Timing of Global Surface Water Transitions Reveals Anthropogenic Dominance of Recent Growth in Water Extent

Timing of Global Surface Water Transitions Reveals Anthropogenic Dominance of Recent Growth in Water Extent | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Timing of Global Surface Water Transitions Reveals Anthropogenic Dominance of Recent Growth in Water Extent Gustavo Nagel, Stephen Darby, Julian Leyland This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3215886/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The extent of coastal and inland surface water resources is constantly varying in response to complex interrelated processes, driven by natural and anthropogenic factors. Recent advance in satellite technology and cloud computing have enabled global-scale monitoring of the changing occurrence and extent of these surface water resources. However, until now, no previous study has sought to estimate the timing of these surface water changes at the global-scale. Here we introduce the first global-scale identification of the year when water advanced or receded within a given pixel, using a 38-year Landsat time series. Our methods focus exclusively on persistent changes in water features, filtering out seasonal or short-lived fluctuations. We use the new algorithm to map the timing of water advance and/or recession events globally, encompassing both inland water bodies and coastal dynamics. Additionally, the timing of water transitions enabled the identification of the primary drivers behind these changes. As a result, we identified that most of the large-scale water change events are related to human influence, such as damming, infrastructure failures and even conflicts. These combined factors contributed to a global shift, with accumulated water advancing surpassing water receding over time. Earth and environmental sciences/Hydrology Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Climate sciences/Hydrology Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts/Environmental health Earth and environmental sciences/Ocean sciences/Physical oceanography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Surface water is an indispensable resource that plays a fundamental role in shaping our planet's landscapes and supporting human development 1 – 6 . The dynamic interplay between wet and dry conditions has driven profound changes throughout Earth's history, sculpting diverse landforms, influencing ecosystem dynamics, and impacting human civilizations. These changes can be attributed variously to human activities such as urban expansion and the construction of dams and levees, as well as to natural processes such as river meandering 2,7−15 . Climate change also influences the distribution of surface water through its impacts on precipitation variability, temperature changes and meting of ice 16 – 18 . Establishing the occurrence and timing of transitions of surface water state (i.e., transitions between wet and dry conditions, or vice versa ) is, therefore, crucial for understanding the complex processes underlying landscape evolution and assessing their implications for sustainable human development 6 , 19 . To address the challenge of studying surface water on a global scale, in recent years researchers have turned to satellite imagery and advances in cloud computing to enable the detection and estimation of various water resource variables at the global scale. Examples of such prior studies include mapping the presence or absence of surface water, assessing its temporal occurrence, classifying the behavior of surface water changes (e.g., do quantified surface water extents vary seasonally, are they permanent?), monitoring reservoir dynamics, and identifying areas of permanent water loss and gain 12 , 19 , 20 . Pekel, et al. 20 and Donchyts, et al. 19 have made significant contributions to this field by mapping global surface water extent and changes using Landsat imagery, with their studies shedding new light on the spatial distribution and interannual variability of Earth’s surface water. However, whilst these previous approaches have focused on identifying areas of changing water occurrence, they ignore the timing of such changes, potentially limiting their utility for evaluating causation. In this study we present the first global analysis of the timing of surface water transitions using Landsat satellites images from 1984 to 2022 within the Google Earth Engine (GEE) cloud computing environment (Fig. 1 ). Herein we define such temporal transitions as occurring either when a pixel was dry, and subsequently becomes persistently flooded or with seasonal water presence (inundated during the flooding period of the region), or when a pixel was wet, and subsequently becomes persistently dry or experiences only scarce flooding events (which occur less frequently than inundation periods). Our algorithm was specifically designed to minimize the impact of short-lived events (lasting one year or less) and, instead, it focuses on identifying transitions that substantively impact the 38-year history of each pixel. This approach lends confidence to the identification of shifts between dry and wet conditions. The derived dataset is publicly available on GEE and is also accessible through a GEE app that aids visual interpretation ( https://gustavoonagel.users.earthengine.app/view/water-change-time-detection ). The novelty of our research lies in its emphasis on understanding the timing of surface water transitions, and the implications of such transition timings for landscape evolution and human development. 2. Results Our surface water transition dataset provides a comprehensive representation of surface water temporal variability, capturing the timings and intricate patterns of change influenced by diverse natural and anthropogenic processes (Fig. 1 ). For example, in regions like China (Fig. 1 .b) and Dubai (Fig. 1 .g), distinctive patterns 21 shaped by human land reclamation and food production activities stand in stark contrast to the natural patterns of change observed on rivers and floodplains (Fig. 1 .c, 1.e, 1.h). Additionally, our dataset reveals that human interventions tend to occur over relatively short time intervals, such as the artificial infilling of the Três Irmãos Reservoir in Brazil that occurred abruptly in 1991 (see histogram below Fig. 1 .f), the construction of the Palm Islands in Dubai, which experienced its peak of land reclamation in 2005 (histogram below Fig. 1 .g), or the fish lakes that were filled in 2015 following Amazonian deforestation (Fig. d). In contrast, natural influences on water dynamics operate over longer time spans, such as in the Ganges delta (Fig. 1 .h), which displays a more distributed and similar rates of water advance and recession over time (histogram below Fig. 1 .h), the gradual evolution of an Amazonian River (Fig. 1 .e), or the long-term droughts affecting a Turkish reservoir (Fig. 1 .a). These natural processes are indicated in Fig. 1 by smooth transitions from darker red and blue colors (indicating earlier timings of transition) to lighter colors (representing more recent transitions). Furthermore, different scales of change are also captured, as exemplified in Fig. 1 .a, which shows a lake of approximately 70.2 km² that experienced drought and a nearby lake of only 0.08 km² that was filled in 1994. Major and rapid events, such as reservoir filling, coastal expansion, and severe droughts, can lead to substantial advance or recession of water bodies within just a few years. Figure 2 illustrates the timing of such transitions, at a global scale, in the period from 1984 to 2022, highlighting portions of the globe where transitions have occurred over spatially-coherent regions. Furthermore, the years in which the biggest water change was related to direct human interventions, such as dam construction, flooded agriculture expansion, poor water management, and poor infrastructure planning, is also highlighted (this information was gathered using visual interpretation and based on the literature). In regions where water is receding, 21% of the recession years were driven by direct human interventions or human-induced events, such as the Aral Sea disaster. The Aral Sea disaster is a prominent example of major water recession caused by the Korakal dike construction in the 1960s by the Soviet Union to divert water to agriculture projects 22 – 24 . Considering the period since 1984, the most significant water recession event in the Aral Sea region occurred in 2004, surpassing any other water recession events in the world in that year and throughout the entire analysis period. In 1996, the Aral Sea, albeit on a smaller scale, was also the largest area of global water recession. Other lakes with recognized human intervention also experienced water recession in other periods, such as the Colhué Huapi in Argentina (1994) and Lake Urmia in Iran (2009), both affected by the over exploitation of water 25 , 26 . Furthermore, although on a smaller scale, in East Asia, particularly the coasts of China and South Korea are expanding, with a general peak in 2012, due to the construction of ports and expansion of cities. The highlighted Saemangeum Seawall, for example, is the largest dike on Earth, and although completed in 2006, the most significant impact was observed in 2012 due to land reclamation behind the 33-km long sea-wall 27 . Moreover, the identification of the year of water transitions has proven valuable in understanding the strategic manipulation of water resources during conflicts. A notable example is the Russian annexation of Crimea in 2014, where Ukraine's response of blocking the North Crimean Canal led to the drying of the canal and associated reservoirs in Crimea 28 (peak of water recession in 2014). In regions where