Translator Disclaimer
2 June 2021 Mountain Observatories: Status and Prospects for Enhancing and Connecting a Global Community
Maria Shahgedanova, Carolina Adler, Aster Gebrekirstos, H. Ricardo Grau, Christian Huggel, Robert Marchant, Nicholas Pepin, Veerle Vanacker, Daniel Viviroli, Mathias Vuille
Author Affiliations +
Abstract

Mountainous regions are globally important, in part because they support large populations and are biodiverse. They are also characterized by enhanced vulnerability to anthropogenic pressures and sensitivity to climate change. This importance necessitates the development of a global reference network of long-term environmental and socioeconomic monitoring—mountain observatories. At present, monitoring is limited and unevenly distributed across mountain regions globally. Existing thematic networks do not fully support the generation of multidisciplinary knowledge required to inform decisions, enact drivers of sustainable development, and safeguard against losses. In this paper, the Mountain Observatories Working Group, established by the Mountain Research Initiative (MRI) Science Leadership Council, identifies geographical and thematic gaps as well as recent advances in monitoring of relevant biophysical and socioeconomic variables in the mountains. We propose principles and ways of connecting existing initiatives, supporting emerging areas, and developing new mountain observatory networks regionally and, eventually, globally. Particularly in the data-poor regions, we aspire to build a community of researchers and practitioners in collaboration with the Global Network on Observations and Information in Mountain Environments, Group on Earth Observations (GEO) Mountains, a GEO Work Programme Initiative.

Introduction

Mountains are among the world's most impressive landscapes and are vital to humanity. Depending on the criteria that are used for defining mountains, they occupy between 12% and 30% of the land surface (Kapos et al 2000; Meybeck et al 2001; Sayre et al 2018). They are home to between 0.9 and 1.2 billion people, accounting for 13% (FAO 2015a) of the global population. These global figures hide large regional variations. It is estimated that about 90% of the mountain population lives in developing and transitional countries. The average density of the mountain population in developing countries (40 inhabitants/km2) is about 5 times higher than that in developed countries (8 inhabitants/km2) (Huddleston et al 2003), and may even exceed 500 inhabitants/km2 on the productive volcanic mountains of East Africa (Himeidan and Kweka 2012). Demographic trends in mountain regions are varied. There are areas where the population continues to increase, some areas with trends toward depopulation and abandonment, and areas where there is no major demographic change, but dramatic shifts in land cover from traditional land uses to new forms such as tourism and recreation are observed (FAO 2015a).

Mountains provide valuable environmental functions that underpin key ecosystem goods and services, a crucial supporting determinant for sustainable development potential. Mountain landscapes, characterized by diverse natural and managed systems (eg agricultural land), support biocultural diversity, food and energy security, tourism and recreation, and have spiritual and intrinsic values (Grêt-Regamey et al 2012; Mengist et al 2020). Mountain environments are directly linked to downstream regions through natural pathways (eg rivers and ecological corridors, which are themselves important habitats), as well as human infrastructure. Through these pathways, mountains provide the essential water, energy, food, and other resources and services, not only to the communities living in close proximity, but also to the downstream societies. They, therefore, play an important role in regional and global sustainable development. Most importantly, mountains act as water towers sustaining continuous flow of most of the world's major rivers (Viviroli et al 2007, 2020). At the same time, mountain environments are particularly threatened by the impacts of global environmental and climate change. Both the production and transmission of these environmental services occur in an environment characterized by prominent biophysical elevational gradients, as well as widespread natural hazards and threats in the form of extreme weather events, earthquakes, landslides, rock falls, avalanches, volcanic eruptions, wildfires, debris flows, and glacier lake outburst floods (GLOFs) (Glade 2013; Balthazar et al 2015; Haeberli et al 2015). Many of these hazards are modulated by highly variable weather conditions and, over the longer term, climatic change and its interactions with land cover and land use change.

Mountain environments are expected to experience particularly wide-ranging effects from current global climate change (Hock et al 2019). Rates of warming often depend on elevation (Pepin et al 2015). Although data availability and quality are lower (particularly at high elevations), changes in precipitation, cloud cover, wind, solar radiation, and other climatic variables are also likely to be strongly dependent on mountain elevation gradients (Lawrimore et al 2011; Viviroli et al 2011). In most mountain regions, snowpack and glaciers are receding (Hock et al 2019), leading to a positive feedback loop as surface albedo decreases and hydrological regimes downstream become less predictable (Huss et al 2017). The compression of ecological zones means that movements of species upslope can be rapid. Some species, typically found in the foothills and low montane zone, responding to drivers such as climate change by shifting upslope may benefit through increases in available area (Elsen and Tingley 2015). However, there is nowhere to go for the true cold-adapted species on high mountain summits (Nogues-Bravo et al 2007; Hagedorn et al 2019), and many may face local extinction (Elsen and Tingley 2015). It is not just impacts of climate change; continued fragmentation of habitats, caused by changes in land use, is another key factor leading to local or even global extinction of mountain species. The interaction among changes in land use, fragmentation, and changing climates is challenging the adaptative capacity of species and ecosystems.

As complex social–ecological systems, mountains exhibit emergent properties, nonlinearity, and dynamic feedback mechanisms (Klein et al 2019). Interacting pressures from changing climates, socioeconomic development, population growth, competing land uses, and national and international policies threaten the potential future sustainability and resilience of mountain systems across the world (Payne et al 2020; Thorn et al 2020). Nowhere are these pressures, and the need to understand the interactions between communities and environment, more acute than in the mountainous regions in developing countries. These regions face multiple and simultaneous threats spanning climatic, political, economic, institutional, and biophysical domains. Compelling evidence has accumulated that these interactions should be viewed as a globally interconnected, complex adaptive system in which heterogeneity, nonlinearity, and innovation play formative roles (Clark and Harley 2020; Payne et al 2020). However, there are also emerging opportunities to foster sustainability that take into account history and the complexity of social–ecological systems and use novel and inclusive participatory tools based on good-quality data and insight.

Interactions among people, ecosystems, and environments within mountain systems, and between mountains and other systems, urgently require integrated observation. This can be used to quantify the signals of change in environmental characteristics, composition, structure, and distribution of ecosystems and should be delivered rapidly and ideally in near real time. Such monitoring strategies are key to understanding and supporting actions that aim to address development and livelihood challenges on a scientifically supported basis. Our potential to adequately observe, monitor, and report these changes and their complex interactions also offers the chance to identify opportunities for development and innovation. Despite recent advances, fully integrated, long-term observations of environmental and social systems have received little attention. This is due to difficulties in combining approaches and methods used in natural and social sciences and bias toward specialization within the limits of established academic disciplines. There are also various institutional, conceptual, and operational challenges to coordinating these efforts (Funnell and Price 2003), which are still relevant today.

The necessity and expectation of engaging human–environment relationships explicitly in knowledge generation are now growing from a policy standpoint (Gleeson et al 2016). Key global policy frameworks have emerged since 2015. These include, but are not limited to, the United Nations (UN) 2030 Agenda and its Sustainable Development Goals (SDGs), the Intergovernmental Platform on Biodiversity and Ecosystem Services, the UN Sendai Framework for Disaster Risk Reduction, the UN Framework Convention on Climate Change, and the Paris Agreement, as well as the current post-2020 process to establish a Global Framework on Biodiversity under the Convention on Biological Diversity (CBD). These global policy frameworks influence the utility of long-term and integrated monitoring campaigns, not least in the mountains. They also come with metrics (ie goals, targets, and/or indicators) that promulgate certain requirements on the types and formats of data to be collected for monitoring purposes. An additional consideration is that reporting and assessing against these metrics can pose some methodological challenges in mountain-specific contexts, such as disaggregated and sparse data at the relevant spatial and temporal scales (Kulonen et al 2019).

A Global Network of Mountain Observatories (GNOMO) was developed in the 2000s. It was spurred forward by multithematic, consistent, comparable, and reasonably uniform monitoring, with adequate precision and accuracy, in mountainous regions, providing data for scientific research, management, and policy requirements (Strachan et al 2016). The GNOMO initiative, originally grassroots, was supported and coordinated by the Mountain Research Initiative (MRI) network. In 2016, the Global Network for Observations and Information in Mountain Environments (now known as GEO Mountains), co-led by the MRI, was established as a Group on Earth Observations (GEO) initiative. It provided additional means to further institutionalize, support, and integrate GNOMO as part of this broader framework (Adler et al 2018). More recently, following an initiative from members of the MRI Science Leadership Council (SLC), the MRI established a Mountain Observatories Working Group (Adler, Balsiger, et al 2020). This working group seeks to extend the work of GNOMO and support GEO Mountains on mountain observatories and observations through the Mountain Observatories initiative—an ongoing effort to coordinate multithematic data collection in the mountains. The Mountain Observatories initiative consists of several projects and tasks aimed at the crowd-sourcing of information on existing and emerging observatories, identifying gaps (both geographical and thematic) in data, and developing protocols for collection and processing of relevant data and information. It aims to build a community of researchers and practitioners as part of GEO Mountains.

The working group defines mountain observatories as sites, networks of sites, or data-rich regions where multidisciplinary, integrated observations of biophysical and human environments are conducted over a lengthy period of time in consistent ways, according to established protocols using both in situ and remote observations. The purpose of mountain observatories is to evaluate the current state of mountain systems and build up an understanding that captures past variations and charts future physical, biological, and social changes in mountain environments. Ideally, mountain observatories need to fulfill a function as supersites or hubs for comprehensive monitoring of mountain environments.

This paper: (1) provides a brief review of the current trends and challenges of socioenvironmental monitoring in the mountains, and available networks and products; (2) assesses challenges for implementing an integrative and holistic approach to monitoring socioenvironmental systems; and (3) proposes principles and ways of supporting the development and connection of mountain observatories. We stress that this paper is not a structured systematic review of the available literature, but a substantive piece by a group of experts and their relevant networks based on published literature and content presented at multiple workshops in the past.

Monitoring in the mountains: status quo

Evolving methods of monitoring mountain environments

Systematic monitoring of mountain environments began in the European Alps in the 19th century by establishing meteorological, hydrological, and glaciological observations. Observations expanded in the 20th century, and continuous monitoring was initiated in many parts of the world during the first International Geophysical Year of 1957–1958 (Zemp et al 2009). At present, the World Meteorological Organization (WMO) runs the Polar and High Mountain Observations, Research and Services (PHORS) program, which coordinates standardized meteorological observations by nations and groups of nations. The World Glacier Monitoring Service (WGMS) coordinates activities on monitoring the state of glaciers, including long-term mass balance measurements (Zemp et al 2009). The Global Observation Research Initiative in Alpine Environments (GLORIA) (Grabherr et al 2000) focuses on monitoring changes in mountain-top vegetation. The global Critical Zone Exploration Network (CZEN;  http://www.czen.org/) has a number of mountain sites where chemical, physical, geological, and biological processes that shape Earth's surface (Brantley et al 2017) are studied. A more recent campaign to highlight the need to support mountain-specific and relevant observation efforts was a topic of the WMO-hosted High Mountains Summit in 2019. The summit provided an opportunity to connect scientific and practitioner communities to take stock of recent developments and discuss prospects for monitoring to support climate services, thereby supporting efforts to safeguard mountain communities and ecosystems from the negative effects of climate change (Adler, Pomeroy, et al 2020).

Currently, there are 2 contrasting trends in environmental monitoring in mountainous regions. On the one hand, the density of in situ monitoring sites is declining, as they are costly and difficult to maintain in high-elevation environments. On the other hand, the availability of remote-sensing data with high spatial, temporal, and spectral resolution is increasing (Weiss and Walsh 2009). This complements and, in many instances, replaces in situ data, especially in the case of land use, land cover, and specific ecological data, such as plant productivity or phenology. Furthermore, the availability and use of proxy data based on paleoenvironmental reconstructions of past mountain environments are additional sources of information that have seen important developments lately. From a mountain observation perspective, paleoenvironmental data offer opportunities to better quantify the dynamics of environmental systems over longer periods of time. They also improve our understanding of environmental processes by putting the current and projected environmental changes in the mountains into a long-term perspective of natural ranges of variability (Marchant et al 2018).

In the following sections we delve into the trends behind in situ and remote-sensing monitoring methods and discuss the availability of mountain-related paleoenvironmental records in more detail.

