Manabendra Saharia

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Manabendra verified their affiliation via an institutional email.

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• PhD
• Associate Professor at Indian Institute of Technology Delhi

The effects of spatial variability of rainfall, geomorphology, and climatology of precipitation and temperature on the hydrologic response remain poorly understood. This study characterizes the catchment response in terms of a variable called flashiness, that describes the severity of the flood response as the rate of rise of the unit discharge. It...

In the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial vari...

Landslides cause significant human and economic losses worldwide, with India accounting for approximately 8% of global fatalities due to landslides. Despite this severe impact, there has been no comprehensive national-scale assessment of infrastructure vulnerability to landslides in India, primarily due to the lack of high-resolution ground data. T...

Gridded precipitation products are inherently uncertain and predominantly deterministic, which limits their applicability in data assimilation systems and hydrologic modeling. This limitation is significant in developing countries such as India, where the observation network is sparse and non-uniform, topography is complex, and hydrometeorological...

Despite floods causing significant loss of life and property, datasets to characterize flooding events in developing countries such as India are not widely available, hampered by limited hydrometric records. Current flood databases are limited to continental river basins, case studies, and government reports, which doesn’t represent the diversity o...

Runoff efficiency (RE) represents the potential of a basin to generate runoff in response to precipitation and it varies based on climatology and physiography. It is a key metric that enables hydrologists to compare the hydrologic responses of basins across diverse climates and landscapes. In the large river basins of South Asia, RE plays a key rol...

Physically informed deep learning models, especially Long Short-Term Memory (LSTM) networks, have shown promise in large-scale streamflow simulations. However, an in-depth understanding of the relative contribution of physical information in deep learning models has been missing. Using a large-sample testbed of 220 catchments in hydrologically dive...

Groundwater is a critical resource for both consumption and food security in India, where groundwater management faces significant challenges due to climate change and anthropogenic activities. Although several studies explored groundwater variability in India, few have focused on the socioeconomic attribution of these changes, utilizing data from...

Landslides pose a significant threat to humans as well as the environment. Rapid and precise mapping of landslide extent is necessary for understanding their spatial distribution, assessing susceptibility, and developing early warning systems. Traditional landslide mapping methods rely on labor-intensive field studies and manual mapping using high-...

Landslides pose a significant threat to humans as well as the environment. Rapid and precise mapping of landslide extent is necessary for understanding their spatial distribution, assessing susceptibility, and developing early warning systems. Traditional landslide mapping methods rely on labor-intensive field studies and manual mapping using high-...

Flash floods are one of the most devastating natural disasters, yet many aspects of their severity and impact are poorly understood. The recession limb is related to post-flood recovery and its impact on communities, yet it remains less documented than the rising limb of the hydrograph to predict the peak discharge and timing of floods. This work i...

Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil...

Ganges and Brahmaputra, two of Asia’s most prominent rivers, have a crucial role in Southeast Asia’s geopolitics and economy and are home to one of the world’s biggest marine ecosystems. Irrigation-driven groundwater depletion and climate change affect the Ganges-Brahmaputra’s hydrology, threatening the stability of the Bay of Bengal. Here, we quan...

Soil erosion generally removes the topmost fertile layer of soil, affecting agricultural productivity on a larger scale. As a significant portion of the Indian economy depends on agricultural productivity, granular assessment of the impact of soil erosion becomes critical. However, a national-scale assessment of soil erosion and an impact classific...

Soil erosion generally removes the topmost fertile layer of soil, affecting agricultural productivity on a larger scale. As a significant portion of the Indian economy depends on agricultural productivity, granular assessment of the impact of soil erosion becomes critical. However, a national-scale assessment of soil erosion and an impact classific...

Surface flow models play a pivotal role in understanding and predicting hydrological processes, significantly influencing water resource management, flood risk assessment, and designing urban drainage systems. These models facilitate the understanding of spatial and temporal water flow patterns, addressing the complex interactions between the atmos...

In this chapter, we develop three finite element models for common contaminant transport problems. The cases represent different scenarios of contaminant transport. Advection is an important process in contaminant transport problems. For the models with an advection component, we use the velocity field data (saved a priori) of the Navier-Stokes sim...

Hydrological relationships are often complex and nonlinear, and being able to simplify and interpret data governing such relationships is a vital component of hydrologic studies. Identifying temporal patterns and trends is a very common form of graphical data analysis in hydrology. Seasonal and long-term variances in such datasets are easier to ins...

Water security is increasingly in jeopardy throughout the world. Too much water causing floods, too little water causing droughts, or poor water quality affecting health can endanger life, economy, and ecosystems. In order to detect, monitor, and mitigate these diverse problems in water and environment, we require actionable intelligence based on d...

