ReaLSAT, a global dataset of reservoir and lake surface area variations

Lakes and reservoirs, as most humans experience and use them, are dynamic bodies of water, with surface extents that increase and decrease with seasonal precipitation patterns, long-term changes in climate, and human management decisions. This paper presents a new global dataset that contains the location and surface area variations of 683,734 medium-sized (0.1 - 100 sq. km.) lakes and reservoirs (south of 50°N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. While HydroLAKES only provides a static shape, the proposed dataset also has a timeseries of surface area and a shapefile containing monthly shapes for each lake. The paper presents the development and evaluation of this dataset and highlights the utility of novel machine learning techniques in addressing the inherent challenges in transforming satellite imagery to dynamic global surface water maps.

Lake Surface Area Forecasting Using Integrated Satellite-SARIMA-Long-Short-Term Memory Model

Lake Water Surface Area (WSA) plays a vital role in environmental preservation and future water resource planning and management. Accurately mapping, monitoring and forecasting Lake WSA changes are of great importance to regulatory agencies. This study used the MODIS satellite images to extract a monthly time series of WSA of two lakes located in Iran from 2001 to 2019. Following a consequence of image and time series preprocessing to obtain the preprocessed lake surface area time series, the outcomes were modeled by the Long-Short-Term Memory (LSTM) deep learning (DL) method, the stochastic Seasonal Auto-Regressive Integrated Moving Average (SARIMA) method and hybridization of these two techniques with the objective of developing WSA forecasts. After separate standardization and normalization of AL TS and reevaluation of the preprocessed data, the SARIMA (1, 0, 0) (0, 1, 1)12 model outperformed sole LSTM models with correlation index of (R) 0.819, mean absolute error (MAE) of 49.425 and mean absolute percentage error (MAPE) of 0.106. On the other hand, the hybridization (stochastic-DL) enhanced the reproduction of the primal statistical properties of WSA data and caused better mediation. However, the other accuracy indices did not change markedly (R 0.819, MAE 49.310, MAPE 0.105). The multi-step preprocessing and reevaluation also caused all LSTM models to produce their best results by less than 12 inputs.

The global lake area, climate, and population dataset

An increasing population in conjunction with a changing climate necessitates a detailed understanding of water abundance at multiple spatial and temporal scales. Remote sensing has provided massive data volumes to track fluctuations in water quantity, yet contextualizing water abundance with other local, regional, and global trends remains challenging by often requiring large computational resources to combine multiple data sources into analytically-friendly formats. To bridge this gap and facilitate future freshwater research opportunities, we harmonized existing global datasets to create the Global Lake area, Climate, and Population (GLCP) dataset. The GLCP is a compilation of lake surface area for 1.42 + million lakes and reservoirs of at least 10 ha in size from 1995 to 2015 with co-located basin-level temperature, precipitation, and population data. The GLCP was created with FAIR (findable, accessible, interoperable, reusable) data principles in mind and retains unique identifiers from parent datasets to expedite interoperability. The GLCP offers critical data for basic and applied investigations of lake surface area and water quantity at local, regional, and global scales.

Changes in Water Surface Area of the Lake in the Steppe Region of Mongolia: A Case Study of Ugii Nuur Lake, Central Mongolia

The Ugii Nuur Lake is not only one of the small hydrologically closed lakes located in the Orkhon River Basin in Central Mongolia but also the most vulnerable area for global climate change. Therefore, this study aims to investigate the impacts of recent global climate change on the water surface area. The data we analyzed were various measured hydro-meteorological variables of the lake basin and the lake surface area, which was estimated from Landsat series satellite data from 1986 to 2018. The methods we used were Mann-Kendall (MK), Innovative trend analysis method (ITAM), Sen’s slope estimator test, correlation, and regression analysis. The variation of lake water surface area has a strong positive correlation with the change of the lake water level (r = 0.95). The Mann-Kendall trend analysis has indicated that under a significant decrease in total annual precipitation ( Z = −0.902) and inflow river discharge ( Z = −5.392) and a considerable increase in total annual evaporation ( Z = 4.385) and annual average air temperature ( Z = 4.595), the surface area of the Ugii Nuur Lake has decreased sharply ( Z = −6.021). The total annual evaporation (r = −0.64) and inflow river discharge (r = 0.67) were the essential hydro-meteorological factors affecting the surface area of the Ugii Nuur Lake. The lake surface area decreased by 13.5% in 2018 compared with 1986. In the near future, it is vital to conduct scientific studies considering the volume of lake water, groundwater, and the anthropogenic impact.

Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events?

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural events is the dominant factor in inland lake surface area change. In this study, we proposed a method that combines the Douglas-Peucker simplification algorithm and the bend simplification algorithm to locate major lake surface area disturbances. These disturbances were used to extract the features that been used to classify disturbances into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31-year long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records were used as references to aid the feature extraction and disturbance identification accuracy assessment. Results of our method for disturbance location and disturbance identification could be concluded as follows: (1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the time series of lake surface area in our study area was 94.73%. (2) Our proposed method achieved an overall accuracy of 87.75%, with an F-score of 85.71 for anthropogenic disturbances and an F-score of 88.89 for natural disturbances. (3) According to our results, lakes in Yunnan Province of China have undergone intensive disturbances. Human-induced disturbances occurred almost twice as much as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or a natural event, and to monitor whether disturbances of lake surface area are intensified for a region.

Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events?

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural event is playing as the dominant factor in inland lake surface area change. In this study, we proposed a Douglas-Peucker simplification algorithm and bend simplification algorithm combined method to locate major lake surface area disturbances; these disturbances were then characterized to extract the time series change features according to documented records; and the disturbances were finally classified into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31 years long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records was used as references to aid the feature extraction and disturbance identification accuracy. Results of our method for both disturbance location and the disturbance identification could be concluded as follows: 1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the lake area time series curves in our study area was 95.24%. 2) Our proposed method achieved an overall accuracy of 91.67%, with F-score of 94.67 for anthropogenic disturbances and F-score of 85.71 for natural disturbances. 3) According to our results, lakes in Yunnan Provence, China, have undergone extensive disturbances, and the human-induced disturbances occurred almost twice as often as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or natural event.