Abstract: Function-as-a-Service (FaaS) platforms and "serverless" cloud computing are becoming increasingly popular due to ease-of-use and operational simplicity. Current FaaS offerings are targeted at stateless functions that do minimal I/O and communication. We argue that the benefits of serverless computing can be extended to a broader range of applications and algorithms while maintaining the key benefits of existing FaaS offerings. We present the design and implementation of Cloudburst, a stateful FaaS platform that provides familiar Python programming with low-latency mutable state and communication, while maintaining the autoscaling benefits of serverless computing. Cloudburst accomplishes this by leveraging Anna, an autoscaling key-value store, for state sharing and overlay routing combined with mutable caches co-located with function executors for data locality. Performant cache consistency emerges as a key challenge in this architecture. To this end, Cloudburst provides a combination of lattice-encapsulated state and new definitions and protocols for distributed session consistency. Empirical results on benchmarks and diverse applications show that Cloudburst makes stateful functions practical, reducing the state-management overheads of current FaaS platforms by orders of magnitude while also improving the state of the art in serverless consistency.

Abstract: The presence of a sparse rain gauge network in complex terrain like Himalaya has encouraged the present study for the concerned evaluation of Indian Meteorological Department (IMD) ground-b...

Abstract: Flash flood is an uncertain and most catastrophic disaster worldwide that causes socio-economic problems, devastation and loss of infrastructure. One of the major triggering factors of flash floods is the extreme events like cloudburst that causes flooding of area within a short span of time. Therefore, this study aims to understand the variations in hydro-meteorological variables during the devastating Kedarnath cloudburst in the Uttarakhand, India. The hydro-meteorological variables were collected from the global satellites such as Moderate Resolution Imaging Spectroradiometer, Tropical Rainfall Measuring Mission, modelled datasets from Decision Support System for Agrotechnology Transfer and National Center for Environmental Prediction (NCEP). For the validation of satellite meteorological data, the NCEP Global analysis data were downscaled using Weather Research and Forecasting model over the study area to achieve the meteorological variables’ information. The meteorological factors such as atmospheric pressure, atmospheric temperature, rainfall, cloud water content, cloud fraction, cloud particle radius, cloud mixing ratio, total cloud cover, wind speed, wind direction and relative humidity were studied during the cloudburst, before as well as after the event. The outcomes of this study indicate that the variability in hydro-meteorological variables over the Kedarnath had played a significant role in triggering the cloudburst in the area. The results showed that during the cloudburst, the relative humidity was at the maximum level, the temperature was very low, the wind speed was slow and the total cloud cover was found at the maximum level. It is expected that because of this situation a high amount of clouds may get condensed at a very rapid rate and resulted in a cloudburst over the Kedarnath region.

Abstract: Flooding events are common at the lower region (Terai) of Nepal in the summer monsoon months (June–August). However, large destruction and heavy floods were observed at Banke-Bardiya districts (western Terai) of Nepal on June 16, 2016 and August 13, 2017. So, this study was carried out to find the causes of these extreme events and probable solution for flood forecasting. We focused on satellite images of these extreme events of those days, and also conducted field observations to measure discharge data and associated impacts of flooding. Satellite-based real- time images were downloaded from various sources, such as NASA and Digital Globe Archive wave systems. NASA cloud images clearly detect the system of cloudburst, cyclone, and smaller tornadoes within a short span of time which caused severe flooding. Uncertainty of weather observations, lack of modern forecasting tool like a Global Flood Awareness System (GloFAS), and trained manpower for the forecasting of extreme events are the existing scenario in the field of hydro-meteorology in Nepal.

Abstract: The entire Himalaya is highly vulnerable to geo-hydrological disasters, among which earthquakes, cloudburst triggered flashfloods and debris flows, landslides and mass movements are prominent. An inventory of these disasters has prepared and presented in this chapter. Forest fires are frequent, causing to heavy loss of biodiversity. Frequency and intensity of these geo-hydrological disasters have increased recently. It has been observed that increasing intensity of these hazards is also due to climate variability and change.

