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Overview Map of Alaska North Slope study site (NSL) with generalized geological subzones. YOCP: Younger Outer Coastal Plain; OCP: Outer Coastal Plain; ICP: Inner Coastal Plain; AF: Arctic Foothills.
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Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying process...
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Land surface-atmosphere interaction is one of the most important characteristic for understanding the terrestrial climate system, as it determines the exchange fluxes of energy and water between the land and the overlying air mass. In several current climate models, it is common practice to use an unphysical approach to close the surface energy bal...
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... i, Warming-driven shrubification or greening of the tundra causes changes in evapotranspiration, surface albedo and snow cover that affect soil temperatures and thaw depths in different ways. The coloured squares in a-c, h and i indicate the sub-regions in Fig. 2 studies actually suggest decreases in Arctic surface water coverage [56][57][58][59] . Accordingly, there is no evidence for a lake-dynamics-driven acceleration of regional permafrost thaw rates. ...
... To capture detailed or localized features of various eco-types, higher-resolution optical satellite data such as Landsat series have been widely used in the subarctic boreal environments [26][27][28]. The optical images have also been used for understanding spatially detailed changes in periglacial landforms, particularly in association with thermokarst processes by analyzing trends of surface reflectance or by using post-classification analysis [9,[29][30][31][32][33][34][35]. ...
... They also reported the presence of lake expansion hot spot areas in the lake-rich terrace in the eastern Lena River. Nitze et al. [34] conducted a Landsat-based comparative analysis on the lake dynamics between 1999 and 2014 for four representative thermokarst regions such as Alaska North Slope, Alaska Kobuk-Selawik Lowlands, and Kolyma Lowland along with thermokarst terrain in the Lena River lowlands. They found that the net lake area in other thermokarst regions, such as the northern coastal regions and western Alaska, tended to decrease between the 1999 and 2014 study period, while the eastern Lena River Lowland was characterized by extreme lake expansion. ...
Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.
... This approach is convenient because the identification process only requires using the least squares algorithm. In addition, the slope coefficient quantifies the trend's strength and has a precise statistical meaning (the Pearson correlation coefficient), enabling statistical tests.Due to the high sensitivity to outliers, simple linear regression is usually replaced by a more robust version (Nitze et al., 2017). However, the assumption of linearity is often unrealistic, and thus curves such as the splines are used to account for nonlinear trends. ...
The pervasive diffusion of information and communication technologies that has characterized the end of the 20th and the beginning of the 21st centuries has profoundly impacted the way water management issues are studied. The possibility of collecting and storing large data sets has allowed the development of new classes of models that try to infer the relationships between the variables of interest directly from data rather than fit the classical physical and chemical laws to them. This approach, known as “data-driven,” belongs to the broader area of machine learning (ML) methods and can be applied to many water management problems.
In hydrological modeling, ML tools can process diverse data sets, including satellite imagery, meteorological data, and historical records, to enhance predictions of streamflow, groundwater levels, and water availability and thus support water allocation, infrastructure planning, and operational decision-making.
In water demand management, ML models can analyze historical water consumption patterns, weather data, and socioeconomic factors to predict future water demands. These models can support water utilities and policymakers in optimizing water allocation, planning infrastructure, and implementing effective conservation strategies.
In reservoir management, advanced ML tools may be used to determine the operating rule of water structures by directly searching for the management policy or by mimicking a set of decisions with some desired properties. They may also be used to develop surrogate models that can be rapidly executed to determine the optimal course of action as a component of a decision-support system.
ML methods have revolutionized water management studies by showing the power of data-driven insights. Thanks to their ability to make accurate forecasts, enhanced monitoring, and optimized resource allocation, adopting these tools is predicted to expand and consistently modify water management practices. Continued advancements in ML tools, data availability, and interdisciplinary collaborations will further propel the use of ML methods to address global water challenges and pave the way for a more resilient and sustainable water future.
... Among permafrost regions in the Northern Hemisphere especially the north-eastern Siberia have experienced dynamic environmental changes induced by climate change (Nitzbon et al., 2020). Recent climate-induced increases in thaw propagation have triggered changes in local relief in the Yedoma uplands, including soil subsidence (Günther et al., 2015), activation of thermokarst and thermoerosion processes (Grigoriev et al., 2009;Morgenstern et al., 2021), and the expansion of pond and thermokarst lake areas (Nitze et al., 2017;Veremeeva et al., 2021). In large rivers, an increase in runoff is both expected and already observed: in the Kolyma, mean annual discharge has increased over the 2010-2020 by 27.7% (94.6-120.7 km 3 year À1 ) compared with a baseline period of 1971-2000 . ...