water advanced, 66% of the years that experienced water change events were driven by direct human intervention, predominantly attributed to the construction of dams, which accounted for 55% of the water advance years. In the Amazon region, the Balbina Dam, filled in 1989, stands out as a significant water advance event that occurred within just one year, resulting in the highest infilling of water anywhere globally in 1989. This example serves as a reminder of the rapid and substantial impact that reservoirs can have on the environment, highlighting the importance of careful planning for additional dams in the region 29 , 30 . Other examples include the Serra da Mesa Dam (1997) and Porto Primavera Dam (1999) in Brazil, the Merowe Dam in Sudan (2005), the Bui Dam in Ghana (2006), and the Murum Dam in Malaysia (2012). However, in a similar way that the Aral Sea disaster was attributed to poor infrastructure planning, the largest recorded water advance event was also linked to another disaster that occurred in the Garabogazki Basin Lake in Turkmenistan, which displays peak water advances in 1993 and 1994. A breach in 1992 of a dam between the lagoon and the Caspian Sea led to a rapid influx of water into the basin 31 , substantially increasing the water surface area by around 2772 km². Overall, these examples demonstrate the significant impact that human activities have on the advance of surface water globally. Besides human-influenced events, the analysis also identified natural or indirectly human-influenced occurrences of water advance and recession. However, even in the absence of direct human influence, these areas might still be susceptible to the effects of climate change in driving drought and flooding regimes. In Southeast Australia, for example, prolonged droughts are completely drying up many reservoirs 32 – 34 , such as in 2001 when the Australian Lake Menindee experienced the world’s largest water recorded recession transition. In Afghanistan, many lakes in the Registan desert completely dried up in 2005 (collectively representing the largest recession of water in the world in that particular year). In South America, the Pantanal wetland region and the downstream Paraná River also experienced severe droughts 35 – 38 , with a water retreat peak displayed in the year 2000 in the Pantanal highlighted region. California in North America also faced reduced rainfall events, leading to a decline in reservoir capacity 39 – 41 (with a water retreat peak in 1991 in the highlighted Lake Malheur). Changes in the climate can also lead to an increase in water extent. The lakes in the Tibet Plateau, for example, experienced the most significant water advance in the world in 2005. These lakes are growing in response to the impacts of climate change, driven by rising precipitation levels, glacier loss, and permafrost thawing in the region 42 , 43 . Higher precipitation rates also contributed to the filling of the highlighted Quill Lakes and surrounding prairie potholes in North Dakota 44 , 45 (USA), with a peak of water advance in 2012, and the Toshka Lakes in Egypt, resulting in significant water expansion in 2020 (the largest advance of water in the world in that year). The Toshka Lakes are closely connected to the Nile River and were influenced by increased flow from the upstream regions of Sudan, which experienced higher precipitation rates 46 . Considering the accumulated amount of water change, global areas of water advance and recession initially grew at similar rates until 2012, when the total areas of water advance began to surpass the total areas of water recession (Fig. 3 .a). The cumulative area of water advance from 1984 to 2022 was estimated as 323,326,000 km², while the extent of water recession was 236,892,000 km². Of the total area of water advance, 56% was observed in high residence time inland water areas, such as reservoirs, lakes, wetlands, and flooded agriculture, while 29% occurred in rivers, 12% in coastal regions, and 3% in deltas. For water recession, the proportions were 60%, 26%, 11%, and 3%, in inland waters, rivers, coasts and deltas, respectively. However, the timings of the transitions of water advance and recession vary substantially across continents. For example, South and Central America and Europe had similar rates of water advance and recession, despite having slightly higher water recession areas overall. In South and Central America, water recession areas were on average 12% higher than areas of water advance, which aligns well with estimates by MAPBIOMAS for Brazil during the same period (15%) 47 . North America, characterized by the highest percentage of water change occurring in lakes and reservoirs (73%), initially had more areas of water recession, but experienced a shift in 2001 when areas of advancing water surpassed areas of receding water. Similar patterns are observed in Africa (lake dominated), where a crossover from water recession to water advance occurred in 2002. Likewise South Asia (the only river dominated region) crossed from water recession to water advance dominant in 2011, while East and Southeast Asia (with more balanced proportions) crossed in 2022 and Central Asia (reservoir dominated) did so in 1993. In contrast, the South Pacific, and especially the Middle East, were the regions with the highest relative proportions of water recession, with a discrepancy of 19.8% and 43.6% between water recession and water advance, respectively. 3. Discussion The new global water transition timing dataset captures a wide range of natural and anthropogenic processes, providing valuable insights into global water dynamics. The emphasis on timing of water transitions allows the identification of human-induced rapid events that changed large swaths of water in a short time interval (one year), revealing the construction of dams around the world, major droughts, infrastructure failures, and even the effects of conflict on water extents. Direct human influence was responsible for the largest water change events in 21% of the years for recession events and 66% of the years for water advances (55% of the years were related to dam construction). The infrastructure failures were responsible for the largest historical water advance, related to a breach in the Caspian Sea 31 in 1992, that inundated large areas in 1993 and 1994, and water recession events, related to the Aral Sea disaster 22 – 24 , with a peak of recession in 2004. These events show how poorly planned human interventions can have huge impacts on the environment. Furthermore, the construction of large dams surpassed any other water advance event in more than half of the analysed years (21 years). The Balbina Dam, for example, which filled its reservoir in 1989 in the Amazon, was the largest artificial reservoir build during the analysed period. This is a stark example of how dams in the low-gradient lowlands of the Amazon produce large reservoirs, which in turn submerge large areas of forest. These dam constructions certainly played a role in the surpassing of the accumulated areas of advance over accumulated areas of recession in the different continents of the world, such as North America, which transitioned to this state in 2002, Africa (transitioned in 2000), South Asia (transitioned 2011), East and Southeast Asia (transitioned in 2002), and Central Asia (transitioned 1993). In East Asia, in addition to the surge of dams 48 and the spread of flooded agriculture (more than 90% of the global rice is produced in the Asia–Pacific region 49 ), climate change is also impacting the rates of precipitation and ice melting, which are filling up the natural lakes of the Tibetan Plateau 42 , 43 . Furthermore, in North America, despite the water retreat observed on the US West Coast (mainly before 2010), the filling of prairie potholes in North Dakota in 2012, mainly caused by higher precipitation 44 , 45 , also contributed to the positive surface water balance of the continent. In South and Central America, despite the great spread of dams, the severe droughts of Pantanal, with peak in 2000 and 1994, and Northwest Brazil (peak in 2012) might have contributed to equilibrate the rates of water advance and recession. However, the Middle East and South Pacific had higher rates of water recession, which means that the overall water surface greatly decreased between 1999 and 2011 in both regions. In both desertic regions, many lakes were completely depleted by droughts 32 – 34 , 50 , 51 , such as the Menindee Lake and many others in Southeast Australia (peak of recession in 2001) and in some lakes of Afghanistan (2005). Nonetheless, the continents with positive water balance are not necessarily in a better water security condition, since the spatial distribution of water transition is not even, as shown in the previous examples. Areas of water advance can also have the opposite effect (on water security), such as the expansion of areas of flooded agriculture, which can reduce water security since it increases water evapotranspiration rates 52 . Water security and conflicts in general can significantly impact water dynamics, as in the case of the Crimean Canal blockage. While currently on a smaller scale, the impact of conflicts on water may escalate in the future due to population growth and climate change, leading to decreased water availability in certain regions 53 , 54 . Overall, the new dataset presented in this paper is able to identify globally important water transitions across the planet, related to drought, reservoir construction and infrastructure projects, and allows comparison of the timing and rates of water transition between continents. The attribution of water changes to human influence was focused on large-scale events, as global information on whether water changes are shaped by humans or natural influences is not readily available. Identifying infrastructure failures and overexploitation of water was even more challenging, and thus we relied on existing literature for these cases. We anticipate that the dataset will be of considerable interest to researchers seeking to decipher the intricate relationships between hydrological processes, erosion rates, landform evolution and the development and sustainability of human activities. The dataset is publicly available on Google Earth Engine (GEE) and an Earth Engine App has been developed for convenient data visualization and retrieval ( https://gustavoonagel.users.earthengine.app/view/water-change-time-detection ). 