Your search found 33 records
1 Amarnath, Giriraj; Inada, Yoshiaki; Ghosh, Surajit; Yakob, Umer; Alahacoon, Niranga; Kota, Harada; Inoue, Ryosuke; Schlaffer, S. 2014. Earth observation technologies for flood-risk mapping, modeling and management. Training manual prepared for Capacity Building Workshop on Earth Observation Technologies for Flood-risk mapping, Modeling and Management, Peradeniya, Sri Lanka, 18-21 November 2014. Peradeniya, Sri Lanka: University of Peradeniya. Postgraduate Institute of Science. 170p.
Earth observation satellites ; Satellite imagery ; Radar satellite ; Early warning systems ; Flood control ; Risk management ; Models ; Capacity building ; Rain ; Runoff ; Climate change ; Impact assessment ; Hydraulics ; Case studies ; Training materials / Sri Lanka / Thailand / Mundeni Aru Basin
(Location: IWMI HQ Call no: IWMI Record No: H046777)
(11.97 MB)
2 Amarasinghe, Upali; Amarnath, Giriraj; Alahacoon, Niranga; Ghosh, Surajit. 2020. How do floods and drought impact economic growth and human development at the sub-national level in India? Climate, 8(11):123. (Special issue: Climate Change and Water-Related Agricultural Risks) [doi: https://doi.org/10.3390/cli8110123]
Flooding ; Drought ; Natural disasters ; Economic growth ; Gross national product ; Climate change adaptation ; Mitigation ; Monsoon climate ; Rain ; Trends ; Satellite observation ; Estimation ; River basins ; Groundwater recharge ; Investment ; Population / India
(Location: IWMI HQ Call no: e-copy only Record No: H050046)
(3.66 MB) (3.66 MB)
This paper tries to shift the focus of research on the impact of natural disasters on economic growth from global and national levels to sub-national levels. Inadequate sub-national level information is a significant lacuna for planning spatially targeted climate change adaptation investments. A fixed-effect panel regression analyses of 19 states from 2001 to 2015 assess the impacts of exposure to floods and droughts on the growth of gross state domestic product (GSDP) and human development index (HDI) in India. The flood and drought exposure are estimated using satellite data. The 19 states comprise 95% of the population and contribute 93% to the national GDP. The results show that floods indeed expose a large area, but droughts have the most significant impacts at the sub-national level. The most affected GSDPs are in the non-agriculture sectors, positively by the floods and negatively by droughts. No significant influence on human development may be due to substantial investment on mitigation of flood and drought impacts and their influence on better income, health, and education conditions. Because some Indian states still have a large geographical area, profiling disasters impacts at even smaller sub-national units such as districts can lead to effective targeted mitigation and adaptation activities, reduce shocks, and accelerate income growth and human development.
3 Ray, A.; Chakraborty, D.; Ghosh, Surajit. 2020. A critical evaluation revealed the proto-indica model rests on a weaker foundation and has a minimal bearing on rice domestication. Ancient Asia, 11:8. [doi: https://doi.org/10.5334/aa.175]
Rice ; Domestication ; Models ; Evaluation ; Species ; Plant genetics ; Genetic processes ; Gene flow ; Mutation ; Seed shattering ; Agriculture ; History / India / China
(Location: IWMI HQ Call no: e-copy only Record No: H050178)
https://www.ancient-asia-journal.com/articles/10.5334/aa.175/galley/207/download/
https://vlibrary.iwmi.org/pdf/H050178.pdf
https://vlibrary.iwmi.org/pdf/H050178.pdf
(1.53 MB) (1.53 MB)
We have evaluated the proto-indica model that is the proponent of multiple domestication of rice but a single origin of the key genes in japonica. Attainment of non-shattering, a marker; appeared least integral to the initial phases of domestication. The other archeological determinants were less discernible in specimens. Existence of the key domestication genes in the wild rice and absence of introgression signature in indica further weakened the hypothesis. Moreover, japonica introduction from China happened in a backdrop of a culture exploiting domesticated rice. Summarizing, we propose that proto-indica model has a little bearing on rice domestication.
