Accurate assessment of flood risk is the basis of various approaches for flood disaster management. For industrial and community sectors, assessing flood inundation risk is particularly important at local level, rather than flooding from rivers, which have been focused on typical flood risk management and forecasting.   Inundation risk assessment at the local level requires high accuracy by reflecting local land conditions. Meanwhile, performing the risk assessment must be implemented with low cost, compared to traditional river management by central or local governments. Recently, the movement to develop a nationwide, but local-level resolution, flood risk map has become active, especially for the insurance industry sectors in Europe and the United States. Those maps show the probability of flooding and the corresponding inundation depths, which are computed by the combination of hydrologic and hydraulic models applied at large scale.      In this presentation, we introduce our recent development of a nation-wide 150 m resolution flood model in Japan based on the Rainfall-Runoff-Inundation (RRI) model. The main objectives of the model development are for flood risk assessment including climate change impact assessment and real-time flood forecasting. One of the advantages for developing such a large-scale and fine-resolution distributed model is that we can evaluate the risk comparatively in different regions with the same framework, so that we will have better understandings in regional characteristics in hydrology and river basins.   Though the development and practical applications of such nation-wide modeling is progressing in the world, there are still many problems related to predictions in ungauged basins, such as the regionalization of model parameters and estimations of river channel cross-sections in tributaries. We will present the issues and some solutions related to the nation-wide application of the RRI model and discuss the risk assessment approaches using the model.

As a basis of flood risk management with high resolution covering wide area, we have been developing a nationwide Rainfall-Runoff-Inundation (RRI) model with 150 m resolution in Japan, and examining the applicability and accuracy of the model through the post analyses of the heavy rain events in July 2018 and typhoon Hagibis in October 2019 using ensemble precipitation forecasts. In this case study of typhoon Haishen in September 2020, we applied the three regional submodels (Chugoku, Shikoku, and Kyushu) of the nationwide RRI to the real-time ensemble flood forecasting during 3 days of the typhoon approach, using MEPS (meso-scale ensemble prediction system, 21 members of 39 hours prediction, updated every 6 hours) as input rainfall. There are two main aims of this study; the first objective is to confirm that the model can complete the calculation of 39 hours MEPS prediction within 12 hours at most, so that we can assure at least 24 hours of lead-time for alert and evacuation; the second objective is to investigate how accurate the spatial distribution of flood and inundation risk are represented on the fine river network of the model. As a result, we confirmed that the longest calculation time for the parallel analysis of MEPS members was around 10 hours, including post-analysis and visualization, and it was for peak-time calculation of Kyushu region where the rainfall was most intense. Also, we found that the spatial distribution of the 5th intense peak runoff of 21 MEPS members on each river cells represents well the spatial peak runoff distribution of observation and post-analysis, and well predicted the high runoff of 30 mm/h in the middle reach of the Gokase river and Omaru rivers in Kyushu.

The 2011 flood in the Chao Phraya River in Thailand caused enormous damage. In addition to human damage, it resulted in significant economic damage to Thailand and other countries around the world due to the interruption of the supply chain caused by the inundation of industrial areas. In order to reduce such damage risk, it is effective to formulate the Business Continuity Plan (BCP) in addition to structural measures. This study developed a basin-scale flood inundation model for the Chao Phraya River basin to understand the flood risk roughly. Next, we developed a high-resolution industrial park-scale model to provide the specific flood information that contributes to business impact assessment and business continuity management. The study combined those models by applying the outputs of the basin-scale model as the boundary conditions of the industrial park-scale model.This study obtained high-resolution elevation data and cross-section data of rivers and canals through field surveys to develop the industrial park-scale model. From the data, we developed a high-resolution flood inundation model of a 40m mesh size industrial park-scale model. Then, the flood analysis using the developed model was sufficiently valid. That is, the study confirmed that flood information such as inundation depth and river discharge between different scales models can be appropriately downscaled by applying the time series outputs of the basin-scale model as the boundary conditions surrounding the industrial park-scale model. The developed model provided detailed flood information, for instance, inundation area and inundation time, which is useful for formulating BCPs for industrial parks and individual companies.

