Surface Water Extraction Research Articles

Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. 71km2 in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring.

Open surface water index: a novel approach for surface water mapping and extraction using multispectral and multisensory data

ABSTRACT In the present study, a novel water index has been proposed for mapping of open surface water in multispectral images and named as open surface water index (OSWI). The proposed index has been applied on Landsat-8 data to delineate the water area and the results are compared with those of existing water indices. The index is also applied on Sentinel-2A data and corresponding water pixels are extracted. The results have been validated with the help of classification as well as density slicing based methods on multisensory data. The preliminary results show that the proposed index is capable of identifying smaller water regions which remain unidentified by other indices.

Assessing species-specific differences for nuclear receptor activation for environmental water extracts

In vitro bioassays are increasingly applied to detect endocrine disrupting chemicals (EDCs) in environmental waters. Most studies use human nuclear receptor assays, but this raises questions about their relevance for evaluating ecosystem health. The current study aimed to assess species-specific differences in the activation or inhibition of a range of human and zebrafish nuclear receptors by different water extracts. Wastewater and surface water extracts were run in transactivation assays indicative of the estrogen receptor (ER), androgen receptor (AR), glucocorticoid receptor (GR), progesterone receptor (PR), mineralocorticoid receptor (MR), pregnane X receptor (PXR) and peroxisome proliferator-activated receptor gamma (PPARγ). The transactivation assays were complemented with competitive binding assays for human AR, GR, PR and MR. In most cases, both human and zebrafish nuclear receptor activity were detected in the water extracts. Only some species-specific differences in potency and activity were observed. Water extracts were more active in zebrafish PXR compared to human PXR whereas the opposite was observed for PPARγ. Further, all water extracts inhibited zebrafish PR, while only one extract showed weak anti-progestagenic activity for human PR. Due to these observed differences, zebrafish nuclear receptor assays may be preferable over human nuclear receptor assays to assess the potential risks of EDCs to aquatic organisms. However, recognizing issues with availability of zebrafish nuclear receptor assays and the relatively small differences in responsiveness for many of the human and zebrafish nuclear receptors, including the widely studied ER, the current study supports the continued use of human nuclear receptor assays for water quality monitoring.

Water storage and irrigation practices for cannabis drive seasonal patterns of water extraction and use in Northern California

There is growing concern over the impacts of cannabis farms on the environment and water resources in particular, yet data on cultivation practices and water use patterns have been limited. Estimates of water use for cannabis cultivation have previously relied on extrapolated values of plant water demand, which do not account for differences in cultivation practices, variation across the growing season, or the role of water storage in altering seasonal extraction patterns. The current study uses data reported by enrollees in California's North Coast Regional Water Quality Control Board Cannabis Program to model how variation in cultivation practices and the use of stored water affect the timing and amount of water extracted from the environment. We found that the supplemental use of stored water resulted in a seasonal pattern of water extraction (water withdrawals from the environment) that was distinct from water demand (water applied to plants). Although water input to storage in the off-season months (November through March) reduced water extraction in the growing season (April through October), farms generally did not have sufficient storage to completely forbear from surface water extraction during the growing season. The most important predictors of storage sufficiency were type of storage infrastructure, type of water source, and farm size, with the likelihood of sufficiency decreasing with increasing cultivation area. As of 2019, state cannabis regulations require forbearance from surface water diversions from April through October. To comply, farms relying on surface water must either develop storage, reduce water demand, or seek alternative water sources, such as groundwater. Our findings indicate that water extraction from farms using groundwater wells generally occurs during the summer dry season and highlight the need to assess their potential impacts to connected surface water in streams. Finally, given that the current study was based on data exclusively from permitted cannabis farms, additional data from unpermitted operations would enhance our overall understanding of cannabis water-use practices and consequences for the environment.

Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data

Freshwater lakes supply a large amount of inland water resources to sustain local and regional developments. However, some lake systems depend upon great fluctuation in water surface area. Poyang lake, the largest freshwater lake in China, undergoes dramatic seasonal and interannual variations. Timely monitoring of Poyang lake surface provides essential information on variation of water occurrence for its ecosystem conservation. Application of histogram-based image segmentation in radar imagery has been widely used to detect water surface of lakes. Still, it is challenging to select the optimal threshold. Here, we analyze the advantages and disadvantages of a segmentation algorithm, the Otsu Method, from both mathematical and application perspectives. We implement the Otsu Method and provide reusable scripts to automatically select a threshold for surface water extraction using Sentinel-1 synthetic aperture radar (SAR) imagery on Google Earth Engine, a cloud-based platform that accelerates processing of Sentinel-1 data and auto-threshold computation. The optimal thresholds for each January from 2017 to 2020 are − 14.88 , − 16.93 , − 16.96 and − 16.87 respectively, and the overall accuracy achieves 92 % after rectification. Furthermore, our study contributes to the update of temporal and spatial variation of Poyang lake, confirming that its surface water area fluctuated annually and tended to shrink both in the center and boundary of the lake on each January from 2017 to 2020.

DELINEATION WATER OF PEARL RIVER BASIN USING LANDSAT IMAGES FROM GOOGLE EARTH ENGINE

Abstract. Surface water plays an important role in ecological circulation. Global climate change and urbanization affect the distribution and quality of water. In order to obtain surface water information quickly and accurately, this study uses Google Earth Engine (GEE) as a data processing tool, 309 Landsat 8 series images from 2016 to 2019 are selected to calculate 4 different water indexes, including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEIsh) and Multi- Band Water Index (MBWI) to extract surface water in Pearl River Basin. In order to remove the influence of other ground objects, Normalized Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Digital Surface Model (DSM) are combined with the above four water indexes, and threshold segmentation is used to eliminate the influence of vegetation, buildings and mountains. Finally, take the advantage of morphological filtering algorithm to eliminate non-water pixels. The results show that GEE is able to extract surface water in a very short time; AWEIsh has the highest overall accuracy of 94.12%, which is 7.20% higher than the classical NDWI method; There is no significant difference in the width and shape of rivers from 2015 to 2018; The locations of the rivers extracted by the four methods are consistent with the 1 : 100,000 river system basic data of 2015 provided by the Ministry of Water Resources of the People’s Republic of China.

Learning vector quantization neural network for surface water extraction from Landsat OLI images

There is a growing concern over surface water dynamics due to an increased understanding of water availability and management with current climate trends. Remote sensing has now become an effective means of water extraction due to the availability of an enormous amount of data with diverse spatial, spectral, and temporal resolutions. However, water extraction from optical remote sensing data is associated with several major difficulties, such as the applicability of the extraction method over large areas and complex environments; shadow contamination from clouds, buildings, and mountains; and disclosure of shadowed water and exclusion of floating and submerged plants. To address these difficulties, a learning vector quantization (LVQ) neural network-based method was proposed and implemented to extract water using Landsat 8 imageries. This method is capable of separating water from clouds, build-up areas, shadows, and shadowed water by the ideal input of bands 1 to 7 and normalized difference vegetation index. This model learns water across Sri Lanka. Eight OLI scenes were tested, and the performance was compared with five widely used machine learning algorithms: support vector machine, K-nearest neighbor, discriminant analysis, combination of modified normalized difference water index and modified fuzzy clustering method, and K-means clustering methods. This method performed the best, achieving overall accuracies and the kappa coefficients between 97.8% and 99.7% and between 0.96 and 0.99, respectively. Results have demonstrated robustness, consistency, and preciseness in various dark surfaces, noisiest water environments, and highly water scarce scenes. LVQ revealed a good generalizing ability to detect all types of water with less amount of training samples. This method can be easily adaptable for other sensors and global water to support water resource studies.

