scholarly journals Recent warming reverses forty-year decline in Catastrophic lake drainage and hastens Gradual lake drainage across northern Alaska

2021 ◽  
Author(s):  
Mark Jason Lara ◽  
Yaping Chen ◽  
Benjamin M. Jones
Keyword(s):  
Future Climate Change ◽  
Support Vector ◽  
Lake Area ◽  
Boosted Regression Tree ◽  
Abrupt Decrease ◽  
Lake Morphology ◽  
Lake Temperature ◽  
Lake Drainage ◽  
Northern Alaska ◽  
Permafrost Regions

Abstract Lakes represent as much as ~25% of the total land surface area in lowland permafrost regions. Though decreasing lake area has become a widespread phenomenon in permafrost regions, our ability to forecast future patterns of lake drainage spanning gradients of space and time remain limited. Here, we modeled the drivers of gradual (steady declining lake area) and catastrophic (temporally abrupt decrease in lake area) lake drainage using 45 years of Landsat observations (i.e., 1975-2019) across 32,690 lakes spanning climate and environmental gradients across northern Alaska. We mapped lake area using supervised support vector machine classifiers and object based image analyses using five-year Landsat image composites spanning ~388,968 km2. Drivers of lake drainage were determined with boosted regression tree (BRT) models, using both static (e.g., lake morphology, proximity to drainage gradient) and dynamic predictor variables (e.g., temperature, precipitation, wildfire). Over the past 45 years, gradual drainage decreased lake area between 10-16%, but rates varied over time as the 1990s recorded the highest rates of gradual lake area losses associated with warm periods. Interestingly, the number of catastrophically drained lakes progressively decreased at a rate of ~37% decade-1 from 1975-1979 (102 to 273 lakes draining year-1) to 2010-2014 (3 to 8 lakes draining year-1). However this 40 year negative trend was reversed during the most recent time-period (2015-2019), with observations of catastrophic drainage among the highest on record (i.e., 100 to 250 lakes draining year-1), the majority of which occurred in northwestern Alaska. Gradual drainage processes were driven by lake morphology, summer air and lake temperature, snow cover, active layer depth, and the thermokarst lake settlement index (R2 adj=0.42, CV=0.35, p<0.0001), whereas, catastrophic drainage was driven by the thawing season length, total precipitation, permafrost thickness, and lake temperature (R2 adj=0.75, CV=0.67, p<0.0001). Models forecast a continued decline in lake area across northern Alaska by 15 to 21% by 2050. However these estimates are conservative, as the anticipated amplitude of future climate change were well-beyond historical variability and thus insufficient to forecast abrupt “catastrophic” drainage processes. Results highlight the urgency to understand the potential ecological responses and feedbacks linked with ongoing Arctic landscape reorganization.

scholarly journals Monitoring Changes in the Transparency of the Largest Reservoir in Eastern China in the Past Decade, 2013–2020

2021 ◽  
Vol 13 (13) ◽  
pp. 2570
Author(s):  
Teng Li ◽  
Bozhong Zhu ◽  
Fei Cao ◽  
Hao Sun ◽  
Xianqiang He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mean Squared Error ◽  
Eastern China ◽  
Water Environment ◽  
Relative Difference ◽  
Boreal Summer ◽  
Support Vector ◽  
Lake Area ◽  
The Past ◽  
Remote Sensing Reflectance

Based on characteristics analysis about remote sensing reflectance, the Secchi Disk Depth (SDD) in the Qiandao Lake was predicted from the Landsat8/OLI data, and its changing rates on a pixel-by-pixel scale were obtained from satellite remote sensing for the first time. Using 114 matchups data pairs during 2013–2019, the SDD satellite algorithms suitable for the Qiandao Lake were obtained through both the linear regression and machine learning (Support Vector Machine) methods, with remote sensing reflectance (Rrs) at different OLI bands and the ratio of Rrs (Band3) to Rrs (Band2) as model input parameters. Compared with field observations, the mean absolute relative difference and root mean squared error of satellite-derived SDD were within 20% and 1.3 m, respectively. Satellite-derived results revealed that SDD in the Qiandao Lake was high in boreal spring and winter, and reached the lowest in boreal summer, with the annual mean value of about 5 m. Spatially, high SDD was mainly concentrated in the southeast lake area (up to 13 m) close to the dam. The edge and runoff area of the lake were less transparent, with an SDD of less than 4 m. In the past decade (2013–2020), 5.32% of Qiandao Lake witnessed significant (p < 0.05) transparency change: 4.42% raised with a rate of about 0.11 m/year and 0.9% varied with a rate of about −0.09 m/year. Besides, the findings presented here suggested that heavy rainfall would have a continuous impact on the Qiandao Lake SDD. Our research could promote the applications of land observation satellites (such as the Landsat series) in water environment monitoring in inland reservoirs.