In situ observations: spatial limitations and technological opportunities: There is poor availability of long-term, homogeneous and comparable ground-based observations in most mountainous regions outside Western Europe and North America (Viviroli et al 2011). The density of high-elevation meteorological stations and hydrological gauging sites with long-term measurements (eg over 50 years) conducted manually has declined in many regions since the 1980s. This is particularly true in the post-Soviet countries, and also in Africa and in the tropical Andes (Beniston et al 1997; Zhou et al 2017; Shahgedanova et al 2018). However, the expansion of automated measurements alleviates this problem to some extent (Strachan et al 2016). The Global Historical Climatology Network dataset (GHCN Version 4) includes 27,467 meteorological stations, of which only 1328 (4.8%) are located above 2000 m above sea level (masl) and 211 (0.8%) are located above 3000 m. Importantly, these statistics refer to the stations that supplied data to GHCN for any period of time and not necessarily the stations with continuing, long-term observations. Our comparison of the elevational distribution of current meteorological stations (as of June 2020) with global hypsography of the land surface area shows that station density is relatively high between 2000 and 3000 masl when averaged globally (Figure 1). However, it still does not meet the WMO criterion of 4 stations per 1000 km2 for measuring precipitation in mountainous areas (WMO 1994; Viviroli et al 2011). The geographical distribution of the high-elevation stations is uneven, with a higher density in North America and Europe (Figure 2A, B) but approximately 10 times lower density elsewhere, especially in Africa, Asia, and South America (Figure 2C, D). This is irrespective of whether all stations (Figure 2A, C) or currently operational stations (Figure 2B, D) are used. The density of meteorological stations declines sharply above 3000 masl, and the GHCN dataset does not feature a single station located above 5000 m (Menne et al 2018). In Africa and South America especially, there has been a dramatic decline in recent years in station coverage (Figure 2D), which is worrying, because there was already a much lower station density on these continents to begin with. Figure 3 further shows details of the geographical distribution and elevation coverage of all GHCN stations. It reveals that high elevations in the Northern Hemisphere are comparatively better covered, especially in parts of North America and Europe, whereas for Asia, more data from high and very high elevations would be highly desirable. In the Southern Hemisphere, as well as in low latitudes, data for high and very high elevations are generally much scarcer. High-elevation regions constitute a small proportion of the global surface area. However, the lack of stations in Africa, South America, and some parts of Asia, as well as the lack of ultrahigh-elevation stations everywhere, cannot be justified given the populations they support and enhanced rates of environmental change they experience (Pepin et al 2015).

FIGURE 1

Global distribution of the GHCN stations with elevation. In this and other figures, stations were mapped by 100 m elevation bands.

img-z4-2_A1.jpg

FIGURE 2

Distribution of GHCN stations with elevation by continent: (A, C) all GHCN stations; (B, D) current GHCN stations as of June 2020.

img-z4-7_A1.jpg

FIGURE 3

Distribution of GHCN stations with data for any period of time. (A) Longitudinal profile with station elevations (colored dots) and maximum elevation of the land mass (gray area); (B) geographical distribution in a Mollweide equal-area projected map, with station elevations shown in colors similar to panel (A).

img-z5-2_A1.jpg

Statistics from another global surface air temperature dataset, Climatic Research Unit Temperature (version 4) (CRUTEM4), are broadly similar (Jones et al 2012; Osborn and Jones 2014; Harris et al 2020). The Global Runoff Data Centre (GRDC) data show that runoff and precipitation measurements are underrepresented above 1500 masl (Viviroli et al 2011). The paucity of measurements is especially evident in Asia, where the density of rain gauges declines to 0.04 per 1000 km2 above 2500 masl; that is about 1% of the WMO-recommended density for mountainous regions (WMO 1994).

The lack of spatiotemporal data coverage in the mountains results in crucial knowledge gaps and hampers our ability to validate remote-sensing data and models and address practical challenges. The usefulness of the available global and regional datasets for practical applications (ie watershed management, infrastructure development, implementation of adaptation options) is limited because solutions depend on topographic categories (eg slope, aspect), and these attributes are not represented. The lack of data from high elevations means that it is all too common for data from lowland sites to be used to evaluate processes in high mountains, resulting in uncertainties and poor characterization and modeling of high-elevation environments. Meteorological stations in the mountains tend to be located in valleys and in urban areas because of accessibility, which has inadvertently created bias in the data. For example, valley stations throughout the world often show strong microclimate effects, such as cold air ponding, urban heat islands, and rain shadows, which can make upslope extrapolation unreliable (Lundquist et al 2008). The Chinese Meteorological Administration currently operates 87 stations across the Tibetan Plateau, but many of these are located in the incised valleys in the south and east of the plateau, and often in towns. Their data cannot be reliably used for detecting signals of climate change at higher elevations, and cross-referencing these data with remotely sensed data is required to understand temperature trends above the highest station (4780 m) (Pepin et al 2019). Similarly, in Central Asia, streamflow data from gauging sites located in the foothills are often used to characterize impacts of glacier change on water availability. Positioning runoff gauges at basin outlets was originally designed to integrate information about runoff from the entire basin, including higher elevations. However, water abstraction and channel modifications often make these data unsuitable for attribution of the observed changes and modeling impacts (Shahgedanova et al 2018). Information on subsurface hydrology, which is an important component in mountain hydrological services to lowlands, is scarce and does not adequately support both research and water management applications (Somers and McKenzie 2020).

In situ observations often focus on the aboveground properties of mountain ecosystems, while long-term monitoring of soil properties in mountain environments is rare. Soils deliver various provisioning and regulating ecosystem services (eg carbon sequestration and storage, water storage and regulation, provision of food). The capacity of soils to deliver these services depends on soil functions and properties. Soil management and policies supporting it require accurate and spatially explicit soil information to meet present-day and near-future demands for food provisioning and regulation of climate and water cycles (Poesen 2018). The GlobalSoilMap.net consortium aimed to produce standardized soil information at an increasingly fine resolution, including mountainous regions. The African Soil Information Service created an online portal for soil information mapped out across the African continent ( http://africasoils.net/services/data/). This has been useful for many different stakeholder groups, from national and regional policy development to farm-level land management, that require soil and landscape information. However, despite these efforts, soil information from the mountainous regions is often limited to local case studies and is fragmented in space and time (FAO 2015b). An integrated landscape approach is crucial in the management of mountain soils, given the variety of land use and soil management systems and the types of soils that can be found in mountain areas. Mountains are important stores of carbon, and montane rivers export sediment and organic carbon downstream and to the world ocean (Wohl et al 2012). Climate change, combined with the naturally high rates of erosion and chemical weathering that characterize montane environments, unsustainable management, and increasing frequency of disturbances such as wildfires, floods, and landslides intensify carbon exchanges in the mountains. A full carbon budget has been quantified in very few montane locations, and expansion of carbon monitoring is required to assess carbon stores and budgets, and the processes affecting their dynamics (Hilton and West 2020).

A positive development, and indeed an efficient way forward, is the increasing density of automated in situ measurements (Strachan et al 2016). The automated approach is particularly common in hydro-meteorological and, more recently, glaciological and ecological monitoring and is used by long-term monitoring programs such as, for example, Glaciers and Observatory of the Climate (GLACIOCLIM) (Shea et al 2015), Stations at High Altitude for Research on the Environment (SHARE) (Bonasoni et al 2008), and the Nevada Climate-Ecohydrological Assessment Network (NevCan) (Strachan et al 2016). Some of the automated sites (eg GLACIOCLIM) archive the collected data. Others (eg NevCan) make data publicly available in real time. Despite these advances, however, the very high-elevation weather stations (>4000 m) are again rare (Viviroli et al 2011). The longest records on mountains such as Kilimanjaro (∼5800 masl) or Quelccaya (∼5700 masl) only extend back some 15–20 years (Yarleque et al 2018), and this hampers our understanding of longer-term climate variability and change. A preliminary global assessment of the spatial distribution of known high-elevation automated meteorological observations suggests a distinct concentration of networks in the midlatitudes of the Northern Hemisphere, but also some sites in Africa and South America. However, at present, there is no global collation of such observations. The recent proposal by the MRI to develop a coordinated database of observations from the automated sites and their transects across elevational gradients (the Unified High-elevation Observation Platform or UHOP) is a welcome example, but it is in its infancy (Adler et al 2018). This makes it almost impossible to know how the number of high-elevation weather stations changes over time, and where spatial coverage is particularly poor and the ensuing need is most acute.

The automated measurements are often funded and operated by individual or national-scale organizations and are associated with short-term research and development projects. They often provide narrowly focused, short time series, which do not necessarily adhere to standard protocols of sensor deployment and data collection. Problems surrounding the deployment of automatic stations, including their siting, continuity of measurements, and data transmission, were comprehensively reviewed by Strachan et al (2016). Here, we note that within the setting of mountain observatories, uniform observational techniques can be introduced, and procedures for operating and maintaining automatic stations can be taught. For example, protocols for uniform observations and data quality control have been introduced within the GLORIA network (Pauli et al 2015).

Many research or development initiatives measure (automatically or manually) the same variables but use different metrics and standards, resulting in datasets that are not directly comparable. While standards for weather station deployment and meteorological data collection (WMO 2008) and guidelines for soil description and classification (FAO 2006) have long existed, standardization of measurements is less common in other areas. Other examples where significant advances toward methodological uniformity have been made include the WGMS (Zemp et al 2009) and the GLORIA (Grabherr et al 2000) networks, concerned with glacier mass balance measurements and ecological monitoring, respectively. However, despite the global sharing of observational protocols, GLORIA sites tend to be concentrated in Europe, the tropical Andes (CONDESAN regional network; Saravia and Bièvre 2013), and western North America (CIRMOUNT regional network; Millar 2004), with limited monitoring elsewhere.

Increasing role of remote sensing in addressing limitations: Remote sensing in general, and remote sensing from space in particular, helps to alleviate logistical challenges of high-elevation monitoring. It helps to provide multiscale and multithematic information obtained in a consistent way over large areas. It is frequently presented as a solution to the problem of declining in situ observations. Depending on the type of sensors used and questions to be addressed, spaceborne—most often satellite—imagery is available at a wide range of scales: from high/fine (<15 m), to medium/moderate (15–500 m), and low/coarse (>500 m) pixel resolution. Spatial resolution is generally traded off against temporal resolution both in terms of frequency of repeat measurements and the temporal extent of the time series.

The monitoring of mountain environments using remote sensing has expanded significantly since the 1980s, when aerial photography was superseded by spaceborne remote sensing (Weiss and Walsh 2009). Currently, however, airborne measurements from unmanned aerial vehicles (UAVs) are expanding in monitoring mountain environments (eg Piras et al 2017). There has been a growth of products, accessibility to data, and number of tools with which to analyze and visualize satellite and other spaceborne datasets that has led to its more widespread application. Remote-sensing products, particularly the repeated imagery from Moderate Resolution Imaging Spectrometer (MODIS), Landsat, ASTER, and Sentinel, have helped to fill a significant data void, especially in developing countries and in remote regions.

Remote sensing, however, has several important limitations when used on its own (Pfeifer et al 2012). First, remote-sensing products are inherently limited in their temporal coverage and are too short to provide a comprehensive long-term monitoring perspective—the world did not start in the 1970s! Second, despite recent advances, some environmental characteristics still cannot be monitored from space or even from UAV. For example, remote sensing captures spatial and temporal changes in vegetation, aboveground plant biomass, phenology, and productivity well, but not soils, or animal and plant biodiversity. Progress is being made, however, in using spaceborne data for plant biodiversity monitoring (Skidmore et al 2015). Third, there are challenges specific to mountain environments. These range from spatial resolution, which is still too low for many applications, and technical problems, associated with complex mountainous topography and their radiative properties, to lack of quality ground-control data (Weiss and Walsh 2009; Balthazar et al 2015).