An integrated study on change detection and environment evaluation of surface water

Surface water pollution is one of the serious environment pollution problems, posing threat to human and other creatures. Extraction, change detection and environment evaluation of surface water are prerequisite for water resource management. Undoubtedly, remote sensing data play an important role in these researches because of its large geographic coverage and high temporal frequency. In this study, the Tianjin Binhai New Area was chosen as the study area and the surface water extraction method Modified Normalized Difference Water Index (MNDWI) was used by combining with adaptive dynamic threshold to extract surface water and detect its change. Comparing with single-band threshold, model of multi-band spectral relationship, Iterative Self-organizing Data Analysis Technique Algorithm and MNDWI, MNDWI-based adaptive dynamic threshold method performed better, which considered the influence of background. Analysis on dynamic change of water showed the area of lake and river had increased and the area of seawater had decreased. Meanwhile, the correlation analysis between area change of surface water and impact factors indicated both climatic and anthropogenic factors made positive contribution to the present water environment situation. Finally, an improved model of surface water environment evaluation was established to evaluate water quality by combining genetic algorithm (GA) and backpropagation (BP) neural network model. And the test results proved that the optimized GA-BP neural network is better than the single BP neural network. The environment evaluation indicated that water quality of the Haihe River section in the study area was poor. Therefore, water environment protection should be strengthened in this area. Some suggestions on practical management were given accordingly.

Historical Morphodynamics Assessment in Bridge Areas using Remote Sensing and GIS Techniques

Currently the Ministry of Construction is responsible for planning and construction of bridges across the country but remote sensing and satellite data are not widely used in the Ministry’s routine process. Although the inspection and monitoring are carried out by the conventional methods, the remote sensing and GIS techniques are available as an alternative way with time and cost saving. From this study, the channel migration in the locations of Ayeyarwady bridges will be analyzed and mapped by identifying temporal changes of channels. Google Earth Engine is used as the primary application in this study and surface water extraction from historical Landsat satellite imagery is done by GEE. River centerline processing and erosion-deposition area identifications are carried out by GIS technique. Study period of each bridge is between 1987 and 2017. Bo Myat Tun Bridge and Ayeyarwady Bridge (Pakokku) are toped in the list with highest migration and erosion-deposition rate according to the study. The goal of this study is to assist the bridge inspections and channel monitoring works by means of remote sensing and GIS techniques which are currently undertaking by Ministry of Construction with conventional techniques.

Following the Water: Characterising de facto Wastewater Reuse in Agriculture in the Netherlands

De facto (or indirect) wastewater reuse is the practice of extracting from surface water bodies which are impacted by treated wastewater (TWW) for anthropogenic use. The extent to which surface water bodies in the Netherlands are impacted by TWW is poorly understood, and the distribution of de facto reuse even more so. This study addresses these knowledge gaps, with a focus on reuse for agricultural irrigation. This is achieved via a novel application of the Water Framework Directive (WFD) Explorer water quality model, allowing for the distribution of different flow components—namely TWW and flow from transboundary rivers—to be discerned for the national surface water network. When paired with data on surface water extractions for irrigation, this identifies notable areas of de facto reuse. Results show that during dry conditions, TWW is a significant flow component in many surface water bodies, particularly in smaller streams located close to WWTPs. De facto reuse is indicated as widespread, with several key areas identified in which extractions are from impacted surface water bodies. This study represents a first attempt to directly link TWW emissions to agricultural irrigation, highlighting a mechanism by which wastewater-associated contaminants can propagate through the hydrological system.

Potential Benefits of Managed Aquifer Recharge MAR on the Island of Gotland, Sweden

The Island of Gotland (3000 km2), east of mainland Sweden, suffers from insufficient water availability each summer. Thin soils and lack of coherent reservoirs in the sedimentary bedrock lead to limited reservoir capacity. The feasibility of Managed Aquifer Recharge (MAR) is explored by identifying suitable areas and estimating their possible contribution to an increased water availability. MAR is compared to alternative water management measures, e.g., increased groundwater abstraction, in terms of costs and water availability potential. Results from GIS analyses of infiltration areas and groundwater storage, respectively proximity to surface water sources and surface water storage were classified into three categories of MAR suitability. An area of ca 7700 ha (2.5% of Gotland) was found to have good local conditions for MAR and an area of ca 22,700 ha (7.5% of Gotland) was found to have moderate local conditions for MAR. These results reveal the MAR potential on Gotland. The water supply potential of MAR in existing well fields was estimated to be about 35% of the forecasted drinking water supply and 7% of the total water demand gap in year 2045. It is similar in costs and water supply potential to increased surface water extraction.