4. Methods The unique combination of its high spatial resolution (30 m), long duration of archive (spanning from 1984 for Landsat 5 to 2023 for Landsat 9), and a revisit time of 16 days positions the Landsat program as a highly suitable tool for undertaking a comprehensive analysis of global inland and coastal surface water. To accurately detect the specific year of change, we developed two algorithms utilizing Landsat Time Series from 1984 to 2023 and the cloud computing platform Google Earth Engine (GEE). The first algorithm, the Water Change Detection (WCD) Algorithm, estimates areas of surface water transition by leveraging the Modified Normalized Difference Water Index (mNDWI) 55 – 64 , while the second algorithm, the Water Time Track Detection (WTTD), identifies the year of change through the use of the Green_Red Normalized Difference Water Index (grNDWI) proposed herein (see Fig. S1 in Supplementary Information, SI). Regions with a Landsat dataset starting only after 1990 were excluded from the analysis (see Fig. S5 in SI) 4.1 Water Change Detection Algorithms To delineate areas of changing water extent, we implemented the Water Change Detection (WCD) algorithm on annual Modified Normalized Difference Water Index (mNDWI) images derived from pre-processed Landsat 5, 8, and 9 data, covering the period from 1984 to 2023 (see Fig. S4 in SI for details and Fig. 4 .a for a summary). Through a pixel-wise analysis, areas of potential change were identified based on the crossing of a pre-determined mNDWI threshold (see Fig. S3 in SI), indicating either water advance (upward crossing) or water recession (downward crossing) (Fig. 4 .b). Recognizing the dynamic nature of surface water, where regions may experience recurrent episodes of flooding or drying, we employed a further analysis of each pixel's mNDWI trend, the trend being determined from Ordinary Least Squares Regression as computed using annual mNDWI over time. Pixels that exhibited both an upward crossing of the threshold and had a regression line slope exceeding 0.005 mNDWI/year were considered as areas with a transition to water advance. Conversely, pixels that displayed both a downward crossing of the threshold and a trend of less than − 0.008 mNDWI/year were classified as regions of water recession (Fig. 4 .c). The determination of these trend values was obtained by extracting slope samples from areas previously identified as undergoing erosion and sedimentation processes (see Fig. S3 in SI for details). To determine the timing (here we resolve timing at annual resolution) of water advance or recession, we employed the Water Time Track Detection (WTTD) algorithm (Fig. 4 ). This algorithm was specifically designed to filter out short-lived events, such as seasonal flooding and droughts. In areas identified by the Water Change Detection (WCD) algorithm as experiencing water advance or recession, we constructed a new time series using the Green_Red Normalized Difference Water Index (grNDWI). Figure 4 .d to 4.k illustrates the processing steps applied to a single pixel identified as water receding, with similar procedures followed for water advancing pixels. The Landsat 5, 8, and 9 images underwent pre-processing procedures including cloud filtering, cloud and snow masks, and the generation of annual grNDWI composites (see Fig. S4 in SI for details). However, even with the use of annual composites, cloud cover can result in missing data for several years (Fig. 4 .d), particularly in regions prone to cloudiness like the Amazon. To address this issue, we developed a technique to fill in data gaps in the time series by utilizing the preceding and subsequent grNDWI years (Fig. 4 .e – details are provided in Fig. S4). With the new, uninterrupted, time series we extracted the maximum and minimum grNDWI values to calculate the mean grNDWI, which served as the pixel-specific water-land threshold (Fig. 4 .2.f). This pixel threshold represents a locally optimized water/land threshold, acknowledging that a pixel transitioning from forest to turbid water would have a different threshold than a pixel transitioning from sand to clear water, even if they were in close proximity. We note that, by relying on the entire time series, the pixel threshold method is susceptible to potential outliers, such as unfiltered clouds and atmospheric constituents, which can affect the accuracy of the results. To address this, we computed the mean between the individual pixel threshold and a regional threshold (calculated using the OTSU method, see Fig. S2 in SI), resulting in the creation of the Optimal Threshold that incorporates both local and regional influences (Fig. 4 .g). The newly derived Optimal Threshold serves as the basis for determining the transition year of water advance or recession. For any individual pixel to be defined as experiencing water advance, it must cross the Optimal Threshold in an upward direction, while water recession pixels must cross it in a downward direction, as depicted in Fig. 4 .h. Considering the influence of short-lived flooding events, an individual pixel may cross the Optimal Threshold multiple times, resulting in several candidate transition years (Fig. 4 .h). To select the actual year of transition used in the analysis, we calculated the mean grNDWI using a 5-year moving window and determined the year at which it crossed the Optimal Threshold, establishing a reference year (Fig. 4 .i). By comparing the candidate years with the reference year, we selected the candidate with the smallest difference (Fig. 4 .j). In the provided example, we encountered two candidate years and chose the more recent one (Fig. 4 .k). The transition year is then stored as part of the pixel dataset that covers the entire Earth. To summarize the water change dataset, we employed a hexagonal grid system with each hexagon covering an area of 7,179 km². Within each hexagon, we determined the year with the most significant water change and quantified the extent of both water recession and advance. This approach allowed us to identify major recession and/or advances and their corresponding years of occurrence. Additionally, we calculated the accumulated area of water change for different continents. To assess the proportion of change occurring in various water environment types, such as high residence time inland waters (reservoirs, lakes, wetlands, and flooded agriculture), rivers, coastal regions, and deltas, we further subdivided the world into smaller hexagons measuring 1,010 km². By estimating the percentage of each water environment within these smaller hexagons (using datasets provided by Google Earth Engine), we associated these percentages with the accumulated areas of water recession and advance within each hexagon. Then, we summed up the water change associated with each water environment type to determine the percentage of change associated to lakes, rivers, coastal, and deltas. Fig. S7 in the Supplementary Information (SI) provides a visual representation of this process. 4.2 Validation We conducted a validation process by collecting 700 geographically diverse data points across the globe. To facilitate this, we developed a user-friendly Google Earth Engine (GEE) App that produced a random distribution of points and displayed a Landsat Time Series with the NIR, Red, and Blue band combination to highlight distinctions between water and land. Through careful visual analysis, we determined the most appropriate year of transition in relation to water advance and recession and compared that year with the year of change as estimated using the WTTD algorithm. The determination of the most appropriate year was performed by identifying the year that water advanced at each point and remained flooded, and the year that the water receded and remained dry. This validation procedure encompassed a range of different types of water bodies such as rivers, floodplains, flooded agricultural areas, lakes, coasts, and delta regions. Key statistical metrics were derived to evaluate the performance of the WTTD algorithm. These metrics include the Mean Absolute Percentage Error for combined water advance and recession points (MAPE), MAPE for water advance points (MAPEadv), MAPE for recession points (MAPErec), the Coefficient of Determination (R²), and the Critical Success Index (CSI), thereby establishing a comprehensive assessment framework (see Fig. 5 ) to ensure the accuracy and reliability of our findings. An overall error distribution can be visualized in Fig. 5 .h, and for each water feature in Fig. S6 in the SI. Our analysis yielded a Mean Absolute Percentage Error (MAPE) of 13.9%, indicating a high level of forecasting accuracy, as supported by previous studies (Montaño Moreno et al., 2013). The coefficient of determination (R²) was 0.82, demonstrating a strong correlation between the predicted and observed data points, while the overall CSI was 95.7% when considering all water bodies in a single regression model (Fig. 5 .a). These statistics indicate the robustness of the WTTD algorithm, considering the intricate and dynamic nature of global water surfaces. Notably, the accuracy of the