A map of high-altitude wetlands in the world's major mountain regions.

Conserving mountains is important for protecting biodiversity because they have high beta diversity and endemicity, facilitate species movement, and provide numerous ecosystem benefits for people. Mountains are often thought to have lower levels of human modification and contain more protected area than surrounding lowlands. To examine this, we compared biogeographic attributes of the largest, contiguous, mountainous region on each continent. In each region, we generated detailed ecosystems based on Köppen-Geiger climate regions, ecoregions, and detailed landforms. We quantified anthropogenic fragmentation of these ecosystems based on human modification classes of large wild areas, shared lands, and cities and farms. Human modification for half the mountainous regions approached the global average, and fragmentation reduced the ecological integrity of mountain ecosystems up to 40%. Only one-third of the major mountainous regions currently meet the Kunming-Montreal Global Biodiversity Framework target of 30% coverage for all protected areas; furthermore, the vast majority of ecosystem types present in mountains were underrepresented in protected areas. By measuring ecological integrity and human-caused fragmentation with a detailed representation of mountain ecosystems, our approach facilitates tracking progress toward achieving conservation goals and better informs mountain conservation.

Snowfall is one of the primary drivers of the global cryosphere and is declining in many regions of the world with widespread hydrological and ecological consequences. Previous studies have shown that the probability of snowfall occurrence is well described by wet-bulb temperatures below 1°C (1.1°C) over land (ocean). Using this relationship, wet-bulb temperatures from three reanalysis products as well as multisatellite and reanalysis precipitation data are analyzed from 1979 to 2017 to study changes in potential snowfall areas, snowfall-to-rainfall transition latitude, snowfall amount, and snowfall-to-precipitation ratio (SPR). Results are presented at hemispheric scales, as well as for three Köppen–Geiger climate classes and four major mountainous regions including the Alps, the western United States, High Mountain Asia (HMA), and the Andes. In all reanalysis products, while changes in the wet-bulb temperature over the Southern Hemisphere are mostly insignificant, significant positive trends are observed over the Northern Hemisphere (NH). Significant reductions are observed in annual-mean potential snowfall areas over NH land (ocean) by 0.52 (0.34) million km2 decade−1 due to an increase of 0.34°C (0.35°C) decade−1 in wet-bulb temperature. The fastest retreat in NH transition latitudes is observed over Europe and central Asia at 0.7° and 0.45° decade−1. Among mountainous regions, the largest decline in potential snowfall areas is observed over the Alps at 3.64% decade−1 followed by the western United States at 2.81% and HMA at 1.85% decade−1. This maximum decrease over the Alps is associated with significant reductions in annual snowfall of 20 mm decade−1 and SPR of 2% decade−1.

Simple SummaryThe coffee berry borer (CBB; Hypothenemus hampei) is an invasive beetle that causes extensive damage to coffee plantations worldwide. Controlling the CBB is difficult because it primarily resides inside coffee berries during its lifecycle, which limits the effectiveness of insecticide applications. Identifying periods of heightened female CBB flight activity can assist growers in making management decisions and evaluating integrated pest management programmes. This study monitored seasonal CBB activity using traps on coffee farms in the high mountain and Blue Mountain regions in Jamaica. Trap collection numbers were compared with berry infestation in the field. The highest CBB infestation levels occurred in November and October in the high mountain region and Blue Mountain region, respectively, coinciding with the presence of susceptible berries. CBB activity and infestation were similar in both study locations and were not significantly influenced by temperature or humidity; however, there was a notable correlation between CBB activity and infestation and the amount of rainfall. Differences in cultural control practices and cropping cycles were also observed between locations. The study lays the groundwork for understanding the dynamics of CBB populations in Jamaica, which is crucial for managing the beetle.Jamaica produces coffee marketed as Blue Mountain and high mountain (grown outside the Blue Mountains). Since the discovery of the coffee berry borer (CBB; Hypothenemus hampei) in Jamaica in 1978, chemical control has traditionally been the primary approach used to protect the crop from the pest. However, in the last 20 years, there has been an effort to shift towards more sustainable management strategies. The study was conducted to determine CBB activity (trap catch) and field infestation on coffee farms in the high mountains and Blue Mountains of Jamaica, over a crop cycle. A total of 27,929 and 12,921 CBBs were captured at high mountain and Blue Mountain farms, respectively. Peak CBB activity occurred in April in the high mountain region (365 CBBs/trap/month) and February in the Blue Mountain region (129 CBBs/trap/month). The highest levels of infestation were in November (33%) and October (34%) in the high mountain region and Blue Mountain region, respectively. There was no significant difference in the patterns of CBB activity and infestation between the study locations, and neither were related to the temperature or relative humidity. However, there was a significant relationship with rainfall. These data suggest that the population dynamics of the CBB may involve complex interactions among weather conditions, berry development, and agronomic practices.