Automated Surface Water Extraction Combining Sentinel-2 Imagery and OpenStreetMap Using Presence and Background Learning (PBL) Algorithm

Surface water bodies play important roles in socioeconomic development and ecosystem balance. The fast-developing technology of remote sensing offers opportunities for the automatic extraction and dynamic monitoring of surface water bodies. Compared with other medium- and low-spatial-resolution remote sensing images, such as Landsat and MODIS, Sentinel-2 imagery provides higher spatial resolution and revisit frequency, making it more suitable for surface water extraction. The existing research works on surface water extraction using Sentinel-2 imagery remain focusing on the construction of water indexes, which is easily affected by shadows and built-up areas. In this study, we propose an automated surface water extraction method based on the presence and background learning algorithm (ASWE-PBL) using Sentinel-2 imagery and OpenStreetMap (OSM) data. The OSM data are used as the auxiliary data to automatically select water samples, and the PBL algorithm is adopted to predict the water presence probability. ASWE-PBL is validated using six typical study areas in China, and the modified normalized difference water index, the automated water extraction index, and the random forest classifier are employed for comparison. Moreover, feature optimization, parameter sensitivity, computation cost, and future work are analyzed and discussed. The experimental results show that ASWE-PBL can effectively suppress noise caused by shadows and built-up areas and can obtain the highest kappa coefficient for five study areas but not for Guangzhou and that the ten-spectral-band composite of Sentinel-2 imagery is a better feature combination scheme than that of spectral and water indexes.

Use of spatial autocorrelation and time series Landsat images for long-term monitoring of surface water shrinkage and expansion in Guanting Reservoir, China

ABSTRACTReservoirs are closely related to anthropic activities, and quantifying the long-term dynamics of surface water in reservoirs could be useful for decision-makers to improve the actual strategies of reservoir management. This study used the global Moran’s I index, modified Normalized Difference Water Index (MNDWI) and a total of 596 Landsat images during 1985–2018 for tracking the annual dynamics of water extent in the process of water shrinkage and expansion in Guanting Reservoir, China. Landscape metrics related to the area, elongation, fragmentation, and edge complexity of surface water in reservoir landscape were computed for tracking the annual dynamics of surface water patterns. Statistical comparison between the results of global Moran’s I index and landscape metrics indicates that except for the complexity of water and non-water edge, global Moran’s I index can successfully estimate the dynamics of the area, elongation and fragmentation of surface water in the reservoir. This study proposed a continuous approach of long-term monitoring of surface water patterns using spatial autocorrelation that might be used in the areas where the surface water extraction is difficult and water dynamics are complex.

Surface water extracts impair gene profiles and differentiation in human mesenchymal stem cells

Low concentrations of pollutants in surface water challenge the assessment of chronic effects on human health. Human bone mesenchymal stem cells (hBMSCs) were employed as a sensitive and relevant in vitro model to evaluate the potential biological effects caused by mixtures of pollutants in surface water. Organic extracts of surface water collected from Hun River inhibited cell viability in a dose-dependent manner. Surface water extracts at noncytotoxic concentrations induced 533 to 1055 differentially expressed genes (DEGs) in hBMSCs after 48 h of exposure. Total of 370 genes were commonly affected by surface water from different sites and accounted for 35–69% of DEGs impaired by individual sample. Pathways related to human diseases, genetic information processing and organismal systems were enriched based on DEGs. Interleukins (IL1B, IL6 and IL8) were affected and involved in most human diseases related pathways. The significantly downregulation of COL1A1 and the variation of rheumatoid arthritis pathway suggested that surface water potentially inhibited osteogenic differentiation of hBMSCs. Clustering analysis and principle component analysis with DEGs distinguish the surface water from tributary and mainstream. The crossing-species comparison of transcriptomic changes identified 923 and 2715 differentially expressed orthologs in hBMSCs and zebrafish, respectively. After the exposure ceased, the followed osteogenic and adipogenic differentiation in hBMSCs for 14 days were inhibited by the treatment of surface water during undifferentiated period, whereas the non-polar fraction exhibited stronger potency in affecting differentiation than the mid to polar fractions. hBMSCs, combining unsupervised transcriptomic technique and specific endpoints test, are promising in screening the health effects of environmental mixtures in surface water.

Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance.

Evaluation of WEAP-MODFLOW Model as an Integrated Water Resources Management Model for Sustainable Development (A Case Study: Gharesoo at Doab-Merek, Kermanshah, Iran)

This paper evaluated an integrated water resources management approach through linked WEAP-MODFLOW model. Study area is Ravasnar-Sanjabi plain located in Kermanshah province in the west of Iran. A MODFLOW model was evaluated and then, accepted as a groundwater model for the region in present research. Schematic WEAP model was provided as representing general features of water resources system after designing a conceptual model for the study area. The simplified rainfall-runoff model in WEAP was used to perform hydrological simulations. In the second step of present research, the groundwater model was linked to WEAP dynamically. Simulation years with 12 time steps per year included years of 2007-2015 for creating and verifying WEAP-MODFLOW model and years of 2015-2030 for performing scenarios. Statistical criteria included mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe (NASH), with Box plot diagram being selected to assess accuracy of calibrated model. Four scenarios were implemented for 2015 until 2030. They included unchanged present situation and situations with 35%, 45% and 57% reduction of groundwater and surface water withdrawal. Results showed that the fourth scenario with a 57% decrease in the extraction of surface water and groundwater resources was the best one. Based on this scenario, exploitation of the system will be sustainable, with the system recovering as 0.023 meter rising per year. Finally, the results of present study indicated that the approach was feasible for planning and managing water resources in spite of the lack of some data.

Near infrared band of Landsat 8 as water index: a case study around Cordova and Lapu-Lapu City, Cebu, Philippines

Monitoring water bodies by extraction using water indexes from remotely sensed images has proven to be effective in delineating surface water against its surrounding. This study tested and assessed the Normalized Difference Water Index, Modified Normalized Difference Water Index, Automated Water Extraction Index, and near infrared (NIR) band using Landsat 8 imagery acquired on September 3, 2016. The threshold method was adapted for surface water extraction. To avoid over and under-estimation of threshold values, the optimum threshold value of each of the water indexes was obtained by implementing a geoprocessing model. Examining images of Landsat 8, NIR band has the largest difference in reflectance values between water and non-water bodies. Thus, NIR band exhibits the highest contrast between water and non-water bodies. An optimum threshold value of 0.128 for NIR band achieved an overall accuracy (OA) and kappa hat (Khat) coefficient of 99.3% and 0.986, respectively. NIR band of Landsat 8 as water index was found more satisfactory in extracting water bodies compared to the multi-band water indexes. This study shows that the optimum threshold values of each of the water indexes considered in this study were determined conveniently, where highest value of OA and Khat coefficient were obtained by creating and implementing a graphical modeler in Quantum Geographic Information System that automates from setting threshold value to accuracy assessment. This study confirms that remote sensing can extract or delineate water bodies rapidly, repeatedly and accurately.

Efficient numerical incorporation of water management operations in integrated hydrosystem models: Application to tile drainage and reservoir operating systems

Hydrologic control structures are designed to connect or disconnect water movement in natural surface water-groundwater systems, and it is important to represent these features effectively within a finite difference/element model without over-discretization. This study presents efficient numerical schemes for operating water allocation and water rights systems in integrated hydrosystem models. We developed a numerical scheme for representing these structures directly within integrated surface/subsurface hydrologic models by creating boundary condition links to route water through control structures or by breaking nodal connections to prevent water flow. The boundary condition linking and mesh cutoff schemes minimize the need for mesh regeneration and modification. Through illustrative examples involving agricultural tile drainage and dam-reservoir operation systems, the numerical connection (boundary condition linking) and disconnection (mesh cutoff) techniques are demonstrated to be effective and efficient at simulating the hydrological processes affected by anthropogenic water resources controls. A proof-of-concept demonstration consisting of complex water supply (dam and groundwater pumping wells) and demand (farms, municipality, and stream) highlights how the numerical schemes for water management operations can be used to optimize surface water and groundwater extraction and distribution using a fully-integrated model.