scholarly journals Winter drainage of surface lakes on the Greenland Ice Sheet from Sentinel-1 SAR imagery

2021 ◽  
Vol 15 (3) ◽  
pp. 1587-1606
Author(s):  
Corinne L. Benedek ◽  
Ian C. Willis
Keyword(s):  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Shape From Shading ◽  
Lake Area ◽  
Ice Flow ◽  
Surface Mass ◽  
Flow Acceleration ◽  
Optical Imagery ◽  
Sar Imagery ◽  
Lake Drainage

Abstract. Surface lakes on the Greenland Ice Sheet play a key role in its surface mass balance, hydrology and biogeochemistry. They often drain rapidly in the summer via hydrofracture, which delivers lake water to the ice sheet base over timescales of hours to days and then can allow meltwater to reach the base for the rest of the summer. Rapid lake drainage, therefore, influences subglacial drainage evolution; water pressures; ice flow; biogeochemical activity; and ultimately the delivery of water, sediments and nutrients to the ocean. It has generally been assumed that rapid lake drainage events are confined to the summer, as this is typically when observations are made using satellite optical imagery. Here we develop a method to quantify backscatter changes in satellite radar imagery, which we use to document the drainage of six different lakes during three winters (2014/15, 2015/16 and 2016/17) in fast-flowing parts of the Greenland Ice Sheet. Analysis of optical imagery from before and after the three winters supports the radar-based evidence for winter lake drainage events and also provides estimates of lake drainage volumes, which range between 0.000046 ± 0.000017 and 0.0200 ± 0.002817 km3. For three of the events, optical imagery allows repeat photoclinometry (shape from shading) calculations to be made showing mean vertical collapse of the lake surfaces ranging between 1.21 ± 1.61 and 7.25 ± 1.61 m and drainage volumes of 0.002 ± 0.002968 to 0.044 ± 0.009858 km3. For one of these three, time-stamped ArcticDEM strips allow for DEM differencing, which demonstrates a mean collapse depth of 2.17 ± 0.28 m across the lake area. The findings show that lake drainage can occur in the winter in the absence of active surface melt and notable ice flow acceleration, which may have important implications for subglacial hydrology and biogeochemical processes.

scholarly journals Performance Comparison of Machine Learning Algorithms for Estimating the Soil Salinity of Salt-Affected Soil Using Field Spectral Data

2019 ◽  
Vol 11 (22) ◽  
pp. 2605 ◽  
Author(s):  
Wang ◽  
Chen ◽  
Wang ◽  
Li
Keyword(s):  
Machine Learning ◽  
Spectral Data ◽  
Soil Salinity ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Percentage Error ◽  
Support Vector ◽  
Boosted Regression Tree ◽  
Data Noise ◽  
Salt Affected Soil

Salt-affected soil is a prominent ecological and environmental problem in dry farming areas throughout the world. China has nearly 9.9 million km2 of salt-affected land. The identification, monitoring, and utilization of soil salinization have become important research topics for promoting sustainable progress. In this paper, using field-measured spectral data and soil salinity parameter data, through analysis and transformation of spectral data, five machine learning models, namely, random forest regression (RFR), support vector regression (SVR), gradient-boosted regression tree (GBRT), multilayer perceptron regression (MLPR), and least angle regression (Lars) are compared. The following performance measures of each model were evaluated: the collinear problems, handling data noise, stability, and the accuracy. In terms of these four aspects, the performance of each model on estimating soil salinity is evaluated. The results demonstrate that among the five models, RFR has the best performance in dealing with collinearity, RFR and MLPR have the best performance in dealing with data noise, and the SVR model is the most stable. The Lars model has the highest accuracy, with a determination coefficient (R2) of 0.87, ratio of performance to deviation (RPD) of 2.67, root mean square error (RMSE) of 0.18, and mean absolute percentage error (MAPE) of 0.11. Then, the comprehensive comparison and analysis of the five models are carried out, and it is found that the comprehensive performance of RFR model is the best; hence, this method is most suitable for estimating soil salinity using hyperspectral data. This study can provide a reference for the selection of regression methods in subsequent studies on estimating soil salinity using hyperspectral data.

