NH9.8

The impact of a warming climate on snow- and rain-dominated river basins such as the Garhwal Himalayas basin constitutes both a major research challenge and the potential of a severe socio-economic risk. The particular combination at the Garhwal, of hydrometeorological and hydrographic conditions entails merging and superposing two presently distinct seasonal phenomena: snowmelt induced spring floods and rainfall generated summer floods. This study focuses on the projection of seasonality changes in floods in a Garhwal Himalayas basin under global warming. The research in this context is rather uncertain in the proposed study area of the Himalayas, mainly due to the scarcity and unavailability of long-term and high-resolution meteorological data in that region. But after setting up Automatic Weather Stations and Gauge and Discharge sites in the Garhwal region in 2016, the observed data of the past five years lay the basis for understanding the different flood generating regimes. We have analysed the IMD historical maximum monthly rainfall (1901-2020) and maximum temperature (1951-2020) over the study region and found evidence of shifting of maximum rainfall peak backward up to the month of June and maximum temperature peak shifting forward to June (earlier triggering snowmelt induced peak then); if warmer climate scenarios are experienced in future. We also compared the different precipitation datasets available with respect to the observed data at daily, monthly, quarterly and yearly time scales. Those data are crucial for any analysis of possible changes in seasonal hydro-meteorological conditions. We found that the IMD precipitation dataset matches best the observations and the projected climate ensemble of chosen dataset (NEX-GDDP) required significant correction with respect to observed data to counter underestimation. Therefore, we have used quantile-based mapping to adjust the biased projected climate dataset of NEX-GDDP. Also, the corrected projected precipitation of time window 2071-2099 of RCP 4.5 and 8.5 scenarios is found to be magnitude wise higher than that of the corrected historical time window 1971-2000. This clearly indicates the possible occurrences of changes in floods, though we are well aware about the high uncertainties of projected future precipitation conditions. Thus, our analysis poses the potential of bridging the gaps of understanding different flood generating regimes and their future possibilities for better preparedness against natural hazards in the Himalayan region.

How to cite: Singhal, P., Goel, N. K., Agarwal, A., Bronstert, A., and Vormoor, K.: Projection of flood seasonality Changes in the Himalayan Region River due to global warming, taking the Garhwal Himalayas river basin as an example, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5512, https://doi.org/10.5194/egusphere-egu22-5512, 2022.

A hydrological model conceptualizing a certain rainfall event of a watershed is capable of reflecting the hydrological situation and assessing its response not only for historical but also projected climate data in future. This works presents a futuristic flood discharge estimation using the established event based HEC-HMS model corresponding to the meteorological forcing from shared socioeconomic pathways (SSPs) of Coupled Model Intercomparison Project-6 (CMIP6). The hydrological model was setup for flood-prone Himalayan Beas river basin, India. The calibration and validation of the model was carried out for the rainfall induced flooding events of monsoon 2005 and 2010, respectively. The coefficient of determination (R²) and Nash–Sutcliffe efficiency (NSE) were achieved to be 0.82 and 0.79 for calibration and, 0.84 and 0.80 for validation at Bhuntar station, respectively. An improved carbon simulated CMIP6 rainfall data holding of ACCESS-ESM1.5, after bias correction and downscaling, was used to simulate the flood hydrographs in the Beas basin till 2100. The peak discharges of each decade from 2021 to 2100 was estimated and analysed, for the SSP245 and SSP585 scenarios. For the climate projection scenario SSP245, the peak flood event was estimated to be in July 2068 with peak discharge of 4446.7 m³/s while a SSP585 scenario observed extreme flood event in July 2057 having a peak discharge of 4817.2 m³/s. The estimated discharge magnitudes from SSP245 and SSP585 schemes are comparable to the 562 years and 706 years return period discharges of the basin, respectively. The study also revealed that the frequency of flooding events are maximum in the endmost decade of 2091-2100, with an increasing trend towards the later decades.

Keywords: Flood, Hydrological Model, CMIP6, HEC-HMS, Himalayas, Beas river, Climate data

How to cite: Moothedan, A. J., Dhote, P. R., Thakur, P. K., and Agarwal, A.: Projection of future pluvial flood events over Himalayan river basin under CMIP6 climate data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-438, https://doi.org/10.5194/egusphere-egu22-438, 2022.

