Spatiotemporal patterns and effects of climate and land use on surface water extent dynamics in a dryland region with three decades of Landsat satellite data

Quantifying the availability of seasonal surface water and identifying the drivers of change within tropical forests in Cambodia

Surface freshwater is a vital resource that is declining globally, predominantly due to climate and land use changes. Cambodia is no exception and the loss threatens many species, such as the giant ibis a Critically Endangered waterbird. We aimed to quantify the spatial and temporal (2000-2020) change of surface water availability across northern and eastern Cambodia and to assess the impact of this on the giant ibis. We used a Random Forest Classifier to determine the changes and we tested the impact of land use and geographical covariates using spatially explicit regression models. We found an overall reduction of surface water availability of 4.16%. This was predominantly driven by the presence of Economic Land Concessions and roads which increased the probability of extreme drying and flooding events. The presence of protected areas reduced these probabilities. We found changes in precipitation patterns over the wider landscape did not correlate with changes in surface water availability, supporting the overriding influence of land use change. 98% of giant ibis nests recorded during the time period were found within 25m of surface water during the dry season, highlighting their dependency on surface water. The overall surface water decline resulted in a 25% reduction in dry season suitable habitat for the giant ibis. Although absolute changes in surface water over the whole area were relatively small, the impact on the highest quality habitat for ibis is disproportionate and therefore threatens its populations. Defining the threats to such an endangered species is crucial for effective management.

Climate Change Will Impact Surface Water Extents and Dynamics Across the Central United States

Climate change is projected to impact river, lake, and wetland hydrology, with global implications for the condition and productivity of aquatic ecosystems. We integrated Sentinel-1 and Sentinel-2 based algorithms to track monthly surface water extent (2017-2021) for 32 sites across the central United States (U.S.). Median surface water extent was highly variable across sites, ranging from 3.9% to 45.1% of a site. To account for landscape-based differences (e.g., water storage capacity, land use) in the response of surface water extents to meteorological conditions, individual statistical models were developed for each site. Future changes to climate were defined as the difference between 2006-2025 and 2061-2080 using MACA-CMIP5 (MACAv2-METDATA) Global Circulation Models. Time series of climate change adjusted surface water extents were projected. Annually, 19 of the 32 sites under RCP4.5 and 22 of the 32 sites under RCP8.5 were projected to show an average decline in surface water extent, with drying most consistent across the southeast central, southwest central, and midwest central U.S. Projected declines under surface water dry conditions at these sites suggest greater impacts of drought events are likely in the future. Projected changes were seasonally variable, with the greatest decline in surface water extent expected in summer and fall seasons. In contrast, many north central sites showed a projected increase in surface water in most seasons, relative to the 2017-2021 period, likely attributable to projected increases in winter and spring precipitation exceeding increases in projected temperature.

Analysis of surface water area dynamics and driving forces in the Bosten Lake basin based on GEE and SEM for the period 2000 to 2021

As an inland dryland lake basin, the rivers and lakes within the Lake Bosten basin provide scarce but valuable water resources for a fragile environment and play a vital role in the development and sustainability of the local societies. Based on the Google Earth Engine (GEE) platform, combined with the geographic information system (GIS) and remote sensing (RS) technology, we used the index WI2019 to extract and analyze the water body area changes of the Bosten Lake basin from 2000 to 2021 when the threshold value is -0.25 and the slope mask is 8°. The driving factors of water body area changes were also analyzed using the partial least squares-structural equation model (PLS-SEM). The result shows that in the last 20 years, the area of water bodies in the Bosten Lake basin generally fluctuated during the dry, wet, and permanent seasons, with a decreasing trend from 2000 to 2015 and an increasing trend between 2015 and 2019 followed by a steadily decreasing trend afterward. The main driver of the change in wet season water bodies in the Bosten Lake basin is the climatic factors, with anthropogenic factors having a greater influence on the water body area of dry season and permanent season than that of wet season. Our study achieved an accurate and convenient extraction of water body area and drivers, providing up-to-date information to fully understand the spatial and temporal variation of surface water body area and its drivers in the basin, which can be used to effectively manage water resources.

