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As an invasive plant species with wide ecological tolerance and fast infestation, kudzu has posed persistent challenges to the ecosystem and economy in the southeastern United States. However, current studies on kudzu mapping and invasive mechanisms are inadequate to unveil the characteristics of kudzu’s spatiotemporal dynamics. In this study, we investigate kudzu distribution and its influential factors based on continuous Sentinel-2 satellite images and a multi-temporal mapping strategy. The study was conducted in Knox County, Tennessee, a representative region highly impacted by kudzu in the southeastern United States. Our results revealed a steady increase in the kudzu invasion area from 2020 to 2022, with a phenology of a growth season from May to September (peak in August) and a rapid defoliation in the fall. We found precipitation is not a limiting factor for kudzu growth, while temperature has strong correlations with kudzu growth, exhibiting seasonal variations in this humid subtropical region. Human activities play an important role in kudzu distribution with a sharp decrease in kudzu density away from road networks, residential clusters, and the edges of urban forests. These findings provide valuable insights into the complex nature of the kudzu invasion in Knox County, as well as other similar areas in the southeastern United States. The observed unique growth patterns of kudzu and its interactions with various environmental factors in this study are also of importance for future initiatives related to mapping the distribution and devising strategies for the prevention and control of kudzu invasion.

Abstract The accuracy and quality of the landslide susceptibility map depend on the available landslide locations and the sampling strategy for absence data (non-landslide locations). In this study, we propose an objective method to determine the critical value for sampling absence data based on Mahalanobis distances (MD). We demonstrate this method on landslide susceptibility mapping of three subdistricts (Upazilas) of the Rangamati district, Bangladesh, and compare the results with the landslide susceptibility map produced based on the slope-based absence data sampling method. Using the 15 landslide causal factors, including slope, aspect, and plan curvature, we first determine the critical value of 23.69 based on the Chi-square distribution with 14 degrees of freedom. This critical value was then used to determine the sampling space for 261 random absence data. In comparison, we chose another set of the absence data based on a slope threshold of

The integration of structure from motion (SFM) and unmanned aerial vehicle (UAV) technologies has allowed for the generation of very high-resolution three-dimensional (3D) point cloud data (up to millimeters) to detect and monitor surface changes. However, a bottleneck still exists in accurately and rapidly registering the point clouds at different times. The existing point cloud registration algorithms, such as the Iterative Closest Point (ICP) and the Fast Global Registration (FGR) method, were mainly developed for the registration of small and static point cloud data, and do not perform well when dealing with large point cloud data with potential changes over time. In particular, registering large data is computationally expensive, and the inclusion of changing objects reduces the accuracy of the registration. In this paper, we develop an AI-based workflow to ensure high-quality registration of the point clouds generated using UAV-collected photos. We first detect stable objects from the ortho-photo produced by the same set of UAV-collected photos to segment the point clouds of these objects. Registration is then performed only on the partial data with these stable objects. The application of this workflow using the UAV data collected from three erosion plots at the East Tennessee Research and Education Center indicates that our workflow outperforms the existing algorithms in both computational speed and accuracy. This AI-based workflow significantly improves computational efficiency and avoids the impact of changing objects for the registration of large point cloud data.

A noise-like pulse (NLP) thulium-doped fiber laser based on single-wall carbon nanotubes (SWCNTs) has been proposed and demonstrated in this paper, which the microfiber decorated with SWCNTs work as a saturable absorber to generate an ultrafast pulse. The length of the cavity is 12 m, corresponding to the fundamental frequency of 17.2 MHz. NLP with a duration of 643 fs can be obtained at 1965 nm under the signal-to-noise ratio of 56 dB. This work may assist us to better understand the dynamic characteristic of NLP with the help of materials and offer a fine laser source to the subsequent applications just like the generation of supercontinuum.

Most lakes have undergone significant changes on the Tibetan Plateau in recent decades, affecting water resources on the Tibetan Plateau and its surrounding areas. In this paper, we investigated the variations of 25 lakes in five sub-regions on the Tibetan Plateau from 1972 to 2019 based on SRTM DEM data and Landsat imagery. We used a method to derive lake-levels based on DEM and lake boundaries delineated from Landsat imagery, and then calculated the changes in lake area, level, and volume in 1972 to 2019. We also analyzed the potential impacts of temperature, precipitation, glacial and permafrost melting in lake changes during this period. The results show that the lakes tended to shrink until 2010 in southern and western plateau, after which they began to expand gradually but the overall trend is still shrinking. Limited meltwater from glaciers and permafrost and low precipitation are the main reasons for their shrinkage. The lakes in the central plateau, northwest plateau and northeast plateau tend to expand overall. The reason for the expansion of the lakes is not only precipitation but also the melting of glaciers and permafrost. Overall, the lake changes have gone through 3 phases, namely a slight decrease during 1972–2000, a rapid increase during 2000–2010, and a slowdown in the last decade (2010–2019). Multiple factors such as temperature, precipitation, the state of glaciers and permafrost have contributed to the changes in the lake.