For example, global precipitation datasets such as Tropical Rainfall Measuring Mission (TRMM) (Huffman et al 2007) and Global Precipitation Measurement (GPM) (Skofronick-Jackson et al 2017) datasets have spatial resolution of 0.25° and 0.1–0.5°. They are available from 1997 and 2000, respectively, and are widely used in environmental analyses. However, data derived from TRMM and GPM are less helpful in characterization of elevation-dependent precipitation trends controlled by topography slope, aspect, and exposure in the mountains. These and other precipitation datasets derived from satellite observations have difficulties in estimating orographic precipitation, particularly in southeastern Asia, in the Himalayas and in the Western Ghats (Sun et al 2018). Soil moisture datasets include the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) (Kerr et al 2012), and the National Aeronautic and Space Administration (NASA) Soil Moisture Active Passive (SMAP) datasets (Entekhabi et al 2010). They have spatial resolutions of 30–50 km and 9 km (downscaled to 3 km), respectively, which is problematic in mountain regions. Efforts are being made to increase the spatial resolution and utility of such datasets. For example, soil moisture data at a scale of meters became available recently from several satellites, albeit at the cost of reduced temporal resolution (Mohanty et al 2017). Another example is TAMSAT (Tropical Applications of Meteorology using SATellite data and ground-based observations), which provides daily rainfall estimates for all of Africa at 4 km resolution (Maidment et al 2014, 2017; Tarnavsky et al 2014). The global Land Surface Temperature (LST) dataset available from 2010, compiled by ESA, is available at 5 km resolution. However, many processes in the mountains (eg cold air drainage, which is critical for ecological responses) are controlled by even smaller-scale topography (Lundquist et al 2008; Morelli et al 2016). In addition, a lack of high-elevation ground-truth data hampers the validation of remote-sensing products in many mountain regions. For example, several sites used for the validation of the LST data are positioned above 1000 m, but none is in complex terrain (Martin et al 2019).

The high-resolution digital elevation models (DEMs) are derived from spaceborne data. They enable mountain topography to be assessed and are extensively used in environmental analyses and models. This is particularly important for natural hazard assessments, predictive species distribution models (Platts et al 2008), and assessing glacier change (Marzeion et al 2017). Elevation data were obtained by the Shuttle Radar Topography Mission (SRTM) in 2000 for the region bound by 60°N and 54°S latitudes. These data were used to generate the 30-m horizontal resolution SRTM30 DEM, representing 80% of Earth's surface with a vertical accuracy of at least 16 m at a 90% confidence level (Rodríguez et al 2006). The DEM captures general topographic gradients but not the finer-scale topographic variations that affect a range of processes—from ecological to glacier change—in regions with high topographic complexity (Marzeion et al 2017). Another example is a multitemporal global land surface altimetry product (GLA14) generated from the Geoscience Laser Altimeter System instrument on the Ice, Cloud and Land Elevation Satellite (ICESat). This is designed to measure changes in ice-sheet elevation with 60 m to 70 m resolution (Schutz et al 2005). The geolocation accuracy of GLA14 is below 1 m and can be a valuable supplement to other DEMs. The recent sub-meter-resolution optical satellites such as GeoEye, Quickbird, WorldView, and Pléiades provide higher-resolution spaceborne DEMs on a scale of mountain systems, for example, the High Mountain Asia 8 m DEM (Shean 2017), and for smaller mountain areas worldwide (eg Berthier et al 2014).

Many practical problems in the routine use of remote sensing in environmental applications that were common a decade ago have been overcome, and spaceborne remote sensing has become a predominant method of monitoring in many disciplines. Most assessments of changes in glacier area are conducted using satellite remote sensing, and comprehensive regional and global datasets characterizing the observed glacier change have been developed (Kargel et al 2014; Pfeffer et al 2014). Monitoring seasonal snow cover is another example. This includes both its spatial extent (eg Gafurov et al 2016) and snow depth using airborne laser altimetry (Deems et al 2013) and satellite data (Marti et al 2016; Lievens et al 2019). However, obtaining data about snow water equivalent, required in many hydrological applications, remains problematic. Similarly, evaluation of many glacier characteristics, such as ice thickness, the main constraint on simulating future glacier evolution and assessment of water resources (Vuille et al 2018; Shahgedanova et al 2020), requires ground-based measurements or airborne remote sensing with ground support (Gärtner-Roer et al 2014) combined with glacier modeling (Linsbauer et al 2012; Farinotti et al 2019). The evolution of mountain lakes, either glacier (eg Gardelle et al 2011) or other (eg Casagranda et al 2019), can be easily measured using satellite imagery. By contrast, data on lake bathymetry require field surveys, which limits data availability to a few regional datasets worldwide (eg Cook and Quincey 2015; Kapitsa et al 2017; Muñoz et al 2020).

A good example of combining the in situ and satellite measurements in a complementary way is the Pléiades Glacier Observatory established by the WGMS in collaboration with the French Space Agency. It provides very high-resolution stereo images (0.5 m), enabling assessment of glacier thickness change in the WGMS benchmark glaciers (Berthier et al 2014; Kutuzov et al 2019; Kapitsa et al 2020). This approach enables expansion of mass balance monitoring from individual glaciers to regional scale through the application of high-resolution satellite DEM validated with ground-control data. Another example is the Virtual Alpine Observatory (VAO), operating under the Alpine Convention ( http://www.alpconv.org) as a network of 16 high-elevation research stations based in 6 Alpine and 4 associated countries. The mountain observation sites, which form the backbone of VAO, are linked to several high-performing computing centers. These enable efficient access to and visualization of in situ data generated by VAO, and relevant remote-sensing and modeling data for the member institutions, wider research community, and stakeholders through the Alpine Environmental Data Analysis Centre (Virtual Alpine Observatory 2017).

Some key challenges to using remotely sensed data, such as the fragmentation of products from different agencies and the range of specialized analytical tools, have been overcome by the Google Earth Engine (Gorelick et al 2017). This combines a wide catalogue of satellite imagery and geospatial datasets with inbuilt modeling tools to map trends and quantify landscape changes and the drivers behind these changes (eg Kumar and Mutanga 2018; Kennedy et al 2018; Bian et al 2020).

Use of proxy data and paleoreconstructions: Understanding how terrestrial ecosystems change at global scales is a fundamental challenge that demands datasets with extensive temporal depth and wide spatial coverage. There is growing interest in harnessing networks of long-term records that extend past the instrumental records. This would allow researchers to explore, contextualize, and predict the responses to abrupt and gradual environmental change, investigate the interplay of abiotic and biotic interactions, understand rates and direction of change (Bi et al 2015; Nolan et al 2018), and characterize mechanisms that promote resilience (Rayback et al 2020). All of these fundamentally require an understanding of response over time. Indeed, placing recent global warming and environmental change in the context of natural climate variability requires the long-term perspective that can be provided only by paleoenvironmental proxy records (Carilla et al 2013; Bi et al 2016; Kaufman et al 2020). This is particularly important in regions where the availability and quality of long-term environmental and climate data are limited.

Paleoclimatic, paleoecological, and paleoenvironmental proxy data are extensively used to reconstruct past changes over a range of different timescales and to examine the changing relationships between people and the environment. Paleoenvironmental research is focused on basins where sediments accumulate over time, archiving signals of their surrounding environment, or it uses data records extracted from ice cores, glacial deposits (moraines), lake cores, tree rings, speleothems, and lichens. Many of these records are obtained in the mountains where lakes, bogs, mires, glaciers, and thermally or hydrologically limited biological growth are common features. As a result, there are numerous paleoreconstructions focusing on individual sites or regions (eg Carilla et al 2013; Wirth et al 2013; Solomina et al 2016; Mokria et al 2017; Fan et al 2019, to name but a few). While the value of these records is in describing change, like any observation, the value diminishes with distance from the observation. This is particularly true in the mountains, where environmental conditions change strongly over small distances. Although there are numerous archives collating paleoenvironmental data, a comprehensive database of paleoclimate records that focuses on mountains is lacking. As for the datasets compiled for the Arctic (Linderholm et al 2018), such a database could be invaluable to place single records into the longer-term and wider spatial context.

A recent global compilation of quality-controlled temperature-sensitive proxy records extends back 12,000 years through the Holocene from 679 sites, many of these from mountain settings (Babst et al 2017). Centralized databases are playing an increasingly important role, for example, National Oceanic and Atmospheric Administration (NOAA) National Centre for Environmental Information and datasets therein ( https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets), Neotoma (Williams et al 2018), Forest Inventory and Analysis (US Department of Agriculture Forest Service;  https://www.fia.fs.fed.us/tools-data/), TRY Plant Trait Database ( www.try-db.org), and the International Tree Ring Data Bank (ITRDB;  https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/tree-ring) (Grissino-Mayer and Fritts 1997). The ITRDB holds data from 238 tree species obtained at more than 4000 sites in 6 continents (Sullivan and Csank 2016) used to quantify and evaluate the long-term environmental and ecological changes across large regions and environmental gradients (Gebrekirstos et al 2014; Babst et al 2017; Zhao et al 2019). The long-term data with annual resolution collected by the Dendro-Ecological Network (DEN;  https://www.uvm.edu/femc/dendro) enable scientists to detect and assess slower ecological processes as well as the rare events and link current environmental change with past human agency and other biological data (Holz et al 2017; Williams et al 2018; Stephens et al 2019; Correa-Díaz et al 2020). There is a recent move to combine these single proxy databases that is galvanizing the community. For example, the multiproxy Neotoma database now holds over 4 million observations of micro- and macrofossil distributions from 31,000 datasets and 15,000 sites. Importantly, these new multiproxy databases include analytical and display functionality to improve access and use: Neotoma data via Neotoma Explorer, Google-Datasets-discoverable landing pages, NOAA/NCEI-Paleoclimatology, Flyover Country, and the Global Pollen Project (Williams et al 2019).

The prospect for stimulating a compilation of relevant paleoscience and paleodata and information sources in a mountain context has long been an aim of the MRI. The MRI SLC led activities for the 2020–2021 period (Adler, Balsiger, et al 2020). These were coupled with renewed efforts, via GEO Mountains, to incorporate paleodata and information for mountains to address current gaps.

Thematic foci and emerging examples of holistic approaches

Multiple assessments of environmental change within mountain regions are usually conducted by networks and programs within subdisciplines. Examples include WGMS's (Zemp et al 2009) and GLORIA's (Grabherr et al 2000) monitoring of glaciers and vegetation, respectively. More recently, other thematic regional and global networks have been established with a focus on the cryosphere, ecology, or biodiversity, such as the Climate and Cryosphere (CliC) project ( http://www.climate-cryosphere.org) supported by the World Climate Research Programme (WRCP). The World Overview of Conservation Approaches and Technologies (WOCAT) has regional initiatives that include mountain regions (eg in Ethiopia, Bhutan, Nepal, Uganda). It has launched efforts to systematize information on sustainable land management (eg Liniger and van Lynden 2005). There are many examples of successful regional thematic monitoring programs, such as the Andean Forest Network, which monitors changes in structure and diversity of montane forests in this vast region (Malizia et al 2020).

Although thematically and locally focused networks make an important contribution to our understanding of environmental change in the mountains, the data they provide are fragmented. There are ongoing efforts to integrate meteorological, glaciological, hydrological, and natural hazard monitoring at high elevations. These are coordinated by the WMO Global Atmospheric Watch (GAW) program. Alternatively, they may be pursued by regional networks, such as GLACIOCLIM (Shea et al 2015) and SHARE (Bonasoni et al 2008), originally established in the Hindu Kush Himalaya region, but now expanding to Africa, Europe, and South America. In particular, SHARE and the Global Change in Mountain Regions (GLOCHAMORE) (Grabherr et al 2005) projects have succeeded in overcoming thematic boundaries, integrating objectives of natural and social sciences, long-term observations, and modeling.

There are examples of integrated multithematic observations operating on regional and local scales. The VAO, operating in the European Alps, Scandinavia, and southern Caucasus and supported by governmental funding, provides an example of how systematic and consistent observations are enhancing our understanding of the interactions among regional development, economy, and environment in the mountains. The International Long-term Ecological Research (LTER) network, which operates globally, adopts an integrated approach to monitoring and research of ecosystem, critical zone, and socioeconomic aspects of landscape evolution (Rogora et al 2018; Angelstam et al 2019). Another example is the long-term environmental and social–ecological monitoring in transboundary landscapes in the Hindu Kush Himalaya region (Chettri et al 2015). An example of an emerging network is Monitoring High Andean Ecosystems of Colombia (EMA), which is building on the existing glaciological, hydrological, biosphere, and land cover and land use monitoring facilities and network with the aim to build an integrated system of transdisciplinary socioenvironmental monitoring in the Andes (Llambí et al 2019). A smaller regional network, NevCAN, established by the University of Nevada and the Desert Research Institute, operates in the southwestern United States monitoring climatic conditions, snow depth, surface hydrology, soil conditions, ecology, and biodiversity (Strachan et al 2016), while the Mountain Social Ecological Observatory Network (MntSEON) focuses on the vulnerabilities of mountain systems and the human communities they support in the United States (Alessa et al 2018). There is a much smaller KiLi Observatory established under the Ecosystems under Global Change project and confined to the limits of Mount Kilimanjaro. KiLi supports research on impacts of climate change, land use change, and a range of human activities on tropical mountain biota, and the resilience and adaptive capacity of natural and modified ecosystems to climate change along elevation gradients (Albrecht et al 2018).