Water contamination is a major concern in modern urban setups. It is a result of the mixing of a toxic substance, liquid or solid, with water that renders the water unusable for most purposes. In many scenarios, contamination happens accidentally, although intentional cases cannot be denied.

The first step in any investigation is to assess and delve into the dataset. It lets you understand data attributes, spot possible anomalies, and formulate well-grounded choices concerning the statistical techniques suitable for the following analysis. For instance, the nature of data—continuous or discrete, Gaussian distributed or skewed, or manif...

Integrated Development Environment (IDE) and virtual environments are two crucial components of Python programming. They are key tools in a Python developer’s toolkit, aiding in streamlining the development process, enhancing productivity, and ensuring code reliability and reproducibility.

Seepage flow models form an integral part of subsurface hydrological studies, focusing on the simulation and quantification of fluid movement through porous media such as soil, rock, and sediment. These models are critical for understanding groundwater dynamics, as they provide valuable insights into the distribution, recharge, and discharge of aqu...

Python has emerged as one of the most popular languages for applications in hydrology, environment, and climate. In this textbook, we have provided extensive codes that can be used for common data analysis and numerical modeling needs.

Forecasting a time series into the future is one of the most common applications of statistical modeling in hydrology. Typically, hydrologists are interested in forecasting streamflow and floods, which itself is a complex nonlinear phenomenon controlled by meteorological and geomorphological factors. Apart from streamflow, hydrologists typically al...

Python is the most popular programming language in the data analysis world and the growing significance of data has made mastering Python an important skill to acquire. Due to its simplicity and code readability, Python is a popular choice among beginners and professionals alike. A wide set of libraries makes it a perfect tool for tasks spanning fr...

Estimating uncertainty is an important part of hydrological data modeling. They offer a measure of confidence in model predictions and aid in decision-making under risk.

Hypothesis testing, also known as significance testing, is widely used in hydrology to determine if the data supports a particular hypothesis. It enables one to make statistical inferences about hydrological phenomena and draw conclusions from data. By designing alternative and null hypotheses, one can test the credibility of model predictions agai...

Curve fitting and regression analysis are powerful statistical tools used widely in hydrological data modeling. They can be used to model underlying relationships between data, allowing you to interpret and predict hydrological behavior under varying conditions. Curve fitting involves fitting a function on a set of data points that best represents...

Flash floods are one of the most devastating natural disasters, yet many aspects of their severity and impact are poorly understood. The recession limb is related to post-flood recovery and its impact on communities, yet it remains less documented than the rising limb of the hydrograph to predict the peak discharge and timing of floods. . This work...

Access to clean water is a fundamental human right, yet millions worldwide face the dire consequences of water scarcity and inadequate sanitation. Water inequality, characterized by disparities in access and availability of water resources, has emerged as a critical global challenge with far-reaching social, economic, and environmental implications...

Access to clean water is a fundamental human right, yet millions worldwide face the dire consequences of water scarcity and inadequate sanitation. Water inequality, characterized by disparities in access and availability of water resources, has emerged as a critical global challenge with far-reaching social, economic, and environmental implications...

Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over Indi...

Landslide susceptibility represents the potential of slope failure for given geo-environmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest. In this study, we overcome all these limitations by...

Landslide susceptibility represents the potential of slope failure for given geo-environmental conditions. The existing landslide susceptibility maps suffer from several limitations, such as being based on limited data, heuristic methodologies, low spatial resolution, and small areas of interest. In this study, we overcome all these limitations by...

Erosion and sedimentation in streams, lakes, reservoirs, and watersheds are a global issue. Runoff and flood waves erode soil and streambank material, destroying agricultural land. Erosion and sedimentation in water bodies induce soil loss in the watershed and diminish channel flow depth and conveyance. Agricultural runoff can pollute aquatic ecosy...

Soil erosion is a major environmental problem worldwide, and almost half of India’s total geographical area is susceptible to it. The Revised Universal Soil Loss Equation (RUSLE) has been widely used globally to estimate soil erosion, and Soil erodibility factor, denoted by K-factor, is an essential component of RUSLE. Although previous studies hav...

The increasing risk of floods across the globe needs focused attention because of the extensive damage to human lives and economy. A comprehensive understanding of its causative factors is of vital importance. Yet catchment characterization studies are generally limited to case studies or regional domains. A comprehensive global characterization is...

The increasing risk of floods across the globe needs focused attention because of the extensive damage to human lives and economy. A comprehensive understanding of its causative factors is of vital importance. Yet catchment characterization studies are generally limited to case studies or regional domains. A comprehensive global characterization is...

Studies of rainfall are usually based on the total amount precipitating throughout a certain period. Compared to rain rates associated with extreme events, the rain rates associated with periods when most of the rainfall occur are not studied extensively. In this study, the characteristics of daily precipitation in India are explored using two metr...