Recently, a project on Regional Resilience Enhancement was launched by establishing the Area-BCM at industrial complexes across Thailand, aiming to enhance regional resilience by visualizing disaster risks through the collaboration of industry, government, and academia. The project is mainly focused on Thailand and is under the SATREPS, a Japanese government program that promotes international joint research. A comprehensive hydrological analysis of a river basin based on historical rainfall is a key component of the project to highlight possible hydrological risk in the industrialized urban areas of Thailand. For such an analysis, we considered the Chao Phraya River Basin, the largest in Asia, which is often vulnerable to water-related disasters. Rainfall data from 119 stations within the basin were collected for frequency analysis. After processing the 36-year daily rainfall data, four types of probability distributions were used to obtain the return period of rainfall for different time intervals. The Gumbel distribution was found to be the best among the four. We calculated the average rainfall of the main basin as well as of five subbasins. The trends of average basin rainfall were different for each subbasin, suggesting that the spatial distribution of rainfall varied greatly over the basin. To perform an inundation analysis over the entire river basin, we designed hyetographs by integrating the various return periods of rainfall separately. We suggested three methods to design hyetographs focusing on their pros and cons. These design hyetographs were considered in an RRI model to calculate the maximum inundation depth profile over the basin for different return periods. We outlined the spatial distribution of simulated inundation profiles over the basin within the next 50, 100, 200, and 500 years, respectively. It is expected that outcomes of this study can be a good reference for risk analysis and evaluation over the basin.

Capacity is one of the most significant factors in coping with disasters. However, capacity has not attracted as much attention in disaster risk reduction research as other factors, such as vulnerability, resilience, and risk, even if capacity is critical for disaster mitigation and resilience. After the 2011 flood in Thailand, the industrial estates/parks established several soft and hard flood countermeasures. One of the typical hard flood countermeasures was building a 5-6meter wall around estates/parks. This, however, means an increased flood risk in the surrounding communities. With considering the situation, the paper aims to examine the present local community’s capacities by conducting　self-capacity assessments, identify the vulnerability groups, and investigate the critical facilities in the communities with collecting their 2011 flood experience. These tasks clarify the communities' updated capacities such as hard countermeasures, disaster planning, disaster system, disaster information, and recovery efforts, examine the communities' vulnerabilities, identify the risks with possible hazard information, and guide to cope with potential flood risk in the area. Questionnaire-style surveys and interviews in the four-target communities have been employed. The findings show differences in the communities' capacities, such as those with urbanized and farmland-based characteristics. The relatively populated urbanized communities tend to have high capacities and motivation to cope with flood disasters. On the other hand, the not-so-populated farmer-based communities have low capacities and motivation, tending to focus more on droughts than floods. The paper investigates the gaps and challenges with visualizing the capacities, vulnerability groups, and critical facilities locations on the map. The paper then examines the connections with damages and lessons learned from the 2011 flood to explore how to support the communities in building their resilience around the industrial park.

Thailand has faced many flooding crises especially in 2011 which led to widespread damage to industrial parks and community zones. However, the negative consequences of flooding on local people and workers in industrial zones have rarely been studied. This paper focuses on the impacts of flooding after 2011 on the well-being of residents in industrial zones in terms of their working and livelihoods. Community-based research was used to explore the impacts of floods on 645 respondents who worked in Rojana Industrial park (245) and those who lived surrounding the park (400). The questionnaire survey was conducted in February 2020. The results found that the adverse impacts on economic, accommodation, and well-being affected both local people and the workers. Their monthly incomes and overtime jobs decreased significantly compared to the time before the disaster. However, local people were more suffer from low income than the workers who worked for the company as their work status was maintained during the crisis. In addition, the workers were evacuated to another accommodation till the company’s recovery while local people remained in their homes. Besides, most of the respondents reported that mental problems were more likely to affect their health rather than a physical problem. For effective recovery, the government should follow up the situation of the employment, accommodation, and livelihood after a sudden the flood, especially the local people.