High Mountain Asia, encompassing the Tibetan Plateau and the surrounding high Asian mountains, has been experiencing a warmer and wetter climate since the 1950s. The amplified climate change has resulted in rapid glacier retreat and permafrost degradation that further cause mountain landscape instability associated with frequent cascading hazards including (rock-ice) avalanches, landslides, debris flows, and outburst floods from glacial- and landslide-dammed lakes. Moreover, the mountain erodible landscapes are expanding and greater amounts of sediment are mobilized in both glacierized and permafrost basins. The river sediment loads in High Mountain Asia have been increasing at a rate of 13% per decade since the 1950s and will likely double by 2050 under an extreme climate change scenario. The climate change-driven mountain landscape instability, increases in river sediment loads and changes in seasonal sediment-transport regimes affect water quality, carbon cycle, floods, infrastructure, and livelihoods. Such findings have implications for other high mountain areas and polar regions and we call for a global assessment of the warming and wetting-driven erosion and sediment transport.

Water hyacinth (Pontederia crassipes) is an invasive weed that covers a significant portion of Lake Tana. The infestation has an impact on the lake’s ecological and socioeconomic systems. Early detection of the spread of water hyacinth using geospatial techniques is crucial for its effective management and control. The main objective of this study was to examine the spatiotemporal distribution of water hyacinth from 2016 to 2022 using a random forest machine learning model. The study used 16 variables obtained from Sentinel-2A, Sentinel-1 SAR, and SRTM DEM, and a random forest supervised classification model was applied. Seven spectral indices, five spectral bands, two Sentinel-1 SAR bands, and two topographic variables were used in combination to model the spatial distribution of water hyacinth. The model was evaluated using the overall accuracy and kappa coefficient. The findings demonstrated that the overall accuracy ranged from 0.91 to 0.94 and kappa coefficient from 0.88 to 0.92 in the wet season and 0.93 to 0.95 and 0.90 to 0.93 in the dry season, respectively. B11 and B5 (2022), VH, soil adjusted vegetation index (SAVI), and normalized difference water index (NDWI) (2020), B5 and B12 (2018), and VH and slope (2016) are the highly important variables in the classification. The study found that the spatial coverage of water hyacinth was 686.5 and 650.4 ha (2016), 1,851 and 1,259 ha (2018), 1,396.7 and 1,305.7 ha (2020), and 1,436.5 and 1,216.5 ha (2022) in the wet and dry seasons, respectively. The research findings indicate that variables derived from optical (Sentinel-2A and SRTM) and non-optical (Sentinel-1 SAR) satellite imagery effectively identify water hyacinth and display its spatiotemporal spread using the random forest machine learning algorithm.