Air pollution has become one of the biggest challenges for human and environmental health. Major pollutants such as Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Ozone (O3), Carbon Monoxide (CO), and Particulate matter (PM10 and PM2.5) are being ejected in a large quantity every day. Initially, authorities did not implement the strictest mitigation...

A R T I C L E I N F O Keywords: Rainfall erosivity factor Modified Fournier Index Rainfall-kinetic energy Fournier Index India A B S T R A C T Rainfall erosivity is a measure of the erosive force of rainfall which represents the potential of rain to cause soil erosion. A large proportion of the total eroded soil in India is due to erosion by water,...

Indian is worst affected by soil erosion, especially due to erosion induced by rainfall. A factor of Universal Soil Loss Equation (rainfall erosivity factor) needs to be estimated throughout the country to assess the soil erosion in the country. Indian climate is dominated by monsoons, and their intensity and distribution vary significantly through...

Soil erosion is a major hydro-geological problem in India. Out of the total geographical area (328.7 M-ha) of the nation, about 45% area (148.8 M-ha) is vulnerable to soil erosion induced by water (National Bureau of Soil Survey and Land Use Planning). Erosion induced by water leads to changes in the river bathymetry and trigger natural hazards lik...

The state of Assam, primarily in the Brahmaputra basin, is one of the most flood-prone states of India, with devastating floods occurring every year. Rapid urbanization in the floodplains and inadequate water management have further exacerbated the human and infrastructural exposure to floods. Despite efforts in organizing relief camps and setting...

This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were exa...

Floods are one of the most devastating natural hazards across the world, with India being one of the worst affected countries in terms of fatalities and economic damage. In-depth research is required in order to understand the complex hydrometeorological and geomorphic factors at play and design solutions to minimize the impact of floods. But the e...

This study assesses sources of variance in stochastic hydrologic modelling to support flood frequency analyses. The major components of the modelling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100,000 years at two watersheds representing di...

Floods are one of the most devastating natural hazards across the world, with India being one of the worst affected countries in terms of fatalities and economic damage. In-depth research is required in order to understand the complex hydrometeorological and geomorphic factors at play and design solutions to minimize the impact of floods. But the e...

To issue early warnings for the public to act, for emergency managers to take preventive actions and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated wit...

Floods have gained increasing global significance in the recent past due to their devastating nature and potential for causing significant economic and human losses. Until now, flood characterization studies in the United States have been limited due to the lack of a comprehensive database matching flood characteristics such as peak discharges and...

Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide due to their multidisciplinary nature, difficulty in forecasting, and fast response that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archi...

Flash floods are a particularly damaging natural hazard worldwide in terms of both fatalities and property damage. In the United States, the lack of a comprehensive database that catalogues information related to flash flood timing, location, causative rainfall, and basin geomorphology has hindered broad characterization studies. First a representa...

Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China's Landslide Inventory Database (CsLID) by utilizing Google’s public cloud computing platform. Firstly, CsLID (Landslide Inventory Database) compiles a...

The critical stage in the evaluation of rainfall-induced landslide failure is in formulating reasonable models to better simulate spatiotemporal changes of slopes in the hilly terrains. A physically based model can take into account the contribution of rainfall infiltration and shear strength of saturated soil layer, and therefore help revealing th...

Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of $1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predi...

This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for c...

The current operational practice for short-range river forecasting at the River Forecast Centers (RFC) is to input Quantitative Precipitation Forecast (QPF) out to only 12 to 24 hours (zero precipitation assumed beyond). In the single-valued forecasting paradigm, such a practice is inevitable to avoid highly erroneous river forecasts. In the ensemb...

By allowing for routine use of longer-lead quantitative precipitation forecast (QPF) in hydrologic prediction, ensemble forecasting offers hope for extending the lead time for short-range streamflow forecasting. In this work, this potential is assessed by comparatively evaluating ensemble streamflow hindcasts forced by Day 1-3 QPF with those forced...

The northeast region (NER) of India covers an area of 0.26 million km 2 . This region is one of the highest rainfall-receiving regions on the planet. Consequently, it has huge water and hydropower potential and analysis of rainfall and temperature trends would be of interest to water and energy planners. Trends in monthly, seasonal, and annual rain...

Artificial Neural Networks have been widely used to develop effective runoff-forecasting models. An overwhelming majority of networks are static in nature and also developed without incorporating geomorphologic information of the watershed. The objective of this study is to develop an efficient dynamic neural network model which also accounts for m...

An experiment on predicting flood flows at each of the upstream and a down stream section of a river network is presented using focused Time Lagged Recurrent Neural Network with three different memories like TDNN memory, Gamma memory and Laguarre memory. This paper focuses on application of memory to the input layer of a TLRN in developing flood...