Ensuring employees' safety and managing resources, supply chain are essential for business continuity for each organization when disaster strikes. For organizations to build feasible business continuity plan, impact analyses considering the hazard information focused on their own facilities and networks and the assumption of consequences reflecting their resources and business processes will be needed. However, these impact analyses need much effort and knowledge, so planning based on scientific data is not advanced enough yet.From these points of view, as an example of facilitation tools of business impact analysis, we developed a toolkit that analyzes the impact of flood disaster on organization's business. We conducted intensive interview survey to the company that is affected by 2011 Thailand floods and gathered needs for the toolkit. Then, we focused on the continuity of the operation of commuter buses and develop an example toolkit.Toolkit has the GIS database that can contain hazard data and transportation network data. We used simulation data of 2011 event for hazard and commuter bus routes for transportation network as an example. When these data are set, toolkit calculates each route's suspension scenario using GIS function with the condition that the area where inundation depth exceeds 30cm is impassable. After this process, toolkit relates the employees' bus usage data which are input from secure web page to calculated bus route suspension scenario data. And toolkit calculates impacts useful for organizations to plan business continuity such as absence of leaders, shortage of manpower in each section and visualizes them in user-interactive dashboard interface. The framework and method developed for this example toolkit will be able to apply and expand to other resource planning and supply chain management. We would like to enhance the functionality and develop various impact analysis toolkit.

Business Continuity Management (BCM) is widely used to manage business continuity in the case of disaster. Lately, it has been found inefficient due to the discontinuity of the stakeholders, which led to Area-Business Continuity Management (Area-BCM). Area- BCM is about managing the business continuity through the event of disasters not only by managing the continuity of one company but also creating plans for the whole area. Many key stakeholders play important roles in maintaining business continuity, such as suppliers, customers, investors, and public sectors (e.g., the operators of lifeline utility and transport infrastructure), especially employees who are one of the essential stakeholders of operating production lines. This study aims to construct an employee assistance program for a plastic automobile parts manufacturer by identifying the factors affecting employee’s decision making for going to work during the event of a flood and creating plans to support them in various ways in order to ease their burden and help them get through their hardship. The factors investigation was carried out using a questionnaire survey distributed in the plastic automobile parts manufacturer company and was then analyzed using cross-tabulation. This study is expected to help the company choose the most suitable employee assistance program that can increase the probability of their employees going to work and continuing the production during the flood.

10 years have passed since the 2011 floods in Thailand, which had a major impact on both community and global supply chains. One of the challenges cited by companies directly affected by the floods was the lack of access to accurate information for decision-making. In addition, there was no coordination function among stakeholders. Even today, it cannot be said that all the issues have been resolved. Therefore, this study is to analyze the information needed for decision-making and what kind of entities need to be coordinated with. In order to protect the safety of their employees and achieve their supply responsibilities in the event of a disaster, companies make various decisions based on the information they have obtained and in cooperation with stakeholders, according to their BCP. However, what became clear in this study is that companies depend on various external resources for their business continuity, which they do not manage themselves and it is difficult for them to collect status information. Electricity, roads, communications, and transportation, in that order, have the highest degree of external dependence, and it is difficult to collect information on these social infrastructures for business continuity. On the other hand, many companies indicate that this information is important for decision-making on business continuity. Looking at past collaboration with stakeholders in disaster response, most of them shared information with their business partners, and there were few cases of collaboration regarding social infrastructure. From this point of view, even for companies that have formulated BCPs, there are limitations to their effectiveness and feasibilities. It is thought that it is possible to achieve interoperability, including social infrastructure, by building a mechanism for cooperation and information sharing with necessary stakeholders throughout the region as Area-BCM, thereby raising the limitations of individual BCPs and strengthening their effectiveness and feasibilities.

Our society's interdependencies have become complex and so have business continuities in an industrial complex where various stakeholders are involved from both private and public sectors. Towards common disaster risks, partial optimizations of each stakeholder for business continuity are possible of causing conflicts on management resources sharing and cross-organizational decision-making among other stakeholders. This results in failures not only at individual business continuity but also at the industrial complex as a whole. The concept of Public-Private Partnerships (PPP) is presented and studied to the issue, yet interdependencies in management resources and decision makings between the sectors are not clearly structured. To fill the research gap, this study introduces preliminary analysis which identifies key factors that act as key interdependencies between the public and the private stakeholders from the perspective of business continuity and operations against a common risk. An approach of system thinking and system dynamics is introduced, and the interdependencies are modeled by using Bayesian Belief Network. Thailand is chosen to be a case since it takes an important role in the hub of global supply chains and has a large number of major industrial parks. Flood, one of the major disasters in Thailand, is adopted as the common risk. Besides, a disaster period is set from hazard detection to reaching of inundation to a target industrial complex; stakeholders take major actions for business continuity and those of early resumptions. Questionnaire surveys and in-depth interviews were conducted with the stakeholders to collect their possible interdependent factors such as materials, equipment and machinery, critical infrastructures, and human resources. The result of the study anticipates contributing of consensus-building between public and private sectors by providing interdependent factors which require prior arrangement on management resource sharing and cross-organizational decision makings.

