3. SDG 6 Clean Water and Sanitation

Target: SDG 6.3 By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globally.

Indicator: SDG 6.3.2 Proportion of bodies of water with good ambient water quality.

Large lakes and reservoirs provide essential water resources, fishery resources, recreational venues, and significant ecological value to human society. They are a crucial component of the terrestrial hydrosphere and play a key role in global biogeochemical cycles. As sensitive indicators of global change, they have been termed the “sentinels” of global change (Ma et al. 2011; Williamson et al. 2009).

In recent decades, the intensification of threats such as rapid population growth, increased industrial and agricultural activities, and marked alterations in the hydrological cycle driven by climate change has led to a continuous increase in pollutant loads in lakes. Consequently, global lake water environments have undergone rapid changes, manifesting as more frequent harmful algal blooms, a decline in aquatic vegetation, a reduction in both the area and number of lakes, and a comprehensive decrease in water and food supply capacity (Woolway et al. 2020; Zhang et al. 2020).

A significant deficit exists in global-scale water quality data for lakes and reservoirs. The spatiotemporal variation of lake water quality, as well as its responses to climate change and human activities, remains an urgent research question in geography and remote sensing (Kavvada et al. 2020; Spyrakos et al. 2020; Tyler et al. 2016). The 2018 UN “Progress on Ambient Water Quality” report noted that, with respect to UN SDG 6, only 52 out of 193 UN member states have submitted surface water quality monitoring data, and in several cases, these data are based on a very limited number of monitoring stations (UN-Water 2018).

With the advancement of satellite remote sensing technology and the progress in theoretical methods for water color remote sensing, significant breakthroughs have been achieved in the optical remote sensing of inland waters in recent years (Duan et al. 2022; Zhang et al. 2021). Satellite remote sensing data are increasingly recognized as the most important and cost-effective source for large-scale surface water quality monitoring. In addition to filling critical data gaps, the ability of satellite data to provide extensive, long-term dynamic monitoring is a powerful tool for tracking global lake water quality trends.

Satellite remote sensing primarily monitors water quality parameters that influence the optical characteristics of water bodies, such as phytoplankton pigment concentrations, suspended sediment levels, colored dissolved organic matter concentrations, and water transparency. Among these, water transparency is a vital parameter for assessing water clarity and is central to understanding lake water environments, as its variations directly impact water security at both regional and global scales. Water transparency is determined by the combined effects of suspended sediments, phytoplankton pigments, and colored dissolved organic matter. Compared with other optical water quality parameters, water transparency more comprehensively reflects the overall water quality status, particularly in terms of turbidity. However, a universally applicable global model and long-term remote sensing inversion products for water transparency in lakes and reservoirs are still lacking, and research on the spatiotemporal changes in lake water transparency and its responses to environmental changes remains insufficient.

This case study leverages Big Earth Data technology and uses water transparency as an indicator for lake water quality monitoring. Based on satellite remote sensing analysis, it examines the spatiotemporal trends of water transparency in large lakes and reservoirs (with an area exceeding 25 km²) worldwide from 2000 to 2021, thereby providing a novel global remote sensing dataset for lake water transparency to support the achievement of SDG 6.

This case study used MODIS surface reflectance products as the primary data source to construct a surface water transparency inversion model based on Forel-Ule index (FUI) and hue angle (Wang et al. 2020). Field-measured water datasets collected from different regions worldwide were used to validate and calibrate the surface water transparency inversion model. Based on MODIS, a product set of water transparency for global large lakes and reservoirs with areas greater than 25 km² from 2000 to 2021 was generated. On this foundation, the spatial and temporal variation trends of water transparency in global large lakes and reservoirs from 2000 to 2021 were analyzed.

The distribution of global climate mean values of water transparency in large lakes and reservoirs from 2000 to 2021 is shown in Fig. 3.1, and the differences in the distribution of water transparency in different continents and climatic zones are shown in Fig. 3.2. From Fig. 3.1, it can be seen that the distribution of global water transparency in large lakes and reservoirs varies greatly by region, and the overall distribution is concave with latitude, i.e., the transparency of lakes and reservoirs near the North and South Poles (high latitude regions) is higher, while the transparency of lakes and reservoirs near the equator (low latitude regions) is lower. As can be seen from Fig. 3.2, in the global continents, from the point of view of the average water transparency, lake water transparency in Asia and Europe is higher, the lowest lake water transparency is in Africa. From the point of view of the number of large lakes and reservoirs, Asia and North America have the largest number of large lakes and reservoirs, while Oceania has the fewest. In different climatic zones of the world, from the point of view of average water transparency of lakes and reservoirs, the water transparency of lakes and reservoirs is higher in polar and cold temperate zones and lower in tropical zones. While from the point of view of quantity, the number of large lakes and reservoirs is higher in cold temperate zones and arid zones, and lowest in polar regions.