Although working at different geographical scales and within different funding frameworks, these are all too infrequent, but successful, examples of integrated multithematic observations focusing on socioeconomic as well as biophysical monitoring.

Thematic gaps and suitability of the available datasets

In this paper, we briefly reviewed, in the context of mountain environments, typical observations and relevant datasets (regional and global, obtained in situ or using remote sensing) available to and widely used by the academic and stakeholder communities. Three important points emerge from this analysis. First, many datasets (eg TRMM, GPM, SMOS, SMAP, CRUTEM4) are not mountain-specific and are not designed to address a concept of large change (eg in temperature, precipitation, species, etc) over a fine spatial scale. Second, while some datasets are easily accessible (eg via Randolph Glacier Inventory [RGI] and WGMS), many regional datasets, both historical and contemporary, remain within institutions and are not shared. Better knowledge about existing datasets, data access, and data rescue is required. Third, most of the above discussion concerns monitoring of the physical environment. Very few observatories and networks combine monitoring of the natural environment with the assessment of socioeconomic and cultural impacts and associated indicators, even though this is important to develop future scenarios of environmental and socioeconomic change (Thorn et al 2020). There are, however, examples of successful integration of biophysical and socioeconomic monitoring by such mountain-specific networks, such as VAO, NevCan, MntSEON, and KiLi, while the EMA (Llambí et al 2019) and other networks explicitly focus on interdisciplinarity in their development plan. In particular, LTER has developed a social–ecological research platform to enhance the links between biophysical and socioeconomic monitoring and research (Angelstam et al 2019). Social, political, and cultural observations are scattered and often based on individual case studies or contexts. This largely reflects a very specific epistemic perspective and set of research questions, methods, and design that may not be amenable to, nor necessarily intended to, align or be integrated with observational data for biophysical variables.

The topography of mountains implies that upstream and downstream regions are connected in terms of impacts, most notably by gravity-driven water and mass flows. Examples include downstream water supply impacted by high-elevation precipitation and snowfall patterns (Viviroli et al 2020) and glacier changes (Huss and Hock 2018), or GLOFs and debris flows produced by upstream geomorphological processes (Haeberli et al 2015). While glacier lakes are often reasonably well monitored, potential or actual impacts of related hazards are often monitored poorly in the absence of socioeconomic data and trends other than national census data.

Efforts to provide a robust basis for systematic, comparable, and coordinated observation frameworks incorporating social dimensions need to capture the unique sociocultural place-based conditions while allowing for cross-comparisons within and across locations and scales. Integrating observations in complex social–ecological systems and contexts is an emerging area that addresses a complex and fundamental methodological challenge. Examples from a few multithematic and mountain-specific initiatives offer promising prospects for their scaling and development. Typologies and frameworks for capturing relevant social data and information are an important basis for designing monitoring systems and networks that are fit-for-purpose and facilitate and respond to these integrated observation needs in mountain social–ecological systems (eg Collins et al 2011; Altaweel et al 2015).

Finally, as highlighted in Hock et al (2019), citing Dickerson-Lange et al (2016) and Wikstrom Jones et al (2018), citizen science can also contribute to generating and integrating diverse observation data, complementing instrumental and modeling efforts. Furthermore, lessons learned from community-based observation methods in the Arctic offer transferable application prospects for the mountains (Griffith et al 2018). These can enhance the value and integration of human dimensions in observation campaigns, as do observations based on indigenous and local knowledge (Abram et al 2019).

BOX 1: Aims of the Mountain Observatories initiative

  1. Crowdsource information, including metadata, on existing or emerging observatories.

  2. Specify or provide guidance for relevant protocols to help mountain observatories in operationalizing mountain-relevant data and information for multiple applications.

  3. Build capacity through concrete activities in mountain monitoring and sustainable development, including provision of on-site courses and training exercises with a particular focus on developing countries with fragile mountainous ecosystems.

  4. Facilitate links between the Mountain Observatories initiative and the GEO Mountains program.

Overall and despite many recent efforts, there is still a lack of datasets for many regions. Where such datasets exist, they still have a disciplinary focus, and their fragmentation and often a lack of accessibility are major challenges (Thornton et al 2021). The question, therefore, remains whether existing datasets from high-elevation regions serve the purpose of a holistic approach to understanding environmental change in mountains and assessment of impacts and vulnerabilities.

Mountain observatories: quo vadis?

The Mountain Observatories initiative, coordinated by the MRI Mountain Observatory Working Group, is expected to build on and go beyond the remits of traditional, highly specialized monitoring programs operating in mountainous regions. It aims to deliver a highly visible and valuable output (Box 1). This will stimulate the development of a comprehensive partnership of key stakeholders and trigger actions leading to realization of the goals stipulated in global frameworks such as the Paris Agreement or the SDGs. It will also support regional and local policymaking and communities in the management and governance of common resources in mountains.

The remits of the mountain observatories are illustrated by Box 2. This list is not exclusive nor exhaustive and intentionally embraces a wide thematic and spatial scope. While it may not be possible or indeed necessary to monitor such a system in its entirety, monitoring at mountain observatories should fulfill several criteria, creating commonality between diverse observational programs. First, observational programs should be multi- and interdisciplinary and include components that can be monitored in a consistent way across the globe (eg climate), as well as those specific to the region (eg glaciers). Second, integrated monitoring of environmental and social processes should be achieved. Third, mountain observatories should aim to effectively integrate in situ measurements, remote sensing, modeling, and other forms of data and information sourced from community-based approaches. Fourth, they should link mountains and downstream regions with regard to both processes and impacts. Recognizing the need to monitor gradient processes for the benefit of both mountain communities and dependent lowland zones is important, as previously emphasized by Pepin et al (2015), Strachan et al (2016), and the recent proposal by the MRI on UHOP development (Adler et al 2018).

BOX 2: Remits of the mountain observatories

Atmosphere and climate

Cryosphere

Hydrology, hydrochemistry, and water quality

Soils and biogeochemical cycles

Vegetation and biodiversity

Land cover and land use

Hazards and disasters

Quantitative demographic, livelihood, and household information

Qualitative sociocultural information, including governance and land tenure systems

Although the reviewed initiatives operate at different scales, their success rests on the same pillars. All are designed and maintained as long-term programs supported by shorter-term projects. The former enables continuity and consistency; the latter brings in new expertise, improves instrumentation and communication, and ensures production and publication of relevant scientific results in a timely fashion. All rely on technology and provide efficient data access. All operate according to the established protocols, collecting and storing data in a consistent and comparable way. All work toward building digital and data infrastructure because generated datasets are too large for manual processing, and automation and workflow architectures are necessary. An important feature is the collaborative approach and sharing of knowledge and resources between countries, institutions, academics, and practitioners. Institutionalization of observational networks or strong links with public and private stakeholders can help to secure long-term monitoring.

The MRI Mountain Observatories Working Group, working closely with GEO Mountains, will support the development of regional and eventually global networks of mountain observatories by focusing on 3 tasks.

While the overall concept of mountain observatories has been developed, problems and mechanisms of delivery vary between regions. The first task, therefore, is the formulation of problems at regional network level, enabling network participants to express their opinion on the agenda of the Mountain Observatories initiative and adjust it to their needs. At this stage, a program of multithematic data collection along mountain gradients in different mountain regions will be created, including, but not limited to:

  • Understanding elevation-dependent warming (Pepin et al 2015) and its broader conception of elevation-dependent climate change. This can be further evidenced through analysis of meteorological and associated data, information on soil characteristics, vegetation, and their response patterns obtained systematically along a variety of elevational gradients.

  • Identifying the changing capacity of mountain regions to store water in its frozen form (snow, glacier ice, ground ice, and permafrost) and supply it to the lowlands (eg Huss et al 2017).

  • Characterizing changes in individual components of the water balance and combined influence of hydrological changes and anthropogenic activities on water availability and quality (Viviroli et al 2011, 2020).

  • Using global and regional climate models to characterize the changing climate of mountainous regions (eg Urrutia and Vuille 2009; Palazzi et al 2013; Platts et al 2014; Kotlarski et al 2015).

  • Assessing the level of adaptation and vulnerability of human mountain communities and relevant resilience traits to climate change and other socioenvironmental changes (Lopez et al 2017; Klein et al 2019).

  • Recording elevation- and topography-dependent changes in human–climate–ecosystem interactions and particularly monitoring changes that are likely to result in nonlinear and potentially abrupt impacts on human and environmental systems (eg changes in mudflows and GLOFs, avalanche occurrence, etc).

  • Identifying key changes that impact mountain biodiversity and ecosystems, including the capacity to support ecosystem services, and interactions between climate change and land use at the relevant spatial and temporal scales (UNEP et al 2020).

  • Recognizing changes in human demography, livelihoods, cultural practices, and governance arrangements, and their interactions with land use, ecosystems, climate change, and topography.

Many of these thematic areas individually comprise major scientific communities and long-standing research and monitoring efforts, some of which are institutionalized in international programs. It is therefore vital to recognize and build on these networks and standards and collaborate with the respective scientific communities. Other thematic areas mentioned above, especially the more interdisciplinary (eg adaptation) one, have emerged more recently but likewise require connections to rapidly growing scientific communities and networks.

The second task is to continue the development of metrics and indicators to be monitored by mountain observatories to ensure consistency and comparability of data in collaboration with GEO Mountains and Global Climate Observing System (GCOS) programs and thematic networks. A set of Essential Climate Variables (ECVs) specific to mountain environments is being developed as an activity of GEO Mountains, with the inputs of the relevant MRI Working Groups (Thornton et al 2021). Mountain-specific ECVs are defined with respect to different mountain processes such as cryospheric change, ecological change, and the hydrological cycle, building on and complementing the existing ECVs as defined by GCOS. Protocols for how these are to be measured using in situ observations and remote sensing, and in models, are being developed in close collaboration with experts in the different thematic areas of existing ECVs (Thornton et al 2021). Principles for the best practice for informatics focused on essential biodiversity variables (EBVs) were compiled recently to support the emerging EBV operational framework (Hardisty et al 2019). Work is ongoing to develop a set of EBVs characterizing biodiversity specifically in the mountains (Kulonen et al 2020).

The third task is that of facilitation. Efforts of the MRI Mountain Observatories Working Group will focus on provision of information about mountain observatories via interactive websites to give access to metadata and, in some cases, navigate users to individual observatories and networks (MRI n.d.). Importantly, the MRI, led by the Mountain Observatories Working Group, will foster and facilitate the development of regional networks of mountain observatories through bringing together regional researchers, practitioners, and international experts experienced in running successful mountain observatories in a series of regional workshops. The following regions will be initially targeted: Central Asia and the Caucasus, East Africa, the Hindu Kush Himalaya region, and the tropical/subtropical Andes. Workshops and targeted campaigns in other regions such as North America and Europe will be pursued with partners in parallel. These workshops will focus on the identification of network-level research issues and foster multidisciplinary observational programs relevant to both regional needs and research issues of global relevance. When possible, regional workshops would be complemented with in situ courses and summer schools or equivalent to promote the use of the data, recruit human resources, and facilitate the development of innovative and transdisciplinary research.

Conclusions

This paper provides a brief review of the current state and challenges of socioenvironmental monitoring in the mountains, available networks, and products. Two opposing trends were identified: reduction in high-elevation in situ observations and increase in the coverage and sophistication of remote sensing. The expansion of automated measurements partly alleviates the lack of in situ observations, but the datasets they generate are often short-lived and inconsistent in time and methods, and there is no database that would compile at least metadata for these measurements. There is no mountain-specific paleodatabase, although many paleo-datasets are obtained from the mountains. Spatial resolution remains a problem: Regional and global datasets are designed to capture large-scale spatial variability and do not resolve smaller-scale processes and sharp gradients characterizing mountain environments. Another major limitation is a lack of integration of biophysical and socioeconomic data and transdisciplinary research collaborations. We identified a number of emerging successful multithematic observatories and networks, acting at different spatial scales and in different financial frameworks. However, most observations retain thematic foci, and integrated, holistic observations are still a rarity in the mountainous regions. This fragmentation hampers our ability to characterize and quantify the observed and projected climate and environmental change, their impacts on high-elevation regions, and their consequences for downstream locations relying on ecosystem services to facilitate sustainable development. The MRI Mountain Observatories Working Group, working in close collaboration with GEO Mountains, as well as researchers and practitioners from various mountain regions in Asia, Africa, and the tropical Andes, aims to facilitate the development of multithematic mountain observatories by identifying mountain-specific essential biophysical and socioeconomic indicators to be monitored, thereby contributing to the development of monitoring protocols and provision of information about mountain observatories, and, importantly, helping to build regional mountain observatories network communities that can inform our understanding of mountain social–ecological and environmental processes and meet future management challenges.