Abstract: The Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 precipitation data has been extensively used for inter-comparison with observations and model validation. The rain distribution over the Northwest Himalaya (NWH) were found to be accurate with a strong positive correlation of 0.88 between TRMM and India Meteorological Department (IMD) station data, supporting the use of 3B42 V7 for the study of extreme rainfall events (ERE’s) over the region. However, many high resolution satellite data sets were made available in the recent past and their potential have not been evaluated for ERE’s like cloudbursts in the NWH. The present endeavor aims to provide guidance to the choice of global precipitation data sets (GPDs). In particular, this study is conducted to evaluate three recent satellite-based rainfall products, i.e. Global Precipitation Measurements (GPM), Indian National Satellite System (INSAT 3D), and CPC Morphing Technique (CMORPH), against the highly used TRMM-RT 3B42 V7 precipitation data for the estimation of rainfall episodes in the recent years (2014–2016). Our results reveal that the magnitude of precipitation and location of peak rainfall are biased in INSAT 3D, whereas CMORPH and the high resolution GPM product capture it with relatively higher values of the employed statistical metrics. Also, the rainfall estimates from GPM and CMORPH are in good agreement with TRMM for cloudbursts events. Particularly, high resolution GPM is useful for monitoring the extreme rainfall event in the region.

Abstract: Although, the cloudburst occurs in the monsoon season but it may also happen in the premonsoon showers in Himalayan mountainous region of Uttarakhand sate. Every year, this causes massive loss of life, property, infrastructure, agricultural lands and other facilities. The earlier disasters show that the growing outbreak of rains and its associated flash floods, debris flows and landslides are important reasons for damages and destructions. It is not yet possible to find out, when and in which area the same events will be occurred, particularly in Uttarakhand region. Change in climate due to Global warming is the major concern for these extreme events. However, it's a topic of detailed research to assess impact of climate change on extreme rainfall pattern. Being a geo-dynamically active young Himalayan belt, here the rocks are highly faulted, folded, jointed and fractured resulting in an excessive amount of mass movements and environmental degradations. So, inherent geology, deep regolith cover and high relief difference are more common in the area and the same increases the vulnerability. In addition, human interventions in terms of construction of houses in instable mountainous slopes, over deep regolith and concentration of the streams/rivulets is enlarged the devastating potential of the disaster. It is very important to know the risk of the area to reduce the impact of the disaster. For this, it is important to know the history of previous disastrous events and take a lesson from them and plan ahead. Keeping all these things in mind, all known cloudburst events and their adverse impacts upon inhabitants of the area has been attempted to be compiled in the present study. Additionally, better land use practices also described in the same by an example. This will be help in further research to prevention, mitigation and disaster risk reduction in near future.

Abstract: Hilly regions are characterized by high spatio-temporal variations in climatic characteristic such as rainfall due to variations in the topography. Uttarakhand State is very susceptible to flooding and cloudburst occasions like one happened at Kedarnath area in June 2013. Estimation of rainfall over a hilly region is a challenging task due to scarcity of rain gauge network. Due to the existing gaps and uncertainty in the rainfall data, these regions are susceptible to disasters such as cloudburst and flash floods. Proper understanding of the precipitation patterns of these regions is required so that disaster mitigation plans can be made and implemented accordingly. Remotely sensed and improved, high-resolution rainfall data derived from Tropical Rainfall Measuring Mission (TRMM) satellite can be used as an alternative to the rain gauge observed rainfall data. However, a proper validation of the satellite-derived products is necessary before using it for various applications. This study aims to compare monthly and monsoon seasons precipitation derived product from Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) with the observed rain gauge analysis from January 1998 to December 2012. Statistical investigation was done for computing relationship of the TMPA product with the rain gauge station data. Statistical indices showing good agreements with the rain gauge data on monthly as well as monsoon seasons time scales. It was observed that the TRMM 3B43 rainfall estimates were much closer to the rain gauge data, with minimal biases. It is suggested to develop satellite precipitation retrieval algorithms by combining the topographical and local climatic factors into consideration.

Abstract: Cloudburst over Tehri district of Uttarakhand resulted in flash flood on 8 August 2019. This catastrophic event killed four persons and damaged few bridges. Results presented in this short communic...