Leveraging google earth engine cloud computing for large-scale arctic wetland mapping

The main objective in our study was to derive an accurate wetland inventory of the Dınàgà Wek’èhodì region, Northwest Territories, while also enhancing our previously established wetland mapping workflow. Our methods used multidate optical and radar satellite imagery and fused these data with ArcticDEM topographic variables. Additionally, few studies to date have assessed the ArcticDEM for wetland mapping; our research helps fill this critical gap in the literature. A machine-learning, object-based approach was employed to classify the fused data stacks and included both mean and standard deviation image-object feature extractions. In this study, 18 random forest models were tested, each including various sensor inputs and feature extractions. The highest accuracy was achieved using a fusion of optical, radar, and ArcticDEM data and included both the mean and standard deviation of image objects (88.17% overall accuracy and kappa 0.858). Vegetated wetlands had producer accuracies ranging from 74% to 86%, whereas open water was 92%. Feature importance rankings indicated that 16 of the top 20 variables were derived from optical data, three from radar, and one from the ArcticDEM. The results of our study will be used to assist governments and other interested parties in advancing conservation initiatives for this significant high-latitude region.

Mountains are important suppliers of freshwater to downstream areas, affecting large populations in particular in High Mountain Asia (HMA). Yet, the propagation of water from HMA headwaters to downstream areas is not fully understood, as interactions in the mountain water cycle between the cryo-, hydro- and biosphere remain elusive. We review the definition of blue and green water fluxes as liquid water that contributes to runoff at the outlet of the selected domain (blue) and water lost to the atmosphere through vapor fluxes, that is evaporation from water, ground, and interception plus transpiration (green) and propose to add the term white water to account for the (often neglected) evaporation and sublimation from snow and ice. We provide an assessment of models that can simulate the cryo-hydro-biosphere continuum and the interactions between spheres in high mountain catchments, going beyond disciplinary separations. Land surface models are uniquely able to account for such complexity, since they solve the coupled fluxes of water, energy, and carbon between the land surface and atmosphere. Due to the mechanistic nature of such models, specific variables can be compared systematically to independent remote sensing observations – providing vital insights into model accuracy and enabling the understanding of the complex watersheds of HMA. We discuss recent developments in spaceborne earth observation products that have the potential to support catchment modeling in high mountain regions. We then present a pilot study application of the mechanistic land surface model Tethys & Chloris to a glacierized watershed in the Nepalese Himalayas and discuss the use of high-resolution earth observation data to constrain the meteorological forcing uncertainty and validate model results. We use these insights to highlight the remaining challenges and future opportunities that remote sensing data presents for land surface modeling in HMA.

High Mountain Asia (HMA) is very sensitive to climate changes. In HMA, air temperature and precipitation shifts or increases are reflected in the timing of snowmelt onset. In this study, a long-term series (1979–2018) of snow melt onset time is first derived using spaceborne microwave radiometer data, following which the long-term trend of snow and ice-melt time of Tienshan, Altay, Karakorom, Hindu Kush, and Pamir is analyzed. Previous studies proposed many algorithms for detecting snowmelt onset and freeze-up using microwave remote sensing. The previously proposed algorithms were extensively applied to polar regions (including the Antarctic, Siberia, Greenland, and sea ice surface), where the impact of topography or mixed pixel problem is relatively small. However, for high mountain regions, complex topography and a potential mixed pixel issue would result in a very complicated and noisy satellite-observed active and passive microwave remote sensing signal. This study thus proposes a recently developed snow and ice-melt detection algorithm in which, for passive microwave data, a median filter is first applied to the original signal (brightness temperature) to suppress random, small, or short-duration signal variations. The differential average derivative of a particular date is then calculated using the time series first-order derivative as the average first-order derivative of a specific count of observations after this particular date. Generally, the differential average derivative is an indicator of sudden changes in time series brightness temperature. Results show that the melt onset time in the majority of HMA occurs earlier, except in the case of the Karakorom Mountains and a part of the West Kunlun Mountains. Moreover, the melt onset time derived from satellite morning (6:00 local time) pass data shows that the melt onset time of Karakorom, the West Kulun Mountains, and southeast Tibet remains stable, or even occurs later. The trend of southeast Tibet is unique, with its earlier melt based on afternoon (18:00 local time) pass data and its later melt based on morning pass data. This suggests that the melt–refreeze period of southeast Tibet is increasing together with the increasing diurnal temperature difference. Then, a 2-m air temperature in ERA5 reanalysis data is used for comparison with melt onset time for validation and analysis. ERA5 is the latest climate reanalysis produced by ECMWF, providing hourly data on many atmospheric, land-surface, and sea-state parameters. A strong correlation exists between monthly average air temperatures and melt onset time, with the maximum linear fit R 2 of 0.76. This strong correlation indicates the good data quality of ERA5 reanalysis and melt onset time. The unique trend of southeast Tibet can also be explained using ERA5 reanalysis data, which show that the monthly mean daily maximum air temperature is increasing, but the monthly mean daily minimum air temperature is decreasing. This renders a slope of the linear fit of melt onset data in the period of 1988–2018 in the whole HMA region. Because the satellite overpasses time difference, data from only 1988 to 2018 is used for trend analysis. The analysis of the relationship between the melt onset time change rate and elevation shows that the areas with an earlier melt are almost all located in low-elevation regions, and the rate of melt time change is positively correlated with elevation. This suggests that low-elevation regions are more affected by climate changes. This study provides objective evidence of the impact of climate change on the cryospheric system in HMA.