scholarly journals Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

2019 ◽  
Vol 11 (16) ◽  
pp. 1943 ◽  
Author(s):  
Omid Rahmati ◽  
Saleh Yousefi ◽  
Zahra Kalantari ◽  
Evelyn Uuemaa ◽  
Teimur Teimurian ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Mountainous Area ◽  
Data Set ◽  
Boosted Regression Tree ◽  
Hazard Exposure ◽  
Learning Techniques

Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models—support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.

scholarly journals Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment

2020 ◽  
Vol 12 (21) ◽  
pp. 3620
Author(s):  
Indrajit Chowdhuri ◽  
Subodh Chandra Pal ◽  
Alireza Arabameri ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Land Use Changes ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Gully Erosion ◽  
Support Vector ◽  
Boosted Regression Tree ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Chotanagpur Plateau ◽  
Additive Regression

The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.

scholarly journals An Improved Long Short-Term Memory Model for Dam Displacement Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jun Zhang ◽  
Xiyao Cao ◽  
Jiemin Xie ◽  
Pangao Kou
Keyword(s):  
Short Term Memory ◽  
Water Pressure ◽  
Extreme Temperature ◽  
Regression Tree ◽  
Support Vector ◽  
Temperature Structure ◽  
Short Term ◽  
Boosted Regression Tree ◽  
Term Memory ◽  
Long Short Term Memory

Displacement plays a vital role in dam safety monitoring data, which adequately responds to security risks such as the flood water pressure, extreme temperature, structure deterioration, and bottom bedrock damage. To make accurate predictions, former researchers established various models. However, these models’ input variables cannot efficiently reflect the delays between the external environment and displacement. Therefore, a long short-term memory (LSTM) model is proposed to make full use of the historical data to reflect the delays. Furthermore, the LSTM model is improved to optimize the performance by making variables more physically reasonable. Finally, a real-world radial displacement dataset is used to compare the performance of LSTM models, multiple linear regression (MLR), multilayer perceptron (MLP) neural networks, support vector machine (SVM), and boosted regression tree (BRT). The results indicate that (1) the LSTM models can efficiently reflect the delays and make the variables selection more convenient and (2) the improved LSTM model achieves the best performance by optimizing the input form and network structure based on a clearer physical meaning.

Concordance of taxonomic richness patterns across multiple assemblages in lakes of the northeastern United States

1999 ◽  
Vol 56 (5) ◽  
pp. 739-747 ◽  
Keyword(s):  
Human Development ◽  
Benthic Macroinvertebrates ◽  
Sampling Effort ◽  
Physical Habitat ◽  
Lake Area ◽  
Taxonomic Richness ◽  
Riparian Birds ◽  
Lake Morphology ◽  
Planktonic Crustaceans ◽  
Richness Patterns

We investigated the concordance of taxonomic richness patterns and their environmental correlates for assemblages of benthic macroinvertebrates, riparian birds, sedimentary diatoms, fish, planktonic crustaceans, and planktonic rotifers in 186 northeastern U.S. lakes. Taxon counts were standardized with respect to sampling effort using rarefaction. The degree of concordance among assemblage richness measures was low, but this was at least partly attributable to measurement precision. Aspects of lake morphology (area, depth) superseded other environmental features (climate, human development, water chemistry, nearshore physical habitat) as correlates of assemblage richness and were the strongest source of concordance. The benthic macroinvertebrates, birds, fish, and zooplankton all showed positive associations between richness and lake area. The diatoms showed negligible associations between richness and area and negative associations between richness and depth. Associations with human development were much weaker than with lake morphology and varied from positive (fish, planktonic crustaceans) to negative (diatoms). We conclude that taxonomic richness alone may be of ambiguous value as an indicator of biological integrity in lakes and that its natural drivers must be controlled for prior to assessing anthropogenic effects.

scholarly journals Modelling channelized surface drainage of supraglacial lakes

2015 ◽  
Vol 61 (225) ◽  
pp. 185-199 ◽  
Author(s):  
J. Kingslake ◽  
F. Ng ◽  
A. Sole
Keyword(s):  
Surface Water ◽  
Ice Sheets ◽  
Frictional Heat ◽  
Lake Area ◽  
Ice Dynamics ◽  
Surface Drainage ◽  
Supraglacial Lakes ◽  
Lake Model ◽  
Lake Drainage ◽  
Area Expansion