Abstract: In recent years the explosion in high-performance computing systems and high-capacity storage has led to an exponential increase in the amount of information, generating the phenomenon of big data and the development of automatic processing models like machine learning analysis. In this paper a machine learning time series analysis was experimentally developed in relation to the paroxysmal meteorological event “cloudburst” characterized by a very intense storm, concentrated in a few hours and highly localized. These extreme phenomena such as hail, overflows and sudden floods are found in both urban and rural areas. The predictability over time of these phenomena is very short and depends on the event considered, therefore it is useful to add data driven methods to the deterministic modeling tools to get the anticipated predictability of the event, also known as nowcasting. The detailed knowledge of these phenomena, together with the development of simulation models for the propagation of cloudbursts, can be a useful tool for monitoring and mitigating risk in civil protection contingency plans.

Abstract: . The North West Himalayan basins have always been prone to hydro-meteorological disasters. Among them Beas Basin is one of the highly affected basins. Beas basin is prone to cloudburst which causes huge loss to life and property every year. Increase in these devastating events have been noticed in the recent years. Climatic change is considered as the major driver for this increased occurrence of these events in the recent past. The analysis of long-term hydrological extremes over the basin will help in understanding the pattern of the hydro-meteorological extremes and also predicting its nature in near and far future. The Variable Infiltration Capacity (VIC) model at the grid size of 0.025° × 0.025° has been used in the present study, for simulating the hydrological behaviour of the Beas Basin. The parameterization of the model inputs is derived from Remote Sensing based and field observed datasets. The model was forced with meteorological dataset of ERA-Interim for the past and present time period and CORDEX dataset for the future time period. The model was calibrated using observed discharge data of Nadaun and Sujanpur stations. The Nash-Sutcliffe model efficiency of calibrated model was achieved to be 0.77 and 0.72 and coefficient of determination (R2) was 0.80 and 0.72, respectively. The validation results of the model for the same stations shows the model efficiency to be 0.73 and 0.74 with coefficient of determination (R2) as 0.67 and 0.82, respectively. The well calibrated model was used to simulate the hydrological behaviour of historic period (1979–2000), present period (2001–2017), near future period (2018–2050) and far future period (2051–2099). The exceedance probability curve method has been utilized in estimating the flood peak value for the future time period. The flood peak discharge value for the future time period comes out to be 1050 m3/s. The hydro-meteorological extremes rate per year in each period was found to be 9, 9, 12 and 14, respectively. The hydro-meteorological extremes rate is showing increasing trend in near future and very high increase in far future. The study highlights the probability of occurrence of catastrophic events in coming future. The methodology and results of the present study can be beneficial for sustainable development of the basin to counter the effect of probable hydro-meteorological extremes in coming future.

Abstract: Hydrological characteristics of the Cold-arid trans-Himalayan region is least known due to lack of studies in the region. Hydrology of a region having around 60 mm of mean annual precipitation can be thought as a paradox. But the area have numerous glaciers constituting more than 75% of the Indian glacier resource and significant snow cover area mainly constrained over the top of the mountain range. These glaciers and snow sustain the livelihood of immediate lowland of arid valley bottom and contribute to the flow of Indus and its major tributaries like Shyok and Zanskar. These big rivers sustain livelihood of millions further downstream. Along with snow and glaciers; large extent of permafrost areas makes the hydrology of the cold-arid regions unique as compared to other regions of Indian Himalaya. Present study focus on the Ladakh range of the cold-arid system. Small glaciers with <1 km2 area is characteristic of the Ladakh and Zanskar ranges. Discharge in the stream reach between glacier and foothill is restricted around 43% days as the mountain reach of the stream freezes in winter. Summer discharge show significant reduction during the lean snow years when ground ice melt component probably contributing to the stream flow. Steep precipitation and temperature gradients are another key feature of the cold-arid system. The temperature gradient during summer months surpasses 9.8 K/km and play a key role in sustaining the mountain cryospheric system in the region. Water related disasters are also very common in the area and have varied genesis such as Cloudburst, Glacial Lake Outburst Floods, Landslide Dam Outburst Flood and glacial surge dam outburst floods. More studies are imperative to comprehend the hydrology of this area in a better way. Mass and Energy balance of bigger glaciers, Permafrost thaw modeling, Groundwater dynamics and recharge areas and climate modeling at valley- ridge scale are some of the key topic to be pursued in greater details in future.