Sunkoshi River Research Articles

Quantifying Streamflow Prediction Uncertainty Through Process‐Aware Data‐Driven Models

ABSTRACTThe hydrological model simulation accompanied with uncertainty quantification helps enhance their overall reliability. Since uncertainty quantification including all the sources (input, model structure and parameter) is challenging, it is often limited to only addressing model parametric uncertainty, neglecting other uncertainty sources. This paper focuses on exploiting the potential of state‐of‐the‐art data‐driven models (or DDMs) in quantifying the prediction uncertainty of process‐based hydrological models. This is achieved by integrating the robust predictive ability of the DDMs with the process understanding ability of the hydrological models. The Bayesian‐based data assimilation (DA) technique is used to quantify uncertainty in process‐based hydrological models. This is accomplished by choosing two DDMs, random forest algorithm (RF) and support vector machine (SVM), which are distinctly integrated with two process‐based hydrological models: HBV and HyMOD. Particle filter algorithm (PF) is chosen for uncertainty quantification. All these combinations led to four different process‐aware DDMs: HBV‐PF‐RF, HBV‐PF‐SVM, HyMOD‐PF‐RF and HyMOD‐PF‐SVM. The assessment of these models on the Baitarani, Beas and Sunkoshi river basins exemplified an improvement in the accuracy of the daily streamflow simulations with a reduction in the prediction uncertainty across all the models for all the basins. For example, the nash‐sutcliffe efficiency improved by 54.69% and 10.61% in calibration and validation of the Baitarani river basin, respectively. Equivalently, average bandwidth improved by 79.37% and 71.59%, respectively. This signified the (a) potential of the DDMs in quantifying and reducing the prediction uncertainty of the hydrological model simulations, (b) transferability of the model with an appreciable performance irrespective of the choice of basins having varying topography and climatology and (c) ability to perform significantly irrespective of different process‐based and DDMs being involved, thereby ensuring generalizability. Thus, the framework is expected to assist in effective decision‐making, including various environmental management and disaster preparedness.

Assessment of Landslide in Singati Sub-Watershed of the Sunkoshi River Basin, Nepal

Assessment of Landslide in Singati Sub-Watershed of the Sunkoshi River Basin, Nepal

Assessing Hydropower Potential in Nepal's Sunkoshi River Basin: An Integrated GIS and SWAT Hydrological Modeling Approach.

This study assessed the hydropower potential of a mountain watershed within the Sunkoshi River basin in Sindhupalchok, Nepal, utilizing geographic information systems (GIS) and the soil and water assessment tool (SWAT) hydrological model. Topographical, soil, land use, meteorological, and discharge data were employed to assess the study area for the appropriateness of hydropower generation. SWAT was utilized to delineate the Sunkoshi basin into 23 distinct subbasins and involved the creation of a detailed river network, incorporating various hydrological attributes including stream links, stream order, stream length, and slope gradient. After that, it was employed to simulate river discharges within these subbasins. The Sequential Uncertainty Fitting Version 2 (SUFI-2) algorithm, integrated within the SWAT Calibration and Uncertainty Program (SWAT-CUP), was employed to calibrate and validate the model. This step involved the adjustment of 25 selected parameters to enhance the model's accuracy and reliability in representing the hydrological processes of the Sunkoshi basin. Model performance was assessed utilizing three well-established efficiency criteria: coefficient of determination (R2 = 0.79), Nash-Sutcliffe efficiency (NSE = 0.73), and percent bias (PBIAS = 17.59). The study identified 36 sites across streams of order 3, 4, and 5 as having potential for hydropower generation. The hydropower potential at each identified site was evaluated using estimated stream flow and topographical head at various probability of exceedance (PoE) levels (40%, 45%, 50%, and 60%). The aggregate hydropower potential of the basin was quantified, yielding a potential of 371.30 MW at a 40% PoE. The findings suggest that an integrated approach combining SWAT-based hydrological modeling within a GIS can accurately assess a river basin's hydropower potential and provide insights into further evaluation of the comprehensive environmental assessment of the fragile Himalayan watersheds.