Bild vergrößern

Bild vergrößern

The annual change rate of water transparency of large lakes and Reservoirs in different climatic zones during 2000–2021 is depicted in Fig. 3.3, and the differences in the changes in different continents and climatic zones are shown in Figs. 3.4 and 3.5. The global change rate of water transparency in large lakes and Reservoirs showed an obvious regional distribution, with significant differences in regions and climatic zones, though an overall upward trend of water transparency has been obvious since 2000. The water transparency of 44.2% of large lakes and Reservoirs showed a significant upward trend (p < 0.05), and only 10.6% of large lakes and Reservoirs showed a significant downward trend in water transparency (p < 0.05). From the statistics of all continents, the average annual change rates of water transparency in large lakes and Reservoirs across the six continents were all positive, as shown in Fig. 3.4. Among them, the average water transparency of large lakes and Reservoirs in Asia and Africa changed slowly, with average annual change rates of 1.3 cm/a. The average water transparency of large lakes and Reservoirs in Europe increased significantly, with an average annual change rate of 7.6 cm/a. From the perspective of different climatic zones (Fig. 3.5), the water transparency of large lakes and Reservoirs in the cold temperate zones increased most obviously, with an average annual change rate of 5.6 cm/a, and the water transparency of polar large lakes and Reservoirs increased significantly, with an average annual change rate of 3.1 cm/a. The increase in water transparency in tropical and arid zones was weak, and the water transparency of turbid lakes (Z_SD < 0.5 m) in the warm temperate zone showed a downward trend.

Bild vergrößern

Bild vergrößern

Bild vergrößern

In this case study, we employed the surface water transparency inversion method based on water color parameters, utilizing satellite remote sensing data as the primary data source and ground-measured data from domestic and international lakes and reservoirs as auxiliary data. We constructed and validated the global water transparency model for large lakes and reservoirs, producing a dataset of water transparency for global large lakes and reservoirs with areas exceeding 25 km² during 2000–2021. Water transparency is used as an indicator of surface water quality to enable global monitoring of the SDG 6.6.1 indicator.

Analyses were conducted using continents and global climatic zones as geographical units, highlighting the differences in water transparency of large lakes and reservoirs across different continents and climatic zones, as well as their changes from 2000 to 2021. This case study provides crucial support for achieving SDG 6 through comprehensive data on spatiotemporal water quality changes and pattern analysis.

Currently, water quality parameters monitored through remote sensing for lakes and reservoirs are primarily optical parameters, such as the water transparency of large lakes and reservoirs monitored in this case study. In future work, we will further explore the relationships between optical water quality parameters and the biochemical indicator-based water quality parameters in SDG 6.3.2, to foster closer integration between remote sensing monitoring of lake and reservoir water quality and the SDG 6.3.2 indicator.

Target: SDG 6.4 By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity.

Indicator: SDG 6.4.1 change in water-use efficiency over time.

Cropland WUE is a commonly used indicator for assessing water efficiency in agriculture. It refers to the amount of biomass produced or the economic value generated per unit of water, reflecting the WUE and the economic benefits generated from the perspective of output. This indicator can comprehensively reflect the trade-off relationship between cultivated land food production and water consumption (Yu et al. 2008). Remote sensing technology has played a pivotal role in the timely, rapid, and large-scale estimation of regional cropland output, water consumption, and WUE. This technology is advantageous in elucidating the patterns of cropland WUE variations, contributing to global and regional sustainable utilization of agricultural water resources, and supporting sustainable agricultural development (Ai et al. 2020). However, there is currently a lack of high temporal and spatial resolution, long-time series datasets on cropland WUE (Cai et al. 2021). Therefore, this case study developed a global assessment methodology for cropland WUE based on multi-source data to generate a 1 km resolution dataset on cropland WUE from 2001 to 2020 and then evaluated the global patterns and temporal variations in cropland WUE, providing valuable data support for sustainable agricultural development and the sustainable utilization of water resources.