WTTD algorithm is seen to vary across different water features, with lakes and coasts exhibiting the best results (Fig. 5 .e and 5.f), characterized by similar MAPE values of 11.1%, CSI values of 96% and 98.9%, and high R² values of 0.85 and 0.93, respectively. Rivers (MAPE: 15.5%, CSI: 95.5%, and R²: 0.79) and deltas (MAPE: 18.5%, CSI = 100%, and R²: 0.85) also demonstrated good accuracy (Fig. 5 .b and 5.g). However, floodplains and flooded agriculture areas, while showing good MAPE values (15.6% and 13.2%, respectively) and good CSI values (88.2% and 100%, respectively), exhibited substantial disparities between the estimated and observed years, resulting in decreased R² values (0.67 and 0.69) (Fig. 5 .c and 5.d). Remarkably, floodplain regions showed a concentration of misclassifications, with 6.7% of the predicted points not correctly experiencing persistent changes. Moreover, it is important to highlight that regions of water advance generally exhibited better estimations compared to water receding regions. The variations in accuracy can be attributed to the inherent complexity of different water features and the extent to which water colour and water extend change over time, which directly impact the performance of both the WCD and WTTD algorithms. Coastal regions, for instance, are influenced by a combination of factors such as ocean currents, wave energy, tides, and coastal morphometric characteristics 65 , 66 . However, these influences tend to persist over longer durations, and it is unlikely for an eroding coast to transition into a sediment-filled one within the time span of our analysis. Additionally, coastal areas generally exhibit lower complexity in terms of their water colour when compared to inland water bodies, conditions that increased the accuracy of the water change algorithms. On the other hand, rivers and floodplains are subject to interannual rainfall variations, seasonality effects, and water turbidity fluctuations, which introduce uncertainties in the analysis. Floodplains, in particular, have relatively shallower inundated areas compared to rivers, thus with a greater colour influence from the submerged surface bottom. Furthermore, they are highly influenced by seasonal variations and short periods of drought and flooding, which adds complexity and potentially leads to reduced accuracy in the assessment of floodplain regions. In the case of flooded agriculture, although they tend to be as shallow as floodplains, they are less affected by rainfall variability as their areas of inundation are artificially controlled. This artificial manipulation contributes to greater stability, resulting in higher accuracy compared to floodplain regions. Lakes generally exhibit higher overall accuracy, primarily attributed to the rapid global construction of new dams 67 . The water change algorithms can easily detect the year when a reservoir is filled, as it undergoes a transition from a dry to a persistently flooded state. However, lakes that are highly dynamic and sensitive to rainfall conditions tend to have lower accuracy. Nonetheless, certain characteristics are common to all water features. In all water features, pixels representing water advance tend to have higher accuracy compared to pixels indicating water recession. This is primarily because inundation or erosion processes tend to occur more rapidly, making it easier to identify the transition year. In the case of meandering rivers, for instance, erosion typically occurs in the deeper outer bank, which experiences lower seasonality. Conversely, sedimentation gradually accumulates over time in the shallower inner bank, which is more influenced by seasonal fluctuations, a more complex condition that reduces the water change accuracy. However, the development of the WCD and WTTD algorithms aimed to mitigate these complexities, enabling the monitoring of surface water bodies worldwide. References Konapala, G., Mishra, A. K., Wada, Y. & Mann, M. E. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. 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Assessment of Surface Water Dynamicsin Bangalore Using WRI, NDWI, MNDWI, Supervised Classification and K-T Transformation. Aquatic Procedia 4, 739–746, doi: https://doi.org/10.1016/j.aqpro.2015.02.095 (2015). Sarp, G. & Ozcelik, M. Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey. Journal of Taibah University for Science 11, 381–391, doi: 10.1016/j.jtusci.2016.04.005 (2017). Rokni, K., Ahmad, A., Selamat, A. & Hazini, S. Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery. Remote Sensing 6, 4173–4189, doi: 10.3390/rs6054173 (2014). Davis, R. A. & Hayes, M. O. What is a wave-dominated coast? Marine Geology 60, 313–329, doi: https://doi.org/10.1016/0025-3227(84)90155-5 (1984). Coca, O. & Ricaurte-Villota, C. Regional Patterns of Coastal Erosion and Sedimentation Derived from Spatial Autocorrelation Analysis: Pacific and Colombian Caribbean. Coasts 2, 125–151, doi: 10.3390/coasts2030008 (2022). Zhang, A. T. & Gu, V. X. Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information. Scientific Data 10, 111, doi: 10.1038/s41597-023-02008-2 (2023). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3215886","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":224684143,"identity":"f404a5f3-dc15-4ec4-8834-560dcc22111c","order_by":0,"name":"Gustavo Nagel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYJCCAwwVEnJIfDaCOhgPMJyxMGZgYCZeC/MBxraKxAaitchH5B44XMAmkT6/vf/g4wIGO3kGibQEvFoMb+QlHJ7BI5G74cxhZuMZDMmGDRJpB/BrmZ1jcJhHAqhFIplNmoeBOYFBIr2BCC0GEunyM8Ba6glrkZcGaUmQSGC4AdZyGKiFgMMM5N8AtRyQMAT6xdiYx+C4YRvPswT8tvScMf7M+69OXr698eFjnopqeX72NAP8tqA6woCIiJRvIKRiFIyCUTAKRgEAU3M9No/AxvEAAAAASUVORK5CYII=","orcid":"","institution":"University of Southampton","correspondingAuthor":true,"prefix":"","firstName":"Gustavo","middleName":"","lastName":"Nagel","suffix":""},{"id":224684144,"identity":"bc640672-0507-493a-a305-8d6b9f457034","order_by":1,"name":"Stephen Darby","email":"","orcid":"https://orcid.org/0000-0001-8778-4394","institution":"University of Southampton","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Darby","suffix":""},{"id":224684145,"identity":"87b20f16-5af0-42b5-a5e9-f20355338c2c","order_by":2,"name":"Julian Leyland","email":"","orcid":"","institution":"University of Southampton","correspondingAuthor":false,"prefix":"","firstName":"Julian","middleName":"","lastName":"Leyland","suffix":""}],"badges":[],"createdAt":"2023-07-29 14:00:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3215886/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3215886/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":41262629,"identity":"36d46636-9e1a-49c4-9401-f328c1bbc82b","added_by":"auto","created_at":"2023-08-08 17:26:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2869465,"visible":true,"origin":"","legend":"\u003cp\u003eYear of global surface water transition (advance or recession). Specific natural and engineered examples from the dataset show: the filling of the Três Irmãos hydroelectric dam in Brazil (a), flood based agricultural expansion in China (b), the delta of the Ganges River in Bangladesh (c), the expansion of small reservoirs for fishing and cattle raising in Amazonian deforested areas (d), the meandering of the Amazon River (e), the drying of Lake Burdur (an important wetland area and one of the deepest lakes in Türkiye) (f), the construction of artificial islands in Dubai (g), and a floodplain in China (h).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3215886/v1/a56100a2a9afcf1b33bd073b.png"},{"id":41261970,"identity":"763c1974-fe36-44da-b989-2cbdfce72f84","added_by":"auto","created_at":"2023-08-08 17:18:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1079022,"visible":true,"origin":"","legend":"\u003cp\u003eTiming of the transition from wet to dry (timing of water recession transitions) and dry to wet (timing of water advance). The size of the points corresponds to the magnitude of change in a particular year, indicated by the colours. Additionally, the two graphs below the maps highlight locations that have experienced substantial changes in water extent and flags the years in which the largest water change was related to human intervention.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3215886/v1/e1d152700e71f58c6104944b.png"},{"id":41261967,"identity":"dc7f336c-6811-40f2-9ef1-cf4252c22087","added_by":"auto","created_at":"2023-08-08 17:18:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":223992,"visible":true,"origin":"","legend":"\u003cp\u003eAccumulated areas of water advance and water recession over time in various regions of the world. The proportion of water change that occurred in different water environments (the bar plots on the right side of the diagrams) are also shown.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3215886/v1/2e595ac1893ce72c857282f4.png"},{"id":41261966,"identity":"7ff23b82-12ab-43d7-b23c-860cd33b0931","added_by":"auto","created_at":"2023-08-08 17:18:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":546481,"visible":true,"origin":"","legend":"\u003cp\u003eWater Change Detection (WCD) and the Water Time Track Detection (WTTD) Algorithm Method flowchart.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3215886/v1/5ea4f1f230938bc6ffda7a53.png"},{"id":41261965,"identity":"18c52f5a-8ade-4db5-b3ae-319df6b9374b","added_by":"auto","created_at":"2023-08-08 17:18:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":377322,"visible":true,"origin":"","legend":"\u003cp\u003eValidation points of the Water Change Detection for River, Floodplain, Flooded Agriculture, Lakes, Coasts and Delta environments.