ACKNOWLEDGMENTS

The authors acknowledge support from their institutions. We acknowledge the MRI for its support of the Mountain Observatories Working Group, and for the resources provided to host the relevant workshops at which these ideas emerged and where the corresponding activities were proposed. The paper itself was supported by funds (open access fees) provided the MRI. The authors are grateful to Dr João Azevedo and Dr Scotty Strachan for their detailed and supportive reviews, and to Dr James Thornton, MRI Scientific Project Officer, for his helpful comments, all of which helped to improve the original manuscript.

OPEN PEER REVIEW

This article was reviewed by João Azevedo and Scotty Strachan. The peer review process for all MountainAgenda articles is open. In shaping target knowledge, values are explicitly at stake. The open review process offers authors and reviewers the opportunity to engage in a discussion about these values.

REFERENCES

1.

Abram N, Gattuso J-P, Prakash A, Cheng L, Chidichimo MP, Crate S, Enomoto H, Garschagen M, Gruber N, Harper S, et al. 2019. Framing and context of the report. In: Pörtner H-O, Roberts DC, Masson-Delmotte V, Zhai P, Tignor M, Poloczanska E, Mintenbeck K, Alegría A, Nicolai M, Okem A, et al, editors. IPCC Special Report on the Ocean and Cryosphere. Geneva, Switzerland: IPCC [Intergovernmental Panel on Climate Change], pp 73–129. Google Scholar

2.

Adler C, Balsiger J, Grêt-Regamey A, Heinimann A, Huggel C, Weingartner R, Alcántara-Ayala I, Gebrekirstos A, Grau R, Jimenez Zamora E, et al. 2020. Making connections for our changing mountains: Future directions for the Mountain Research Initiative (MRI). Mountain Research and Development 40(3):P1–P6,  https://doi.org/10.1659/MRD-JOURNAL-D-20-00045.1Google Scholar

3.

Adler C, Palazzi E, Kulonen A, Balsiger J, Colangeli G, Cripe D, Forsythe N, Goss-Durant G, Guigoz Y, Krauer J, et al. 2018. Monitoring mountains in a changing world: New horizons for the Global Network for Observations and Information on Mountain Environments (GEO-GNOME). Mountain Research and Development 38(3):265–269.  https://doi.org/10.1659/MRD-JOURNAL-D-8-00065.1Google Scholar

4.

Adler C, Pomeroy J, Nitu R. 2020. High Mountain Summit: Outcomes and outlook. WMO Bulletin 69(10):34–37.  https://public.wmo.int/en/resources/bulletin/articles-by-themes?tid-type-bulletin=589; accessed on 20 July 2020. Google Scholar

5.

Albrecht J, Classen A, Vollstädt MGR, Mayr A, Mollel NP, Costa DS, Dulle HI, Fischer M, Hemp A, Howell KM, et al. 2018. Plant and animal functional diversity drive mutualistic network assembly across an elevational gradient. Nature Communications 9(1):3177.  https://doi.org/10.1038/s41467-018-05610-wGoogle Scholar

6.

Alessa L, Kliskey A, Gosz J, Griffith D, Ziegler A. 2018. MtnSEON and social–ecological systems science in complex mountain landscapes. Frontiers in Ecology and the Environment 16:S4–S10.  https://doi.org/10.1002/fee.1753Google Scholar

7.

Altaweel M, Virapongse A, Griffith D, Alessa L, Kliskey A. 2015. A typology for complex social–ecological systems in mountain communities. Sustainability: Science, Practice and Policy 11:1–13.  https://doi.org/10.1080/15487733.2015.11908142Google Scholar

8.

Angelstam P, Manton M, Elbakidze M, Sijtsma F, Adamescu MC, Avni N, Beja P, Bezak P, Zyablikova I, Cruz F, et al. 2019. LTSER platforms as a place-based transdisciplinary research infrastructure: Learning landscape approach through evaluation. Landscape Ecology 34:1461–1484.  https://doi.org/10.1007/s10980-018-0737-6Google Scholar

9.

Babst F, Poulter B, Bodesheim P, Mahecha MD, Frank DC. 2017. Improved tree-ring archives will support earth-system science. Nature Ecology and Evolution 1:0008.  https://doi.org/10.1038/s41559-016-0008Google Scholar

10.

Balthazar V, Vanacker V, Molina A, Lambin EF. 2015. Impacts of forest cover change on ecosystem services in high Andean mountains. Ecological Indicators 48:63–75. Google Scholar

11.

Beniston M, Diaz HF, Bradley RS. 1997. Climatic change at high elevation sites: An overview. Climatic Change 36:233–251.  https://doi.org/10.1023/A:1005380714349Google Scholar

12.

Berthier E, Vincent C, Magnússon E, Gunnlaugsson P, Pitte P, Le Meur E, Masiokas M, Ruiz L, Pálsson F, Belart JMC, et al. 2014. Glacier topography and elevation changes derived from Pléiades sub-meter stereo images. Cryosphere 8:2275–2291.  https://doi.org/10.5194/tc-8-2275-2014Google Scholar

13.

Bi Y, Xu J, Gebrekirstos A, Guo L, Zhao M, Liang E, Yang X. 2015. Assessing drought variability since 1650 AD from tree-rings on the Jade Dragon Snow Mountain, southwest China. International Journal of Climatology 35:4057–4065.  https://doi.org/10.1002/joc.4264Google Scholar

14.

Bi Y, Xu J, Yang J, Li Z, Gebrekirstos A, Liang E, Zhang S, Yang Y, Yang Y, Yang X. 2016. Ring-widths of the above tree-line shrub Rhododendron reveal the change of minimum winter temperature over the past 211 years in southwestern China. Climate Dynamics 91:1–15.  https://doi.org/10.1007/s00382-016-3311-4Google Scholar

15.

Bian J, Li A, Lei G, Zhang Z, Nan X. 2020. Global high-resolution mountain green cover index mapping based on Landsat images and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing 162:63–76. Google Scholar

16.

Bonasoni P, Laj P, Angelini F, Arduini J, Bonafè U, Calzolari F, Cristofanelli P, Villani P, Vuillermoz E. 2008. The ABC-Pyramid Atmospheric Research Observatory in Himalaya for aerosol, ozone and halocarbon measurements. Science of the Total Environment 391:252–261.  https://doi.org/10.1016/j.scitotenv.2007.10.024Google Scholar

17.

Brantley SL, McDowell WH, Dietrich WE, White TS, Kumar P, Anderson SP, Chorover J, Lohse KA, Bales RC, Richter DD, et al. 2017. Designing a network of critical zone observatories to explore the living skin of the terrestrial Earth. Earth Surface Dynamics 5:841–860.  https://doi.org/10.5194/esurf-5-841-2017Google Scholar

18.

Carilla J, Grau HR, Paolini L, Mariano M. 2013. Lake fluctuations, plant productivity, and long-term variability in high-elevation tropical Andean ecosystems. Arctic, Antarctic and Alpine Research 45:179–189.  https://doi.org/10.1657/1938-4246-45.2.179Google Scholar

19.

Casagranda E, Navarro C, Grau HR, Izquierdo AE. 2019. Interannual lake fluctuations in the Argentine Puna: Relationships with its associated peatlands and climate change. Regional Environmental Change 19:1737–1750.  https://doi.org/10.1007/s10113-019-01514-7Google Scholar

20.

Chettri N, Bubb P, Kotru R, Rawat G, Ghate R, Murthy MSR, Wallrapp C, Pauli H, Shrestha AB, Mool PK, et al. 2015. Long-term Environmental and Socioecological Monitoring in Transboundary Landscapes: An Interdisciplinary Implementation Framework. ICIMOD Working Paper 2015/2. Kathmandu, Nepal: ICIMOD [International Centre for Integrated Mountain Development].  https://lib.icimod.org/record/30619; accessed on 20 July 2020. Google Scholar

21.

Clark WC, Harley AG. 2020. Sustainability science: Toward a synthesis. Annual Review of Environment and Resources 45:331–386.  https://doi.org/10.1146/annurev-environ-012420-043621Google Scholar

22.

Collins SL, Carpenter SR, Swinton SM, Orenstein DE, Childers DL, Gragson TL, Grimm NB, Grove JM, Harlan SL, Kaye JP, et al. 2011. An integrated conceptual framework for long-term social–ecological research. Frontiers in Ecology and the Environment 9:351–357.  https://doi.org/10.1890/100068Google Scholar

23.

Cook SJ, Quincey DJ. 2015. Estimating the volume of Alpine glacial lakes. Earth Surface Dynamics 3:559–575.  https://doi.org/10.5194/esurf-3-559-2015Google Scholar

24.

Correa-Díaz A, Silva LCR, Horwath WR, Gómez-Guerrero A, Vargas-Hernández J, Villanueva-Díaz J, Suárez-Espinoza J, Velázquez-Martínez A. 2020. From trees to ecosystems: Spatiotemporal scaling of climatic impacts on montane landscapes using dendrochronological, isotopic, and remotely sensed data. Global Biogeochemical Cycles 34:e2019GB006325.  https://doi.org/10.1029/2019GB006325Google Scholar

25.

Deems JS, Painter TH, Finnegan DC. 2013. Lidar measurement of snow depth: A review. Journal of Glaciology 59:467–479.  https://doi.org/10.3189/2013JoG12J154Google Scholar

26.

Dickerson-Lange S, Eitel K, Dorsey L, Link T, Lundquist J. 2016. Challenges and successes in engaging citizen scientists to observe snow cover: From public engagement to an educational collaboration. Journal of Science Communication 15(1):A01.  https://doi.org/10.22323/2.15010201Google Scholar

27.

Elsen PR, Tingley MW. 2015. Global mountain topography and the fate of montane species under climate change. Nature Climate Change 5(8):772–776.  https://doi.org/10.1038/nclimate2656Google Scholar

28.

Entekhabi D, Njoku EG, O'Neill PE, Kellogg KH, Crow WT, Edelstein WN, Entin JK, Goodman SD, Jackson TJ, Johnson J, et al. 2010. The soil moisture active passive (SMAP) mission. Proceedings of the IEEE 98(5):704–716.  https://doi.org/10.1109/JPROC.2010.2043918Google Scholar

29.

Fan Z, Bräuning A, Fu P-L, Yang R-Q, Qi J-H, Grießinger J, Gebrekirstos A. 2019. Intra-annual radial growth of Pinus kesiya var. langbianensis is mainly controlled by moisture availability in the Ailao Mountains, southwestern China. Forests 10(10):899.  https://doi.org/10.3390/f10100899Google Scholar

30.

FAO [Food and Agricultural Organization of the United Nations]. 2006. Guidelines for Soil Description. 4th edition (1st edition 1968). Rome, Italy: FAO. Google Scholar

31.

FAO [Food and Agricultural Organization of the United Nations]. 2015a. Mapping the Vulnerability of Mountain Peoples to Food Insecurity [ Romeo R, Vita A, Testolin R, Hofer T, editors]. Rome, Italy: FAO. Google Scholar

32.

FAO [Food and Agricultural Organization of the United Nations]. 2015b. Understanding Mountain Soils: A Contribution from Mountain Areas to the International Year of Soils 2015. Rome, Italy: FAO. Google Scholar

33.

Farinotti D, Huss M, Fürst JJ, Landmann J, Machguth H, Maussion F, Pandit A. 2019. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nature Geoscience 12:168–173.  https://doi.org/10.1038/s41561-019-0300-3Google Scholar

34.

Funnell, DC, Price MF. 2003. Mountain geography: A review. The Geographical Journal 169:183–190.  https://doi.org/10.1111/1475-4959.00083Google Scholar

35.