4 Amarasinghe, Upali A.; Amarnath, Giriraj; Alahacoon, Niranga; Aheeyar, Mohamed; Chandrasekharan, Kiran; Ghosh, Surajit; Nakada, Toru. 2021. Adaptation to climate variability in Sri Lanka: a case study of the Huruluwewa Irrigation System in the Dry Zone. Colombo, Sri Lanka: International Water Management Institute (IWMI). 30p. (IWMI Working Paper 200) [doi: https://doi.org/10.5337/2021.229]
Climate variability ; Climate change adaptation ; Irrigation systems ; Arid zones ; Tank irrigation ; Irrigation canals ; Irrigation management ; Land use ; Cropping patterns ; Water supply ; Water depletion ; Crop production ; Water use efficiency ; Irrigation efficiency ; Water productivity ; Water availability ; Drought ; Rainfall patterns ; Risk ; Resilience ; Water scarcity ; Water management ; Reservoirs ; Water spreading ; Catchment areas ; Water storage ; Groundwater recharge ; Water accounting ; Water policies ; Seasonal cropping ; Diversification ; Crop water use ; Consumptive use ; Farmers ; Farm income ; Remote sensing ; Geographical information systems ; Case studies / Sri Lanka / India / North Central Province / Huruluwewa Irrigation System / Sina Irrigation System
(Location: IWMI HQ Call no: IWMI Record No: H050737)
(7.75 MB)
This paper assesses how the Huruluwewa tank (HWT) irrigation system in the North Central Province of Sri Lanka adapts to climate variability. The lessons learned in the HWT will be helpful for many water-scarce irrigation systems in the country, which bear high climate risks. Recurrent droughts are the bane of agriculture in the Dry Zone, comprising three-fourths of the land area spread over the Northern, North Central and Eastern provinces. In the HWT, the fifteenth largest canal irrigation system in the country, adaptation to climate variability happens on several fronts: changes made by the irrigation management to the water release regime; changes in the cropping patterns practiced by farmers in the command area; and the use of groundwater, which is recharged from rainfall, reservoir storage and canal irrigation, as supplemental irrigation. Such adaptation measures ensure that the available water supply in the reservoir is adequate for 100% cropping intensity over two cropping seasons, even in drought years, and enhances economic water productivity in terms of value per unit of consumptive water use. Moreover, irrigation management should consider groundwater recharge through canal irrigation as a resource, which brings substantial agricultural and economic benefits not only for the command area but also outside the command area. The adaptation patterns implemented in HWT demonstrate how water-scarce irrigation systems can achieve higher economic water productivity, i.e., generate ‘more income per drop’ to enhance climate resilience for people in and outside the canal command areas.
5 Srinet, R.; Nandy, S.; Padalia, H.; Ghosh, Surajit; Watham, T.; Patel, N. R.; Chauhan, P. 2020. Mapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engine. International Journal of Remote Sensing, 41(18):7296-7309. [doi: https://doi.org/10.1080/01431161.2020.1766147]
Forests ; Highlands ; Normalized difference vegetation index ; Ecosystems ; Time series analysis ; Moderate resolution imaging spectroradiometer ; Digital elevation models ; Climatic factors ; Mapping / India / Himalayan Foothills
(Location: IWMI HQ Call no: e-copy only Record No: H050791)
(6.51 MB)
Plant functional types (PFTs) have been widely used to represent the vegetation characteristics and their interlinkage with the surrounding environment in various earth system models. The present study aims to generate a PFT map for the Northwest Himalayan (NWH) foothills of India using seasonality parameters, topographic conditions, and climatic information from various satellite data and products using Random Forest (RF) algorithm in Google Earth Engine (GEE) platform. The seasonality information was extracted by carrying out a harmonic analysis of Normalized Difference Vegetation Index (NDVI) time-series (2008 to 2018) from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance 8 day 500 m data (MOD09A1). For topographic information, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) derived aspect and Multi-Scale Topographic Position Index (MTPI) were used, whereas, for climatic variables, WorldClim V2 Bioclimatic (Bioclim) variables were used. RF, a machine learning classifier, was used to generate a PFT map using these datasets. The overall accuracy of the resulting PFT map was found to be 83.33% with a Kappa coefficient of 0.71. The present study provides an effective approach for PFT classification using different well-established, freely available satellite data and products in the GEE platform. This approach can also be implemented in different ecological settings by using various meaningful variables at varying resolutions.