Cropland WUE was calculated as the ratio of net primary productivity (NPP) to evapotranspiration (ET) using the following equation:where:

The global average cropland WUE over multiple years (2001–2020) is approximately 0.98 g C/kg H₂O/a. There are noticeable spatial variations in the distribution of global cropland WUE. The pattern reveals that tropical regions exhibit the highest cropland WUE, with an average of approximately 1.13 g C/kg H₂O/a, followed by arid and semi-arid regions approximately 0.95 g C/kg H₂O/a, and cold regions approximately 0.95 g C/kg H₂O/a. Temperate regions, comparatively, have a relatively lower average of 0.88 g C/kg H₂O/a.

From 2001 to 2020, there was a significant increasing trend in cropland WUE in agricultural areas worldwide (Fig. 3.6), with an overall rise of 3.48%. However, spatial differences were observed. On the continental scale, differences were evident. Asia had the largest increase in cropland WUE (8.9%), followed by Oceania (7.2%). North America also showed an increasing trend, but with a lower rate (4.6%) compared to Asia and Oceania. Europe, Africa, and South America had relatively small changes in cropland WUE (all less than 1%). The global increase in cropland WUE is primarily attributed to the greater increase in agricultural NPP (9.31%) compared to the increase in evapotranspiration (6.39%). This is due to the significant advancements in agricultural technology and infrastructure, particularly in countries like China, as well as improvements in water-saving irrigation and agricultural water management techniques.

Bild vergrößern

To further clarify the changing characteristics of global cropland WUE, this case study selected typical countries from major grain-producing countries, and countries associated with the BRI and their neighboring countries in North Africa and Central Asia for cropland WUE analysis. Major grain-producing countries include China, the United States, Brazil, Indonesia, Argentina, France, and Canada. North African countries comprise Morocco, Algeria, Tunisia, Libya, and Egypt. Countries in Central Asia include Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan.

From 2001 to 2020, the cropland WUE of the world’s major grain-producing countries showed an upward trend (Fig. 3.7). Canada and China experienced the highest increases, with growth rates of 18.2% and 13.3%, respectively. Indonesia, Brazil, and France, with higher average cropland WUE, had smaller increases, all below 2%. The United States and Argentina had increases of 3.2% and 2.6%, respectively. The significant improvement in China is attributed to the much larger increase in cropland NPP (over 25%) compared to the increase in evapotranspiration (approximately 10%). This improvement is mainly due to factors such as advances in agricultural technology (e.g., field management, water-saving measures, fertilization, breeding, etc.), adjustments in cropping structure and intensity, and climate change (e.g., elevated CO₂ concentration, etc.) (Chen et al. 2019; Yang et al. 2022; Zhai et al. 2021). For example, from 2001 to 2021, due to advancements in agricultural technology, China’s irrigation WUE continuously improved from 0.43 to 0.568, and the net water consumption per unit of grain production decreased from 0.469 m³/kg to 0.404 m³/kg, with an average annual reduction of 0.003 m³/kg (Kang 2022).

Bild vergrößern

In Central Asia (Fig. 3.8), Turkmenistan, Tajikistan, and Uzbekistan, which have relatively low average cropland WUE, experienced an increasing trend with a significant growth of approximately 10.3%. Kyrgyzstan, with higher average cropland WUE, also showed an upward trend, but the increase was relatively small, at only 3.3%. Kazakhstan, however, experienced a decreasing trend with a substantial decrease of 13.9% (Fig. 3.8). The primary reason for the improvement in cropland WUE in Turkmenistan, Tajikistan, and Uzbekistan may be attributed to the adjustment of agricultural planting structures in irrigation areas. From 2001 to 2020, in response to food crises, a considerable number of cotton fields in irrigation areas were converted to winter wheat cultivation. Winter wheat has higher WUE compared to cotton, leading to an overall enhancement in WUE (Zou et al. 2017). In the case of Kazakhstan, its decreasing cropland WUE is likely due to drought conditions in the rainfed agricultural region in the northwest (Dubovyk et al. 2019; Karatayev et al. 2022), and land degradation exacerbated by human activities such as grassland cultivation and land abandonment (Hu et al. 2020). This collective impact significantly reduced the cropland NPP (by approximately 11.4%), contributing to the observed decline in cropland WUE.