Spatio-temporal change analysis and prediction of land use and land cover changes using CA-ANN model

The spatial and temporal representation of land use and land cover (LULC) changes helps to understand the interactions between natural habitats and other areas and to plan for sustainability. Research on the models used to determine the spatio-temporal change of LULC and simulation of possible future scenarios provides a perspective for future planning and development strategies. Landsat 5 TM for 1990, Landsat 7 ETM + for 2006, and Landsat 8 OLI for 2022 satellite imageries were used to estimate spatial and temporal variations of transition potentials and future LULC simulation. Independent variables (DEM, slope, and distances to roads and buildings) and the cellular automata-artificial neural network (CA-ANN) model integrated in the MOLUSCE plugin of QGIS were used. The CA-ANN model was used to predict the LULC maps for 2038 and 2054, and the results suggest that artificial surfaces will continue to increase. The Düzce City center's artificial surfaces grew by 100% between 1990 and 2022, from 16.04 to 33.10 km2, and are projected to be 41.13 km2 and 50.32 km2 in 2038 and 2054, respectively. Artificial surfaces, which covered 20% of the study area in 1990, are estimated to cover 64.07% in 2054. If this trend continues, most of the 1st-class agricultural lands may be lost. The study's results can assist local governments in their land management strategies and aid them in planning for the future. The results suggest that policies are necessary to control the expansion of artificial surfaces, ensuring a balanced distribution of land use.

A series of spatio-temporal analyses and predicting modeling of land use and land cover changes using an integrated Markov chain and cellular automata models

For sustainable land cover planning, spatial land cover models are essential. Deforestation, loss of agriculture, and conversion of pasture land to urban and industrial uses are only some of the negative consequences of human kind's insatiable need for more land. Using remote sensing multi-temporal data, spatial criteria, and prediction models can effectively monitor these changes and plan for sustainable land use. This research aims to predict the land use and land cover (LULC) with cellular automata (CA) and Markov chain models. Landsat TM, ETM + , and OLI/TIRS data were used for mapping LULC distributions for the years 1990, 2006, and 2022. A CA-Markov chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2054. Analysis of urban sprawl was carried out by using the support vector machine (SVM). Through the CA-Markov chain analysis, we expect that built-up area will grow from 285.68 km² (22.59%) to 383.54 km² (30.34%) in 2022 and 2054, as inferred from the changes that occurred from 1990 to 2022. Therefore, substantial deforestation area reduction will result if existing tendencies in change continue despite sustainable development efforts. The findings of this research can inform land cover management strategies and assist local authorities in preparing for the present and the future. They can balance expanding the city and preserving its natural resources.

High-frequency time series comparison of Sentinel-1 and Sentinel-2 satellites for mapping open and vegetated water across the United States (2017-2021)

Frequent observations of surface water at fine spatial scales will provide critical data to support the management of aquatic habitat, flood risk and water quality. Sentinel-1 and Sentinel-2 satellites can provide such observations, but algorithms are still needed that perform well across diverse climate and vegetation conditions. We developed surface inundation algorithms for Sentinel-1 and Sentinel-2, respectively, at 12 sites across the conterminous United States (CONUS), covering a total of >536,000 km2 and representing diverse hydrologic and vegetation landscapes. Each scene in the 5-year (2017-2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables from Sentinel-1 and Sentinel-2, as well as variables derived from topographic and weather datasets. The Sentinel-1 algorithm was developed distinct from the Sentinel-2 model to explore if and where the two time series could potentially be integrated into a single high-frequency time series. Within each model, open water and vegetated water (vegetated palustrine, lacustrine, and riverine wetlands) classes were mapped. The models were validated using imagery from WorldView and PlanetScope. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for the Sentinel-1 algorithm and 3.1% and 0.5% for the Sentinel-2 algorithm, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. The Sentinel-2 algorithm showed higher accuracy (10.7% omission and 7.9% commission error) relative to the Sentinel-1 algorithm (28.4% omission and 16.0% commission error). Patterns over time in the proportion of area mapped as open or vegetated water by the Sentinel-1 and Sentinel-2 algorithms were charted and correlated for a subset of all 12 sites. Our results showed that the Sentinel-1 and Sentinel-2 algorithm open water time series can be integrated at all 12 sites to improve the temporal resolution, but sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for mixed-pixel, vegetated water. The methods developed here provide inundation at 5-day (Sentinel-2 algorithm) and 12-day (Sentinel-1 algorithm) time steps to improve our understanding of the short- and long-term response of surface water to climate and land use drivers in different ecoregions.