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3215886/v1/c023ee71bbaf228c76c50084.png"},{"id":68934188,"identity":"76453560-cec6-4a59-9693-db7a96ca5f35","added_by":"auto","created_at":"2024-11-13 16:13:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4989118,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3215886/v1/627a4802-2047-4c63-b7d5-ca7bdd0d52e3.pdf"},{"id":41261969,"identity":"af55b317-7fef-4fd2-a7e9-598adf79878b","added_by":"auto","created_at":"2023-08-08 17:18:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4376971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3215886/v1/88d4f729c14cd49bdc5be528.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Timing of Global Surface Water Transitions Reveals Anthropogenic Dominance of Recent Growth in Water Extent","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSurface water is an indispensable resource that plays a fundamental role in shaping our planet's landscapes and supporting human development \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The dynamic interplay between wet and dry conditions has driven profound changes throughout Earth's history, sculpting diverse landforms, influencing ecosystem dynamics, and impacting human civilizations. These changes can be attributed variously to human activities such as urban expansion and the construction of dams and levees, as well as to natural processes such as river meandering \u003csup\u003e2,7\u0026minus;15\u003c/sup\u003e. Climate change also influences the distribution of surface water through its impacts on precipitation variability, temperature changes and meting of ice \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Establishing the occurrence and timing of transitions of surface water state (i.e., transitions between wet and dry conditions, or \u003cem\u003evice versa\u003c/em\u003e) is, therefore, crucial for understanding the complex processes underlying landscape evolution and assessing their implications for sustainable human development \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address the challenge of studying surface water on a global scale, in recent years researchers have turned to satellite imagery and advances in cloud computing to enable the detection and estimation of various water resource variables at the global scale. Examples of such prior studies include mapping the presence or absence of surface water, assessing its temporal occurrence, classifying the behavior of surface water changes (e.g., do quantified surface water extents vary seasonally, are they permanent?), monitoring reservoir dynamics, and identifying areas of permanent water loss and gain \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Pekel, et al. \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and Donchyts, et al. \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e have made significant contributions to this field by mapping global surface water extent and changes using Landsat imagery, with their studies shedding new light on the spatial distribution and interannual variability of Earth\u0026rsquo;s surface water. However, whilst these previous approaches have focused on identifying areas of changing water occurrence, they ignore the timing of such changes, potentially limiting their utility for evaluating causation.\u003c/p\u003e \u003cp\u003eIn this study we present the first global analysis of the timing of surface water transitions using Landsat satellites images from 1984 to 2022 within the Google Earth Engine (GEE) cloud computing environment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Herein we define such temporal transitions as occurring either when a pixel was dry, and subsequently becomes persistently flooded or with seasonal water presence (inundated during the flooding period of the region), or when a pixel was wet, and subsequently becomes persistently dry or experiences only scarce flooding events (which occur less frequently than inundation periods). Our algorithm was specifically designed to minimize the impact of short-lived events (lasting one year or less) and, instead, it focuses on identifying transitions that substantively impact the 38-year history of each pixel. This approach lends confidence to the identification of shifts between dry and wet conditions. The derived dataset is publicly available on GEE and is also accessible through a GEE app that aids visual interpretation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gustavoonagel.users.earthengine.app/view/water-change-time-detection\u003c/span\u003e\u003cspan address=\"https://gustavoonagel.users.earthengine.app/view/water-change-time-detection\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The novelty of our research lies in its emphasis on understanding the timing of surface water transitions, and the implications of such transition timings for landscape evolution and human development.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003eOur surface water transition dataset provides a comprehensive representation of surface water temporal variability, capturing the timings and intricate patterns of change influenced by diverse natural and anthropogenic processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For example, in regions like China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.b) and Dubai (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.g), distinctive patterns\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e shaped by human land reclamation and food production activities stand in stark contrast to the natural patterns of change observed on rivers and floodplains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.c, 1.e, 1.h). Additionally, our dataset reveals that human interventions tend to occur over relatively short time intervals, such as the artificial infilling of the Tr\u0026ecirc;s Irm\u0026atilde;os Reservoir in Brazil that occurred abruptly in 1991 (see histogram below Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.f), the construction of the Palm Islands in Dubai, which experienced its peak of land reclamation in 2005 (histogram below Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.g), or the fish lakes that were filled in 2015 following Amazonian deforestation (Fig. d). In contrast, natural influences on water dynamics operate over longer time spans, such as in the Ganges delta (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.h), which displays a more distributed and similar rates of water advance and recession over time (histogram below Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.h), the gradual evolution of an Amazonian River (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.e), or the long-term droughts affecting a Turkish reservoir (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.a). These natural processes are indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e by smooth transitions from darker red and blue colors (indicating earlier timings of transition) to lighter colors (representing more recent transitions). Furthermore, different scales of change are also captured, as exemplified in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.a, which shows a lake of approximately 70.2 km\u0026sup2; that experienced drought and a nearby lake of only 0.08 km\u0026sup2; that was filled in 1994.\u003c/p\u003e\u003cp\u003eMajor and rapid events, such as reservoir filling, coastal expansion, and severe droughts, can lead to substantial advance or recession of water bodies within just a few years. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the timing of such transitions, at a global scale, in the period from 1984 to 2022, highlighting portions of the globe where transitions have occurred over spatially-coherent regions. Furthermore, the years in which the biggest water change was related to direct human interventions, such as dam construction, flooded agriculture expansion, poor water management, and poor infrastructure planning, is also highlighted (this information was gathered using visual interpretation and based on the literature).\u003c/p\u003e \u003cp\u003eIn regions where water is receding, 21% of the recession years were driven by direct human interventions or human-induced events, such as the Aral Sea disaster. The Aral Sea disaster is a prominent example of major water recession caused by the Korakal dike construction in the 1960s by the Soviet Union to divert water to agriculture projects \u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Considering the period since 1984, the most significant water recession event in the Aral Sea region occurred in 2004, surpassing any other water recession events in the world in that year and throughout the entire analysis period. In 1996, the Aral Sea, albeit on a smaller scale, was also the largest area of global water recession. Other lakes with recognized human intervention also experienced water recession in other periods, such as the Colhu\u0026eacute; Huapi in Argentina (1994) and Lake Urmia in Iran (2009), both affected by the over exploitation of water \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Furthermore, although on a smaller scale, in East Asia, particularly the coasts of China and South Korea are expanding, with a general peak in 2012, due to the construction of ports and expansion of cities. The highlighted Saemangeum Seawall, for example, is the largest dike on Earth, and although completed in 2006, the most significant impact was observed in 2012 due to land reclamation behind the 33-km long sea-wall \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Moreover, the identification of the year of water transitions has proven valuable in understanding the strategic manipulation of water resources during conflicts. A notable example is the Russian annexation of Crimea in 2014, where Ukraine's response of blocking the North Crimean Canal led to the drying of the canal and associated reservoirs in Crimea \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e (peak of water recession in 2014).