Bild vergrößern

In the North African countries (Fig. 3.9), Algeria, Libya, and Tunisia all showed an increasing trend in cropland WUE, with growth rates of 14.2%, 11.5%, and 7.5%, respectively. In contrast, Morocco and Egypt experienced a decreasing trend, with reductions of 8.9% and 9.9%, respectively. The rise in cropland WUE in Algeria, Libya, and Tunisia is primarily attributed to a significant increase in cropland NPP, far exceeding the increase in water consumption through ET. In the case of Morocco, the decrease in cropland WUE mainly results from a reduction in cropland NPP (approximately 4%) caused by decreased precipitation, and a slight increase in water consumption through ET (3%) due to rising temperatures. For Egypt, the substantial increase in ET (about 10%) is mainly due to a significant rise in temperatures, and a slight decrease in cropland NPP (a decrease of 0.7%), leading to a notable decrease in cropland WUE.

Bild vergrößern

Highlights

This case study developed the global dataset of annual cropland WUE, defined as the ratio of cropland NPP to ET, at a 1 km resolution from 2001 to 2020, based on multi-source data and incorporating models. This case study assessed and analyzed the spatiotemporal variations in cropland WUE globally and in typical countries, providing data support for the evaluation of agricultural WUE and its time-series changes aligned with SDGs.

The results indicate that there was a significant increase in cropland WUE in agricultural areas worldwide, but spatial differences were observed. The global increase in cropland WUE is primarily attributed to significant advancements in agricultural technology, substantial improvements in agricultural infrastructure conditions, and the adoption of water-saving irrigation and agricultural water management technology, with countries like China leading the way. It is recommended that countries and regions with lower WUE should increase investments in agricultural technology and infrastructure to enhance cropland WUE, promoting sustainable water resource utilization. Furthermore, against the backdrop of climate change, different regions exhibit varied responses of cropland WUE to climate change. For regions where climate change leads to a decrease in cropland WUE, tailored measures are suggested to minimize the adverse effects of climate change (grain reduction, water consumption increase, etc.), considering the specific conditions of each area.

Target: SDG 6.6 By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers, and lakes.

Indicator: SDG 6.6.1 Change in the extent of water-related ecosystems over time.

Lakes, including natural lakes and manmade reservoirs, play vital roles in the global hydrological and biogeochemical cycles and underpin vital ecosystem functions and services. However, rapid lake changes have been identified worldwide in response to changing climate and escalating human activities, threatening the ecosystem services provided by these lacustrine habitats. A spatially explicit understanding of their extent changes is essential for evaluating the associated ecological, environmental, and societal impacts, which is also a key indicator within the UN SDG 6 framework (SDG 6.6.1). This particular indicator includes five sub-indicators, with this case study focusing on the changes in the spatial extent of both natural and artificial water bodies. Natural water bodies refer primarily to natural lakes, while artificial water bodies are designated as reservoirs developed with regulatory facilities like dams for water storage and management (Cooley et al. 2021; Yao et al. 2019). Specifically, human management practices have intensified seasonal variations in water storage, resulting in reservoirs exhibiting distinct ecohydrological characteristics compared to their natural counterparts (Cooley et al. 2021). Accurately distinguishing these two categories is essential for evaluating the available water resources for humans. The identification and extraction of natural lakes and reservoirs through Big Earth Data, such as multi-source remote sensing methodologies, can provide vital data support for assessing SDG 6.6.1.

In response to this need, the research team developed an innovative method for delineating the extent of natural lakes and reservoirs and created the Global Lakes (GLAKES) dataset. This dataset includes over 3.4 million natural lakes and reservoirs (with an area ≥0.03 km²) and shows marked improvements over previous datasets in terms of temporal range, spatial coverage, and detailed delineation of small water bodies (Pi et al. 2022). This established a foundational resource for accurately and comprehensively capturing the spatiotemporal trends of natural lakes and reservoirs at the global scale. Utilizing GLAKES and various global datasets (Pekel et al. 2016; Donchyts et al. 2022; Wang et al. 2022), this case study assessed changes in the extent of natural lake and reservoir water bodies from 2001 to 2021 through remote sensing technology and Big Earth Data. By constructing a detailed surface water area time series dataset on a global scale, this case study explored the temporal trends, spatial variations, and underlying driving factors of the water area changes in global natural lakes and reservoirs, thereby providing critical support for water resource management and sustainable development.

This case study first constructed a global water occurrence layer for each three-year period based on the monthly water classification layers from the JRC GSW dataset. Water occurrence refers to the proportion of observed instances classified as water to the total number of valid observations during a specific period, reflecting the frequency of water presence over the entire historical period. The water occurrence maps were then overlaid with the delineated water extent of GLAKES to construct a time-series dataset of water occurrence-weighted lake areas at different time periods. Based on this dataset, the temporal trends and spatial variations in natural lake and reservoir water area changes were analyzed at both 1° × 1° grid and national scale, and their responses to both climate change and human activities were investigated for typical countries/regions.