Land cover change in global drylands: A review

As a sensitive region, identifying land cover change in drylands is critical to understanding global environmental change. However, the current findings related to land cover change in drylands are not uniform due to differences in data and methods among studies. We compared and judged the spatial and temporal characteristics, driving forces, and ecological effects by identifying the main findings of land cover change in drylands at global and regional scales (especially in China) to strengthen the overall understanding of land cover change in drylands. Four main points were obtained. First, while most studies found that drylands were experiencing vegetation greening, some evidence showed decreases in vegetation and large increases in bare land due to inconsistencies in the datasets and the study phases. Second, the dominant factors affecting land cover change in drylands are precipitation, agricultural activities, and urban expansion. Third, the impact of land cover change on the water cycle, especially the impact of afforestation on water resources in drylands, is of great concern. Finally, drylands experience severe land degradation and require dataset matching (classification standards, resolution, etc.) to quantify the impact of human activities on land cover.

Combining historical maps and landsat images to delineate the centennial-scale changes of lake wetlands in Taihu Lake Basin, China

Lake wetlands (LWs) are essential components of the ecosystem and play an irreplaceable role in flood regulation, carbon fixation, and biodiversity maintenance. Continuous monitoring of LWs' change is necessary in the context of increased human disturbance and climate change, particularly in Taihu Lake Basin, China, an area exposed to early human exploitation. Yet, long-time series of LWs detection in this region is still unavailable due to the data limitation. To quantify the spatiotemporal dynamics of LWs and the associated driving forces, we combined 236 historical topographic maps and thousands of Landsat satellite images from the 1910s to 2021 to delineate the centennial-scale changes of lake wetlands for the first time in this region. We also applied land use transitions and statistical analyses to quantitively explore the climatic and anthropogenic factors behind LWs variations. Our results document a dramatic decline in the area and number of LWs in the Taihu Lake Basin over the last century and a shift in the 2000s: Taihu Lake Basin has seen a total of 89.15% loss in lake littoral wetlands and a decrease of 14.5% in the whole lake wetlands area, with a net reduction of 68 (from 156 in the 1910s to 88 in the 2021) lakes. This decrease has been especially predominant during the 1910s-2000s, because of the policy initiatives for reclamation and aquacultural industries. The area and number of LWs have gradually been recovered since the 2000s as the country strengthened concern on the ecological restoration and sustainable development. The statistical results suggested that human activities played a dominant role in the LWs changes, with GDP and population explained 80.74% of the changes, coupled with climatic contribution of only around 20%. This long-term investigation will provide baseline information for future lake wetlands monitoring. Our findings could also provide a guidance for decision makers regarding water resources management, environmental protection and land-use planning in urban areas.

Relations between physical and ecosystem service flows of freshwater are critical for water resource security in large dryland river basin

Freshwater ecosystem services are the link between ecological systems and social systems, which is an important guarantee of the freshwater safety particularly in dryland regions. However, more quantitative research has been based on the freshwater ecosystem services of static situations, and less on the flow conditions. We established a comprehensive modeling framework for the analysis of water security pattern based on the physical flow (PF) and ecosystem service flow (ESF) of freshwater. The results for Yellow River Basin showed that the water-scarce area have reduced in the past two decades. The PF of freshwater relieves water stress on an average of 52.1 % of the static water in scarce areas per year. The problem in water-deficient areas meanly lies on the water supply side. These results highlight the importance of PF from the upstream to downstream, which is critical for formulating sustainable management strategies in safeguarding long-term regional freshwater resource security.

Long-term detection and spatiotemporal variation analysis of open-surface water bodies in the Yellow River Basin from 1986 to 2020

Accurately investigating long-term information about open-surface water bodies can contribute to water resource protection and management. However, due to the limits of big-data calculations for remote sensing, there has been no specific study on the long-term changes in the water bodies in the Yellow River Basin. Thus, in this study, we developed a new combined extraction rule to build an entire annual-scale open-surface water body dataset for 1986-2020 with excellent effectiveness in eliminating the interference of shadows in the Yellow River Basin using all of the available Landsat images. For the first time, the spatial distribution, change trends, conversion processes, and the heterogeneity of the surface water bodies in the Yellow River Basin were analyzed comprehensively to the best of our knowledge. The extraction results had an overall accuracy of 99.70 % and a kappa coefficient of 0.90, which were validated using 34,073 verification points selected on high-resolution Google Earth images and random Landsat images. The total area of water bodies initially decreased (1986-2000) and then increased (2001-2020); however, only the size of the permanent water bodies increased in most areas, while the size of most of the seasonal water bodies decreased. In regions with human-made water bodies, the non-water areas were substantially converted to seasonal and permanent water bodies; however, in areas with natural water bodies, many permanent and seasonal water bodies were gradually converted to non-water areas. Thus, most of the increases in the water bodies occurred in the form of artificial lakes and reservoirs, while most of the decreases in the water body area occurred in natural wetlands and lakes. The areas of both the permanent and seasonal water bodies were positively correlated with precipitation, but only the area of the seasonal water bodies was negatively correlated with temperature.