Permafrost regions are under particular pressure from climate change resulting in widespread landscape changes, which impact also freshwater chemistry. We investigated a snapshot of hydrochemistry in various freshwater environments in the lower Kolyma river basin (North‐East Siberia, continuous permafrost zone) to explore the mobility of metals, metalloids and non‐metals resulting from permafrost thaw. Particular attention was focused on heavy metals as contaminants potentially released from the secondary source in the permafrozen Yedoma complex. Permafrost creeks represented the Mg‐Ca‐Na‐HCO 3 ‐Cl‐SO 4 ionic water type (with mineralisation in the range 600–800 mg L ⁻¹ ), while permafrost ice and thermokarst lake waters were the HCO 3 ‐Ca‐Mg type. Multiple heavy metals (As, Cu, Co, Mn and Ni) showed much higher dissolved phase concentrations in permafrost creeks and ice than in Kolyma and its tributaries, and only in the permafrost samples and one Kolyma tributary we have detected dissolved Ti. In thermokarst lakes, several metal and metalloid dissolved concentrations increased with water depth (Fe, Mn, Ni and Zn – in both lakes; Al, Cu, K, Sb, Sr and Pb in either lake), reaching 1370 μg L ⁻¹ Cu, 4610 μg L ⁻¹ Mn, and 687 μg L ⁻¹ Zn in the bottom water layers. Permafrost‐related waters were also enriched in dissolved phosphorus (up to 512 μg L ⁻¹ in Yedoma‐fed creeks). The impact of permafrost thaw on river and lake water chemistry is a complex problem which needs to be considered both in the context of legacy permafrost shrinkage and the interference of the deepening active layer with newly deposited anthropogenic contaminants.
... The snow season spans from October to mid-May of the next year. Melting of snow increases soil moisture due to continuous permafrost and the low topographic relief (Hobbie 1984, Jepsen et al 2013, Carroll and Loboda 2017, Nitze et al 2017. ...
Earlier snowmelt, warmer temperatures and herbivory are among the factors that influence high-latitude tundra productivity near the town of Utqiaġvik in northern Alaska. However, our understanding of the potential interactions between these factors is limited. MODIS observations provide cover fractions of vegetation, snow, standing water, and soil, and fractional absorption of photosynthetically active radiation by canopy chlorophyll (fAPARchl) per pixel. Here, we evaluated a recent time-period (2001 – 2014) that the tundra experienced large interannual variability in vegetation productivity metrics (i.e., fAPARchl and APARchl), which was explainable by both abiotic and biotic factors. We found earlier snowmelt to increase soil and vegetation cover, and productivity in June, while warmer temperatures significantly increased monthly productivity. However, abiotic factors failed to explain stark decreases in productivity during August of 2008, which coincided with a severe lemming outbreak. MODIS observations found this tundra ecosystem to completely recover two years later, resulting in elevated productivity. This study highlights the potential roles of both climate and herbivory in modulating the interannual variability of remotely retrieved plant productivity metrics in Arctic coastal tundra ecosystems.
... There was no need to exclude lake and river ice from the analysis since all lakes and rivers were completely ice-free at the time. To determine snow cover, we used an object-oriented image analysis and support vector machine learning classification algorithm (Nitze et al., 2017(Nitze et al., , 2018. Following the classification of lingering summer snow cover, we converted the rasterized data to vector polygons for further analysis in a GIS. ...
Snowdrifts formed by wind transported snow deposition represent a vital component of the earth surface processes on Arctic tundra. Snow accumulation on steep slopes particularly at the margins of rivers, coasts, lakes, and drained lake basins (DLBs) comprise a significant water storage component for the ecosystem during spring and summer snowmelt. The tundra landscape is in constant change as lakes drain, substantially altering the surface morphology that partially controls how snow drifts and accumulates throughout the cold seasons. Here, we combine field measurements, remote sensing observations, and snow modeling to investigate how lake drainage affects snow redistribution at Inigok on the Arctic Coastal Plain of Alaska, where the snow movement is controlled by wind. Field observations included measurements of snow depth using ground penetrating radar and probe. We mapped mid‐July snow cover and modeled snow redistribution before and after drainage simulation for 33 lakes (∼30 km²) in our study area (∼140 km²). Our results show the advantage of using a wide range of snow depth measurements on frozen lakes, DLBs, and upland to validate the snow modeling in order to capture the variability inherent in the landscape. The lake drainage simulation suggests an increase in snow storage of up to ∼24% at DLBs compared to extant lakes, ∼35% considering only snowdrifts (assumed as ≥1 m depth), and ∼4% considering the whole study area. This increase in snow accumulation could significantly impact the landscape when it melts, including wildlife, vegetation, biogeochemical processes, and potential natural hazards like snow‐dam outburst floods.
