Hydrological Parameters Estimation Using Remote Sensing and GIS for Indian Region: A Review

Thakur, Praveen K.; Nikam, Bhaskar R.; Garg, Vaibhav; Chouksey, Arpit; Dhote, Pankaj R.; Ghosh, Surajit

doi:10.1007/s40010-017-0440-z

Cited by 31 publications

(18 citation statements)

References 129 publications

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“…The stream parameters and river cross sections are derived using ALOS 12.5 and CartoDEMs in HEC-GeoRAS tool of ArcGIS and Mike hydro tool of Mike-11 HD model. The generated river network and cross-sections are used in Mike-11 HD model along with boundary condition and HD parameter settings to simulate the river flows due to heavy rainfall events based flash floods (flood hydrographs as input boundary condition are output of hydrological model) GLOF (glacier lake breach scenarios) events (Thakur et al, 2016(Thakur et al, , 2017. Evaluation of terrain data from DEMs is also utilized in the Height Above Nearest Drainage (HAND) tool (Renno et al, 2008) to identify the flood prone areas of NWH region.…”

Section: Methods For Satellite Based Terrain Data and Hydrodynamic Modelingmentioning

confidence: 99%

“…The major driving parameter for all hydrological studies in any basin or watershed is accurate spatial and temporal representation of precipitation. Traditional methods of measuring precipitation using rain or snow gauges gives accurate and temporal point based estimates of rainfall, but fails to represent spatial variations in precipitation, especially in mountainous regions or river basins such as in Himalayas, where such field instruments are sparse (Bhatt and Nakamura, 2005;Khandelwal et al, 2015;Thakur et al, 2017). In such scenarios, gridded rainfall products from satellites, climate and weather models, re-analysis data and interpolation gauge data have gained significance in the last 20 years and these rainfall products are used extensively in many validation and hydrological studies in Himalayas Burbank, 2006, 2010;Shukla et al, 2014b;Thakur et al, 2015;Bharti et al, 2016;Li et al, 2018;Banerjee et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Significance of Remote Sensing Based Precipitation and Terrain Information for Improved Hydrological and Hydrodynamic Simulation in Parts of Himalayan River Basins

Thakur

Dhote

Roy

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The Himalayan region are home to the world’s youngest and largest mountains, and origins of major rivers systems of South Asia. The present work highlight the importance of remote sensing (RS) data based precipitation and terrain products such as digital elevation models, glacier lakes, drainage morphology along with limited ground data for improving the accuracy of hydrological and hydrodynamic (HD) models in various Himalayan river basins such as Upper Ganga, Beas, Sutlej, Teesta, Koshi etc. The satellite based rainfall have mostly shown under prediction in the study area and few places have are also showing over estimation of rainfall. Hydrological modeling results were most accurate for Beas basin, followed by Upper Ganga basin and were least matching for Sutlej basin. Limited ground truth using GNSS measurements showed that digital elevation model (DEM) for carto version 3.1 is most accurate, followed by ALOS-PALSAR 12.5 DEM as compared to other open source DEMs. Major erosion and deposition was found in Rivers Bhagirathi, Alakhnanda, Gori Ganga and Yamuna in Uttarakhand state and Beas and Sutlej Rivers in Himachal Pradesh using pre and post flood DEM datasets. The terrain data and river cross section data showed that river cross sections and water carrying capacity before and after 2013 floods have changed drastically in many river stretches of upper Ganga and parts of Sutlej river basins. The spatio-temporal variation and evolution of glacier lakes was for lakes along with GLOF modeling few lakes of Upper Chenab, Upper Ganga, Upper Teesta and Koshi river basin was done using time series of RS data from Landsat, Sentinel-1 and Google earth images.

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Section: Methods For Satellite Based Terrain Data and Hydrodynamic Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Significance of Remote Sensing Based Precipitation and Terrain Information for Improved Hydrological and Hydrodynamic Simulation in Parts of Himalayan River Basins

Thakur

Dhote

Roy

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In the last 40 years, the evolution of Geo-Spatial Technology (GST) have occurred, which involves integrated use of Remote Sensing (RS), Geographical Information System (GIS) and Global Navigation System of System (GNSS). The GST has proven its effectiveness in repeated and large area mapping and monitoring of all natural resources including water resources and major components of hydrological cycle (Chakraborty, 1999(Chakraborty, , 2000Thakur et al, 2017c).…”

Section: Geospatial Technology Application In Water Resourcesmentioning

confidence: 99%

“…Revised RS data policy of Government of India (GoI), advent of google earth, availability of free Landsat and other RS data at medium to high resolution from USGS-Earth Explorer (NASA-2018), IRS from ISRO-Bhuvan webGIS portal (NRSC-2018), Sentinel from ESA (ESA, 2014). Various NASA water related dedicated RS missions such as Terra/Aqua, Tropical Rainfall Measuring Mission (TRMM) (Huffman et al, 2007) are few milestones in this drastically changing scenario, where focus has shifted towards retrieval of hydrological parameters (Thakur et al, 2017c). These vast improvements in sensor and geospatial technology has opened many new applications in hydrology and water resources sector in India along with even greater need of training and capacity building to implement these application operationally.…”