Terrestrial LiDAR (light detection and ranging) has been used to quantify micro-topographic changes using high-density 3D point clouds in which extracting the ground surface is susceptible to off-terrain (OT) points. Various filtering algorithms are available in classifying ground and OT points, but additional research is needed to choose and implement a suitable algorithm for a given surface. This paper assesses the performance of three filtering algorithms in classifying terrestrial LiDAR point clouds: a cloth simulation filter (CSF), a modified slope-based filter (MSBF), and a random forest (RF) classifier, based on a typical use-case in quantifying soil erosion and surface denudation. A hillslope plot was scanned before and after removing vegetation to generate a test dataset of ground and OT points. Each algorithm was then tested against this dataset with various parameters/settings to obtain the highest performance. CSF produced the best classification with a Kappa value of 0.86, but its performance is highly influenced by the ‘time-step’ parameter. MSBF had the highest precision of 0.94 for ground point classification but the highest Kappa value of only 0.62. RF produced balanced classifications with the highest Kappa value of 0.75. This work provides valuable information in optimizing the parameters of the filtering algorithms to improve their performance in detecting micro-topographic changes.

Topography plays an important role in shaping the patterns of sediment erosion and deposition of different landscapes. Studies have investigated the role of topography at basin scales, whereas little work has been conducted on hillslopes, partially due to the lack of high-resolution topographic data. We monitored detailed topographic changes of a rilled hillslope in the southeastern United States using terrestrial laser scanning and investigated the influences of various microtopographic factors on erosion and deposition. The results suggest that the contributing area is the most important factor for both rill erosion and deposition. Rills with large contributing areas tend to have high erosion and deposition. Slope is positively related to erosion but negatively related to deposition. Roughness, on the other hand, is positively related to deposition but negatively related to erosion. Higher erosion and lower deposition likely occur on north-facing aspects, possibly because of higher soil moisture resulting from less received solar insolation. Similarly, soil moisture is likely higher in areas with higher terrain wetness index values, leading to higher erosion. This work provides important insight into the sediment dynamic and its microtopographic controls on hillslopes.

The advances of remote sensing techniques allow for the generation of dense point clouds to detect detailed surface changes up to centimeter/millimeter levels. However, there is still a need for an easy method to derive such surface changes based on digital elevation models generated from dense point clouds while taking into consideration spatial varied uncertainty. We present a straightforward method, Las2DoD, to quantify surface change directly from point clouds with spatially varied uncertainty. This method uses a cell-based Welch’s t-test to determine whether each cell of a surface experienced a significant elevation change based on the points measured within the cell. Las2DoD is coded in Python with a simple graphic user interface. It was applied in a case study to quantify hillslope erosion on two plots: one dominated by rill erosion, and the other by sheet erosion, in southeastern United States. The results from the rilled plot indicate that Las2DoD can estimate 90% of the total measured sediment, in comparison to 58% and 70% from two other commonly used methods. The Las2DOD-derived result is less accurate (65%) but still outperforms the other two methods (30% and 48%) for the plot dominated by sheet erosion. Las2DoD captures more low-magnitude changes and is particularly useful where surface changes are small but contribute significantly to the total surface change when summed.

High-resolution terrestrial laser scanning (TLS) provides a unique opportunity to monitor short-term erosion and deposition processes in gully systems. This study quantified the pattern of erosion and deposition within an active gully in the sub-tropical environment of west Tennessee. Two TLS surveys were conducted on December 2014 and February 2015 to generate digital elevation models (DEMs) of different resolutions. The volumes of erosion and deposition were estimated by differencing the DEMs of these two dates with consideration of the spatially propagated errors associated with TLS-measured gully topography. The detected erosional and depositional volumes were 11.0 and 8.2 m3, respectively, with a net loss of 2.8 m3 of sediment at the DEM resolution of 2-cm. We found that both estimated volumes of erosion and deposition decrease as the DEM resolution becomes coarser. The estimated erosional volume decreases at a relatively high rate because erosion mainly occurs on steeper slopes where the propagated errors in TLS-measured topography are relatively higher, leading to rapid smoothing at coarser resolutions. In contrast, the depositional areas on gentler slopes have less propagated errors. This bias in the smoothing behavior of erosional and depositional areas appears to make coarser resolution DEMs dominated by deposition, a misleading interpretation of the sediment dynamics within the gully. We therefore suggest caution when using DEM difference to interpret the erosion-deposition processes within a gully system.