\u003c/p\u003e \u003cp\u003eIn regions where water advanced, 66% of the years that experienced water change events were driven by direct human intervention, predominantly attributed to the construction of dams, which accounted for 55% of the water advance years. In the Amazon region, the Balbina Dam, filled in 1989, stands out as a significant water advance event that occurred within just one year, resulting in the highest infilling of water anywhere globally in 1989. This example serves as a reminder of the rapid and substantial impact that reservoirs can have on the environment, highlighting the importance of careful planning for additional dams in the region\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Other examples include the Serra da Mesa Dam (1997) and Porto Primavera Dam (1999) in Brazil, the Merowe Dam in Sudan (2005), the Bui Dam in Ghana (2006), and the Murum Dam in Malaysia (2012). However, in a similar way that the Aral Sea disaster was attributed to poor infrastructure planning, the largest recorded water advance event was also linked to another disaster that occurred in the Garabogazki Basin Lake in Turkmenistan, which displays peak water advances in 1993 and 1994. A breach in 1992 of a dam between the lagoon and the Caspian Sea led to a rapid influx of water into the basin\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, substantially increasing the water surface area by around 2772 km\u0026sup2;. Overall, these examples demonstrate the significant impact that human activities have on the advance of surface water globally.\u003c/p\u003e \u003cp\u003eBesides human-influenced events, the analysis also identified natural or indirectly human-influenced occurrences of water advance and recession. However, even in the absence of direct human influence, these areas might still be susceptible to the effects of climate change in driving drought and flooding regimes. In Southeast Australia, for example, prolonged droughts are completely drying up many reservoirs \u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, such as in 2001 when the Australian Lake Menindee experienced the world\u0026rsquo;s largest water recorded recession transition. In Afghanistan, many lakes in the Registan desert completely dried up in 2005 (collectively representing the largest recession of water in the world in that particular year). In South America, the Pantanal wetland region and the downstream Paran\u0026aacute; River also experienced severe droughts \u003csup\u003e\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, with a water retreat peak displayed in the year 2000 in the Pantanal highlighted region. California in North America also faced reduced rainfall events, leading to a decline in reservoir capacity \u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e (with a water retreat peak in 1991 in the highlighted Lake Malheur). Changes in the climate can also lead to an increase in water extent. The lakes in the Tibet Plateau, for example, experienced the most significant water advance in the world in 2005. These lakes are growing in response to the impacts of climate change, driven by rising precipitation levels, glacier loss, and permafrost thawing in the region \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Higher precipitation rates also contributed to the filling of the highlighted Quill Lakes and surrounding prairie potholes in North Dakota\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e (USA), with a peak of water advance in 2012, and the Toshka Lakes in Egypt, resulting in significant water expansion in 2020 (the largest advance of water in the world in that year). The Toshka Lakes are closely connected to the Nile River and were influenced by increased flow from the upstream regions of Sudan, which experienced higher precipitation rates \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsidering the accumulated amount of water change, global areas of water advance and recession initially grew at similar rates until 2012, when the total areas of water advance began to surpass the total areas of water recession (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.a). The cumulative area of water advance from 1984 to 2022 was estimated as 323,326,000 km\u0026sup2;, while the extent of water recession was 236,892,000 km\u0026sup2;. Of the total area of water advance, 56% was observed in high residence time inland water areas, such as reservoirs, lakes, wetlands, and flooded agriculture, while 29% occurred in rivers, 12% in coastal regions, and 3% in deltas. For water recession, the proportions were 60%, 26%, 11%, and 3%, in inland waters, rivers, coasts and deltas, respectively. However, the timings of the transitions of water advance and recession vary substantially across continents. For example, South and Central America and Europe had similar rates of water advance and recession, despite having slightly higher water recession areas overall. In South and Central America, water recession areas were on average 12% higher than areas of water advance, which aligns well with estimates by MAPBIOMAS for Brazil during the same period (15%)\u003csup\u003e47\u003c/sup\u003e. North America, characterized by the highest percentage of water change occurring in lakes and reservoirs (73%), initially had more areas of water recession, but experienced a shift in 2001 when areas of advancing water surpassed areas of receding water. Similar patterns are observed in Africa (lake dominated), where a crossover from water recession to water advance occurred in 2002. Likewise South Asia (the only river dominated region) crossed from water recession to water advance dominant in 2011, while East and Southeast Asia (with more balanced proportions) crossed in 2022 and Central Asia (reservoir dominated) did so in 1993. In contrast, the South Pacific, and especially the Middle East, were the regions with the highest relative proportions of water recession, with a discrepancy of 19.8% and 43.6% between water recession and water advance, respectively.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThe new global water transition timing dataset captures a wide range of natural and anthropogenic processes, providing valuable insights into global water dynamics. The emphasis on timing of water transitions allows the identification of human-induced rapid events that changed large swaths of water in a short time interval (one year), revealing the construction of dams around the world, major droughts, infrastructure failures, and even the effects of conflict on water extents. Direct human influence was responsible for the largest water change events in 21% of the years for recession events and 66% of the years for water advances (55% of the years were related to dam construction). The infrastructure failures were responsible for the largest historical water advance, related to a breach in the Caspian Sea\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e in 1992, that inundated large areas in 1993 and 1994, and water recession events, related to the Aral Sea disaster\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, with a peak of recession in 2004. These events show how poorly planned human interventions can have huge impacts on the environment. Furthermore, the construction of large dams surpassed any other water advance event in more than half of the analysed years (21 years). The Balbina Dam, for example, which filled its reservoir in 1989 in the Amazon, was the largest artificial reservoir build during the analysed period. This is a stark example of how dams in the low-gradient lowlands of the Amazon produce large reservoirs, which in turn submerge large areas of forest.\u003c/p\u003e \u003cp\u003eThese dam constructions certainly played a role in the surpassing of the accumulated areas of advance over accumulated areas of recession in the different continents of the world, such as North America, which transitioned to this state in 2002, Africa (transitioned in 2000), South Asia (transitioned 2011), East and Southeast Asia (transitioned in 2002), and Central Asia (transitioned 1993). In East Asia, in addition to the surge of dams\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and the spread of flooded agriculture (more than 90% of the global rice is produced in the Asia\u0026ndash;Pacific region\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e), climate change is also impacting the rates of precipitation and ice melting, which are filling up the natural lakes of the Tibetan Plateau\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Furthermore, in North America, despite the water retreat observed on the US West Coast (mainly before 2010), the filling of prairie potholes in North Dakota in 2012, mainly caused by higher precipitation\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, also contributed to the positive surface water balance of the continent. In South and Central America, despite the great spread of dams, the severe droughts of Pantanal, with peak in 2000 and 1994, and Northwest Brazil (peak in 2012) might have contributed to equilibrate the rates of water advance and recession. However, the Middle East and South Pacific had higher rates of water recession, which means that the overall water surface greatly decreased between 1999 and 2011 in both regions. In both desertic regions, many lakes were completely depleted by droughts \u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, such as the Menindee Lake and many others in Southeast Australia (peak of recession in 2001) and in some lakes of Afghanistan (2005).