Atmospheric rivers (ARs) that reach the complex terrain of High Mountain Asia (HMA) cause significant hydrological impacts for millions of people. While ARs are often associated with precipitation extremes and can cause floods and debris flows affecting populated communities, little is known about ARs that reach as far inland as HMA. This paper characterizes AR types and investigates dynamical mechanisms associated with the development of ARs that typically affect HMA. Combined empirical orthogonal function (cEOF) analysis using integrated water vapor transport (IVT) is applied to days where an AR reaches HMA. K-means cluster analysis applied to the first two principal components uncovered three subtypes of AR events with distinct synoptic characteristics during winter and spring months. The first subtype increases precipitation and IVT in Western HMA and is associated with a zonally oriented wave train propagating within the westerly jet waveguide. The second subtype is associated with enhanced southwesterly IVT, anomalous upper-level cyclonic circulation centered on 45^circ E, and precipitation in Northwestern HMA. The third subtype shows anomalous precipitation in Eastern HMA and southwesterly IVT across the Bay of Bengal. Interannual variations in the frequency of HMA ARs and relationships with various teleconnection patterns show that western HMA AR subtypes are sensitive to well-known remote large-scale climate factors, such as the El Niño Southern Oscillation, Arctic Oscillation, and the Siberian High. These results provide synoptic characterization of the three types of ARs that reach HMA and reveal the previously unexplored significance of their contribution to winter and spring precipitation.

The effects of different factors on soil water infiltration properties in High Mountain Asia: A meta-analysis

Under the context of climate change, significant variations in snow density have been observed in the High Mountain Asia, however, its spatiotemporal patterns and underlying drivers remain incompletely understood. By integrating ERA5 and ERA5-Land reanalysis datasets with large-scale atmospheric circulation data, combined with advanced statistical methods, this study systematically analyzes the spatiotemporal patterns and driving factors of snow density across multiple scales in the High Mountain Asia. The results indicate that: The snow density exhibited a significant decreasing trend at a rate of −0.4 kg/m3·per decade (p < 0.01) from 1960 to 2023. Spatially, snow density consistently demonstrated a “high in mountains, low in plateaus” distribution pattern, which is closely associated with snow depth and snow accumulation. Significant decreases in snow density were concentrated in areas with relatively low snow accumulation, such as the southwestern (S2) and southeastern (S3) Tibetan Plateau, where snowpack exhibits higher sensitivity to temperature variations. Snow depth and air temperature serves as key geographical factor influencing snow density, the latter primarily affects snow density by modulating the proportion of snowfall in total precipitation and altering snow phenology. The East Atlantic/Western Russia (EA/WR) teleconnection pattern indirectly influences snow density through its control on temperature. A weakened EA/WR pattern facilitates increased advection of warm air from the southeast into the Asian High Mountain region, thereby elevating summer temperatures and contributing to reduced snow density.