Gafurov A, Lüdtke S, Unger-Shayesteh K, Vorogushyn S, Schöne T, Schmidt S, Kalashnikova O, Merz B. 2016. MODSNOW-Tool: An operational tool for daily snow cover monitoring using MODIS data. Environmental Earth Sciences 75:1078.  https://doi.org/10.1007/s12665-016-5869-xGoogle Scholar

36.

Gardelle J, Arnaud Y, Berthier E. 2011. Contrasted evolution of glacial lakes along the Hindu Kush Himalaya mountain range between 1990 and 2009. Global and Planetary Change 75:47–55.  https://doi.org/10.1016/j.gloplacha.2010.10.003Google Scholar

37.

Gärtner-Roer I, Naegeli K, Huss M, Knecht T, Machguth H, Zemp M. 2014. A database of worldwide glacier thickness observations. Global and Planetary Change 122:330–344.  https://doi.org/10.1016/j.gloplacha.2014.09.003Google Scholar

38.

Gebrekirstos A, Bräuning A, Sass Klassen U, Mbow C. 2014. Opportunities and applications of dendrochronology in Africa. Current Opinion in Environmental Sustainability 6:48–53. Google Scholar

39.

Giglio L, Randerson JT, van der Werf GR, Kasibhatla PS, Collatz GJ, Morton DC, DeFries RS. 2010. Assessing variability and long-term trends in burned area by merging multiple satellite fire products. Biogeosciences 7:1171–1186.  https://doi.org/10.5194/bgd-6-11577-2009Google Scholar

40.

Glade T. 2013. Landslide occurrence as a response to land use change: A review of evidence from New Zealand. Catena 51:297–314. Google Scholar

41.

Gleeson EH, Wymann von Dach S, Flint CG, Greenwood GB, Price MF, Balsiger J, Nolin A, Vanacker V. 2016. Mountains of our future Earth: Defining priorities for mountain research. Mountain Research and Development 36(4):537–548.  https://doi.org/10.1659/MRD-JOURNAL-D-16-00094.1Google Scholar

42.

Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27.  https://doi.org/10.1016/j.rse.2017.06.031Google Scholar

43.

Grabherr G, Björnsen Gurung A, Dedieu J-P, Haeberli W, Hohenwallner D, Lotter AF, Nagy L, Pauli H, Psenner R. 2005. Long-term environmental observations in mountain biosphere reserves: Recommendations from the EU GLOCHAMORE Project. Mountain Research and Development 25:376–385.  https://doi.org/10.1659/0276-4741(2005)025[0376:LEOIMB]2.0.CO;2Google Scholar

44.

Grabherr G, Gottfried M, Pauli H. 2000. GLORIA: Global Observation Research Initiative in Alpine Environments. Mountain Research and Development 20:190–191.  https://doi.org/10.1659/0276-4741(2000)020[0190:GAGORI]2.0.CO;2Google Scholar

45.

Grêt-Regamey A, Brunner SH, Kienast F. 2012. Mountain ecosystem services: Who cares? Mountain Research and Development 32(S1):S23–S34.  https://doi.org/10.1659/MRD-JOURNAL-D-10-00115.S1Google Scholar

46.

Griffith DL, Alessa L, Kliskey A. 2018. Community-based observing for social–ecological science: Lessons from the Arctic. Frontiers in Ecology and the Environment 16:S44–S51.  https://doi.org/10.1002/fee.1798Google Scholar

47.

Grissino-Mayer HD, Fritts HC. 1997. The International Tree-Ring Data Bank: An enhanced global database serving the global scientific community. The Holocene 7:235–238.  https://doi.org/10.1177/095968369700700212Google Scholar

48.

Haeberli W, Whiteman C., Shroder JF, editors. 2015. Snow and Ice-Related Hazards, Risks and Disasters. London, United Kingdom: Elsevier. Google Scholar

49.

Hagedorn F, Gavazov K, Alexander JM. 2019. Above- and belowground linkages shape responses of mountain vegetation to climate change. Science 365:1119–1123.  https://doi.org/10.1126/science.aax4737Google Scholar

50.

Hardisty AR, Michener WK, Agosti D, Alonso García E, Bastin L, Belbin L, Bowser A, Buttigieg PL, Canhos DAL, Egloff W, et al. 2019. The Bari Manifesto: An interoperability framework for essential biodiversity variables. Ecological Informatics 49:22–31.  https://doi.org/10.1016/j.ecoinf.2018.11.003Google Scholar

51.

Harris I, Osborn TJ, Jones PD, Lister DH. 2020. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data 7(1):109.  https://doi.org/10.1038/s41597-020-0453-3Google Scholar

52.

Hilton, RG, West, AJ. 2020. Mountains, erosion and the carbon cycle. Nature Reviews Earth and Environment 1:284–299.  https://doi.org/10.1038/s43017-020-0058-6Google Scholar

53.

Himeidan YE, Kweka EJ. 2012. Malaria in East African highlands during the past 30 years: Impact of environmental changes. Frontiers in Physiology 3:315.  https://doi.org/10.3389/fphys.2012.00315Google Scholar

54.

Hock R, Rasul G, Adler C, Cáceres B, Gruber S, Hirabayashi Y, Jackson M, Kääb A, Kang S, Kutuzov S, et al. 2019. High mountain areas. In: Pörtner H-O, Roberts DC, Masson-Delmotte V, Zhai P, Tignor M, Poloczanska E, Mintenbeck K, Alegría A, Nicolai M, Okem A, et al, editors. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. Geneva, Switzerland: IPCC [Intergovernmental Panel on Climate Change], pp 131–202. Google Scholar

55.

Holz A, Paritsis J, Mundo IA, Veblen TT, Kitzberger T, Williamson GJ, Aráoz E, Bustos-Schindler C, González ME, Grau HR, et al. 2017. Southern Annular Mode drives multicentury wildfire activity in southern South America. Proceedings of the National Academy of Science of the United States of America 114:9552–9557.  https://doi.org/10.1073/pnas.1705168114Google Scholar

56.

Huddleston B, Ataman E, de Salvo P, Zanetti M, Bloise M, Bel J, Franceschini G, Fè d'Ostiani L. 2003. Towards a GIS-Based Analysis of Mountain Environments and Populations. Rome, Italy: FAO [Food and Agriculture Organization of the United Nations]. Google Scholar

57.

Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF. 2007. The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology 8:38–55.  https://doi.org/10.1175/JHM560.1Google Scholar

58.

Huss M, Bookhagen B, Huggel C, Jacobsen D, Bradley RS, Clague JJ, Vuille M, Buytaert W, Cayan DR, Greenwood G, et al. 2017. Toward mountains without permanent snow and ice. Earth's Future 5:418–435.  https://doi.org/10.1002/2016EF000514Google Scholar

59.

Huss M, Hock R. 2018. Global-scale hydrological response to future glacier mass loss. Nature Climate Change 8(2):135–140.  https://doi.org/10.1038/s41558-017-0049-xGoogle Scholar

60.

Jones PD, Lister DH, Osborn TJ, Harpham C, Salmon M, Morice CP. 2012. Hemispheric and large-scale land surface air temperature variations: An extensive revision and an update to 2010. Journal of Geophysical Research 117:D05127.  https://doi.org/10.1029/2011JD017139Google Scholar

61.

Kapitsa V, Shahgedanova M, Machguth H, Severskiy I, Medeu A. 2017. Assessment of evolution of mountain lakes and risks of glacier lake outbursts in the Djungarskiy (Jetysu) Alatau, Central Asia, using Landsat imagery and glacier bed topography modelling. Natural Hazards and Earth System Sciences 7:1837–1856.  https://doi.org/10.5194/nhess-17-1837-2017Google Scholar

62.

Kapitsa V, Shahgedanova M, Severskiy I, Kasatkin N, White K, Usmanova Z. 2020. Assessment of changes in mass balance of the Tuyuksu group of glaciers, northern Tien Shan, between 1958 and 2016 using ground-based observations and Pléiades satellite imagery. Frontiers in Earth Science 8:259.  https://doi.org/10.3389/feart.2020.00259Google Scholar

63.

Kapos V, Rhind J, Edwards N, Price MF, Ravilious C. 2000. Developing a map of the world's mountain forests. In: Price MF, Butt N, editors. Forests in Sustainable Mountain Development: A State of Knowledge Report for 2000. IUFRO Research Series Vol 5. New York, NY: CAB International, pp 4–9. Google Scholar

64.

Kargel JS, Leonard GJ, Bishop MP, Kaab A, Raup B, editors. 2014. Global Land Ice Measurements from Space. Chichester, United Kingdom: Springer-Praxis. Google Scholar

65.

Kaufman D, McKay N, Routson C, Erb M, Davis B, Heiri O, Jaccard S, Tierney J, Dätwyler C, Axford Y, et al. 2020. A global database of Holocene paleotemperature records. Scientific Data 7(1):115.  https://doi.org/10.1038/s41597-020-0445-3Google Scholar

66.

Kennedy RE, Yang Z, Gorelick N, Braaten J, Cavalcante L, Cohen WB, Healey S. 2018. Implementation of the LandTrendr algorithm on Google Earth Engine. Remote Sensing 10(5):691.  https://doi.org/10.3390/rs10050691Google Scholar

67.

Kerr YH, Waldteufel P, Richaume P, Wigneron J-P, Ferrazzoli P, Mahmoodi A, Bitar AA, Cabot F, Gruhier C, Juglea SE, et al. 2012. The SMOS soil moisture retrieval algorithm. Geoscience and Remote Sensing 50(5):1384–1403.  https://doi.org/10.1109/TGRS.2012.2184548Google Scholar

68.

Klein JA, Tucker CM, Steger CE, Nolin A, Reid R, Hopping KA, Yeh ET, Pradhan MS, Taber A, Molden D, et al. 2019. An integrated community and ecosystem-based approach to disaster risk reduction in mountain systems. Environmental Science and Policy 94:143–152.  https://doi.org/10.1016/j.envsci.2018.12.034Google Scholar

69.

Kotlarski S, Lüthi D, Schär C. 2015. The elevation dependency of 21st century European climate change: An RCM ensemble perspective. International Journal of Climatology 35:3902–3920.  https://doi.org/10.1002/joc.4254Google Scholar

70.

Kulonen A, Adler C, Bracher C, von Dach SW. 2019. Spatial context matters in monitoring and reporting on sustainable development goals: Reflections based on research in mountain regions. GAIA–Ecological Perspectives on Science and Society 28:90–94.  https://doi.org/10.14512/gaia.28.2.5Google Scholar

71.

Kulonen A, Adler C, Palazzi E. 2020. Identifying Essential Biodiversity Variables (EBVs) and Essential Societal Variables (ESVs) in Mountain Environments. GEO-GNOME [Global Network for Observations and Information in Mountain Environments] Workshop Report. Bern, Switzerland: MRI [Mountain Research Initiative].  https://doi.org/10.7892/boris.144872Google Scholar

72.

Kumar L, Mutanga O. 2018. Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing 10(10):1509.  https://doi.org/10.3390/rs10101509Google Scholar

73.

Kutuzov S, Lavrentiev I, Smirnov A, Nosenko G. 2019. Volume changes of Elbrus glaciers from 1997 to 2017. Frontiers in Earth Science 7:153.  https://doi.org/10.3389/feart.2019.00153Google Scholar

74.

Lawrimore JH, Menne MJ, Gleason BE, Williams CN, Wuertz DB, Vose RS, Rennie J. 2011. An overview of the Global Historical Climatology Network monthly mean temperature dataset, version 3. Journal of Geophysical Research Atmospheres 116:1–18.  https://doi.org/10.1029/2011JD016187Google Scholar

75.

Lievens H, Demuzere M, Marshall H-P, Reichle RH, Brucker L, Brangers I, de Rosnay P, Dumont M, Girotto M, Immerzeel WW, et al. 2019. Snow depth variability in the Northern Hemisphere mountains observed from space. Nature Communications. 10:4629.  https://doi.org/10.1038/s41467-019-12566-yGoogle Scholar

76.

Linderholm HW, Nicolle M, Francus P, Gajewski K, Helama S, Korhola A, Solomina O, Yu Z, Zhang P, D'Andrea WJ, et al. 2018. Arctic hydroclimate variability during the last 2000 years: Current understanding and research challenges. Climate of the Past 14:473–514.  https://doi.org/10.5194/cp-14-473-2018Google Scholar

77.