Landslide susceptibility depends on various causal factors such as geology, land use/land cover (LULC), slope, and elevation. Unlike other factors that are relatively stable over time, LULC is a dynamic factor associated with human activities. This study evaluates the impact of LULC change on landslide susceptibility in the Rangamati municipality of Rangamati district, Bangladesh, based on three LULC scenarios—the existing (2018) LULC, the proposed LULC (proposed in 2010, but not yet implemented), and the simulated LULC of 2028—using artificial neural network (ANN)-based cellular automata. The random forest model was used for landslide susceptibility mapping. The model showed good accuracy for all three LULC scenarios (existing: 82.7%; proposed: 81.4%; and 2028: 78.3%) and strong positive correlations (>0.8) between different landslide susceptibility maps. LULC is either the third or fourth most important factor in these scenarios, suggesting that is has a moderate impact on landslide susceptibility. Future LULC changes will likely increase landslide susceptibility, with up to 14.5% increases in the high susceptibility zone for both the proposed and simulated LULC scenarios. These findings may help policymakers carry out proper urban planning and highlight the importance of considering landslide susceptibility in LULC planning.

As an invasive plant species, kudzu has been spreading rapidly in the Southeastern United States in recent years. Accurate mapping of kudzu is critical for effective invasion control and management. However, the remote detection of kudzu distribution using multispectral images is challenging because of the mixed reflectance and potential misclassification with other vegetation. We propose a three-step classification process to map kudzu in Knox County, Tennessee, using multispectral Sentinel-2 images and the integration of spectral unmixing analysis and phenological characteristics. This classification includes an initial linear unmixing process to produce an overestimated kudzu map, a phenological-based masking to reduce misclassification, and a nonlinear unmixing process to refine the classification. The initial linear unmixing provides high producer’s accuracy (PA) but low user’s accuracy (UA) due to misclassification with grasslands. The phenological-based masking increases the accuracy of the kudzu classification and reduces the domain for further processing. The nonlinear unmixing further refines the kudzu classification via the selection of an appropriate nonlinear model. The final kudzu classification for Knox County reaches relatively high accuracy, with UA, PA, Jaccard, and Kappa index values of 0.858, 0.907, 0.789, and 0.725, respectively. Our proposed method has potential for continuous monitoring of kudzu in large areas.

Hillslope rill/interrill erosion has been investigated from the perspective of runoff transport of sediment. Recent advances in terrestrial laser scanning can provide high-resolution elevation data up to centimeter levels, and temporal digital elevation models (DEMs) enabled the detection and quantification of sediment redistribution. Erosion and deposition are spatially heterogeneous across hillslopes, and the choice of resolution is critical when using a DEM to study the spatial pattern of the processes. This study investigates the influence of grid size on the sediment change calculation and rill network delineation based on two surveys using a terrestrial laser scanner on a hillslope with well-developed rills in 2014 and 2015. Temporal DEMs were used to quantify elevation changes and used to delineate rill networks. We produced DEM pairs of incremental grid sizes (1-cm, 2-cm, 5-cm, 8-cm, 10-cm, 15-cm, 20-cm, and 30-cm) for DEM difference and rill network delineation. We used the 1-cm DEM as the reference to compare the results produced from other DEMs. Our results suggest that erosion mainly occurs on the rill sidewalls, and deposition on the rill floors, with patches of erosion/deposition within the interrill areas. Both the area and volume of detectable change decrease as the grid size increases, while the area and volume of erosion are less sensitive compared to those of deposition. The total length and number of rills decrease with the increased grid size, whereas the average length of rills increases. The mean offset between delineated rill network and the reference increases with larger grid sizes. In contrast to the erosion and deposition detected within rills, minor changes are detected on the interrill areas, indicating that either no topographic changes occurred or the changes were too small to be detected on the interill areas by our finest 1-cm DEMs. We recommend to use the finest possible grid size that can be achieved for future studies.

