substantial. The permanent SWA presents an increasing trend in Shandong Province, especially from 1990 to 2004. Multitemporal monitoring results show that the permanent SWA of Dongping Lake, Xiashan Reservoir, and Chanzhi Reservoir fluctuate most in 1990–1994 and 2013–2018. Precipitation and water supply significantly affect the permanent SWA , with R of 0.38 and −0.70. The results will provide a critical guidance for implementing sustainable water resources management so as to avoid flood-hazards and drought disasters.

LONG-TERM LAND SURFACE WATER MONITORING IN THE YELLOW RIVER BASIN OF CHINA BASED ON LANDSAT IMAGERY ON THE GOOGLE EARTH ENGINE

Abstract. Land surface water is the most active part of the earth's surface layer and plays an important role in material balance and energy transformation. The monitoring of dynamic changes in surface open water helps to grasp the distribution and changes of water resources, which is important for understanding the impact of human activities on water resources and safeguarding the ecological security of the basin. In the study, 72,520 Landsat images were processed using the Google Earth Engine (GEE) cloud platform to study the long-term changes of the open surface water in the Yellow River Basin (YRB) from 2000 to 2020. The results show that (1) The multi-index water body extraction rules (MIWER) is suitable for long-term and large-scale surface water mapping based on Landsat images; (2) From 2000 to 2020, both permanent and seasonal water in the whole YRB and sub-basins have increased. In general, the total open surface water in the YRB show an increasing trend with obvious spatial heterogeneity; (3) The changes of open surface water in the YRB are related to climate change and intensive human activities, but the influencing factors vary in different regions and need to be further studied in the future. The findings of this study can be used to help the policy-makers understand changing water resources and their driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.

Water Body Super-Resolution Mapping Based on Multiple Endmember Spectral Mixture Analysis and Multiscale Spatio-Temporal Dependence

Water body mapping is an effective way to monitor dynamic changes in surface water, which is of great significance for water resource management. Super-resolution mapping is a valid method to generate high-resolution dynamic water body maps from low-spatial-resolution images. However, the accuracy of existing super-resolution mapping methods is not high due to the low accuracy of fraction images and the insufficiency of spatial pattern information. To solve this problem, this paper proposes a spectral similarity scale-based multiple-endmember spectral mixture analysis (SSS-based MESMA) and a multiscale spatio-temporal dependence method based on super-resolution mapping (MESMA_MST_SRM) for water bodies. SSS-based MESMA allows different coarse pixels to have different endmember combinations, which can effectively improve the accuracy of spectral unmixing and then improve the accuracy of fraction images. Multiscale spatio-temporal dependence adopts both pixel-based and subpixel-based spatial dependence. In this study, eight different types of water body mappings derived from the Landsat 8 Operational Land Imager (OLI) and Google Earth images were employed to test the performance of the MESMA_MST_SRM method. The results of the eight experiments showed that compared with the other four tested methods, the overall accuracy (OA) value, as well as the overall distribution and detailed information of the water map generated by the MESMA_MST_SRM method, were the best, indicating the great potential and efficiency of the proposed method in water body mapping.

The Application of Satellite Image Analysis in Oil Spill Detection

In recent years, there has been an increasing use of satellite sensors to detect and track oil spills. The satellite bands, namely visible, short, medium infrared, and microwave radar bands, are used for this purpose. The use of satellite images is extremely valuable for oil spill analysis. With satellite images, we can identify the source of leakage and assess the extent of potential damage. However, it is not yet clear how to approach a specific leakage case methodologically. The aim of this study is the remote sensing analysis of environmental changes with the development of oil spill detection processing methods. Innovative elements of the work, in addition to methodological proposals, include the long-term analysis of surface water changes. This is very important because oil is very likely to enter the soil when water levels change. The classification result was satisfactory and accurate by 85%. The study was carried out using images from Landsat 5, Landsat 7, Landsat 8, Sentinel-1, and Sentinel-2 satellites. The results of the classification of the oil stains in active and passive technologies differ. This difference affects the methodology for selecting processing methods in similar fields. In the case of this article, the oil spill that occurred on 29 May 2020 in Norilsk was investigated and compared with data from other years to determine the extent of biodegradation. Due to the tank failure that occurred at the Nornickel power plant on that day, a large amount of crude oil leaked into the environment, contaminating the waters and soil of local areas. Research shows that oil spills may be caused by human error or may be the effect of climate change, particularly global warming.