Liniger H, van Lynden G. 2005. Building up and sharing knowledge for better decision-making on soil and water conservation in a changing mountain environment—The WOCAT experience. In: Stocking H, Hellemann H, White R, editors. Renewable Natural Resources Management for Mountain Regions. Katmandu, Nepal: International Centre for Integrated Mountain Development, pp 103–114. Google Scholar

78.

Linsbauer A, Paul F, Haeberli W. 2012. Modeling glacier thickness distribution and bed topography over entire mountain ranges with GlabTop: Application of a fast and robust approach. Journal of Geophysical Research Earth Surface 117: F03007.  https://doi.org/10.1029/2011JF002313Google Scholar

79.

Llambí LD, Becerra MT, Peralvo M, Avella A, Baruffol M, Flores LJ. 2019. Monitoring biodiversity and ecosystem services in Colombia's high Andean ecosystems: Toward an integrated strategy. Mountain Research and Development 39:A8–A20.  https://doi.org/10.1659/MRD-JOURNAL-D-19-00020.1Google Scholar

80.

Lopez S, Jung JK, Lopez MF. 2017. A hybrid-epistemological approach to climate change research: Linking scientific and smallholder knowledge systems in the Ecuadorian Andes. Anthropocene 17:30–45.  https://doi.org/10.1016/j.ancene.2017.01.001Google Scholar

81.

Lundquist JD, Pepin NC, Rochford C. 2008. Automated algorithm for mapping regions of cold-air pooling in complex terrain. Journal of Geophysical Research Atmospheres 113:D22107.  https://doi.org/10.1029/2008jd009879Google Scholar

82.

Maidment RI, Grimes D, Allan RP, Tarnavsky E, Stringer M, Hewison T, Roebeling R, Black E. 2014. The 30 year TAMSAT African Rainfall Climatology and Time series (TARCAT) dataset. Journal of Geophysical Research Atmospheres 119(18):10619–10644.  https://doi.org/10.1002/2014JD021927Google Scholar

83.

Maidment RI, Grimes D, Black E, Tarnavsky E, Young M, Greatrex H, Allan RP, Stein T, Nkonde E, Senkunda S, et al. 2017. A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa. Scientific Data 4:170063.  https://doi.org/10.1038/sdata.2017.63Google Scholar

84.

Malizia A, Blundo C, Carilla J, Acosta OO, Cuesta F, Duque A, Aguirre N, Aguirre Z, Ataroff M, Baez S, et al. 2020. Elevation and latitude drives structure and tree species composition in Andean forests: Results from a large-scale plot network. PLoS One 15:1–18.  https://doi.org/10.1371/journal.pone.0231553Google Scholar

85.

Marchant R, Richer S, Boles O, Capitani C, Courtney-Mustaphi CJ, Lane P, Prendergast ME, Stump D, De Cort G, Kaplan JO, et al. 2018. Drivers and trajectories of land cover change in East Africa: Human and environmental interactions from 6000 years ago to present. Earth Science Reviews 178:322–378.  https://doi.org/10.1016/j.earscirev.2017.12.010Google Scholar

86.

Marti R, Gascoin S, Berthier E, Pinel MD, Houet T, Laffly D. 2016. Mapping snow depth in open alpine terrain from stereo satellite imagery. Cryosphere 10:1361–1380.  https://doi.org/10.5194/tc-10-1361-2016Google Scholar

87.

Martin MA, Ghent D, Pires AC, Göttsche FM, Cermak J, Remedios JJ. 2019. Comprehensive in situ validation of five satellite land surface temperature datasets over multiple stations and years. Remote Sensing 11(5):479.  https://doi.org/10.3390/rs11050479Google Scholar

88.

Marzeion B, Champollion N, Haeberli W, Langley K, Leclercq P, Paul F. 2017. Observation-based estimates of global glacier mass change and its contribution to sea-level change. Surveys in Geophysics 38:105–130.  https://doi.org/10.1007/s10712-016-9394-yGoogle Scholar

89.

Mengist W, Soromessa T, Legese G. 2020. Ecosystem services research in mountainous regions: A systematic literature review on current knowledge and research gaps. Science of the Total Environment 702:134581.  https://doi.org/10.1016/j.scitotenv.2019.134581Google Scholar

90.

Menne MJ, Williams CN, Gleason BE, Rennie JJ, Lawrimore JH. 2018. The Global Historical Climatology Network monthly temperature dataset, Version 4. Journal of Climate 31(24):9835–9854.  https://doi.org/10.1175/JCLI-D-18-0094.1Google Scholar

91.

Meybeck M, Green PA, Vörösmarty CJ. 2001. A new typology for mountains and other relief classes: An application to global continental water resources and population distribution. Mountain Research and Development 21(1):34–45. Google Scholar

92.

Millar CI. 2004. The Consortium for Integrated Climate Research in Western Mountains (CIRMOUNT). In: Lee C, Schaaf T, editors. Global Change Research in Mountain Biosphere Reserves. Proceedings of the International Launching Workshop, 10–13 November 2003. Paris, France: UNESCO [United Nations Educational, Scientific and Cultural Organization], pp 154–158. Google Scholar

93.

Mohanty BP, Cosh MH, Lakshmi V, Montzka C. 2017. Soil moisture remote sensing: State-of-the-science. Vadose Zone Journal 16(1):vzj2016.10.0105.  https://doi.org/10.2136/vzj2016.10.010Google Scholar

94.

Mokria M, Gebrekirstos A, Abiyu A, Van Noordwijk M, Bräuning A. 2017. Multicentury tree-ring precipitation record reveals increasing frequency of extreme dry events in the upper Blue Nile River catchment. Global Change Biology 23:5436–5454.  https://doi.org/10.1111/gcb.13809Google Scholar

95.

Morelli TL, Daly C, Dobrowski SZ, Dulen DM, Ebersole JL, Jackson ST, Lundquist JD, Millar CI, Maher SP, Monahan WB, et al. 2016. Managing climate change refugia for climate adaptation. PLoS ONE 11(8):e0159909.  https://doi.org/10.1371/journal.pone.0159909Google Scholar

96.

MRI [Mountain Research Initiative]. n.d. Working groups: Mountain observatories. Mountain Research Initiative.  https://mountainresearchinitiative.org/activities/community-led-activities/working-groups/2097-mountain-observatories; accessed on 15 March 2021. Google Scholar

97.

Muñoz R, Huggel C, Frey H, Cochachin A, Haeberli W. 2020. Glacial lake depth and volume estimation based on a large bathymetric dataset from the Cordillera Blanca, Peru. Earth Surface Processes and Landforms 45:1510–1527.  https://doi.org/10.1002/esp.4826Google Scholar

98.

Nogues-Bravo D, Araujo MB, Errea MP, Martınez-Rica JP. 2007. Exposure of global mountain systems to climate warming during the 21st century. Global Environmental Change 17:420–428.  https://doi.org/10.1016/j.gloenvcha.2006.11.007Google Scholar

99.

Nolan C, Overpeck JT, Allen JRM, Anderson PM, Betancourt JL, Binney HA, Brewer S, Bush MB, Chase BM, Cheddadi R, et al. 2018. Past and future global transformation of terrestrial ecosystems under climate change. Science 361(6405):920–923.  https://doi.org/10.1126/science.aan5360Google Scholar

100.

Osborn TJ, Jones PD. 2014. The CRUTEM4 land-surface air temperature dataset: Construction, previous versions and dissemination via Google Earth. Earth System Science Data 6:61–68.  https://doi.org/10.5194/essd-6-61-2014Google Scholar

101.

Palazzi E, Von Hardenberg J, Provenzale A. 2013. Precipitation in the Hindu-Kush Karakoram Himalaya: Observations and future scenarios. Journal of Geophysical Research Atmospheres 118:85–100.  https://doi.org/10.1029/2012JD018697Google Scholar

102.

Pauli H, Gottfried M, Lamprecht A, Niessner S, Rumpf S, Winkler M, Steinbauer K, Grabherr G, coordinating authors and editors. 2015. The GLORIA Field Manual—Standard Multi-Summit Approach, Supplementary Methods and Extra Approaches. 5th edition. Vienna, Austria: GLORIA [Global Observation Research Initiative IN Alpine Environments]-Coordination, Austrian Academy of Sciences and University of Natural Resources and Life Sciences. Google Scholar

103.

Payne D, Snethlage M, Geschke J, Spehn EM, Fisher M. 2020. Nature and people in the Andes, East African Mountains, European Alps, and Hindu Kush Himalaya: Current research and future directions. Mountain Research and Development 40(2):A1–A4.  https://doi.org/10.1659/MRD-JOURNAL-D-19-00075.1 Google Scholar

104.

Pepin N, Bradley RS, Diaz HF, Baraer M, Caceres EB, Forsythe N, Fowler H, Greenwood G, Hashmi MZ, Liu XD, et al. 2015. Elevation-dependent warming in mountain regions of the world. Nature Climate Change 5:424–430.  https://doi.org/10.1038/nclimate2563Google Scholar

105.

Pepin N, Deng H, Zhang H, Zhang F, Kang S, Yao T. 2019. An examination of temperature trends at high elevations across the Tibetan Plateau: The use of MODIS LST to understand patterns of elevation-dependent warming. Journal of Geophysical Research Atmospheres 124:5738–5756.  https://doi.org/10.1029/2018JD029798Google Scholar

106.

Pfeffer WT, Arendt AA, Bliss A, Bolch T, Cogley JG, Gardner AS, Hagen J-O, Hock R, Kaser G, Kienholz C, et al. 2014. The Randolph Glacier Inventory: A globally complete inventory of glaciers. Journal of Glaciology 60 (221):537–552.  https://doi.org/10.3189/2014JoG13J176Google Scholar

107.

Pfeifer M, Disney M, Quaife T, Marchant R. 2012. Terrestrial ecosystems from space: A review of earth observation products for macroecology applications. Global Ecology and Biogeography 21(6):603–624.  https://doi.org/10.1111/j.1466-8238.2011.00712.xGoogle Scholar

108.

Piras M, Taddia G, Forno MG, Gattiglio M, Aicardi I, Dabove P, Russo SL, Lingua A. 2017. Detailed geological mapping in mountain areas using an unmanned aerial vehicle: Application to the Rodoretto Valley, NW Italian Alps. Geomatics, Natural Hazards and Risk 8:137–149.  https://doi.org/10.1080/19475705.2016.1225228Google Scholar

109.

Platts PJ, Garcia RA, Hof C, Foden W, Hansen LA, Rahbek C, Burgess ND. 2014. Conservation implications of omitting narrow-ranging taxa from species distribution models, now and in the future. Diversity and Distributions 20:1307–1320.  https://doi.org/10.1111/ddi.12244Google Scholar

110.

Platts PJ, McClean CJ, Lovett JC, Marchant R. 2008. Predicting tree distributions in an East African biodiversity hotspot: Model selection, data bias and envelope uncertainty. Ecological Modelling 218(1–2):121–134.  https://doi.org/10.1016/j.ecolmodel.2008.06.028Google Scholar

111.

Poesen J. 2018. Soil erosion in the Anthropocene: Research needs. Earth Surface Processes and Landforms 43:64–84.  https://doi.org/10.1002/esp.4250Google Scholar

112.

Rayback SA, Duncan JA, Schaberg PG, Kosiba AM, Hansen CF, Murakami PF. 2020. The DendroEcological Network: A cyberinfrastructure for the storage, discovery and sharing of tree-ring and associated ecological data. Dendrochronologia 60:125678.  https://doi.org/10.1016/j.dendro.2020.125678Google Scholar

113.

Rodríguez E, Morris CS, Belz JE. 2006. A global assessment of the SRTM performance. Photogrammetric Engineering and Remote Sensing 72:249–260.  https://doi.org/10.14358/PERS.72.3.249Google Scholar

114.

Rogora M, Frate L, Carranza ML, Freppaz M, Stanisci A, Bertani I, Bottarin R, Brambilla A, Canullo R, Carbognani M, et al. 2018. Assessment of climate change effects on mountain ecosystems through a cross-site analysis in the Alps and Apennines. Science of the Total Environment 624:1429–1442.  https://doi.org/10.1016/j.scitotenv.2017.12.155Google Scholar

115.

Saravia A, Bièvre D. 2013. CONDESAN: Better knowledge, better decisions: Supporting sustainable Andean mountains development. Mountain Research and Development 33:339–342.  https://doi.org/10.1659/MRD-JOURNAL-D-13-00056.1Google Scholar

116.