Section: Status Of Geospatial Technology In Water Resources and Capacmentioning

confidence: 99%

Training, Education, Research and Capacity Building Needs and Future Requirements in Applications of Geospatial Technology for Water Resources Management

Thakur

Nikam

Garg

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> In India, water resources are managed at different levels, i.e. at central level by Ministry of Water Resources, River Development &amp; Ganga Rejuvenation, Central Water Commission and Central Ground Water Board, at states level by state water resources departments, and at local level by Municipal Corporation and Panchayati Raj Institutions (PRIs). As per India’s national water policy of year 2012 focuses on adaption to climate change, enhancement of water availability, water demand management by efficient water use practices, management of floods and droughts, water supply and sanitation, trans-boundary rivers, conservation of water bodies and infrastructure, and finally research and training needs for each theme. Geospatial technology has unique role in all aforementioned themes. Therefore, research and training in use of Geospatial Technology (GST) in water sector is needed for each theme at different levels of water administration and water utilisation. The current paper discusses the existing framework and content of capacity building in water sector and geospatial technology in use at various government organizations and institutes. The major gap areas and future capacity building requirements are also highlighted, along with duration and timelines of training/capacity building programs. The use of distance learning/educations tools, social media, and e-learning are also highlighted in promoting use of GST in water sector. The emerging technological trends such as, new remote sensing sensors for measuring water cycle components, ground sensors based field instruments, cloud based data integration and computational models, webGIS based water information portals and training needs of new technologies are also emphasised.</p>

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“…Indirect estimation of DP could be, economically, a more realistic mean especially, on large scale studies. Remote sensing (RS) is a trusted tool for precise estimation of evapotranspiration (ET), rainfall (P), infiltration rate, water level, surface runoff (R s ), change in stored water, river flow rates, and change in soil moisture content (∆S) (Thakur et al 2017;Lakshmi, 2013 andDobriyal et al 2012). The relatively few studies conducted on the Nile basin level were mainly aiming to estimate ET and P in irrigated and rainfed lands.…”

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confidence: 99%

Indirect estimation of deep percolation using soil water balance equation and NASA Land Simulation Model (LIS) for more sustainable water management

et al. 2019

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Sustainable management to water resources involves precise estimation for groundwater recharge. The better understanding of the estimated amount of deep percolation and behavior would contribute to more sustainable water resources planning. Soil water balance (SWB) equation computes deep percolation (DP) precisely if adequately applied. Lysimeters are efficient instruments used to compute each component of SWB model with high accuracy. This method is very sophisticated and costs A CCURATE estimation for groundwater recharge is required for more sustainable management to water resources. On-farm level, deep percolation is one of the water loss sources that researchers and agronomists work hard to minimize. However, on a large scale or district level, it is considered as a valuable source for recharging the aquifer. Indirect estimation to deep percolation would be an economic effective mean, especially for large scale studies. The presented research study aims at checking the capability of Land Information System (LIS) model in estimating deep percolation by soil-water balance equation. The model was validated using precipitation data extracted from meteorological stations randomly selected and distributed along the Nile basin. Multi-thematic map layers including all soil water balance model parameters, i.e. deep percolation and change in soil moisture content were developed to the year 2013 at a scale of 10 km 2. The relationship between measured rainfall values and estimated ones by LIS has a coefficient of determination (R 2) of 0.88. The obtained results revealed a good capacity of LIS model in estimating deep percolation on large scale from its direct simulation to the soil-water balance parameters. It was found that deep percolation rates were generally higher in the Nile River's downstream than in upstream especially in Egypt, Sudan, and parts of Uganda. This is in contrary to the behavior of precipitation rates and both surface and sub-surface runoff. They were higher in upstream countries than in downstream ones. The highest deep percolation rate (19.63 mm day-1) was observed in Ethiopia and parts of Sudan during May 2013. It was found that low elevated lands with loose texture tend to have higher rates of deep percolation than elevated rocky lands. The current research results could be of great benefit for sustainable water management in Egypt. However, further study should be conducted in different agro-ecological zones in Egypt for more precise model calibration and validation. This may require access to large amount of field data and long-term meteorological data to run the model.

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Hydrological Parameters Estimation Using Remote Sensing and GIS for Indian Region: A Review

Cited by 31 publications

References 129 publications

Significance of Remote Sensing Based Precipitation and Terrain Information for Improved Hydrological and Hydrodynamic Simulation in Parts of Himalayan River Basins

Significance of Remote Sensing Based Precipitation and Terrain Information for Improved Hydrological and Hydrodynamic Simulation in Parts of Himalayan River Basins

Training, Education, Research and Capacity Building Needs and Future Requirements in Applications of Geospatial Technology for Water Resources Management

Indirect estimation of deep percolation using soil water balance equation and NASA Land Simulation Model (LIS) for more sustainable water management

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