\u003c/p\u003e \u003cp\u003eNonetheless, the continents with positive water balance are not necessarily in a better water security condition, since the spatial distribution of water transition is not even, as shown in the previous examples. Areas of water advance can also have the opposite effect (on water security), such as the expansion of areas of flooded agriculture, which can reduce water security since it increases water evapotranspiration rates\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Water security and conflicts in general can significantly impact water dynamics, as in the case of the Crimean Canal blockage. While currently on a smaller scale, the impact of conflicts on water may escalate in the future due to population growth and climate change, leading to decreased water availability in certain regions \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Overall, the new dataset presented in this paper is able to identify globally important water transitions across the planet, related to drought, reservoir construction and infrastructure projects, and allows comparison of the timing and rates of water transition between continents. The attribution of water changes to human influence was focused on large-scale events, as global information on whether water changes are shaped by humans or natural influences is not readily available. Identifying infrastructure failures and overexploitation of water was even more challenging, and thus we relied on existing literature for these cases. We anticipate that the dataset will be of considerable interest to researchers seeking to decipher the intricate relationships between hydrological processes, erosion rates, landform evolution and the development and sustainability of human activities. The dataset is publicly available on Google Earth Engine (GEE) and an Earth Engine App has been developed for convenient data visualization and retrieval (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gustavoonagel.users.earthengine.app/view/water-change-time-detection\u003c/span\u003e\u003cspan address=\"https://gustavoonagel.users.earthengine.app/view/water-change-time-detection\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cp\u003eThe unique combination of its high spatial resolution (30 m), long duration of archive (spanning from 1984 for Landsat 5 to 2023 for Landsat 9), and a revisit time of 16 days positions the Landsat program as a highly suitable tool for undertaking a comprehensive analysis of global inland and coastal surface water. To accurately detect the specific year of change, we developed two algorithms utilizing Landsat Time Series from 1984 to 2023 and the cloud computing platform Google Earth Engine (GEE). The first algorithm, the Water Change Detection (WCD) Algorithm, estimates areas of surface water transition by leveraging the Modified Normalized Difference Water Index (mNDWI)\u003csup\u003e\u003cspan additionalcitationids=\"CR56 CR57 CR58 CR59 CR60 CR61 CR62 CR63\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, while the second algorithm, the Water Time Track Detection (WTTD), identifies the year of change through the use of the Green_Red Normalized Difference Water Index (grNDWI) proposed herein (see Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in Supplementary Information, SI). Regions with a Landsat dataset starting only after 1990 were excluded from the analysis (see Fig. S5 in SI)\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Water Change Detection Algorithms\u003c/h2\u003e \u003cp\u003eTo delineate areas of changing water extent, we implemented the Water Change Detection (WCD) algorithm on annual Modified Normalized Difference Water Index (mNDWI) images derived from pre-processed Landsat 5, 8, and 9 data, covering the period from 1984 to 2023 (see Fig. S4 in SI for details and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.a for a summary). Through a pixel-wise analysis, areas of potential change were identified based on the crossing of a pre-determined mNDWI threshold (see Fig. S3 in SI), indicating either water advance (upward crossing) or water recession (downward crossing) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.b). Recognizing the dynamic nature of surface water, where regions may experience recurrent episodes of flooding or drying, we employed a further analysis of each pixel's mNDWI trend, the trend being determined from Ordinary Least Squares Regression as computed using annual mNDWI over time. Pixels that exhibited both an upward crossing of the threshold and had a regression line slope exceeding 0.005 mNDWI/year were considered as areas with a transition to water advance. Conversely, pixels that displayed both a downward crossing of the threshold and a trend of less than \u0026minus;\u0026thinsp;0.008 mNDWI/year were classified as regions of water recession (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.c). The determination of these trend values was obtained by extracting slope samples from areas previously identified as undergoing erosion and sedimentation processes (see Fig. S3 in SI for details).\u003c/p\u003e \u003cp\u003eTo determine the timing (here we resolve timing at annual resolution) of water advance or recession, we employed the Water Time Track Detection (WTTD) algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This algorithm was specifically designed to filter out short-lived events, such as seasonal flooding and droughts. In areas identified by the Water Change Detection (WCD) algorithm as experiencing water advance or recession, we constructed a new time series using the Green_Red Normalized Difference Water Index (grNDWI). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.d to 4.k illustrates the processing steps applied to a single pixel identified as water receding, with similar procedures followed for water advancing pixels. The Landsat 5, 8, and 9 images underwent pre-processing procedures including cloud filtering, cloud and snow masks, and the generation of annual grNDWI composites (see Fig. S4 in SI for details). However, even with the use of annual composites, cloud cover can result in missing data for several years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.d), particularly in regions prone to cloudiness like the Amazon. To address this issue, we developed a technique to fill in data gaps in the time series by utilizing the preceding and subsequent grNDWI years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.e \u0026ndash; details are provided in Fig. S4). With the new, uninterrupted, time series we extracted the maximum and minimum grNDWI values to calculate the mean grNDWI, which served as the pixel-specific water-land threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.2.f). This pixel threshold represents a locally optimized water/land threshold, acknowledging that a pixel transitioning from forest to turbid water would have a different threshold than a pixel transitioning from sand to clear water, even if they were in close proximity. We note that, by relying on the entire time series, the pixel threshold method is susceptible to potential outliers, such as unfiltered clouds and atmospheric constituents, which can affect the accuracy of the results. To address this, we computed the mean between the individual pixel threshold and a regional threshold (calculated using the OTSU method, see Fig. S2 in SI), resulting in the creation of the Optimal Threshold that incorporates both local and regional influences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.g).\u003c/p\u003e \u003cp\u003eThe newly derived Optimal Threshold serves as the basis for determining the transition year of water advance or recession. For any individual pixel to be defined as experiencing water advance, it must cross the Optimal Threshold in an upward direction, while water recession pixels must cross it in a downward direction, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.h. Considering the influence of short-lived flooding events, an individual pixel may cross the Optimal Threshold multiple times, resulting in several candidate transition years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.h). To select the actual year of transition used in the analysis, we calculated the mean grNDWI using a 5-year moving window and determined the year at which it crossed the Optimal Threshold, establishing a reference year (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.i). By comparing the candidate years with the reference year, we selected the candidate with the smallest difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.j). In the provided example, we encountered two candidate years and chose the more recent one (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.k). The transition year is then stored as part of the pixel dataset that covers the entire Earth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo summarize the water change dataset, we employed a hexagonal grid system with each hexagon covering an area of 7,179 km\u0026sup2;. Within each hexagon, we determined the year with the most significant water change and quantified the extent of both water recession and advance. This approach allowed us to identify major recession and/or advances and their corresponding years of occurrence. Additionally, we calculated the accumulated area of water change for different continents. To assess the proportion of change occurring in various water environment types, such as high residence time inland waters (reservoirs, lakes, wetlands, and flooded agriculture), rivers, coastal regions, and deltas, we further subdivided the world into smaller hexagons measuring 1,010 km\u0026sup2;. By estimating the percentage of each water environment within these smaller hexagons (using datasets provided by Google Earth Engine), we associated these percentages with the accumulated areas of water recession and advance within each hexagon. Then, we summed up the water