... As we mentioned before, surface hydrology is key information for assessing the snow melting associated with winter heatwaves, and the detection of freshwater (streams, lakes, and ponds) in concomitance with seasonal snow represents major knowledge for cryospheric studies. Surface hydrology is a task already approached in Arctic areas using both optical [61,62] and microwave remote sensing [63], but the complexity of the selected study area was in this case contributed by the simultaneous occurrence of seasonal snow, water bodies, and outcropping bedrock during the snow melting event. A key role was played by the dynamic of braided channels in the riverine system [61], and the capacity to detect water-saturated sediments was the major feature that differentiated the water output in the summer season in comparison to the winter heatwave. ...
The occurrence of extreme warm events in the Arctic has been increasing in recent years in terms of their frequency and intensity. The assessment of the impact of these episodes on the snow season requires further observation capabilities, where spatial and temporal resolutions are key constraints. This study targeted the snow season of 2022 when a winter rain-on-snow event occurred at Ny-Ålesund in mid-March. The selected methodology was based on a multi-scale and multi-platform approach, combining ground-based observations with satellite remote sensing. The ground-based observation portfolio included meteorological measurements, nivological information, and the optical description of the surface in terms of spectral reflectance and snow-cover extent. The satellite data were obtained by the Sentinel-2 platforms, which provided ten multi-spectral acquisitions from March to July. The proposed strategy supported the impact assessment of heat waves in a periglacial environment, describing the relation and the timing between rain-on-snow events and the surface water drainage system. The integration between a wide range of spectral, time, and spatial resolutions enhanced the capacity to monitor the evolution of the surface water drainage system, detecting two water discharge pulsations, different in terms of duration and effects. This preliminary study aims to improve the description of the snow dynamics during those extreme events and to assess the impact of the produced break during the snow accumulation period.
... Compared to thermokarst lake drainage events, the expansion of thermokarst lakes is a relatively slow process, with typical expansion rates ranging from tens to hundreds of centimeters per year [30]. In previous studies, Nitze et al. (2017) [27] analyzed lake dynamics in the Kolyma Lowland of Northeastern Siberia, reporting a 0.51% decrease in lake area between 1999 and 2014, indicating that the region experienced more lake area loss due to lake drainage events than gain from lake expansion. However, Veremeeva et al. (2021) [24] reported an increase in the lake area by 0.89% (1999-2013) and 4.15% (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) in the Kolyma Lowland, suggesting a dominant role in lake expansion. ...
Thermokarst lakes in permafrost regions are highly dynamic due to drainage events triggered by climate warming. This study focused on mapping lake drainage events across the Northeast Siberian coastal tundra from 2000 to 2020 and identifying influential factors. An object-based lake analysis method was developed to detect 238 drained lakes using a well-established surface water dynamics product. The LandTrendr change detection algorithm, combined with continuous Landsat satellite imagery, precisely dated lake drainage years with 83.2% accuracy validated against manual interpretation. Spatial analysis revealed the clustering of drained lakes along rivers and in subsidence-prone Yedoma regions. The statistical analysis showed significant warming aligned with broader trends but no evident temporal pattern in lake drainage events. Our machine learning model identified lake area, soil temperature, summer evaporation, and summer precipitation as the top predictors of lake drainage. As these climatic parameters increase or surpass specific thresholds, the likelihood of lake drainage notably increases. Overall, this study enhanced the understanding of thermokarst lake drainage patterns and environmental controls in vulnerable permafrost regions. Spatial and temporal dynamics of lake drainage events were governed by complex climatic, topographic, and permafrost interactions. Integrating remote sensing with field studies and modeling will help project lake stability and greenhouse gas emissions under climate change.