Ground water availability assessment for a data-scarce river basin in Nepal using SWAT hydrological model

Abstract Mountainous river basins are expected to experience significant seasonal fluctuations in water supply due to climate change. Thus, hydrological modeling becomes further challenging while accounting for data-scarce mountainous basins observing climate change impacts. In this study, Soil and Water Assessment Tool (SWAT) was used to predict hydrological flow in the Sunkoshi River Basin (SRB) based on daily rainfall and temperature data spanning 36 years. The specific objectives of this study were: (i) to use the SWAT model to simulate the long-term hydrological response, (ii) to generate spatially distributed rainfall–runoff and subbasin-wise water balance components using well-established performance indicators. Calibration and validation at the outlet of the study area were successful, with the values of R2/Nash–Sutcliffe efficiency (NSE) calculated as 0.91/0.82 for monthly data and 0.79/0.73 for daily data. In the validation phase, the values of R2/NSE were 0.91/0.84 for monthly data and 0.82/0.75 for daily data, respectively. This study predicted the average yearly flow and precipitation at the SRB outlet to be 279 m3/s and 368.25 mm, respectively. Approximately, 30% of water loss was attributed to evapotranspiration, 18% to runoff, and 30% to lateral flow. The findings of this study will contribute to water resource management.

How Effective Is Inter-Basin Transfer to Manage Temporal Variation Of “Too Much” And “Too Little” Water Conditions for Irrigation in a Himalayan Basin?

Better irrigation facilities leading to “more crop per drop” are today’s needs. This study examines how the overall water balance of Sunkoshi, Marin, and Bagmati Basins will be altered by implementing the Sunkoshi-Marin Diversion Project (SMDP) to fulfill the unmet demands of the Bagmati Irrigation Project (BIP) in the Bagmati Basin via the Marin Basin in Nepal. The specific objectives are to: i) quantify “too much” and “too little” water peculiar to the study basin based on historical data; ii) evaluate water availability, irrigation water requirement, and deficits with and without SMDP; and iii) provide evidence-based recommendations on the effectiveness of the SMDP considering hydropower generation, irrigation and low- and high-flow conditions. Thirty years of historical daily flow data (two gauging stations) and precipitation data (15 stations) were obtained from the Department of Hydrology and Meteorology. Other spatial input data were acquired from relevant authorized sources. Water availability has been estimated using flow at the diversion sites while the irrigation requirements have been calculated based on secondary information. Results show that additional water is not at all required for Marin and Bagmati Basins in the monsoon season. Rather, the diverted water increases the flood hazard. Moreover, the contribution of hydropower from SMDP to the national energy demand is insignificant in the monsoon. However, the SMDP was found to play an important role in meeting the irrigation deficits during the dry season. Its contribution to hydropower production in the lean period is also commendable. However, the proposed irrigation requirement of the BIP cannot be met even after the implementation of the SMDP. More importantly, there is a high probability of the intended diversion flow not being available in the Sunkoshi River (donor) which could have severe consequences downstream. Therefore, additional options for conjunctive use need to be explored.

Engineering geological characteristics and failure mechanics of Jure rock avalanche, Nepal

IntroductionThe rock avalanches are a frequent and disruptive phenomenon in the Himalayas and other mountain chains. To minimize future losses, it is essential to investigate the engineering geological causative factors and mechanism of these mass wasting events.Study areaThe present work is aimed at assessing the failure mechanism of the disastrous 2014 Jure rock avalanche along Araniko Highway, Northern Nepal. The event had blocked the Sunkoshi River and blocked an economically significant route to China.Geotechnical properties and analysisInitially, rockmass characterization and intact strength attribute were determined for the site to classify the failure zone. The parameters measured and obtained from the field and laboratory were integrated into the analytical models to obtain a conclusive interpretation of the failure mechanism. Structural, kinematic, and key block theory analyses have been carried out for decipher the evolution of the failure zone.Results and discussionRock mass was found to be of fair quality, however, the structural instabilities and the presence of water has led to a progressive failure. Movement of the key block and subsequent sliding of wedges and foot failure appears to be a possible failure mechanism.ConclusionThe present research explores the contributory engineering geological aspects of the Jure rock avalanche. The investigation results can be used to tackle similar large scale rock avalanches in similar geological terrains and thus minimizing the losses.