Abstract. Atmospheric warming is intensifying glacier melting and glacial-lake development in High Mountain Asia (HMA), and this could increase glacial-lake outburst flood (GLOF) hazards and impact water resources and hydroelectric-power management. There is therefore a pressing need to obtain comprehensive knowledge of the distribution and area of glacial lakes and also to quantify the variability in their sizes and types at high resolution in HMA. In this work, we developed an HMA glacial-lake inventory (Hi-MAG) database to characterize the annual coverage of glacial lakes from 2008 to 2017 at 30 m resolution using Landsat satellite imagery. Our data show that glacial lakes exhibited a total area increase of 90.14 km2 in the period 2008–2017, a +6.90 % change relative to 2008 (1305.59±213.99 km2). The annual increases in the number and area of lakes were 306 and 12 km2, respectively, and the greatest increase in the number of lakes occurred at 5400 m elevation, which increased by 249. Proglacial-lake-dominated areas, such as the Nyainqêntanglha and central Himalaya, where more than half of the glacial-lake area (summed over a 1∘ × 1∘ grid) consisted of proglacial lakes, showed obvious lake-area expansion. Conversely, some regions of eastern Tibetan mountains and Hengduan Shan, where unconnected glacial lakes occupied over half of the total lake area in each grid, exhibited stability or a slight reduction in lake area. Our results demonstrate that proglacial lakes are a main contributor to recent lake evolution in HMA, accounting for 62.87 % (56.67 km2) of the total area increase. Proglacial lakes in the Himalaya ranges alone accounted for 36.27 % (32.70 km2) of the total area increase. Regional geographic variability in debris cover, together with trends in warming and precipitation over the past few decades, largely explains the current distribution of supraglacial- and proglacial-lake area across HMA. The Hi-MAG database is available at https://doi.org/10.5281/zenodo.4275164 (Chen et al., 2020), and it can be used for studies of the complex interactions between glaciers, climate and glacial lakes, studies of GLOFs, and water resources.

&amp;lt;p&amp;gt;Snow cover strongly modulates the energy fluxes between the atmosphere and the Earth's surface. Indeed, snow has generally a much higher albedo compared to other surfaces and therefore reduces the amount of solar radiation absorbed by the surface. Moreover, because of its low conductivity, snow isolates the ground from the atmosphere, impacting soil surface temperatures and energy balance (Zhang 2005). In general circulation models (GCMs) the snow cover fraction (SCF) is usually a diagnostic variable derived from other snow quantities, as for instance, the snow water equivalent (SWE) or the snow depth (SD). The relationship between SWE and SCF varies from simple linear relationships to more advanced parameterizations taking into account the snow density allowing to represent the hysteresis effect between the accumulation phase and the more disparate melting phase (e.g., Niu and Yang 2007). Swenson and Lawrence (2012) highlighted strong differences of snow cover extents between plains and mountainous areas, which may be explained by the persistence of snow on the summits whereas a faster melting occurs in the valleys. However, the dependency of SCF on the topography is considered only in a reduced number of GCMs, whereas mountainous areas represent nearly 1/5 of the world's surface area (Huddlestone et al., 2003). In this study, we designed three new snow parameterizations that include the impact of the sub-grid topography on the SCF in the ORCHIDEE land surface model (LSM) coupled to the LMDZ atmospheric model (part of the French GCM of IPSL). This model shows a strong cold bias and an excess of SCF over the High Mountains of Asia (HMA)&amp;amp;#160; (Lalande et al., 2021). The new SCF parameterizations are based on the following existing ones: Swenson and Lawrence (2012; hereafter SL12), Roesch et al. (2001; hereafter R01), and a modified version of Niu and Yang (2007; hereafter NY07). These new parameterizations were calibrated over HMA using a high-resolution snow reanalysis (Liu et al., 2021), and compared to a deep learning model trained on the reanalysis dataset. The calibrated parameterizations SL12, R01, and the modified version of NY07 were then tested in coupled ORCHIDEE/LMDZ simulations. Preliminary results show improvements in simulated snow cover in HMA but slight deterioration in other areas. They suggest also that calibration should be extended to other snow-covered areas and should include other parameters such as the type of vegetation in particular.&amp;lt;/p&amp;gt;