... The Mann-Kendall test is a non-parametric test recommended by the World Meteorological Organization to test for the presence of trends in time series [46]. It is considered a robust linear trend regression method [45,[47][48][49][50]. The Mann-Kendal test (Z Kendall coefficient) provides information on the significance of the trend [46]. ...
Spatio-temporal analysis of rainfall trends in a watershed is an effective tool for sustainable water resources management, as it allows for an understanding of the impacts of these changes at the watershed scale. The objective of the present study is to analyze the impacts of climate change on the availability of surface water resources in the Nakanbe-Wayen watershed over the period from 1981 to 2020. The analysis was conducted on in situ rainfall data collected from 14 meteorological stations distributed throughout the watershed and completed with CHIRPS data. Ten precipitation indices, recommended by the ETCCDI (Expert Team on Climate Change Detection and Indices), were calculated using the RClimDex package. The results show changes in the distribution of annual precipitation and an increasing trend in annual precipitation. At the same time, a trend towards an increase in the occurrence and intensity of extreme events was also observed over the last 4 decades. In light of these analyses, it should be emphasized that the increase in precipitation observed in the Nakanbe-Wayen watershed is induced by the increase in the occurrence and intensity of events, as a trend towards an increase in persistent drought periods (CDD) is observed. This indicates that the watershed is suffering from water scarcity. Water stress and water-related hazards have a major impact on communities and ecosystems. In these conditions of vulnerability, the development of risk-management strategies related to water resources is necessary, especially at the local scale. This should be formulated in light of observed and projected climate extremes in order to propose an appropriate and anticipated management strategy for climate risks related to water resources at the watershed scale.
... Recently, satellitebased earth observation techniques and data-based analytical methods have been increasingly used by scientists (Huang and Zhao, 2018). Machine learning algorithms, a typical data-based method, have been applied to the dynamics of thermokarst lakes Nitze et al., 2017;Veremeeva et al., 2021). For assessing the susceptibility of thermokarst lakes, by combining the thermokarst lake inventory and the conditioning factors for machine learning modeling, the contribution of each conditioning factor to the occurrence of the thermokarst lake is obtained, and then the prediction of the thermokarst lake occurrence possibility in the future becomes more feasible (Luo et al., 2022;Yin et al., 2021). ...
... We excluded rivers and streams to refine this thermokarst lake sample dataset with a global river and stream dataset (Allen and Pavelsky, 2018). In addition, this thermokarst lake inventory for machine learning modeling also included data from an existing study (Nitze et al., 2017). We randomly generated non-thermokarst lake points with the Google Earth Engine. ...
... Most previous studies have focused on the dynamics of thermokarst lakes at local or regional scales in the Arctic (Frohn et al., 2005;Muster et al., 2017;Nitze et al., 2017;Olthof et al., 2015;Turner et al., 2022), which have helped understand thermokarst processes and provided datasets with better resolution accuracy. Additionally, Olefeldt et al. (2016) provides a geospatial assessment of typical thermokarst landscapes (i.e., wetland, lake, and hillslope) in the circum-Arctic region, which is important to understand the response of thermokarst processes to climate change. ...
Ice-rich permafrost thaws in response to rapid Arctic warming, and ground subsidence facilitates the formation of thermokarst lakes. Thermokarst lakes transform the surface energy balance of permafrost, affecting geomorphology, hydrology, ecology, and infrastructure stability, which can further contribute to greenhouse gas emissions. Currently, the spatial distribution of thermokarst lakes at large scales remains a challenging task. Based on multiple high-resolution environmental factors and thermokarst lake inventories, we used machine learning methods to estimate the spatial distributions of present and future thermokarst lake susceptibility (TLS) maps. We also identified key environmental factors of the TLS map. At 1.8 × 106 km2, high and very high susceptible regions were estimated to cover about 10.4 % of the region poleward of 60°N, which were mainly distributed in permafrost-dominated lowland regions. At least 23.9 % of the area of TLS maps was projected to disappear under representative concentration pathway scenarios (RCPs), with increased susceptibility levels in northern Canada. The slope was the key conditioning factor for the occurrence of thermokarst lakes in Arctic permafrost regions. Compared with similar studies, the reliability of the TLS map was further evaluated using probability calibration curve and coefficient of variation (CV). Our results provide a means for assessing the spatial distribution of thermokarst lakes at the circum-Arctic scale but also improve the understanding of their dynamics in response to the climate system.