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

AbstractOccurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time.

Climate change impact on water availability in the Himalaya: Insights from Sunkoshi River basin, Nepal

Changing streamflow is one of the most visible consequences of climate change. In this study, the Soil and Water Assessment Tool (SWAT) model was used to investigate the significant effects of climate change on streamflow in a Himalayan River. We incorporated information from several Global Climate Models (GCM), considering two climate scenarios: SSP 2.45 and SSP 5.85, which are part of Shared Socioeconomic Pathways (SSPs) for the future periods of 2022–2030 and 2031–2050. Substantial patterns in temperature and rainfall changes were identified using ensemble modeling. Under both SSPs 2.45 and 5.85, the results indicated a rising trend in temperatures from January to June, a decline from June to September, and a marginal increase from October to December. Additionally, annual rainfall, real evapotranspiration, and river flow are anticipated to rise by 17.67–21.79%, up to 0.93%, and 23–53%, respectively in the upper region of the study basin. Conversely, across the two future periods and scenarios, the lower region is depicted to have a decline in rainfall of 20.84–36.34%, evapotranspiration of up to 4.35%, and river flow of 38–65%. These findings will be invaluable for the design and construction of climate-resilient water resources related projects in the Himalayan regions, such as Nepal.

Factors Affecting the Farming System of Gwaltar (Sindhuli) and Birta (Ramechhap) Village, Nepal


 
 
 Farming in Nepal is subsistence-based and is mostly integrated with livestock. Farming is seasonal and weather dependent. Different factors of agricultural system such as soil, water, livestock, labor, climate change and other resources within a given environment have affected farming. Primary data was collected using the tools like household survey, key informants’ interview, focus group discussion and field observations. In order to select the households for the household questionnaires, a random sampling technique was carried out. The study area consists of two villages viz. Birta and Gwaltar divided by a Sunkoshi River, Gwaltar having wet land on the other hand, Birta having dried land and thus 30 households from each village were selected. Despite of lying the village in the same region, there is variation in the selection of crops, cropping pattern and amount of production. It has found that policy, infrastructure, market, lack of irrigation water, climate change etc. are some of the major constraints for Birta while these factors affecting the farming in Gwaltar was less. Water scarcity in Birta was increasing, affecting the agricultural production and resulting in food insecurity. They were accessed to limited irrigation, low income level, limited institut.ional capacity resulting these effects in crop types, production, household food security and household income.
 
 

Flood risk mapping and analysis: A case study of Andheri Khola catchment, Sindhuli district, Nepal

Nepal is one of the world's most vulnerable countries to a variety of risks, including severe floods. This could result in the loss of lives and property, the relocation of people, the damage of physical infrastructures, homes, and the disruption of people's socioeconomic functions and the country's economy in a variety of ways. River flooding is caused by heavy monsoon rainfall, weak geology, unplanned infrastructure construction along the embankments, and mining in upstream riverbeds. The Andheri Khola (river) is a tributary of the Sunkoshi River that frequently floods, affecting the inhabitants along the way. In Nepal, little effort has been made to comprehend the flood risk in tiny catchment regions such as Andheri Khola, despite the fact that this sort of small catchment is affecting Nepal's numerous new rising towns and urban areas in many ways. To analyse the one-dimensional flood plain, HEC-RAS, Ras Mapper, and ArcGIS were used. The WECS/DHM approach was used to estimate flood frequency in different return periods in order to determine the flood risk in the research area. The study finds that the floods of 2, 50, 100, and 1000 years return periods cover a maximum of 35, 41.9, 42.7, and 49.72 hectare area, respectively. The majority of the flooded sites had water depths of more than 3 meters. More than 70% of the sandy area in the study region is prone to flooding. Furthermore, the cultivated areas are located in a low to moderate risk area.