Cloud obstruction is a major challenge among the application of remote sensing. Here, the Balkhab river basin (BRB) in northern Afghanistan was selected for the study area. BRB is located in a mountainous region with arid to semi-arid climate. The spatial snow cover extent was extracted from the MODIS Aqua and Terra NDSI snow cover products. MODIS snow cover products were subjected to three-steps combinations to reduce the cloud cover. 1) Spatial combination where two satellite images were combined for the same day. 2) Temporal combination one where the images of step one were combined with its proceeding day images. 3) Temporal combination two where the images in step two were combined with previous day images. Then Snowmelt Runoff Model (SRM) was adopted for the runoff estimation. SRM parameters were calibrated for 2014, and then, validated for 2012 and 2013 with same model setup. In the simulations process, six snow cover inputs with different levels of cloud coverage (without snow cover input, raw Aqua and raw Terra snow covers, and three combination outputs) were used to investigate the effects of cloud coverage in the snow cover input on runoff. As a result, the values of determination coefficient (R²) and Nash-Sutcliff efficiency (NSE) has been gradually improved after each combination steps in snow cover, and the volume differences (D_v) between the observed and simulated discharge are gradually reduced. The final statistic values for 2012-2014 estimated discharges are as follow: R² are 0.62, 0.60, and 0.62; NSE are 0.53, 0.48, and 0.62 and; D_v are 16.08%, −and 0.37%. The SRM results clearly depict the improvement in the simulated discharges with a reduction in the cloud cover in the snow cover extent. Thus, careful attention is needed while applying the remotely-sensed snow cover products to hydrological models as input.

The headwater of Yellow River (HYR) locates in the northeastern Qinghai-Tibet Plateau whose water resource is greatly important for the entire Yellow River Basin. Streamflow in the HYR has decreased over past several decades and many hydrological models have been used to simulate runoff of the river. However, the performance of runoff simulation in this head region is limited by widespread frozen soil and dramatic changes in terrestrial water storage in the HYR. In this study, Variable Infiltration Capacity (VIC) model with frozen soil module (freezing and thawing progress of frozen soil) was used to simulate streamflow in the HYR and the terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE) was incorporated for multi-objective calibration (GRAEC-MC). Simulation with or without GRACE data and with or without frozen soil module were performed to study the isolated and combined effects of frozen soil degradation and GRAEC-MC for improving the performance of runoff simulation in the HYR. Results show that considering frozen soil module could significantly improve runoff simulation effect, especially in cold and dry season. The VIC model with both frozen soil module and GRACE-MC has best performance, with the increasing accuracy of not only runoff simulation but also the evaporation and soil moisture simulation. The GRACE-MC-only model shows a worse performance compared with the VIC model with frozen soil module, although GRACE-MC indeed increases the ability of calibration for the same models. These results highlight the importance of considering frozen soil in the runoff simulation and suggest the significance of GRACE as an additional constraint to improve performances of hydrological models in cold regions.

Yangtze River and Huang River are the top two largest rivers in China. Their headwaters (HYR and HHR) locate in the central east Tibetan Plateau where increase in terrestrial water storage (TWS) during past few decades has been reported. Runoff ratio (annual runoff/precipitation, denoted as RR) in this two basins have experienced significant decrease over past several decades, which is closely related to TWS changes. However, the magnitude and mechanism of this effects are still unclear. In this study, long-term TWS change over the period of 1980-2015 in HYR and HHR were detected from a high-quality TWS reconstruction product (TWSrec), after the TWSrec were validated in this headwater regions by comparing with GRACE observation and using combined water balance method. Trend detection, change points analysis and path analysis were further used to quantify the impact of long-term TWS changes on RR. Results show that the TWS_rec can well capture TWS variation derived from GRACE and had a good performance in terrestrial water balance in the both headwater regions. The TWS increased significantly from 1980 to 2015 with rate of 27.6 mm/10a and 18.8 mm/10a in both HYR and HHR, respectively. RR showed significantly downward trends with rate of 2.0%/10a in HYR and of 3.6%/10a in HHR. Increasing potential evaporation was the dominant driver (>75%) on RR decrease while increase of water store capacity had noticeable contribution (>19%) in both headwaters. Increase in basin water store capacity was possible resulted from climate warming which thickened the annual active layer of permafrost. This study highlights that precipitation-runoff relationship had been changed in theses headwater regions and change in water store capacity due to permafrost degradation have to be considered for improving capability of runoff simulation in the Tibetan Plateau.