Increasing rate of flash-floods in Himalayan river basin causes immediate damage to human lives, daily living and infrastructure. The present work proposed flood risk assessment framework by blending the hydrodynamic modelling outputs and risk evaluation concepts. The different notions of risk as in hazard, vulnerability and exposure were evaluated over flood-prone Beas river with focus at Bhuntar, Kullu and Manali. The hydrodynamic model (MIKE 11) was established for 56 km river stretch right from Manali to Bhuntar. The flood depth and flow velocity outputs from the calibrated and validated hydrodynamic model were used for the estimation of flood hazard rating. Vulnerability maps were generated using depth-damage curves prepared by Joint Research Centre, EU, for each exposure of agriculture, settlement and roads. The 100 year return period flood risk maps were prepared and analysed for all the three towns. Key interviews and community focus group discussions were held further to strengthen, compare and verify the achieved outcomes. For a 100 year return period flood risk assessment, a total of 0.054 km², 0.226 km² and 0.334 km² area was flooded and extreme flood risk zones were identified with 4.7%, 6.8%, and 10.9% area of the total inundated area at the settlement regions of towns of Manali, Kullu and Bhuntar, respectively. The area on right bank of the river was inundated severely and got classified into extreme flood risk zones. The major settlements at all the towns under consideration are at the right bank due to relatively flat, low lying terrain leading to the dire risk. The outcome of the work can assist disaster managers and local administrations for flood disaster planning in advance, thus reducing human and economic loss.  Further, flood risk map could serve as catastrophic product to define flood insurance rate for various exposures in floodplain.

Keywords: Flood Risk Assessment, MIKE11, Hazard, Vulnerability, Hydrodynamic Modelling

How to cite: Dhote, P. R., Moothedan, A. J., Thakur, P. K., and Agarwal, A.: Flood risk assessment framework for Himalayan river basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-595, https://doi.org/10.5194/egusphere-egu22-595, 2022.

The river morphologies and the associated landscape experience considerable changes in response to landslides and floods. The young and tectonically active Himalayan region is more prone to such natural hazards. The impacts of climate change and anthropogenic activities have further increased the frequency and intensity of such natural disasters in this already active region. These disasters cause vast losses of life, property, infrastructure and disturb the ecological balance. This study explores the geomorphological changes occurring in the downstream river reaches of the Alaknanda River using the Google Earth Engine (GEE) cloud-based computing tool. We extract the active river channel width using Landsat multispectral images. The initial results show considerable changes in width over the years (1990-2021) and the changes start from the knickpoint continuing towards downstream. The changes in the river’s bank line indicate the bank erosion and relocation of sediments along the river, likely supplied by erosion processes at upstream reaches. Here, we try to identify the critical point where the deposition process first starts to highlight the most vulnerable zone geomorphologically. We further check whether there has been an increase in sediment deposition in recent years due to likely increased erosion related to deforestation on higher reaches of the Alakananda catchment. We try to achieve this goal by correlating the river landform changes and land cover changes along riparian areas of the river temporally. Our overall objective is to develop a framework to correlate changes or processes in upstream reaches to depositions or erosions along the downstream sections of a high-energy river.

Keywords: Landscape evolution, natural hazards, erosion, deposition, River-line

How to cite: Malakar, B., Ozturk, U., and Sen, S.: The link between downstream river planform changes and upstream changes or processes in high energy mountain rivers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4284, https://doi.org/10.5194/egusphere-egu22-4284, 2022.