Assessment on the 2014 Jure Landslide in Nepal – a disaster of extreme tragedy

The landslides kill many people every year in Nepal and in the Himalayan region, which devastates society in the local area. A famous living memory of an example of landslide induced devastation was the landslide at Tinau river that took place in early September 1981 damming the river completely, which breached after few days causing huge flood downstream. The flood swept away concrete tower of the Tinau Hydropower Project, two suspension bridges, one concrete bridge at Butwal, part of Butwal city was completely damaged and many hundred people lost their life. Similarly, on the night of 2nd August 2014 (02:30 AM), a huge landslide, famously known as “Jure Slide”, took place along Sunkoshi River valley on Araniko Highway that connects Kathmandu with Tibet. The landslide killed 156 people who were on sleep in their houses, savaged 120 houses, partially damaged 37 more, dammed Sunkoshi river creating an artificial dam of approximately 50 m high and an approximately 3 km long reservoir was formed. A huge flood occurred after partial breach of the dam damaging barrage structures of the Sunkoshi Hydropower Project located about one kilometre downstream from the slide area. It took over two months to drain artificially created reservoir. The main aim of this manuscript is to describe and evaluate Jure landslide. Critical aspects on the causes of landslide will be highlighted giving emphasis on topography, geology, rock mass, monsoon, and earthquake.

WITHDRAWN: Identification of landslide/Man-made structures along transboundary rivers

Roughly 40 percent of the total populace lives in waterway and lake bowls that contain at least two nations, and maybe considerably more essentially, more than 90 percent lives in nations that share basins. It is important for a downstream country to have sufficient information on the changes in the upstream river basin to understand the water availability in future and to plan necessary measures. Natural disasters like landslides, man-made structures like construction of Dams, etc, will directly influence the water availability for the downstream countries. This study focuses on the obstructions to flow of water in the upstream areas/countries, with reference to landslides and man-made structures, based on remote sensing data. Brahmaputra river in Tibet area is chosen to identify man-made structure. The objective is to identify Landslide/Man-made structures and check for the temporal consistencies across the trans-boundary rivers using multi-temporal satellite data. Two methods, Spatial profiling and Buffering were employed to understand the changes along the river. Spatial profiling provides the changes along the river in a linear manner, whereas buffering method provides the river stretch-wise details of the area. The methods have been validated using IRS-AWiFS for Brahmaputra river and MODIS, Landsat-8 data of the Sun koshi river. It is concluded that satellite remote sensing is quite useful in detecting these changes in the upstream river areas, which are inaccessible for the down-stream countries.

Characterizing the evolution life cycle of the Sunkoshi landslide in Nepal with multi-source SAR data

A catastrophic landslide disaster happened on 2 August 2014 on the right bank of Sunkoshi River in Nepal, resulting in enormous casualties and severe damages of the Araniko highway. We collected multi-source synthetic aperture radar (SAR) data to investigate the evolution life cycle of the Sunkoshi landslide. Firstly, Distributed Scatterers SAR Interferometry (DS-InSAR) technology is applied to analyze 20 ALOS PALSAR images to retrieve pre-disaster time-series deformation. The results show that the upper part, especially the top of the landslide, has long been active before collapse, with the largest annual LOS deformation rate more than − 30 mm/year. Time series deformations measured illustrate that rainfall might be a key driving factor. Next, two pairs of TerraSAR-X/TanDEM-X bistatic data are processed to identify the landslide affected area by intensity change detection, and to generate pre- and post-disaster DSMs. Surface height change map showed maximum values of − 150.47 m at the source region and 55.65 m in the deposit region, leading to a debris volume of 5.4785 ± 0.6687 million m3. Finally, 11 ALOS-2 PALSAR-2 and 82 Sentinel-1 SAR images are analyzed to derive post-disaster annual deformation rate and long time series displacements of the Sunkoshi landslide. The results illustrated that the upper part of the landslide were still in active deformation with the largest LOS displacement velocity exceeding − 100 mm/year.

A sudden outburst of erosion

Geomorphology Glacial lake outburst floods (GLOFs) are exactly what they sound like. The sudden emptying of a glacial lake in high-topography regions like the Himalaya can quickly destroy everything in its path. Cook et al. intercepted a GLOF in the Bhotekoshi and Sunkoshi river valleys in central Nepal as they were monitoring the region in the aftermath of the 2015 Gorkha earthquake. They found that a massive amount of erosion occurred during the outburst flood, which suggests that GLOFs may be the primary factor in landscape evolution for these regions. Science , this issue p. [53][1] [1]: /lookup/doi/10.1126/science.aat4981

Glacial lake outburst floods as drivers of fluvial erosion in the Himalaya.