AbstractSupraglacial lakes can drain to the bed of ice sheets, affecting ice dynamics, or over their surface, relocating surface water. Focusing on surface drainage, we first discuss observations of lake drainage. In particular, for the first time, lakes are observed to drain >70 km across the Nivlisen ice shelf, East Antarctica. Inspired by these observations, we develop a model of lake drainage through a channel that incises into an ice-sheet surface by frictional heat dissipated in the flow. Modelled lake drainage can be stable or unstable. During stable drainage, the rate of lake-level drawdown exceeds the rate of channel incision, so discharge from the lake decreases with time; this can prevent the lake from emptying completely. During unstable drainage, discharge grows unstably with time and always empties the lake. Model lakes are more prone to drain unstably when the initial lake area, the lake input and the channel slope are larger. These parameters will vary during atmospheric-warming-induced ablation-area expansion, hence the mechanisms revealed by our analysis can influence the dynamic response of ice sheets to warming through their impact on surface-water routing and storage.

scholarly journals Modelled present and future thaw lake area expansion/contraction trends throughout the continuous permafrost zone

2014 ◽  
Vol 8 (4) ◽  
pp. 3603-3627 ◽  
Author(s):  
Y. Mi ◽  
J. van Huissteden ◽  
A. J. Dolman
Keyword(s):  
Greenhouse Gas ◽  
Ground Subsidence ◽  
Permafrost Degradation ◽  
Lake Area ◽  
Dominant Feature ◽  
Lake Drainage ◽  
Area Expansion ◽  
Thaw Lakes ◽  
Lake Dynamics ◽  
Lake Basins

Abstract. Thaw lakes and drained lake basins are a dominant feature of Arctic lowlands. Thaw lakes are a source of the greenhouse gas methane (CH4), which is produced under anaerobic conditions, while drained lake basins are carbon sinks due to sedimentation. Besides feedbacks on climate, the development of thaw lakes due to the melt-out of ground ice and subsequent ground subsidence, can have significant impacts on the regional morphology, hydrology, geophysics and biogehemistry. Permafrost degradation as a result of climate warming, which is proceeding considerably faster in high latitude regions than the global average, could lead to either an increases in lake area due to lake expansion, or decrease due to lake drainage. However, which process will dominate is elusive. Therefore understanding thaw lake dynamics and quantifying the feedbacks related to thaw lake expansion and contraction are urgent questions to solve. We apply a stochastic model, THAWLAKE, on four representative Arctic sites, to reproduce recent lake dynamics (1963–2012) and predict for the future changes under various anticipated climate scenarios. The model simulations of current thaw lake cycles and expansion rates are comparable with data. Future lake expansions are limited by lake drainage. We suggest further improvements in the area of enhancing the hydrology component, and operation on larger scales to gauge the impacts on lacustrine morphology and greenhouse gas emissions.

scholarly journals Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1840 ◽  
Author(s):  
Meng Zhang ◽  
Hui Lin ◽  
Guangxing Wang ◽  
Hua Sun ◽  
Jing Fu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Land Surface ◽  
Single Channel ◽  
Paddy Rice ◽  
Support Vector ◽  
Landsat 8 ◽  
Lake Area ◽  
Machine Method

Rice is one of the world’s major staple foods, especially in China. Highly accurate monitoring on rice-producing land is, therefore, crucial for assessing food supplies and productivity. Recently, the deep-learning convolutional neural network (CNN) has achieved considerable success in remote-sensing data analysis. A CNN-based paddy-rice mapping method using the multitemporal Landsat 8, phenology data, and land-surface temperature (LST) was developed during this study. First, the spatial–temporal adaptive reflectance fusion model (STARFM) was used to blend the moderate-resolution imaging spectroradiometer (MODIS) and Landsat data for obtaining multitemporal Landsat-like data. Subsequently, the threshold method is applied to derive the phenological variables from the Landsat-like (Normalized difference vegetation index) NDVI time series. Then, a generalized single-channel algorithm was employed to derive LST from the Landsat 8. Finally, multitemporal Landsat 8 spectral images, combined with phenology and LST data, were employed to extract paddy-rice information using a patch-based deep-learning CNN algorithm. The results show that the proposed method achieved an overall accuracy of 97.06% and a Kappa coefficient of 0.91, which are 6.43% and 0.07 higher than that of the support vector machine method, and 7.68% and 0.09 higher than that of the random forest method, respectively. Moreover, the Landsat-derived rice area is strongly correlated (R2 = 0.9945) with government statistical data, demonstrating that the proposed method has potential in large-scale paddy-rice mapping using moderate spatial resolution images.

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