Turbidity is an important indicator of riverine conditions, especially in a fragile environment such as the Tibetan Plateau. Remote sensing, with the advantages of large-scale observations, has been widely applied to monitor turbidity change in lakes and rivers; however, few studies have focused on turbidity change of rivers on the Tibetan Plateau. We investigated the pattern of turbidity change in the middle reaches of the Yarlung Zangbo River, southern Tibetan Plateau, based on multispectral satellite imagery and in situ measurements. We developed empirical models from in situ measured water leaving reflectance and turbidity, and applied the best performed s-curve models on satellite imagery from Sentinel-2, Landsat 8, and Landsat 5 to derive turbidity change in 2007–2017. Our results revealed an overall decreasing spatial trend from the upper to lower streams. Seasonal variations were observed with high turbidity from July to September and low turbidity from October to May. Annual turbidity showed a temporally slightly declining trend from 2007 to 2017. The pattern of turbidity change is affected by the confluence of tributaries and the changes in precipitation and vegetation along the river. These findings provide important insights into the responses of riverine turbidity to climate and environmental changes on the Tibetan Plateau.

Landslide susceptibility mapping is of critical importance to identify landslide-prone areas to reduce future landslides, causalities, and infrastructural damages. This paper presents landslide susceptibility maps at a regional scale for the Chittagong Hilly Areas (CHA), Bangladesh. The frequency ratio (FR) was integrated with the analytical hierarchy process (AHP) (FR_AHP) and logistic regression (LR) (FR_LR). A landslide inventory of 730 landslide locations and 13 landslide predisposing factors including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), land use/land cover, rainfall, distance from drainage network, distance from fault lines, lithology, and normalized difference vegetation index (NDVI) were used. Landslide locations were randomly split into training (80%) and validation (20%) sites to support the susceptibility analysis. A safe zone was determined based on a slope threshold for logistic regression using the exploratory data analysis. The same number of non-landslide locations were randomly selected from the safe zone to train the model (FR_LR). Success and prediction rate curves and statistical indices, including overall accuracy, were used to assess model performance. The success rate curves show that FR_LR showed the highest area under the curve (AUC) (79.46%), followed by the FR_AHP (77.15%). Statistical indices also showed that the FR_LR model gave the best performance as the overall accuracy was 0.86 for training and 0.82 for validation datasets. The prediction rate curve shows similar results. The correlation analysis shows that the landslide susceptibility maps produced by FR and FR_AHP are highly correlated (0.95). In contrast, the correlation between the maps produced by FR and FR_LR was relatively lower (0.85). It indicates that the three models are highly convergent with each other. This study’s integrated methods would be helpful for regional-scale landslide susceptibility mapping, and the landslide susceptibility maps produced would be useful for regional planning and disaster management of the CHA, Bangladesh.

The Yarlung Zangbo River basin is an important alley to transport moisture from the Indian Ocean to the inner Tibetan Plateau. With a wide range of elevations from 147 m to over 7000 m above sea level (a.s.l.), ecosystems respond differently to climate change at various elevations. However, the pattern of elevation-dependent vegetation change and how it responds to recent warming have been rarely reported. Here, we investigated the pattern of vegetation greening at different elevations in this river basin using SPOT normalized difference vegetation index (NDVI) data during 1999–2013, and examined its relationship with elevation-dependent changes in temperature and precipitation. The annual NDVI has increased by 8.83% from 1999 to 2013. In particular, the NDVI increased more apparently at lower elevations, but remained relatively stable or even decreased at high elevations. It seems that rising temperature has driven the basin-wide vegetation greening, but the greening rate is in contrast to the pattern of elevation-dependent warming (EDW) with more significant temperature increase at higher elevations. It appears that decreasing precipitation does not reverse the overall increasing trend in NDVI, but relatively limited precipitation (4000 m).

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

Landslides are a frequent natural hazard in Chittagong Hilly Areas (CHA), Bangladesh, which causes the loss of lives and damage to the economy. Despite this, an official landslide inventory is still lacking in this area. In this paper, we present a landslide inventory of this area prepared using the visual interpretation of Google Earth images (Google Earth Mapping), field mapping, and a literature search. We mapped 730 landslides that occurred from January 2001 to March 2017. Different landslide attributes including type, size, distribution, state, water content, and triggers are presented in the dataset. In this area, slide and flow were the two dominant types of landslides. Out of the five districts (Bandarban, Chittagong, Cox’s Bazar, Khagrachari, and Rangamati), most (55%) of the landslides occurred in the Chittagong and Rangamati districts. About 45% of the landslides were small (2) in size, while the maximum size of the detected landslides was 85202 m2. This dataset will help to understand the characteristics of landslides in CHA and provide useful guidance for policy implementation.