Sayre A, Breyer S, Aniello P, Wright DJ, Payne D. 2018. A new high-resolution map of world mountains and an online tool for visualizing and comparing characterizations of global mountain distributions. Mountain Research and Development 38:240–249.  https://doi.org/10.1659/MRD-JOURNAL-D-17-00107.1Google Scholar

117.

Schutz BE, Zwally HJ, Shuman CA, Hancock D, DiMarzio JP. 2005. Overview of the ICESat mission. Geophysical Research Letters 32:1–4.  https://doi.org/10.1029/2005GL024009Google Scholar

118.

Shahgedanova M, Afzal M, Hagg W, Kapitsa V, Kasatkin N, Mayr E, Rybak O, Saidaliyeva Z, Severskiy I, Usmanova Z, et al. 2020. Emptying water towers? Impacts of future climate and glacier change on river discharge in the northern Tien Shan, Central Asia. Water 12:627.  https://doi.org/10.3390/w12030627Google Scholar

119.

Shahgedanova M, Afzal M, Severskiy I, Usmanova Z, Saidaliyeva Z, Kapitsa V, Kasatkin N, Dolgikh S. 2018. Changes in the mountain river discharge in the northern Tien Shan since the mid-20th century: Results from the analysis of a homogeneous daily streamflow dataset from seven catchments. Journal of Hydrology 564:1133–1152.  https://doi.org/10.1016/j.jhydrol.2018.08.001Google Scholar

120.

Shea JM, Wagnon P, Immerzeel WW, Biron R, Brun F, Pellicciotti F. 2015. A comparative high-altitude meteorological analysis from three catchments in the Nepalese Himalaya. International Journal of Water Resources Development 31:174–200.  https://doi.org/10.1080/07900627.2015.1020417Google Scholar

121.

Shean D. 2017. High Mountain Asia 8-meter DEMs Derived from Cross-Track Optical Imagery, Version 1. Boulder, CO: NASA [National Aeronautics and Space Administration] National Snow and Ice Data Center Distributed Active Archive Center.  https://doi.org/10.5067/0MCWJJH5ABYO; accessed on 12 August 2020. Google Scholar

122.

Skidmore AK, Pettorelli N, Coops NC, Geller GN, Hansen M, Lucas R, Mücher CA, O'Connor B, Paganini M, Pereira HM, et al. 2015. Environmental science: Agree on biodiversity metrics to track from space. Nature 523:403–405.  https://doi.org/10.1038/523403aGoogle Scholar

123.

Skofronick-Jackson G, Petersen WA, Berg W, Kidd C, Stocker EF, Kirschbaum DB, Kakar R, Braun SA, Huffman GJ, Iguchi T, et al. 2017. The Global Precipitation Measurement (GPM) mission for science and society. Bulletin of the American Meteorological Society 98:1679–1695.  https://doi.org/10.1175/BAMS-D-15-00306.1Google Scholar

124.

Solomina ON, Bradley RS, Jomelli V, Geirsdottir A, Kaufman DS, Koch J, McKay NP, Masiokas M, Miller G, Nesje A, et al. 2016. Glacier fluctuations during the past 2000 years. Quaternary Science Reviews 149:61–90.  https://doi.org/10.1016/j.quascirev.2016.04.008Google Scholar

125.

Somers LD, McKenzie JM. 2020. A review of groundwater in high mountain environments. WIREs Water 7:e1475.  https://doi.org/10.1002/wat2.1475Google Scholar

126.

Stephens L, Fuller D, Boivin N, Rick T, Gauthier N, Kay A, Marwick B, Armstrong CG, Barton CM, Denham T, et al. 2019. Archaeological assessment reveals Earth's early transformation through land use. Science 365:897–902.  https://doi.org/10.1126/science.aax1192Google Scholar

127.

Strachan S, Kelsey EP, Brown RF, Dascalu S, Harris F, Kent G, Lyles B, McCurdy G, Slater D, Smith K. 2016. Filling the data gaps in mountain climate observatories through advanced technology, refined instrument siting, and a focus on gradients. Mountain Research and Development 36:518–527.  https://doi.org/10.1659/MRD-JOURNAL-D-16-00028.1Google Scholar

128.

Sullivan PF, Csank AZ. 2016. Contrasting sampling designs among archived datasets: Implications for synthesis efforts. Tree Physiology 36:1057–1059.  https://doi.org/10.1093/treephys/tpw067Google Scholar

129.

Sun Q, Miao C, Duan Q, Ashouri H, Sorooshian S, Hsu KL. 2018. A review of global precipitation datasets: Data sources, estimation, and intercomparisons. Reviews of Geophysics 56:79–107.  https://doi.org/10.1002/2017RG000574Google Scholar

130.

Tarnavsky E, Grimes D, Maidment R, Black E, Allan RP, Stringer M, Chadwick R, Kayitakire F. 2014. Extension of the TAMSAT satellite-based rainfall monitoring over Africa and from 1983 to present. Journal of Applied Meteorology and Climatology 53:2805–2822.  https://doi.org/10.1175/JAMC-D-14-0016.1Google Scholar

131.

Thorn JPR, Klein JA, Steger C, Hopping KA, Capitani C, Tucker CM, Nolin AW, Reid RS, Seidl R, Chitale VS, et al. 2020. A systematic review of participatory scenario planning to envision mountain social–ecological systems futures. Ecology and Society 25(3):6.  https://doi.org/10.5751/ES-11608-250306Google Scholar

132.

Thornton JM, Palazzi E, Pepin N, Cristofanelli P, Essery R, Kotlarski S, Giuliani G, Guigoz G, Kulonen A, Li X, et al. 2021. Toward a definition of Essential Mountain Climate Variables. One Earth 4(6):S2590–3322(21)00248-7.  https://doi.org/10.1016/j.oneear.2021.05.005Google Scholar

133.

UNEP [United Nations Environment Programme], GRID-Arendal, GMBA, and MRI. 2020. Elevating Mountains in the Post-2020 Global Biodiversity Framework 2.0. Policy Brief. Bern, Switzerland: MRI [Mountain Research Initiative].  https://www.mountainresearchinitiative.org/images/Articles_Newsletters_2020/FEB_2020/ElevatingMountainsPolicyBrief.pdf; accessed on 20 July 2020. Google Scholar

134.

Urrutia R, Vuille M. 2009. Climate change projections for the tropical Andes using a regional climate model: Temperature and precipitation simulations for the end of the 21st century. Journal of Geophysical Research 114:D02108.  https://doi.org/10.1029/2008JD011021Google Scholar

135.

Virtual Alpine Observatory. 2017. Strategy: Virtual Alpine Observatory. Munich, Germany: Bavarian State Ministry of the Environment and Consumer Protection.  https://www.vao.bayern.de/doc/vao_strategy_v18.pdf; assessed on 20 July 2020. Google Scholar

136.

Viviroli D, Archer DR, Buytaert W, Fowler HJ, Greenwood GB, Hamlet AF, Huang Y, Koboltschnig G, Litaor MI, López-Moreno JI, et al. 2011. Climate change and mountain water resources: Overview and recommendations for research, management and policy. Hydrology and Earth System Sciences 15:471–504.  https://doi.org/10.5194/hess-15-471-2011Google Scholar

137.

Viviroli D, Dürr HH, Messerli B, Meybeck M, Weingartner R. 2007. Mountains of the world, water towers for humanity: Typology, mapping, and global significance. Water Resources Research 43:1–13.  https://doi.org/10.1029/2006WR005653Google Scholar

138.

Viviroli D, Kummu M, Meybeck M, Kallio M, Wada Y. 2020. Increasing dependence of lowland populations on mountain water resources. Nature Sustainability 3:917–928.  https://doi.org/10.1038/s41893-020-0559-9Google Scholar

139.

Vuille M, Carey M, Huggel C, Buytaert W, Rabatel A, Jacobsen D, Soruco A, Villacis M, Yarleque C, Elison Timm O, et al. 2018. Rapid decline of snow and ice in the tropical Andes: Impacts, uncertainties and challenges ahead. Earth-Science Reviews 176:195–213.  https://doi.org/10.1016/j.earscirev.2017.09.019Google Scholar

140.

Weiss DJ, Walsh SJ. 2009. Remote sensing of mountain environments. Geography Compass 3:1–21.  https://doi.org/10.1111/j.1749-8198.2008.00200.xGoogle Scholar

141.

Wikstrom Jones K, Wolken GJ, Hill D, Crumley R, Arendt A, Joughin J, Setiawan L. 2018. Community Snow Observations (CSO): A citizen science campaign to validate snow remote sensing products and hydrological models. International Snow Science Workshop Proceedings: Proceedings, International Snow Science Workshop, Innsbruck, Austria, 2018.  http://arc.lib.montana.edu/snow-science/item/2566; accessed on 26 March 2021. Google Scholar

142.

Williams JW, Blois J, Goring SJ, Grimm EC, Smith AJ, Uhen MD. 2019. The Neotoma Paleoecology Database and EarthLife Consortium: Building community data resources to mobilize dark, long-tail records of past biodiversity dynamics. Abstract B53O-2614, American Geophysical Union Fall Meeting, 9–13 December 2019, San Francisco, USA. Astrophysics Data System.  https://ui.adsabs.harvard.edu/abs/2019AGUFM.B53O2614W/abstract; accessed on 26 March 2021. Google Scholar

143.

Williams JW, Grimm EC, Blois JL, Charles DF, Davis EB, Goring SJ, Graham RW, Smith AJ, Anderson M, Arroyo-Cabrales J, et al. 2018. The Neotoma Paleoecology Database, a multiproxy, international, community-curated data resource. Quaternary Research 89(1):156–177.  https://doi.org/10.1017/qua.2017.105Google Scholar

144.

Wirth SB, Gilli A, Simonneau A, Ariztegui D, Vannière B, Glur L, Chapron E, Magny M, Anselmetti FS. 2013. A 2000 year long seasonal record of floods in the southern European Alps. Geophysical Research Letters 40:4025–4029.  https://doi.org/10.1002/grl.50741Google Scholar

145.

WMO [World Meteorological Organization]. 1994. Guide to Hydrological Practice: Data Acquisition and Processing, Analysis, Forecasting and Other Applications. WMO Publication 168. Geneva, Switzerland: WMO. Google Scholar

146.

WMO [World Meteorological Organization]. 2008. Guide to Meteorological Instruments and Methods of Observation. WMO No 8. 7th edition (1st edition 1954). Geneva, Switzerland: WMO. Google Scholar

147.

Wohl E, Dwire K, Sutfin N, Polvi L, Bazan R. 2012. Mechanisms of carbon storage in mountainous headwater rivers. Nature Communications 3:1263.  https://doi.org/10.1038/ncomms2274Google Scholar

148.

Yarleque C, Vuille M, Hardy DR, Timm OE, Cruz JD, Ramos H, Rabatel A. 2018. Projections of the future disappearance of the Quelccaya ice cap in the Central Andes. Scientific Reports 8:15564.  https://doi.org/10.1038/s41598-018-33698-zGoogle Scholar

149.

Zemp M, Hoelzle M, Haeberli W. 2009. Six decades of glacier mass-balance observations: A review of the worldwide monitoring network. Annals of Glaciology 50:101–111.  https://doi.org/10.3189/172756409787769591Google Scholar

150.

Zhao S, Pederson N, D'Orangeville L, HilleRisLambers J, Boose E, Penone C, Bauer B, Jiang Y, Manzanedo RD. 2019. The International Tree-Ring Data Bank (ITRDB) revisited: Data availability and global ecological representativity. Journal of Biogeography 46:355–368.  https://doi.org/10.1111/jbi.13488Google Scholar

151.

Zhou H, Aizen E, Aizen V. 2017. Constructing a long-term monthly climate dataset in Central Asia. International Journal of Climatology 38(3):1463–1475.  https://doi.org/10.1002/joc.5259Google Scholar
© 2021 Shahgedanova et al.
Maria Shahgedanova, Carolina Adler, Aster Gebrekirstos, H. Ricardo Grau, Christian Huggel, Robert Marchant, Nicholas Pepin, Veerle Vanacker, Daniel Viviroli, and Mathias Vuille "Mountain Observatories: Status and Prospects for Enhancing and Connecting a Global Community," Mountain Research and Development 41(2), A1-A15, (2 June 2021). https://doi.org/10.1659/MRD-JOURNAL-D-20-00054.1
Received: 27 August 2020; Accepted: 5 February 2021; Published: 2 June 2021
JOURNAL ARTICLE
PAGES


SHARE
ARTICLE IMPACT
Back to Top