Rock falls, rock slides and rock avalanches occurring in glaciated environments and permafrost regions are characterized by their sudden and complex character, high magnitude-mobility and cascading secondary hazards. The flow mobility is enhanced by the presence of ice and snow by up to 25% to 30%, with respect to rock avalanches of comparable magnitude evolving in non-glacial settings. Their dynamics are controlled by interaction between the detached rock and the icy component during all phases of motion, from initiation to the final deposition (Sosio et al., 2015).  The 7^th Feb'21 catastrophe in the Upper part of the Chamoli district of Uttarakhand, India was one such event that impacted the catchments of Ronti Gad, Rishiganga and Dhauliganga valleys by a high magnitude debris flow, triggered by a massive rock-ice slide of 25-27 million cubic meters (ICIMOD, 2021; Pandey et al., 2021; Thaiyan et al., 2021; Shugar et al., 2021). The initial rockslide entrained glacier ice and continued as a rock-ice avalanche which fluidized along the path, evolving into a massive debris flow, traversing 21-22 km downstream in around 16 to 18 minutes (ICIMOD, 2021; Pandey et al., 2021; Thaiyan et al., 2021; Shugar et al., 2021). It destroyed two hydroelectric projects (HEP) enroute, and killed more than 100 workers at the Tapovan HEP. This also led to the formation of the lake at the confluence of Ronti Gad and Rishiganga and a small lake was also observed at the confluence of Rishiganga and Dhauliganga, which was instantaneously breached. This event accentuated the fragility of the Indian Himalayas and its complex periglacial terrain.

In the present work, we try to numerically and conceptually reconstruct the cascade from the initial rockslide to 21 km downstream, till the Tapovan HEP. We segmented the flow path into four major sections based on: i) gradient changes; ii) observed flow physical parameters; iii) channel characteristics; iv) erosion-deposition and entrainment. For each of the four sections, we present the section wise peak velocity and energy calculations based on the fundamental Voellmy-Perla equations and present the result as profile graphs to better understand the velocity-energy changes along the longitudinal profile of the flow path. Next, we estimate the section wise sediment-rock to water amount at the end of each section, using pre-post DEM profile-differencing, satellite images and field data, based on certain logical assumptions. Thus, proposing the plausible stepwise processes and sediment-water interaction as occurred on the morning of 7^th Feb'21. The results, hence obtained were found to be in-line with the available literature and were able to logically justify the so-far-known event parameters. Future work is intended on better validation of the obtained results by using flow models. Thus, aiming to better comprehend and understand such events in the complex Himalayan terrain and being able to predict and mitigate them in the future.

Keywords: Rockslides, rock falls, rock avalanches, debris flows, hydroelectric projects, Indian Himalayas, Glacier, Climate change

How to cite: Lakhera, S., Jaboyedoff, M., Derron, M.-H., and Goswami, A.: Numerical Calculations & Scenario Reconstruction of the 7th Feb'21, Chamoli Event-In Terms of Velocity-Energy and Sediment-Water Amount Changes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11477, https://doi.org/10.5194/egusphere-egu22-11477, 2022.

Rainfall intensity-duration (ID) thresholds are helpful to estimate the likelihood of natural hazards during extreme precipitation events. Sub-daily time-series of weather data is necessary to define precise ID thresholds of sediment disasters. The Himalayas, vulnerable to extreme precipitation events, experience large-scale sediment disasters, i.e., landslides, debris flows, and flash floods. Present early warning systems currently in operation encounter difficulties forecasting sub-daily time-series of weather due to instrumental and operational challenges. Here, we present a new framework to analyse and predict extreme rainfall-induced landslides using a weather research and forecasting model (WRF) followed by a spatially distributed numerical model. The operational framework starts with the WRF model running at 1.8 km × 1.8 km resolution. Then, the spatiotemporal numerical model for landslide forecasting at the same resolution uses the WRF model outputs. We calibrate the models using Uttarakhand, India's 2013 heavy rainfall-induced landslide events. We perform parametric numerical simulations to identify critical ID thresholds of landslides under different precipitation intensities, i.e., moderate rain, rather heavy, heavy rain, very heavy rain, and extremely heavy rain according to the India Meteorological Department (IMD) glossary. Our analysis opens avenues for integrating the WRF model with rainfall ID threshold-based territorial early warning of landslides. 

How to cite: Siva Subramanian, S., Srivastava, P., and Sen, S.: Numerical weather prediction model outputs define intensity-duration thresholds of extreme-precipitation-induced sediment disasters, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7804, https://doi.org/10.5194/egusphere-egu22-7804, 2022.