Permafrost thawing due to global warming has a significant impact on ecosystems, the water cycle, and climate. However, the effect of permafrost thaw on freshwater flux into the Arctic Ocean is not well understood. In the four major Siberian rivers (Obi, Enysei, Lena, and Kolyma), the accumulated freshwater flux to the Arctic Ocean increased by approximately 4 km³/yr/yr from 1978 to 2016. Unlike the other three major Siberian rivers, the Kolyma River exhibited a decrease in the discharge during 1927–2000 and 1978–2000 despite an increase in precipitation in the basin. To understand the changes in the basin hydrological cycle due to global warming and permafrost thawing in the Kolyma River basin, it is desirable to analyze the long-term data in terms of hydrolometeorological elements together with active layer thickness. This study aims to clarify the impact of permafrost thawing on river discharge in the Kolyma River basin, located in North-East Siberia, using data from 1979 to 2012. To analyze the changes in the active layer and evapotranspiration due to permafrost thawing, the long-term climate data provided by Princeton University was utilized for carrying out the simulations of the coupled hydrological and biogeochemical model (CHANGE). The CHANGE model was validated by comparing it with the Global Land Data Assimilation System ver2.0 (GLDAS2), satellite observation, and in-situ observation data. The causal relationship between permafrost thawing and changes in runoff and terrestrial water storage was clarified on the basis of the CHANGE model simulations of the active layer. We found that the increase in the active layer thickness due to permafrost thawing suppressed the increase in runoff owing to increased precipitation. This suggests that permafrost thawing affected runoff volume by increasing the basin terrestrial water storage capacity.

General circulation models (GCMs) are indispensable for climate change adaptive study over the Tibetan Plateau (TP), which is the potential trigger and amplifier in global climate fluctuations. With the release of Coupled Model Intercomparison Project Phase 6 (CMIP6), 24 GCMs from CMIP5 and CMIP6 were comparatively evaluated for precipitation and air temperature simulations based on the China Meteorological Forcing Dataset (CMFD). Rank score results showed that CMIP6 models generally performed better than CMIP5 for precipitation and surface air temperature over the TP. According to multimodel ensembles (MMEs) of the optimal GCMs, the overestimation of precipitation was both present in CMIP5 and CMIP6, but the results of CMIP6 MMEs were relatively lower in the mid-west and northern edge of the  TP. For temperature, CMIP6 MMEs were able to reduce the relatively large cold bias that appeared in CMIP5 MMEs in northwest areas to about 1℃. Moreover, the investigation into the simulation effects of precipitation at different elevation zones demonstrated that the improved ability of CMIP6 MMEs to reduce bias was mainly concentrated in the elevation zones of 2,000–3,000 m and over 5,000 m, where the precipitation bias was more than 200%. Additionally, CMIP6 MMEs of temperature were able to reduce the bias to less than 2℃ in each  elevation zone, with the minimum bias of −0.22℃ distributed in the region with altitudes from 3,000 to  4,000 m. Findings obtained in this study could provide a scientific reference for related climate change research over the TP. Multimodel ensembles (MMEs) of CMIP6 effectively reduce the overestimation of precipitation from CMIP5 MMEs by 40 mm at the annual scale. Improved ability of CMIP6 MMEs shows a significant elevation dependency, especially in elevation zones of 2,000–3,000 m and over 5,000 m for precipitation.

Aiming to further assesses and reduces the uncertainty from ensemble members and emission scenarios existing in the global climate models, this paper incorporates a new method based on Empirical Orthogonal Function analysis and Structural Similarity Index Measure to evaluate both the spatial structure and temporal variation of the models. The sea ice thickness combined with the sea ice concentration is incorporated into the assessment of the model performance to enhance the explanation of physical mechanism and a new score-based method is proposed for model selection. Finally, the selected models have been used to predict the future opening period of the Northern Sea Route, showing it will reach to 3-5 months. 