The classification of natural lakes and reservoirs was determined by the spatial relationships between GLAKES and two global reservoir datasets. On the one hand, compared to the most comprehensive World Register of Dams (WRD), the total area and capacity of reservoirs recorded in GeoDAR accounted for 93.5% and 95.6% of those in WRD, respectively. On the other hand, the reservoir dataset constructed by Donchyts et al. included 71,208 small- and medium-sized reservoirs (0.1–100 km²) globally, largely compensating for the gaps in GeoDAR regarding small- and medium-sized reservoirs. This case study combined both datasets to effectively characterize the changes in global reservoirs.

From 2001 to 2021, the average water area of global natural lakes and reservoirs reached 2.7 × 10⁶ km², with reservoirs constituting 19.0% and natural lakes 81.0%. Figure 3.10 illustrates the proportion of water area in global reservoirs and natural lakes within each 1° × 1° grid, defined as the ratio of water surface area to grid area (range: 0%-100%). These water bodies can be further divided into three size groups: small (< 1 km²), medium (1–100 km²), and large (> 100 km²). These size groups accounted for 95.53%, 4.43%, and 0.04% of the total number of water bodies, respectively, and 14.4%, 24.7%, and 60.9% of the total area, respectively.

Bild vergrößern

From 2001 to 2021, the global coverage of natural lakes and reservoirs showed an overall expansion trend (Fig. 3.11). Among them, reservoirs exhibited continuous and significant expansion, while natural lakes underwent a cycle of initial shrinkage, subsequent expansion, and further shrinkage, leading to a slight overall decline. Specifically, the area change rate of reservoirs was 1,133.5 km²/a, whereas natural lakes experienced a negative change rate of − 414.4 km²/a, primarily due to the considerable shrinkage in large natural lakes (−1,598.8 km²/a). The growth rate of natural lakes in glacial and permafrost regions was 525.95 km²/a, which was 2.72 times higher than that in non-glacial and permafrost regions, yet only 46.4% of the reservoir area expansion rate during the same period. This indicates that the overall water expansion from 2001 to 2021 was largely due to human-regulated reservoirs rather than replenishment from glacial melt attributable to climate change.

Bild vergrößern

Reservoirs are mainly concentrated in North America, Asia, and Europe, which together accounted for 81.4% of the total number and 70.6% of the total area globally. Developed countries represented 37.4% of the total number and 38.0% of the total area worldwide, while the corresponding proportions for the BRICS countries were 29.8% and 37.6%. In terms of natural lakes, North America and Asia had significantly higher area proportions compared to other continents, reaching 45.8% and 36.6%, respectively. Additionally, 51.0% of the number and 27.4% of the area of natural lakes were found to be located north of 60°N.

At the continental scale, except for South America, the global reservoir water area showed an increasing trend from 2001 to 2021, with Asia (835.4 km²/a) and Africa (187.4 km²/a) exhibiting significant expansion. At the national scale (Fig. 3.12a), the reservoir water area significantly increased in 46 countries, while only 7 countries showed a significant decreasing trend. The primary countries and regions displaying expansion trends include China, Russia, parts of Southeast and South Asia, Iran, Türkiye, Northeast Africa, Canada, and Greenland. Meanwhile, the countries and regions with shrinking trends are mainly concentrated in the United States, South America, Southern Africa, and the Northern Mediterranean coast.

Bild vergrößern

Regarding natural lakes, no significant interannual area changes were observed from 2001 to 2021 on the continental scale, with positive changes only occurring in Africa, Europe, and North America. At the national scale (Fig. 3.12b), the lake water area significantly expanded in 68 countries, while 12 countries showed a significant shrinking trend. Countries or regions demonstrating expansion trends include China, parts of Southeast and South Asia, central and western Europe, the Arabian Peninsula, and the central and northwestern coast of Africa and Greenland. Meanwhile, countries or regions with shrinking trends are primarily located in West Asia, Ukraine, southern Africa, Australia, North America, and southern South America.