Himalayan rivers are frequently hit by catastrophic floods that are caused by the failure of glacial lake and landslide dams; however, the dynamics and long-term impacts of such floods remain poorly understood. We present a comprehensive set of observations that capture the July 2016 glacial lake outburst flood (GLOF) in the Bhotekoshi/Sunkoshi River of Nepal. Seismic records of the flood provide new insights into GLOF mechanics and their ability to mobilize large boulders that otherwise prevent channel erosion. Because of this boulder mobilization, GLOF impacts far exceed those of the annual summer monsoon, and GLOFs may dominate fluvial erosion and channel-hillslope coupling many tens of kilometers downstream of glaciated areas. Long-term valley evolution in these regions may therefore be driven by GLOF frequency and magnitude, rather than by precipitation.

Rehabilitation of Sunkoshi Small Hydropower Plant (2.6 MK), Nepal after Sequences of Natural Disaster

Sunkoshi Small Hydropower Plant (SSHP) is a run-of-river type project constructed in Sunkoshi River located in Sindhupalchowk District of Bagmati zone of Central Development Region, Nepal. The plant started its commercial operation since March 2005. During the year 2014, 2015 and 2016 the plant faced series of natural disaster events; landslide at Jure village on 2 August 2014, earthquake (7.8 magnitude) on 25 April 2015, landslide dammed flood in Sunkoshi River on 11 August 2015 and Glacier Lake Outburst Flood (GLOF) in Sunkoshi River on 5 July 2016. The Jure landslide created 55 m dam across Sunkoshi River. Thus created landslide dammed lake inundated the powerhouse of SSHP for 36 days. About seven million cubic meters of water was reserved in the lake. The event damaged entire powerhouse building, Electromechanical Equipment (EM), tailrace culvert, portion of steel penstock pipe and staff quarter. The rehabilitation works after Jure landslides included strengthening of powerhouse building and tailrace culvert, winding of generators, replacement of electromechanical equipment and portion of penstock pipe and change of transmission line alignment. On 25 April 2015, most of the rehabilitation works were completed. The EM experts were conducting a wet test for power generation when the power-plant was hit by other natural disaster - earthquake of 7.8 Rector Scale. The earthquake and its aftershocks followed by landslide dammed outburst flood of 11 August 2015 damaged headworks structures - formed a cavern of 56.6 m3 below gravel trap, collapsed entire powerhouse building and damaged newly installed electromechanical equipment, created several landslides along water way alignment and damaged about 900 m of penstock alignment. Some innovations in designs were introduced such as construction of sliding type saddle supports, bio-engineering combined with civil engineering structures to stabilize landslides, retrofitting of powerhouse building, use of crackamite and rock drill and avoidance of heavy masonry wall and gable wall in powerhouse. After rehabilitation works, the power plant re-operated since 4 January 2016 - after about 18 months. However, the plant was again affected by a Glacier Lake Outburst Flood (GLOF) in Botekoshi River on 5 July 2016.

Postearthquake Landslides Mapping From Landsat-8 Data for the 2015 Nepal Earthquake Using a Pixel-Based Change Detection Method

The 2015 Nepal earthquake and its aftershocks not only caused huge damage with severe loss of life and property, also induced many geohazards with the major type of landslide which should bring continuous threats to the affected region. To gain a better understanding of the landslides induced by this earthquake, we proposed a pixel-based change detection method for postearthquake landslide mapping by using bitemporal Landsat-8 remote sensing data [May 29, 2014 (pre-earthquake) and June 1, 2015 (postearthquake)]. Two river valleys (Trishuli river valley and Sun Koshi river valley) that contain important economic arteries linking Nepal and China were selected as the study areas. Validation of the mapping results with postearthquake high-resolution images from Google Earth shows that the pixel-based landslide mapping method is able to identify landslides with relatively high accuracy, and it also approves the applicability of Landsat-8 satellite for landslide mapping with its multispectral information. The spatial distribution analysis indicates that both river valleys are substantially affected by landslides, and the situation is even more serious in the high mountain areas. Landslides are generally found in areas of high elevation and large surface slopes, with mean values above 1600 m and 30°, respectively. These findings suggest that these areas suffer greatly from these geohazards, and the threat will continue for the next few years.