Surface water classification from GF-4 images using a time series water index

ABSTRACTAlthough satellite remote sensing imagery is suitable for mapping and monitoring the surface water, the application of surface water classification techniques in the literatures is still constrained by low accuracy in various situations. As the GF-4 satellite is one of the few moderate spatial resolution geostationary orbit remote sensing satellites, the purpose of this study is to introduce a method that consistently improves the accuracy of surface water classification by utilizing the temporal characteristics of the dense time series of GF-4 images. A new time series water index called the spectrum and solar altitude angle water index (SSWI) is proposed based on the characteristics of the variation in the top of atmosphere (TOA) radiance with the change in solar altitude angle in the near-infrared (NIR) band of GF-4 images. The thresholding technique is applied to the SSWI map to automatically extract surface water. The performance of the SSWI method for surface water classification is validated and compared with the results of two widely used water classification methods, the Normalized Difference Water Index (NDWI) and maximum likelihood (ML) classifier, at four representative sites in different parts of China, including Beijing, Hubei, Tibet and Guangdong. The results show that at all four test sites, the classification accuracy of SSWI is significantly higher than that of NDWI and ML. Averaged over the four test sites, the total error of the SSWI for water bodies is only approximately 36.9% of that of the NDWI method and 23.5% of that of the ML classifier. In particular, the SSWI method shows outstanding performance in terms of distinguishing surface water from backgrounds that are usually sources of surface water classification errors, e.g. mountain shadows and low-albedo built-up areas. Therefore, the SSWI method can be used to classify surface water from GF-4 images and holds the potential to be a useful surface water classification technology for water resource studies and applications.

Impact of Climate Variabilities and Human Activities on Surface Water Extents in Reservoirs of Yongding River Basin, China, from 1985 to 2016 Based on Landsat Observations and Time Series Analysis

Yongding River is the largest river flowing through Beijing, the capital city of China. In recent years, Yongding River Basin (YDRB) has witnessed increasing human impacts on water resources, posing serious challenges in hydrological and ecological health. In this study, remote sensing techniques and statistical time series approaches for hydrological studies were combined to characterize the dynamics and driving factors of reservoir water extents in YDRB during 1985–2016. First, 107 Landsat 4, 5, 7 and 8 images were used to extract surface water extents in YDRB during 1985–2016 using a combination of water indices and Otsu threshold algorithm. Significant positive correlation was found between water extents and the annual inflow for the two biggest reservoirs, the downstream Guanting and upstream Cetian reservoirs, proving their representativeness of surface water availability in this basin. Then, statistical time series approaches including trend-free pre-whitening Mann-Kendall trend test, Pettit change-point test and double mass curve method, which are frequently used in hydrological studies, were adopted to quantify the trend of reservoir water extents dynamics and the relative contributions of climate variability and human activities. Results showed that the water extents in both reservoirs exhibited significant downward trend with change point occurring in 2001 and 2005 for Guanting and Cetian, respectively. About 74%~75% of the shrinkage during the post-change period can be attributed to human activities, among which GDP, population, electricity power production, raw coal production, steel and crude iron production, value of agriculture output, and urban area were the major human drivers. Hydrological connectivity between the upstream Cetian and downstream Guanting reservoirs declined during the post-change period. Since 2012, water extents in both reservoirs recovered as a result of various governmental water management policies including the South-to-North Water Diversion Project. The methodology presented in this study can be used for analyzing the dynamics and driving mechanism of surface water resources, especially for un-gauged or poorly-gauged watersheds.