change associated with each water environment type to determine the percentage of change associated to lakes, rivers, coastal, and deltas. Fig. S7 in the Supplementary Information (SI) provides a visual representation of this process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Validation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe conducted a validation process by collecting 700 geographically diverse data points across the globe. To facilitate this, we developed a user-friendly Google Earth Engine (GEE) App that produced a random distribution of points and displayed a Landsat Time Series with the NIR, Red, and Blue band combination to highlight distinctions between water and land. Through careful visual analysis, we determined the most appropriate year of transition in relation to water advance and recession and compared that year with the year of change as estimated using the WTTD algorithm. The determination of the most appropriate year was performed by identifying the year that water advanced at each point and remained flooded, and the year that the water receded and remained dry. This validation procedure encompassed a range of different types of water bodies such as rivers, floodplains, flooded agricultural areas, lakes, coasts, and delta regions. Key statistical metrics were derived to evaluate the performance of the WTTD algorithm. These metrics include the Mean Absolute Percentage Error for combined water advance and recession points (MAPE), MAPE for water advance points (MAPEadv), MAPE for recession points (MAPErec), the Coefficient of Determination (R\u0026sup2;), and the Critical Success Index (CSI), thereby establishing a comprehensive assessment framework (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) to ensure the accuracy and reliability of our findings. An overall error distribution can be visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.h, and for each water feature in Fig. S6 in the SI.\u003c/p\u003e \u003cp\u003eOur analysis yielded a Mean Absolute Percentage Error (MAPE) of 13.9%, indicating a high level of forecasting accuracy, as supported by previous studies (Monta\u0026ntilde;o Moreno et al., 2013). The coefficient of determination (R\u0026sup2;) was 0.82, demonstrating a strong correlation between the predicted and observed data points, while the overall CSI was 95.7% when considering all water bodies in a single regression model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.a). These statistics indicate the robustness of the WTTD algorithm, considering the intricate and dynamic nature of global water surfaces. Notably, the accuracy of the WTTD algorithm is seen to vary across different water features, with lakes and coasts exhibiting the best results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.e and 5.f), characterized by similar MAPE values of 11.1%, CSI values of 96% and 98.9%, and high R\u0026sup2; values of 0.85 and 0.93, respectively. Rivers (MAPE: 15.5%, CSI: 95.5%, and R\u0026sup2;: 0.79) and deltas (MAPE: 18.5%, CSI\u0026thinsp;=\u0026thinsp;100%, and R\u0026sup2;: 0.85) also demonstrated good accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.b and 5.g). However, floodplains and flooded agriculture areas, while showing good MAPE values (15.6% and 13.2%, respectively) and good CSI values (88.2% and 100%, respectively), exhibited substantial disparities between the estimated and observed years, resulting in decreased R\u0026sup2; values (0.67 and 0.69) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.c and 5.d). Remarkably, floodplain regions showed a concentration of misclassifications, with 6.7% of the predicted points not correctly experiencing persistent changes. Moreover, it is important to highlight that regions of water advance generally exhibited better estimations compared to water receding regions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe variations in accuracy can be attributed to the inherent complexity of different water features and the extent to which water colour and water extend change over time, which directly impact the performance of both the WCD and WTTD algorithms. Coastal regions, for instance, are influenced by a combination of factors such as ocean currents, wave energy, tides, and coastal morphometric characteristics \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. However, these influences tend to persist over longer durations, and it is unlikely for an eroding coast to transition into a sediment-filled one within the time span of our analysis. Additionally, coastal areas generally exhibit lower complexity in terms of their water colour when compared to inland water bodies, conditions that increased the accuracy of the water change algorithms. On the other hand, rivers and floodplains are subject to interannual rainfall variations, seasonality effects, and water turbidity fluctuations, which introduce uncertainties in the analysis. Floodplains, in particular, have relatively shallower inundated areas compared to rivers, thus with a greater colour influence from the submerged surface bottom. Furthermore, they are highly influenced by seasonal variations and short periods of drought and flooding, which adds complexity and potentially leads to reduced accuracy in the assessment of floodplain regions. In the case of flooded agriculture, although they tend to be as shallow as floodplains, they are less affected by rainfall variability as their areas of inundation are artificially controlled. This artificial manipulation contributes to greater stability, resulting in higher accuracy compared to floodplain regions.\u003c/p\u003e \u003cp\u003eLakes generally exhibit higher overall accuracy, primarily attributed to the rapid global construction of new dams \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. The water change algorithms can easily detect the year when a reservoir is filled, as it undergoes a transition from a dry to a persistently flooded state. However, lakes that are highly dynamic and sensitive to rainfall conditions tend to have lower accuracy. Nonetheless, certain characteristics are common to all water features. In all water features, pixels representing water advance tend to have higher accuracy compared to pixels indicating water recession. This is primarily because inundation or erosion processes tend to occur more rapidly, making it easier to identify the transition year. In the case of meandering rivers, for instance, erosion typically occurs in the deeper outer bank, which experiences lower seasonality. Conversely, sedimentation gradually accumulates over time in the shallower inner bank, which is more influenced by seasonal fluctuations, a more complex condition that reduces the water change accuracy. However, the development of the WCD and WTTD algorithms aimed to mitigate these complexities, enabling the monitoring of surface water bodies worldwide.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKonapala, G., Mishra, A. K., Wada, Y. \u0026amp; Mann, M. E. 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Scientific Data 10, 111, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41597-023-02008-2\u003c/span\u003e\u003cspan address=\"10.1038/s41597-023-02008-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3215886/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3215886/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe extent of coastal and inland surface water resources is constantly varying in response to complex interrelated processes, driven by natural and anthropogenic factors. Recent advance in satellite technology and cloud computing have enabled global-scale monitoring of the changing occurrence and extent of these surface water resources. However, until now, no previous study has sought to estimate the timing of these surface water changes at the global-scale. Here we introduce the first global-scale identification of the year when water advanced or receded within a given pixel, using a 38-year Landsat time series. Our methods focus exclusively on persistent changes in water features, filtering out seasonal or short-lived fluctuations. We use the new algorithm to map the timing of water advance and/or recession events globally, encompassing both inland water bodies and coastal dynamics. Additionally, the timing of water transitions enabled the identification of the primary drivers behind these changes. As a result, we identified that most of the large-scale water change events are related to human influence, such as damming, infrastructure failures and even conflicts. These combined factors contributed to a global shift, with accumulated water advancing surpassing water receding over time.\u003c/p\u003e","manuscriptTitle":"Timing of Global Surface Water Transitions Reveals Anthropogenic Dominance of Recent Growth in Water Extent","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-08 17:18:17","doi":"10.21203/rs.3.rs-3215886/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f3441453-8f4b-4d19-ae3e-36418ff3b915","owner":[],"postedDate":"August 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":23868122,"name":"Earth and environmental sciences/Hydrology"},{"id":23868123,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":23868124,"name":"Earth and environmental sciences/Climate sciences/Hydrology"},{"id":23868125,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts/Environmental health"},{"id":23868126,"name":"Earth and environmental sciences/Ocean sciences/Physical oceanography"}],"tags":[],"updatedAt":"2024-11-13T16:05:42+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-08 17:18:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3215886","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3215886","identity":"rs-3215886","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}