Autonomous System of Surface Water Bodies Monitoring for Real Time

Autonomous remote monitoring system of pollutants in site controlled sections for measuring water bodies pollution was documented. It locates the self-contained module for remote monitoring of water bodies surface in automatic mode according to the predetermined path of point-integration sampling for water bodies pollution measuring and technological parameters processing followed by subsequent data transmission via GSM to the state register of negative environmental sources. Obtained data enable the modelling of substances concentration changes in surface waters.

Analysis and evaluation of surface water changes in the lower reaches of the Yangtze River using Sentinel-1 imagery

Study regionThe lower reaches of the Yangtze River, China. Study focusA novel framework for analysis and evaluation of surface water changes in the lower reaches of the Yangtze River was proposed in this study, which was carried out using the Google Earth Engine (GEE) cloud computing platform. The Sentinel-1A images of dry and wet seasons in the study area during 2016 and 2020 were initially synthesized based on the median value of the backscatter coefficient. Then, water extraction results were obtained from the combination of the Random Forest (RF) algorithm, digital elevation model (DEM), and mathematical morphology model according to preferred textural features. New hydrological insights for the regionVerification regions were selected to evaluate the performance of proposed framework; the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method was 94.13% and 0.89, respectively. Meanwhile, the proposed water extraction framework can provide a better performance when compared with state-of-the-art methods (Otsu threshold method, k-nearest neighbors (KNN), and support vector machine (SVM) algorithms) regarding qualitative and quantitative evaluations. The mountain shadows could be eliminated and water continuity improved when the DEM and mathematical morphology model were introduced in the proposed framework. The results indicate that frequent floods and droughts occur in the study area. Additionally, this area is experiencing because of the increases in the water levels and a decline in the regulation and storage capacity of lakes.

Can Surface Soil Moisture Information Identify Evapotranspiration Regime Transitions?

The transition of evapotranspiration between energy‐ and water‐limitation regimes also denotes a nonlinear change in surface water and energy coupling strength. The regime transitions are primarily dominated by available moisture in the soil, although other micro‐meteorological factors also play a role. Remotely sensed soil moisture is frequently used for detecting evapotranspiration regime transitions during inter storm dry downs. However, its sampling depth does not include the entire soil profile, over which water uptake is dominated by plant root distribution. We use flux tower, surface (θ s ; observations at 5 cm), and vertically integrated in situ soil moisture (θv; 0–50 cm) observations to address the question: Can surface soil moisture robustly identify evapotranspiration regime transitions? Results demonstrate that θ s and θ v are hydraulically linked and have synchronized evapotranspiration regime transitions. As such, θ s and θ v capture comparable statistics of evapotranspiration regime prevalence, which supports the utility of remote‐sensing θ s for large‐scale land‐atmosphere exchange analysis.

Characterizing surface water changes across the Tibetan Plateau based on Landsat time series and LandTrendr algorithm

ABSTRACT Recently, the Tibetan Plateau (TP) has experienced dramatic climate change, which influence atmospheric and hydrological cycles. The understanding of surface water dynamics is essential to further investigate the water balance and the hydrologic cycle in this region. In this study, we used all available Landsat Surface Reflectance imagery that generated Modified Normalized Difference Water Index (MNDWI) overlapped the TP in time-series for 1996–2019. We detected the annual changes in surface water using the LandTrendr algorithm on the Google Earth Engine (GEE), and examined impact factors on surface water area dynamics. During 1996–2019, the area of 17,065 km2 had changed from land to water body and 3511 km2 had changed from water to land in the entire TP. The drivers of surface water change are spatially heterogeneous. Precipitation is a main cause of surface water variability in the central, northern and eastern parts of the TP. Meanwhile, temperature is the dominant factor affecting the western and southern parts of the TP. Our results proved that LandTrendr could be applied to monitor surface water changes over larger spatial and temporal scales. In conclusion, our study focus on understanding the process of climate change and the hydrological cycle across the TP.