To further explain the characteristics of water area changes in global reservoirs and natural lakes, this case study selected 10 typical countries/regions worldwide based on factors including interannual water area change rates, geographical location, and the focus of similar studies. There were significant differences in the proportions of reservoir and natural lake areas among different countries/regions, with specific values shown in Table 3.2. Temporally, the areas of natural lakes and reservoirs in China, Sudan, and Greenland exhibited a significant expansion trend, while the reservoirs in the Democratic Republic of the Congo demonstrated a significant contraction trend, although natural lakes in this region showed a significant expansion trend. In addition, natural lakes in Kazakhstan were experiencing a significant declining trend, while reservoirs in Türkiye showed a significant expansion trend. No significant trends were observed in the water area changes in the remaining countries/regions.

A further investigation reveals that from 2001 to 2021, there were also variations in natural lake and reservoir areas within the inside regions of different typical countries/regions, with diverse causes for these differences (Fig. 3.13). In the northern plains of the United States, water bodies expanded due to a climatic progression from dry to wet period, while those in the western coastal areas decreased due to drought and groundwater extraction (Scanlon et al. 2012; Rodell et al. 2018; Leeper et al. 2022). Natural lakes in Greenland (Denmark) expanded due to increased glacial melt resulting from climate warming (Shugar et al. 2020). In western Australia, natural lakes shrank due to a drying climate (Rodell et al. 2018), whereas natural lakes and reservoirs in the eastern regions expanded locally after experiencing the worst drought in over 100 years followed by recovery with heavy rains (van Dijk et al. 2013; Evans et al. 2012). The expansion of reservoirs in northern Sudan, as well as upstream natural lakes in the south, was associated with dam construction in the north and subsequent upstream replenishment of water storage (Alrajoula et al. 2016; Ahmed et al. 2014). Reservoir expansion in China was primarily concentrated in the eastern and southern regions, while parts of Nei Mongol experienced a decline in water area due to mining and irrigation (Zhu et al. 2022; Song et al. 2022; Tao et al. 2020, 2015). Climatic factors, such as precipitation and glacial melt, also significantly influenced water area changes in China, leading to considerable expansion in natural lakes in the Qinghai-Xizang Plateau and Northeast Plain (Tao et al. 2020). The area dynamics of water bodies in the Southeastern Democratic Republic of the Congo were affected by the El Niño and Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) phenomena (Sogno et al. 2022). The contraction of the Caspian Sea and the Aral Sea largely contributed to the decline of natural lakes in the western region of Kazakhstan, primarily owing to local drought and excessive water extraction (Zmijewski and Becker 2014; Chen et al. 2017; Joodaki et al. 2014). In contrast, changes in natural lakes in the northeastern region were attributed to the melting of permafrost due to climate warming, which has led to the formation and expansion of numerous small thermal karst lakes (Polishchuk et al. 2015). In Central Brazil, the water area of natural lakes and reservoirs expanded due to drought alleviation and dam construction (Zarfl et al. 2015; Chen et al. 2010), while those in the eastern region declined because of continued suffering from drought (Getirana et al. 2016). The expansion of reservoirs in Türkiye primarily resulted from a series of government measures to replenish existing water bodies and construct new artificial ones after the drought in 2008 (Donchyts et al. 2022). Lastly, in central and southern Argentina, reservoir areas decreased due to reduced precipitation (Wang et al. 2018; Garreaud et al. 2017).

Bild vergrößern Bild vergrößern

Highlights

Leveraging remote sensing technology and Big Earth Data, this case study quantitatively evaluated changes in the water areas of global reservoirs and natural lakes between 2001 and 2021. It also examined spatial variations in water area changes across different countrie and regions, along with the mechanisms driving these changes. The findings indicate an overall expansion in the extent of global natural lakes and reservoirs during this period. At the continental scale, the water area of reservoirs increased in all continents except South America, with particularly notable expansion in Asia and Africa. In contrast, natural lakes did not exhibit significant interannual variation trends across the continents. Analysis of typical countries/regions identified glacial meltwater replenishment, increased precipitation, and reservoir water storage as key factors contributing to the expansion of natural lakes and reservoirs, whereas drought and excessive water extraction were associated with reductions in surface water area. The research findings can provide data support for countries to evaluate progress toward SDG 6.6.1, contributing to the protection and restoration of water ecosystems. To this end, countries/regions should closely monitor water resource status and take measures to protect freshwater resources, including drafting water resource management regulations, improving remote sensing monitoring networks, strengthening the construction of water resource storage and scheduling facilities, and raising public awareness of water resource protection through education. These measures are crucial for achieving the sustainable utilization of water resources.