Aufeis are produced annually in the rivers valleys in permafrost environment as the result of layer-by-layer freezing of groundwater flowing to the surface. Aufeis are widespread in the territory of the North-East of Russia with total area about 2 mln. km² including the basins of the large rivers such as the Yana, Indigirka, Kolyma and Anadyr.Based on the analysis of Landsat satellite images for the period 2013-2019 the number and total maximum area were estimated. Total number of giant aufeis (>0.1 km²) formed by groundwater reaches 6217 with maximum area of about 4500 km² (in average 0.22 % of studied area). For each aufeis field the assessment of maximum ice reserves was conducted. The aufeis resources of the North-East are at least 10.6 km³ or 5 mm of aufeis runoff. The aufeis resources vary from 0.4 to 4.25 km³ (or 3.7 – 11 mm) for individual basins of large rivers. The greatest aufeis resources in absolute values are found in the Indigirka River basin. The contribution of aufeis runoff to streamflow in different seasons was calculated for 58 hydrological gauges (area 523 – 526000 km²). Aufeis annual runoff varies from 0.3 to 29 mm (0.1 – 22%, average 3.8%) with the share in winter runoff amount about 6 – 712 % (average 112%) and the spring freshet 0.2 – 43% (average 7.1%). The response of aufeis to climate change depends on different factors of the natural system. The dynamics of aufeis formation is directly related to the winter runoff, which changes are observed in different parts of the cryolithozone. The presented results are relevant for studying the impact of climate change on the hydrological cycle and its components in the permafrost regions of the Northern Hemisphere.The study was carried out with the support of RFBR (projects 19-35-90090, 19-55-80028, 20-05-00666).

Gridded digital elevation models (DEM) are used in many disciplines due to their ability to represent topographic characteristics required in landscape, hydrological and erosional modelling. However, the currently available global topographic data is confronted with limitations due to the existence of vegetation bias, random errors, and insufficient spatial resolution. The objective of this study is to evaluate the vertical and horizontal accuracy of three mid-resolution DEMs – SRTM-30, ALOS-30 and the recent ALOS-12.5 in the Vakhsh watershed in Tajikistan. We examined the vertical accuracy (the precision with which give an altitude on a given point) as well as the horizontal accuracy (i.e. the precision with which the DEMs represent the topographic elements that make up the terrain) by comparing more accurate truth datasets. For vertical accuracy, we used the ICEsat version 2 dataset available on the OpenAltimetry Platform. In total in our study area, 16,112 elevation points were collected across 32 tracks using “gt3r channel signal photons”. For horizontal accuracy, we compared the streams derived from the 3 DEMs with digitized streams from SPOT 1.5 m. The results of the vertical accuracy showed a RMSE of 16.49 for ALOS-12.5 m, 39.97 m for ALOS-30 and 40.16 m for SRTM-30. Regarding the vertical accuracy, the distance between the digitized and the DEM-derived-streams were all similar: RMSE of 45.09 m for ALOS-12.5, 46.61 m for ALOS-30 and 48.22 m for SRTM-30. The resampling ALOS-12.5 DEM showed some elevation thresholds in our study area between the tiles, which might affect the accuracy of any modelling. However, the vertical accuracy and the horizontal accuracy of ALOS-12.5 are promising for higher resolution analysis compared to SRTM-30 and ALOS-30.

Glaciers play a significant role in livelihood of mountain societies whose social and economic development completely remains on water resources availability in Central Asian region. Climate change and its variabilities have had significant impact on glaciers and water resources in Pamir mountains, water tower of the region. With total number of 9139, glaciers occupy more than 6 % of Tajikistan’s territory although, alteration of glaciers retreatment have been widely pronounced during recent decades, systematic assessment of glaciers has not been widely and successfully carried out due to decreasing number of “in-situ” monitoring stations. Therefore, there are uncertainties related to rate of glaciers retreat and they extent. Recently, alternative remotely-sensed methods successfully have been used for glacier monitoring and mapping. Remote sensing data from different land observation satellites and advanced GIS techniques are allowing scientists to carry out a large-scale glacier at varying spatial and temporal scales. In this study we reviewed applications of remote sensing and historical data for mapping glaciers of Pamir mountains. Global Land Ice Measurements from Space (GLISM) datasets were used to map spatial distribution of glaciers. Moreover, we performed a statistical analysis to categorize glaciers to three large, moderate and small groups and then to assess feasibility of using different land observation satellites in monitoring each category.  Based on the results of the research new data on glaciers boundaries and they inventory will be created. The outputs of this study will assist us in better understanding of spatial and temporal distribution of glaciers as well as in designing new monitoring stations.