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource—if not overexploited—sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.

Spatial and Temporal Changes in Surface Water Area of Sri Lanka over a 30-Year Period

Sri Lanka contains a large number of natural and man-made water bodies, which play an essential role in irrigation and domestic use. The island has recently been identified as a global hotspot of climate change extremes. However, the extent, spatial distribution, and the impact of climate and anthropogenic activities on these water bodies have remained unknown. We investigated the distribution, spatial and temporal changes, and the impacts of climatic and anthropogenic drivers on water dynamics in Dry, Intermediate, and Wet zones of the island. We used Landsat 5 and Landsat 8 images to generate per-pixel seasonal and annual water occurrence frequency maps for the period of 1988–2019. The results of the study demonstrated high inter- and intra-annual variations in water with a rapid increase. Further, results showed strong zonal differences in water dynamics, with most dramatic variations in the Dry zone. Our results revealed that 1607.73 km2 of the land area of the island is covered by water bodies, among this 882.01 km2 (54.86%) is permanent and 725.72 km2 (45.14%) is seasonal water area. Total inland seasonal water increased with a dramatic annual growth rate of 7.06 ± 1.97 km2 compared to that of permanent water (4.47 ± 2.08 km2/year). Sri Lanka has the highest permanent water area during December–February (1045.97 km2), and drops to the lowest in May–September (761.92 km2) when the seasonal water (846.46 km2) is higher than permanent water. The surface water area was positively related to both precipitation and Gross Domestic Product, while negatively related to the temperature. Findings of our study provide important insights into possible spatiotemporal changes in surface water availability in Sri Lanka under certain climate change and anthropogenic activities.

Open-Surface Water Bodies Dynamics Analysis in the Tarim River Basin (North-Western China), Based on Google Earth Engine Cloud Platform

The Tarim River Basin (TRB), located in an arid region, is facing the challenge of increasing water pressure and uncertain impacts of climate change. Many water body identification methods have achieved good results in different application scenarios, but only a few for arid areas. An arid region water detection rule (ARWDR) was proposed by combining vegetation index and water index. Taking computing advantages of the Google Earth Engine (GEE) cloud platform, 56,284 Landsat 5/7/8 optical images in the TRB were used to detect open-surface water bodies and generated a 30-m annual water frequency map from 1992 to 2019. The interannual changes and trends of the water body area were analyzed and the impacts of climatic and anthropogenic drivers on open-surface water body area dynamics were examined. The results show that: (1) ARWDR is suitable for long-term and large-scale water body identification, especially suitable for arid areas lacking vegetation. (2) The permanent water area was 2093.63 km2 and the seasonal water area was 44,242.80 km2, accounting for 4.52% and 95.48% of the total open-surface water area of he TRB, respectively. (3) From 1992 to 2019, the permanent and seasonal water bodies of the TRB all showed an increasing trend, with obvious spatial heterogeneity. (4) Among the effects of human activities and climate change, precipitation has the largest impact on the water area, which can explain 65.3% of the change of water body area. Our findings provide valuable information for the entire TRB’s open-surface water resources planning and management.

Influence of global climate on freshwater changes in Africa's largest endorheic basin using multi-scaled indicators

The poor investments in gauge measurements for hydro-climatic research in Africa has necessitated the need to investigate how decision makers can leverage on sophisticated space-borne measurements to improve knowledge on surface water hydrology that can feed directly into water accounting processes, and risk assessment from extreme droughts and its impacts. To demonstrate such potential, a suite of satellite earth observations (Sentinel-2, altimetry, Landsat, GRACE, and TRMM) and model data are combined with the standardized precipitation evapotranspiration index to assess the impacts of global climate on freshwater dynamics over the LCB (Lake Chad basin), Africa's largest endorheic basin. As shown in the results of this study, the significant relationship of climate modes (AMO; r=0.68 and 0.59; and AMM; r=0.42 and 0.47) with drought patterns in the LCB highlights the evidence of global climate influence in the region. The significant declines in drought extents and their intensities (2004 - 2015) over LCB coincide with the rise in surface water extent of the Lake Chad during the same period. Change detection analysis of open water features in the southern pool of Lake Chad during the 2015 - 2019 period shows that on the average, only 28.4% of inundated areas within the vicinity of the Lake persisted during the period. While the association of terrestrial water storage (TWS) with model-derived surface water storage (SWS) is strongest (r=0.89) in the catchments that provide the most nourishment to the Lake Chad, the relationship of rainfall (2002 - 2017) with TWS (r=0.85), model TWS (r=0.87) and SWS (r=0.88) confirm that the LCB's hydrology is predominantly climate-driven. This notion is further reinforced as the predicted SWS over the LCB using a support vector machine regression scheme was found to be strongly correlated (r=0.95 at α=0.05) with observed SWS.