Inland water bodies, which are critical freshwater resources for arid and semi-arid areas, are very sensitive to climate change and human disturbance. In this paper, we derived a time series of major lake surface areas across Xinjiang Uygur Autonomous Region (XUAR), China, based on an eight-day MODIS time series in 500 m resolution from 2000 to 2014. A classification approach based on water index and dynamic threshold selection was first developed to accommodate varied spectral features of water pixels at different temporal steps. The overall classification accuracy for a MODIS-derived water body is 97% compared to a water body derived using Landsat imagery. Then, monthly composites of water bodies were derived for the months of April, July, and September to identify seasonal patterns and inter-annual dynamics of 10 major lakes (>100 km2) in XUAR. Our results indicate that the changing trends of surface area of major lakes varied across the region. The surface areas of the Ebinur and Bosten Lakes showed a significant shrinking trend. The Ulungur-Jili Lake remained relatively stable during the entire period. For mountain lakes, the Barkol Lake showed a decreasing trend in April and July, but the Sayram Lake showed a significant expanding trend in September. The four plateau lakes exhibited significant expanding trends in all three seasons except for Arkatag Lake in July. The shrinking of major lakes reflects severe anthropogenic impacts due to agricultural and industrial needs, in addition to the impact of climate change. The pattern of lake changes across the XUAR can provide insight into the impact of climate change and human activities on regional water resources in this arid and semi-arid region.

Assessing long-term hydrological impacts of land use/land cover (LULC) change is of critical importance for land use planning and water resource management. The Little River Watershed, Tennessee, is an important watershed supporting drinking water and recreational activities within and around the Great Smoky Mountains National Park in the Unites States. However, the potential hydrological impacts of LULC change, especially urbanization in recent decades, are not quantified. This paper assessed the long-term impacts of LULC change on streamflow and non-point source pollution using the Soil and Water Assessment Tool (SWAT) and a detailed LULC record from 1984 to 2010. The SWAT was first calibrated and validated using observed streamflowin 2010 and then simulated using different LULC patterns in 1984-2010 to quantify the long-term hydrological impacts caused by the LULC change. Simulated results indicated a minor 3% increase in streamflow for the whole watershed from 1984 to 2010, but with a distinct spatial pattern. The increase in streamflow is closely related to urban development. Almost no streamflow increase occurred in the upper watershed within the national park, whereas >10% increase occurred in the lower watershed, especially in areas close to cities. Model simulation also suggested 34.6% reduction in sediment and about 10% reduction in nutrient loads from 1984 to 2010, closely related to the decrease in agricultural land. However, without calibration and validation, the simulated reduction in the sediment and nutrient loads may be problematic because SWAT mainly simulates the static LULC patterns, whereas LULC transitions, such as construction phases, may generate more sediment and nutrient loads. In addition, the simulation also did not account for the sediment and nutrients generated from stream bank erosion.

A critical component of environmental education is to ensure student understanding and use of available technologies to better experience and analyze spatially distributed features of the environment. Combining mobile technologies with geographic information systems in field data collection may provide a unique opportunity for students to feel engaged in what they are learning and take ownership of their learning process. We customized an open access data collection application using Collector for ArcGIS and investigated its impacts on student engagement and perception of the incorporation of technology within an environmental science curriculum. Analyses of pre- and post-surveys indicate that the inclusion of geospatial technologies as a part of environmental curricula allows students to take the lead on their own research, view field data interactively as opposed to looking at a database in hindsight and analyze multiscale data as it is presented during field data collection. The findings of this study are consistent with previous studies, suggesting a strong association between the inclusion of geospatial technologies as a part of curricula and student engagement.