A UNIFIED FRAMEWORK FOR WATER SURFACE EXTRACTION AND CHANGE PREDICTION IN IMAGERY DATA STREAMS

Changes in surface water might result in natural disasters such as floods, water shortages, landslides, waterborne diseases, which lead to loss of lives. Timely extracting for surface water and predicting its movement is essential for planning activities and decision-making processes. Most existing works on extracting water surface using satellite images focus on static spectral images and ignore the temporal evolution of data in streams, leading to less accuracy and lack of prediction power. Although some works realize that modeling temporal information of satellite signals could boost the forecasting capability on environmental changes, most of them only focus on prediction tasks independently and separately from the extraction task. In this paper, we propose a unified framework for water extraction and change prediction (WECP) built on top of imagery data streams, which are free to access from orbiting satellites, to locate water surface and predict its changes over time. Our framework is evaluated on Landsat 8 data due to its high spatial resolution. Empirical evaluations on real imagery datasets of different landscapes reveal that our framework is robust in extracting and capturing spatio-temporal changes in the water surface.

Identification and Apportionment of Potential Pollution Sources Using Multivariate Statistical Techniques and APCS-MLR Model to Assess Surface Water Quality in Imjin River Watershed, South Korea

Reliable water quality monitoring data, identifying potential pollution sources, and quantifying the corresponding potential pollution source apportionment are essential for future water resource management and pollution control. Here, we collected water quality data from seven monitoring sites to identify spatiotemporal changes in surface water in the Imjin River Watershed (IRW), South Korea, distinguish potential pollution sources, and quantify the source apportionment from 2018–2020. An analysis was performed based on multivariate statistical techniques (MST) and the absolute principal component score-multiple linear regression (APCS-MLR) model. Statistically significant groups were created based on spatiotemporally similar physicochemical water quality characteristics and anthropogenic activities: low-pollution (LP) and high-pollution (HP) regions, and dry season (DS) and wet season (WS). There were statistically significant mean differences in water quality parameters between spatial clusters, rather than between temporal clusters. We identified four and three potential factors that could explain 80.75% and 71.99% in the LP and HP regions, respectively. Identification and quantitative evaluation of potential pollution sources using MST and the APCS-MLR model for the IRW may be useful for policymakers to improve the water quality of target watersheds and establish future management policies.

Surface Water Change Detection via Water Indices and Predictive Modeling Using Remote Sensing Imagery: A Case Study of Nuntasi-Tuzla Lake, Romania

Water body feature extraction using a remote sensing technique represents an important tool in the investigation of water resources and hydrological drought assessment. Nuntasi-Tuzla Lake, a component of the Danube Delta Natural Reserve, is located on the Romanian Black Sea littoral. On account of an event in summer 2020, when the lake surface water decreased significantly, this study aims to identify the variation of the Nuntasi-Tuzla Lake surface water over a long-term period in correlation with human intervention and climate change. To this end, it provides an analysis in the period 1965–2021 via hydrological drought indices and data mining classification. The latter approach is based on several water indices derived from Landsat TM/ETM+/OLI and MODIS full-time series datasets: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (NDVI), Modified NDWI (MNDWI), Weighted Normalized Difference Water Index (WNDWI), and Water Ratio Index (WRI). The experimental results indicate that the proposed classification methods can extract relevant features from waterbodies using remote sensing imagery with a high accuracy. Moreover, the study shows a similarity in the evolution of surface water cover identified with the data mining classification and the drought periods detected in the flow data series for the Nuntasi and Sacele Rivers that supply the Nuntasi-Tuzla Lake. Overall, the results of our investigation show that human intervention and hydrological drought had an extensive impact on the long-term changes in surface water of the Nuntasi-Tuzla Lake.

Surface and Intermediate Water Changes Triggering the Future Collapse of Deep Water Formation in the North Western Mediterranean

AbstractDeep water formation (DWF) in the North Western Mediterranean (NWMed) is a key feature of Mediterranean overturning circulation. DWF changes under global warming may have an impact on the Mediterranean biogeochemistry and marine ecosystem. Here we analyze the deep convection in the Gulf of Lions (GoL) in a changing climate using a regional climate system model with a horizontal resolution high enough to represent DWF. We find that under the RCP8.5 scenario the NWMed DWF collapses by 2040–2050, leading to a 92% shoaling in the winter mixed layer by the end of the century. The collapse is related to a strengthening of the vertical stratification in the GoL caused by changes in properties of Modified Atlantic Water and Levantine Intermediate Water, being their relative contribution to the increase of the stratification 57.8% and 42.2%, respectively. The stratification changes also alter the Mediterranean overturning circulation and the exchange with the Atlantic.