&amp;lt;p&amp;gt;The High Mountains of Asia (HMA) region and the Tibetan Plateau (TP), with an average altitude of 4000 m, are hosting the third largest reservoir of glaciers and snow after the two polar ice caps, and are at the origin of strong orographic precipitation. Climate studies over HMA are related to serious challenges concerning the exposure of human infrastructures to natural hazards and the water resources for agriculture, drinking water, and hydroelectricity to whom several hundred million inhabitants of the Indian subcontinent are depending. However, climate variables such as temperature, precipitation, and snow cover are poorly described by global climate models because their coarse resolution is not adapted to the rugged topography of this region. Since the first CMIP exercises, a cold model bias has been identified in this region, however, its attribution is not obvious and may be different from one model to another. Our study focuses on a multi-model comparison of the CMIP6 simulations used to investigate the climate variability in this area to answer the next questions: (1) are the biases in HMA reduced in the new generation of climate models? (2) Do the model biases impact the simulated climate trends? (3) What are the links between the model biases in temperature, precipitation, and snow cover extent? (4) Which climate trajectories can be projected in this area until 2100? An analysis of 27 models over 1979-2014 still show a cold bias in near-surface air temperature over the HMA and TP reaching an annual value of -2.0 &amp;amp;#176;C (&amp;amp;#177; 3.2 &amp;amp;#176;C), associated with an over-extended relative snow cover extent of 53 % (&amp;amp;#177; 62 %), and a relative excess of precipitation of 139 % (&amp;amp;#177; 38 %), knowing that the precipitation biases are uncertain because of the undercatch of solid precipitation in observations. Model biases and trends do not show any clear links, suggesting that biased models should not be excluded in trend and projections analysis, although non-linear effects related to lagged snow cover feedbacks could be expected. On average over 2081-2100 with respect to 1995-2014, for the scenarios SSP126, SSP245, SSP370, and SSP585, the 9 available models shows respectively an increase in annual temperature of 1.9 &amp;amp;#176;C (&amp;amp;#177; 0.5 &amp;amp;#176;C), 3.4 &amp;amp;#176;C (&amp;amp;#177; 0.7 &amp;amp;#176;C), 5.2 &amp;amp;#176;C (&amp;amp;#177; 1.2 &amp;amp;#176;C), and 6.6 &amp;amp;#176;C (&amp;amp;#177; 1.5 &amp;amp;#176;C); a relative decrease in the snow cover extent of 10 % (&amp;amp;#177; 4.1 %), 19 % (&amp;amp;#177; 5 %), 29 % (&amp;amp;#177; 8 %), and 35 % (&amp;amp;#177; 9 %); and an increase in total precipitation of 9 % (&amp;amp;#177; 5 %), 13 % (&amp;amp;#177; 7 %), 19 % (&amp;amp;#177; 11 %), and 27 % (&amp;amp;#177; 13 %). Further analyses will be considered to investigate potential links between the biases at the surface and those at higher tropospheric levels as well as with the topography. The models based on high resolution do not perform better than the coarse-gridded ones, suggesting that the race to high resolution should be considered as a second priority after the developments of more realistic physical parameterizations.&amp;lt;/p&amp;gt;