Landslide is the most significant natural hazard that causes socio-economic devastation in mountainous terrains around the world. In India, lands of mountains especially the Himalayas are vulnerable to landslide due to the high intensity of seismic shaking, prolonged rainfall and complex lithological setting. In the present study, Landslide Susceptibility Zonation (LSZ) has been carried out using Random Forest technique on Geographical Information System by combining different landslide causative factors i.e. slope angle, slope aspect, drainage density, distance to drainage, elevation, shape of slope, distance to lineament, lineament density, surface geology, soil, geomorphology, landform, rainfall, epicenter proximity, Normalize Differences Vegetation Index, Landuse/Landcover, road density and distance to road are integrated to model Landslide Susceptibility Index, thus classifying the terrain in terms of  ‘None’, ‘Low’, ‘Moderate’, ‘High’, ‘Very High’ and ‘Severe’. It is observed that around 45% of the terrain falls under the ‘High’ to ‘Severe’ landslide susceptibility zones. Receiver Operating Characteristics (ROC) places an 85% confidence level that predicts a strong correlation between LSZ and landslide inventory dataset of the region. Thus, this study suggests that a comprehensive approach for slope failure mapping can be used to develop appropriate mitigational strategies for landslide disaster management in the socio-economic context.

Keywords: Landslide Susceptibility Zonation; Northwest Himalaya.

How to cite: Sengupta, A. and Nath, S. K.: Landslide Susceptibility & Risk Mapping for the Northwest Himalayan State of Uttarakhand, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8461, https://doi.org/10.5194/egusphere-egu22-8461, 2022.

Landslide is a disaster which is affecting countries with high-relief topography and large-amount precipitation. A typical example is Bhutan. Major roads are sometimes blocked by landslides caused by monsoon-derived intensive precipitation events. To understand where landslide is prone to occur in Bhutan, we need geographical assessments focused on both of spatial distribution of past landslide mass movements and that of precipitation. In this study, landslide features were delineated from high-resolution satellite imagery and Digital Surface Model (DSM) collected by the Advanced Land Observing Satellite (ALOS) operated by the Japan Aerospace Exploration Agency (JAXA). We define three domains located in different river basins as our study site. They are located in the Mangde river tributary, the Wang river tributary, and the Drangme river tributary. Multiple geographical parameters were calculated from ALOS-derived DSM data; i.e. elevation, slope angle, distance from the river, curvature, topographic wetness index (TWI), stream power index (SPI), and sedimentary transport index (STI). Frequency ratio (FR) was calculated by the number of pixels in each class of parameter to evaluate the geographic conditions that are known to be associated with landslides.

The results show that the FR was greater in places with (1) lower elevation, (2) closer distance from the river relative to the entire watershed, and that landslides are more likely to occur under these conditions in all three study areas. The larger FR at lower elevations is presumably due to other factors, such as weathering, which are affected by elevation. The finding that the FR was larger in the area closer to the river is explained by a hypothesis that erosion of the lower part of the slope reduces the stability of the slope and makes landslides more likely to occur. In addition, representative values of geographical parameters in the study areas were compared with each other. The Drangme river tributary area with the smallest elevation and distance from the river has the largest percentage of landslide features. They indicate the same trend as (1) and (2). Thus, elevation and distance from river are important parameters to know landslide prone area in these districts.

How to cite: Tada, K. and Nagai, H.: Relationship between spatial distribution of landslides in Bhutan and geographical parameters, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1857, https://doi.org/10.5194/egusphere-egu22-1857, 2022.