Assessment of the Water, Environmental, Economic and Social Vulnerability of a Watershed to the Potential Effects of Climate Change and Land Use Change

In semi-arid regions, where hydrological resources are very vulnerable and where there are water shortages in many regions of the world, it is of great importance to assess the vulnerability that a system is facing or will face to the potential impacts of climatic changes and changes on the use of land. For that reason, this research focuses on evaluating the global vulnerability of a hydrological basin, taking into consideration these changes. Being different from the existing methodologies that assess the vulnerability, our methodology interconnects through a new interface a distributed hydrological model, global climate models, climate change scenarios, land use change scenarios and the largest number of system variables calculated with information from official sources. Another important point of our methodology is that it quantifies the global vulnerability of the system, taking into consideration hydrological, environmental, economic and social vulnerabilities. The results obtained show that the proposed methodology may provide a new approach to analyze vulnerability in semi-arid regions. Moreover, it made it possible to diagnose and establish that the greatest current and future vulnerabilities of the system are the result of activities in agricultural areas and urban centers.

Regional Impacts of Climate and Land Cover on Ecosystem Water Retention Services in the Upper Yangtze River Basin

Water retention is an important factor in ecosystem services, owing to its relationships with climate and land-cover change; however, quantifying the independent and combined impacts of these variables remains a challenge. We use scenario analysis and the InVEST model to assess individual or combined impacts of climate and land cover on water retention in the Upper Yangtze River Basin. Water retention decreased from 1986 to 2015 at a rate of 2.97 mm/10a in response to increasing precipitation (3.94 mm/10a) and potential evapotranspiration (16.47 mm/10a). The rate of water retention change showed regional variability (from 68 to −18 mm/a), with some eastern regions experiencing an increase and most other regions experiencing a decrease. Farmland showed the highest decrease (10,772 km2), with land mainly converted into forest (58.17%) and shrub land (21.13%) from 2000 to 2015. The impact of climate change (−12.02 mm) on water retention generally was greater than the impact of land cover change (−4.14 mm), at the basin scale. Among 22 climate zones, 77.27% primarily were impacted by climate change; 22.73% primarily were impacted by land cover change. Our results demonstrate that both individualistic and integrated approaches toward climate and vegetation management is necessary to mitigate the impacts of climate change on water resources.

Long-Term Changes of Open-Surface Water Bodies in the Yangtze River Basin Based on the Google Earth Engine Cloud Platform

The spatiotemporal changes of open-surface water bodies in the Yangtze River Basin (YRB) have profound influences on sustainable economic development, and are also closely relevant to water scarcity in China. However, long-term changes of open-surface water bodies in the YRB have remained poorly characterized. Taking advantage of the Google Earth Engine (GEE) cloud platform, this study processed 75,593 scenes of Landsat images to investigate the long-term changes of open-surface water bodies in the YRB from 1984 to 2018. In this study, we adopted the percentile-based image composite method to collect training samples and proposed a multiple index water detection rule (MIWDR) to quickly extract the open-surface water bodies. The results indicated that (1) the MIWDR is suitable for the long-term and large-scale Landsat water bodies mapping, especially in the urban regions. (2) The areas of permanent water bodies and seasonal water bodies were 29,076.70 km2 and 21,526.24 km2, accounting for 57.46% and 42.54% of the total open-surface water bodies in the YRB, respectively. (3) The permanent water bodies in the YRB increased along with the decreases in the seasonal water bodies from 1984 to 2018. In general, the total open-surface surface water bodies in the YRB experienced an increasing trend, with an obvious spatial heterogeneity. (4) The changes of open-surface water bodies were associated with the climate changes and intense human activities in the YRB, however, the influences varied among different regions and need to be further investigated in the future.