ANALYSIS OF JURE LANDSLIDE DAM, SINDHUPALCHOWK USING GIS AND REMOTE SENSING

On 2<sup>nd</sup> August 2014, a rainfall-induced massive landslide hit Jure village, Sindhupalchowk killing 156 people at a distance of 70 km North-East of Kathmandu, Nepal. The landslide was a typical slope failure with massive rock fragments, sand and soil. A total of estimated 6 million cubic meters debris raised more than 100 m from the water level and affected opposite side of the bank. The landslide blocked the Sunkoshi River completely forming an estimated 8 million cubic meter lake of 3km length and 300-350m width upstream. It took nearly 12 hour to fill the lake and overflow the debris dam. The lake affected five Village Development Committees (VDC) including highway, school, health post, postal service, police station, VDC office and temple upstream. The bottom of the dam was composed of highly cemented material and the derbies affected Sunkoshi hydropower downstream. Moreover, it caused the potential threat of Lake Outburst Flood. The lake was released by blasting off part of the landslide blockade and facilitated release of water from the lake. With the help of Remote Sensing (RS), series satellite images were used to identified, compared with previous state and quick estimation of potential treat was analysed. Using geographic information System (GIS) technology, estimation of volume, affected households, service centres, parcels etc. in the area was possible. In such hilly regions where disaster are very frequent, using GIS and RS technology comes very handy for immediate planning and response.

ANALYSIS OF JURE LANDSLIDE DAM, SINDHUPALCHOWK USING GIS AND REMOTE SENSING

Abstract. On 2nd August 2014, a rainfall-induced massive landslide hit Jure village, Sindhupalchowk killing 156 people at a distance of 70 km North-East of Kathmandu, Nepal. The landslide was a typical slope failure with massive rock fragments, sand and soil. A total of estimated 6 million cubic meters debris raised more than 100 m from the water level and affected opposite side of the bank. The landslide blocked the Sunkoshi River completely forming an estimated 8 million cubic meter lake of 3km length and 300-350m width upstream. It took nearly 12 hour to fill the lake and overflow the debris dam. The lake affected five Village Development Committees (VDC) including highway, school, health post, postal service, police station, VDC office and temple upstream. The bottom of the dam was composed of highly cemented material and the derbies affected Sunkoshi hydropower downstream. Moreover, it caused the potential threat of Lake Outburst Flood. The lake was released by blasting off part of the landslide blockade and facilitated release of water from the lake. With the help of Remote Sensing (RS), series satellite images were used to identified, compared with previous state and quick estimation of potential treat was analysed. Using geographic information System (GIS) technology, estimation of volume, affected households, service centres, parcels etc. in the area was possible. In such hilly regions where disaster are very frequent, using GIS and RS technology comes very handy for immediate planning and response.

Hazard assessment of the formation and failure of the Sunkoshi landslide dam in Nepal

A massive landslide occurred at Jure village of Nepal on August 2, 2014. The landslide mass blocked the Sunkoshi River and created a landslide dam. Thirty-seven days after the formation, the Sunkoshi landslide dam breached on September 7, 2014. In this study, flood hazards due to the formation and failure of the Sunkoshi landslide dam were analyzed. Flood inundation due to impounded water in the upstream river valley of the dam was also analyzed. The outburst discharge from the dam breach on September 7, 2014 and flood characteristics along the downstream river valley were computed by using an erosion-based dam breach and flow model. Flood inundation maps were prepared by using the results obtained from the numerical simulations and geographical information system tools. The peak flow discharge of the Sunkoshi landslide dam outburst at the dam outlet was found to be 6436 m3/s, and the estimated time of the dam breach was found to be about 26 min. The estimated volume of water released from the impounded water in the upstream river valley of the dam was approximately 4.5 million m3. The calculated peak flow velocity and maximum flow depth along the river ranged mostly from 3 to 11 m/s and from 4 to 10 m, respectively. The dam breach had considerable impact on areas more than 30 km downstream from the dam.