Northeast India is the most seismically active region being located in Seismic Zone-V and experienced liquefaction phenomenon triggered by large earthquakes with maximum MM Intensity of X. The 1950 Assam earthquake of M_w 8.7, 1897 Shillong earthquake of M_w 8.1, 1869 Cachar earthquake of M_w 7.4 and 1988 India-Burma border earthquake of M_w 7.2 reportedly induced scattered liquefaction phenomenon with the surface exposure of sand boils, ground subsidence and lateral spreading in the Northeast India region. Having a shallow groundwater condition in major populated areas of the region located on the alluvium-rich Bramhaputra river system with deltaic plains, lacustrine swamp and marsh geomorphological conditions, Northeast India region presents a strong case for systematic liquefaction potential modelling using modern multivariate techniques. In the present investigation, we delivered synthesised bedrock ground motion for the aforementioned earthquakes using finite fault stochastic simulation followed by 1-D non-linear/equivalent linear site response analysis using DEEPSOIL module for Site Amplification and Peak Ground Acceleration assessment at the surface. Factor of Safety (FOS), Liquefaction Potential Index (LPI), Probability of Liquefaction (P_L), and Liquefaction Risk Index (I_R) are estimated to make a more subtle understanding of the severity of liquefaction under the impact of earthquake loading and also to predict deterministic liquefaction scenario in the event of a surface-consistent probabilistic seismic hazard condition at 10% probability of exceedance in 50 years with a return period of 475 years. From the results, it is observed that, ‘Severe’ (LPI>15)  liquefaction susceptible zone exists around the cities of Guwahati and Digboi in Assam, while Silchar and Jorhat are lying in ‘High’ (5<LPI≤15) liquefaction potential zone. Imphal, Agartala, and Itanagar are the other major cities that fall under the ‘moderate’ liquefaction potential (0<LPI≤5) zone. The entire Northeast India region has been classified into ‘Severe’, ‘High’, ‘Moderate’ and ‘Non-liquefiable’ zones based on LPI distribution while the Liquefaction Risk map classified the terrain into ‘Low (I_R≤20)', ‘High (20<I_R≤30)’ and ‘Extremely High (I_R>30)’  Risk zones. The results of this investigation are very useful to identify liquefaction susceptible areas, as well as for future development and planning of cities against liquefaction failure.

 Keywords: Northeast India, Liquefaction, Factor of Safety, Liquefaction Potential Index, Liquefaction Risk Index, Landslide Susceptibility.

How to cite: Madan, J., Nath, S. K., and Srivastava, A.: Liquefaction Potential Assessment of Northeast India Region: Its earthquake and deterministic scenario, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11104, https://doi.org/10.5194/egusphere-egu22-11104, 2022.

Northeast India region presenting the most complex neotectonic assemblage is one of the world’s deadliest seismic territory being struck time and again by devastating earthquakes like the 1897 Shillong earthquake of M_w 8.1, 1934 Bihar-Nepal earthquake of M_w 8.1, 1950 Assam earthquake of M_w 8.7, and 1988 Burma-India border earthquake of M_w 7.2 being triggered from the Shillong, Eastern Himalaya, Mishmi tectonic block and Eastern Boundary zones. Ground motion of an impending earthquake in the Northeast India region is amplified due to trapping up of incident energy in the overburden soft sediments/soils thus necessitating site classification and its characterization to understand Seismic Hazard potential of the region. Shear wave velocity (V_s³⁰) is estimated from empirical relation obtained through nonlinear regression analysis of geology, geomorphology, slope and landform in conforming to NEHRP and UBC nomenclature which together with measured (V_s³⁰)  and liquefaction susceptibility assessment  classifies the region into Site Class A, B, C1, C2, C3, C4, D1, D2, D3, D4, E and F. 1-D nonlinear/equivalent linear site response analysis performed using DEEPSOIL package estimates spectral  site amplification of  4.28 in E/F), 3.64 in D4, 2.95 in D3), 2.91 in D2, 2.80 in D1, 2.71 in C4, 2.29 in C3, 2.16 in C2, 1.98 in C1 and 1.53 in B at corresponding predominant frequencies of 0.76Hz (in E/F), 1.05Hz (in D4), 1.1Hz (in D3), 2.21Hz (in D2), 2.95Hz (in D1), 3.0Hz (in C4), 3.37Hz (in C3), 3.45Hz (in C2), 5.41Hz (in C1) and 4.42Hz (in B) along with the absolute site amplification factor 2.1  in E/F, 1.93 in D4, 1.9 in D3, 1.85 in D2, 1.81 in D1, 1.78 in C4, 1.71 in C3, 1.68 in C2, 1.6 in C1 and 1.56 in B respectively. Surface Consistent Probabilistic Seismic Hazard Assessment of this region for 10% probability of exceedance in 50 years with a return period of 475 years considered both polygonal and tectonic seismogenic sources, wherein the entire region predicted Peak Ground Acceleration (PGA) variation within 0.34-1.88g placing Dispur in the ‘Severe’ hazard regime (PGA:1.5-1.88g) while  Kohima, Shillong, Itanagar and Imphal are in the ‘Moderate’ to ‘High’ hazard (PGA:0.73-1.12g), but Agartala, Aizawal and Gangtok in the ‘Low’ hazard (PGA:0.34-0.73g) domain correlating well with the isoseismal distributions of the great historical earthquakes impeded in this region. The assessment is expected to be useful for updating the urban development plan, developing design principles for future earthquake-resistant structures.