Climate change and increasing human activities have induced lake expansion or shrinkage, posing a serious threat to the ecological security on the Inner Mongolian Plateau, China. However, the pattern of lake changes and how it responds to climate change and revegetation have rarely been reported. We investigated the pattern of lake-area changes in the Dalinor National Nature Reserve (DNR) using Landsat imagery during 1976–2015, and examined its relationship with changes in climate and vegetation factors. The total lake-area in the DNR has decreased by 11.6% from 1976 to 2015 with a rate of −0.55 km2 year−1. The largest Dalinor Lake reduced the most (by 32.7 km2) with a rate of −0.79 km2 year−1. The air temperature has increased significantly since 1976, with a rate of 0.03 °C year−1 (p < 0.05), while the precipitation slightly decreased during 1976–2015, with a rate of −0.86 mm year−1. The Normalized Difference Vegetation Index (NDVI) increased by 27.7% from 1976 to 2015, especially after 2001 when vegetation has been promoted greatly as a result of the successful ecological protection and restoration in the Dalinor basin. The decrease in lake-areas for the DNR exhibited a negative correlation with NDVI (r = −0.397, p < 0.05) during 1976–2015. It seems that decreasing precipitation drives the reduction in lake-area, while rising temperature and vegetation greenness accelerated this decreasing trend by increasing evapotranspiration. The continuous lake shrinkage increases the ecological risks to the habitat of birds, causing a challenge to the management in the DNR.

Most glaciers in the Himalayas and the Tibetan Plateau are retreating, and glacier melt has been emphasized as the dominant driver for recent lake expansions on the Tibetan Plateau. By investigating detailed changes in lake extents and levels across the Tibetan Plateau from Landsat/ICESat data, we found a pattern of dramatic lake changes from 1970 to 2010 (especially after 2000) with a southwest-northeast transition from shrinking, to stable, to rapidly expanding. This pattern is in distinct contrast to the spatial characteristics of glacier retreat, suggesting limited influence of glacier melt on lake dynamics. The plateau-wide pattern of lake change is related to precipitation variation and consistent with the pattern of permafrost degradation induced by rising temperature. More than 79% of lakes we observed on the central-northern plateau (with continuous permafrost) are rapidly expanding, even without glacial contributions, while lakes fed by retreating glaciers in southern regions (with isolated permafrost) are relatively stable or shrinking. Our study shows the limited role of glacier melt and highlights the potentially important contribution of permafrost degradation in predicting future water availability in this region, where understanding these processes is of critical importance to drinking water, agriculture, and hydropower supply of densely populated areas in South and East Asia.

A significant challenge posed by changing climates is how water cycling and surficial and subsurface water availability will be affected at global and regional scales. Such alterations are critical as they often lead to increased vulnerability in ecosystems and human society. Understanding specifically how climate change affects water resource variability in different locations is of critical importance to sustainable development in different parts of the world. The papers included in this special issue focus on three broad perspectives associated with water resource variability and climate change. Six papers employ remote sensing, meteorological station-based observational data, and tree-ring records to empirically determine how water resources have been changing over historical time periods. Eight of the contributions focus on modeling approaches to determine how known processes are likely to manifest themselves as climate shifts over time. Two others focus on human perceptions and adaptation strategies in the midst of unstable or unsettled water availability. The findings and methods presented in this collection of papers provide important contributions to the increased study and awareness of climate change on water resources.

In recent decades, most glaciers have been melting, thinning and retreating globally in response to continuously increasing temperatures. We simulated ice thicknesses and volumes on the east and west slopes of Mt Noijin Kang-Sang, Southern Tibetan Plateau (TP), since the Last Glacial Maximum (LGM), using a glacial flowline model. The simulated average ice thicknesses during the LGM and Lateglacial (LG), Early Holocene, Neoglacial and the Little Ice Age (LIA) periods were 1.4, 1.3, 1.1 and 1.2 times greater than those of modern glaciers in the Gangbu (eastern slope) and western valleys, respectively. In the Gangbu Valley, areas from the LGM-LG to LIA periods were 1.7, 1.5, 1.4 and 1.2 times greater than the modern glacier area, and the volume expansion indexes were 2.2, 1.9, 1.5 and 1.4. In the western valleys, the area expansion indexes were 2.2, 1.9, 1.5 and 1.4 times greater than the modern glacier, and volumes were 5.4, 4.4, 3.4 and 2.9 times greater, respectively. Glaciers in the western valleys retreated more extensively than those on the eastern slopes of Gangbu Valley after the LGM-LG, probably due to the smaller glaciers on the leeward western slopes with lower elevations. Equilibrium-line altitudes dropped ~425 m in Gangbu Valley and ~385 m in the western valleys during the LGM-LG, corresponding to a 3.8-4.1 ℃ temperature decrease in this region, which was consistent with cooling on the central Tibetan Plateau, but lower than cooling at the southeastern edge of the Tibetan Plateau. [ABSTRACT FROM AUTHOR]