Monitoring snowpack depth is essential in many applications at regional and global scales. Space-borne passive microwave (PMW) remote sensing observations have been widely used to estimate snow depth (SD) information for over four decades due to their responsiveness to snowpack characteristics. Many approaches comprised of static and dynamic empirical models, non-linear, machine-learning-based models, and assimilation approaches have been developed using spaceborne PMW observations. These models cannot be applied uniformly over all regions due to inherent limitations in the modelling approaches. Further, the global PMW SD products have masked out in their coverage critical regions such as the Himalayas, as well as very high SD regions, due to constraints triggered by prevailing topographical and snow conditions. Therefore, the current review article discusses different models for SD estimation, along with their merits and limitations. Here in the review, various SD models are grouped into four types, i.e., static, dynamic, assimilation-based, and machine-learning-based models. To demonstrate the rationale behind these drawbacks, this review also details various causes of uncertainty, and the challenges present in the estimation of PMW SD. Finally, based on the status of the available PMW SD datasets, and SD estimation techniques, recommendations for future research are included in this article.

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Disaster management is a critical component in mitigating the impacts of various natural, and other disasters such as floods, cyclones, forest fires, earthquakes, disease spreading etc. The primary aim of disaster management in a specific region is to empower the local neighborhood to higher determine its natural danger instincts and therefore migrate closer to options for lowering that risk. Conventional techniques of disaster management are majorly driven by the quantitative information collected from various events. Some of the recent techniques have used more advanced data-driven and non-linear approaches such as machine learning, and spatial analysis tools such as GIS for making more informed decisions. However, these techniques cannotoften represent the dynamics of demographic units, and event impact in small regions due to a multitude of reasons such as lack of data, equipment, more generalized approaches, etc. Participatory Geographic Information System (PGIS) overcomes some of the limitations present in the traditional techniques by incorporating local communities as stakeholders in making various policies, distributing risk information etc. PGIS has been adopted in various fields such as land cover planning, agriculture information systems, data collection systems etc. Other than these applications, the effectiveness of PGIS in disaster management in handling various natural disasters such as floods, cyclones, forest fires, and disease spread has been demonstrated in several studies. However, in manyplaces, PGIS is not yet evolved and its implementation is still at the infancy level due to several reasons. Despite many advantages, PGIS presents many problems comprising insufficient infrastructure, training facilities, engagement and education of the community members towards a combined decision, etc. therefore provision of necessary infrastructure can improve the overall impact of implementing PGIS. Involving the local community and educating them on the right approach for the success of PGIS is a complex task. Further, the conflict of opinions between technical personnel and locals can be another factor that limits the usage. However, from the results of various studies, the advantages of PGIS implementation can outweigh the limitations of implementation.

Abstract. Spatiotemporal snow depth (SD) mapping in the Indian Western Himalayan (WH) region is essential in many applications pertaining to hydrology, natural disaster management, climate, etc. In-situ techniques for SD measurement are not sufficient to represent the high spatiotemporal variability of SD in WH. Currently, low-frequency passive microwave (PMW) remote sensing-based algorithms are extensively used to monitor SD at regional and global scales. However, only a limited number of PMW SD estimation studies are carried out for WH till date. In addition, the majority of the available PMW SD models for WH locations are developed using limited data and less parameters, therefore cannot be implemented for the entire region. Further, these models have not considered the auxiliary parameters such as location, topography, snow cover days (SCD) into consideration and have poor accuracy (particularly in deep snow), and coarse spatial resolution. Considering the high spatiotemporal variability of snow depth characteristics across WH region, region wise multifactor models are developed for the first time to estimate SD at high spatial resolution of 500 m x 500 m for three different WH zones i.e., Lower Himalayan Zone (LHZ), Middle Himalayan Zone (MHZ), and Upper Himalayan Zone (UHZ). Multifrequency brightness temperature (TB) observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), SCDs data, terrain parameters (i.e., elevation, slope and ruggedness), geolocation for the winter period (October to March) during 2012–13 to 2016–17 are used for developing the SD models. Different regression approaches (i.e., linear, logarithmic, reciprocal, and power) are developed and evaluated to find if any of these models can address the heterogeneous association between SD observations and PMW TB. The results indicate the following observations: (a) multifactor model developed using power regression has shown improved accuracy in SD retrievals compared to other regression approaches in all WH zones; (b) spatial variability in SD is highly affected by SCDs, terrain parameters, geolocation parameters; (c) compared to the currently operational AMSR2 SD products, the proposed models have shown better SD estimates in all WH zones particularly when SD > 25 cm; (d) the Root Mean Square Error (RMSE) of multifactor models SD estimates increased with an increase in SCD in all WH zones; The multifactor model of MHZ has shown lesser RMSE (i.e., 27.21 cm) compared to LHZ (32.87 cm) and UHZ (42.81 cm). Overall results indicate that the proposed multifactor SD models have achieved higher accuracy in deep snowpack (i.e., SD >25 cm) of WH when compared to various previously developed SD models.