Keywords: Northeast India; Shear Wave Velocity; Site Class; Peak Ground Acceleration; Site Characterization.

How to cite: Srivastava, A., Nath, S. K., and Madan, J.: Site Characterization and Assessment of Probabilistic Seismic Hazard in Northeast India Region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10956, https://doi.org/10.5194/egusphere-egu22-10956, 2022.

The Indian Himalayan Region possesses a unique place among the world's mountain ecosystems. Being a geographic young region and tectonically active, it is subject to multiple hazards and has seen a significant loss of life and property each year. Historically, the Himalayas have been subject to various disasters (earthquakes, landslides, floods, etc.), resulting in devastating socio-economic effects on the country's population, further straining an already stressed economy.

Haridwar, the most populous city in Uttarakhand, attracts tourists from all over the world. It is a state in northern India with young mountains and is affected by multiple disasters every year. Many national and international organizations are doing disaster risk reduction research, studies, and initiatives in the Himalayas.

Educational institutions, such as schools, act as lifeline structures in the case of a crisis. As a result, it's critical to protect these structures for those who rely on the school as a disaster shelter and help center. Schools and hospitals, which are considered lifeline structures, play a critical role in the aftermath of disasters. The essential elements to recognize are coping capability, multi-hazard vulnerability, and their risk should be readily available for better planning and decision-making.

In Haridwar District, multi-hazard risk assessment assessments were undertaken at 50 schools (with 285 building blocks) with the same goal. The hazard assessment is divided into two types: building-level surveys that include Rapid Visual Screening (RVS), Non-Structural Risk Assessment (NSRA), and Fire Safety Audit, and campus-level surveys that include vulnerability analysis for earthquakes, floods, industrial hazards, landslides, and wind. The Rapid Visual Screening will highlight potential weaknesses in a building's wall, roof, site condition, block geometry, foundation, seismic band availability, and other components.

This research aims to find hazard vulnerabilities and overlooked behavioral patterns in the region that raise the multi-hazard risk of the schools and the community. The analysis findings should be utilized to prioritize hazard preparedness, retrofitting, prospective building activities, and decision-making to decrease risk and prepare the school for possible catastrophes.

Multiple surveys are employed in this study to identify deficiencies/gaps in building methods and development patterns in existing Haridwar district schools, and solutions for risk assessment and retrofitting are proposed based on the findings. The research findings can be utilized to prioritize disaster preparedness, retrofitting, future building practices, and decision-making to lower risk and better prepare the school for future calamities.

How to cite: Chouhan, S., Narang, A., and Mukherjee, M.: Multi-Hazard Risk Assessment of Schools in Lower Himalayas: Haridwar District, Uttarakhand, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4333, https://doi.org/10.5194/egusphere-egu22-4333, 2022.

Himalayan region is a critical part of the globe. In recent years,  vegetation cover in this region is undergoing considerable changes attributed ongoing to climatic and anthropogenic factors. The present study aims to capture the interannual vegetation changes over 19 years and explore how topographic and climatic variables contribute to the observed changes. Satellite-derived Normalized Difference Vegetation Index (NDVI) dataset (2001–2019) was used to examine the spatio-temporal patterns of vegetation in Uttarakhand state in the Indian western Himalayas. Further analysis explored variation across elevation, temperature, precipitation, and vegetation types. Most parts of the Uttarakhand region experienced increasing NDVI trends, particularly in the Needleleaved Evergreen and Broadleaved Deciduous forest types; however, negative trends were observed in shrublands.

How to cite: Singh, K. N., Nocke, T., Shukla, R., Joshi, P. K., Agarwal, A., and Kurths, J.: Quantifying emerging patterns of greening and browning in the Himalayan region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8654, https://doi.org/10.5194/egusphere-egu22-8654, 2022.