6 Modak, S.; Ghosh, Surajit. 2021. Floods as agents of vitality: reaffirming human-nature synergies. Neuotting, Germany: Water Science Policy (WSP). 7p. (Water Science Policy Brief) [doi: https://doi.org/10.53014/REHQ6535]
Flooding ; Floodplains ; Flood control ; Governance ; Policies ; Zoning ; Regulations ; River basins ; Hydrological factors ; Riparian zones ; Communities ; Risk reduction ; Water resources ; International waters ; Ecosystems ; Wetlands ; Nutrients ; Monsoons / India / Nepal / Bangladesh / Ganga-Brahmaputra-Meghna Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050792)
https://firebasestorage.googleapis.com/v0/b/water-science-policy.appspot.com/o/policyBriefs%2Fwsp%2Fflood_agents%2FWSP_10.53014%3AREHQ6535_Floods%20as%20agents.pdf?alt=media&token=59c06ed0-869d-47e7-a0c3-ec145f536a0a
https://vlibrary.iwmi.org/pdf/H050792.pdf
https://vlibrary.iwmi.org/pdf/H050792.pdf
(1.72 MB) (1.72 MB)
7 Nandy, S.; Ghosh, Surajit; Singh, S. 2021. Assessment of sal (Shorea robusta) forest phenology and its response to climatic variables in India. Environmental Monitoring and Assessment, 193(9):616. [doi: https://doi.org/10.1007/s10661-021-09356-9]
Forests ; Phenology ; Climatic factors ; Shorea robusta ; Moderate resolution imaging spectroradiometer ; Time series analysis ; Remote sensing ; Temperature ; Rain ; Vegetation index / India / Assam / Chhattisgarh / Jharkhand / Madhya Pradesh / Meghalaya / Uttarakhand / West Bengal
(Location: IWMI HQ Call no: e-copy only Record No: H050795)
(2.27 MB)
Remote sensing-based observation provides an opportunity to study the spatiotemporal variations of plant phenology across the landscapes. This study aims to examine the phenological variations of different types of sal (Shorea robusta) forests in India and also to explore the relationship between phenology metrics and climatic parameters. Sal, one of the main timber-producing species of India, can be categorized into dry, moist, and very moist sal. The phenological metrics of different types of sal forests were extracted from Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Enhanced Vegetation Index (EVI) time series data (2002–2015). During the study period, the average start of season (SOS) was found to be 16 May, 17 July, and 29 June for very moist, moist, and dry sal forests, respectively. The spatial distribution of mean SOS was mapped as well as the impact of climatic variables (temperature and rainfall) on SOS was investigated during the study period. In relation to the rainfall, values of the coefficient of determination (R2) for very moist, moist, and dry sal forests were 0.69, 0.68, and 0.76, respectively. However, with temperature, R2 values were found higher (R2 = 0.97, 0.81, and 0.97 for very moist, moist, and dry sal, respectively). The present study concluded that MODIS EVI is well capable of capturing the phenological metrics of different types of sal forests across different biogeographic provinces of India. SOS and length of season (LOS) were found to be the key phenology metrics to distinguish the different types of sal forests in India and temperature has a greater influence on SOS than rainfall in sal forests of India.
8 Marandi, M.; Parida, B. R.; Ghosh, Surajit. 2022. Retrieving vegetation biophysical parameters and GPP [Gross Primary Production] using satellite-driven LUE [Light Use Efficiency] model in a national park. Environment, Development and Sustainability, 24(7):9118-9138. [doi: https://doi.org/10.1007/s10668-021-01815-0]
Normalized difference vegetation index ; Photosynthetically active radiation ; Air temperature ; Moisture ; Leaf Area Index ; Land use ; Land cover ; National parks ; Satellite observation ; Moderate resolution imaging spectroradiometer ; Models / India / Assam / Dibru Saikhowa National Park
(Location: IWMI HQ Call no: e-copy only Record No: H050796)
(3.70 MB)
The terrestrial biosphere plays an active role in governing the climate system by regulating carbon exchange between the land and the atmosphere. Analysis of vegetation biophysical parameters and gross primary production (GPP) makes it convenient to monitor vegetation's health. A light use efficiency (LUE) model was employed to estimate daily GPP from satellite-driven data and environmental factors. The LUE model is driven by four major variables, namely normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and moisture for which both satellite-based and ERA5-Land data were applied. In this study, the vegetation health of Dibru Saikhowa National Park (DSNP) in Assam has been analyzed through vegetation biophysical and biochemical parameters (i.e., NDVI, EVI, LAI, and chlorophyll content) using Sentinel-2 data. Leaf area index (LAI) varied between 1 and 5.2, with healthy forests depicted LAI more than 2.5. Daily GPP was estimated for January (winter) and August (monsoon) 2019 for tropical evergreen and deciduous forest types. A comparative analysis of GPP for two seasons has been performed. In January, GPP was found to be 3.6 gC m-2 day-1, while in August, GPP was 5 gC m-2 day-1. The outcome of this study may be constructive to forest planners to manage the National Park so that net carbon sink may be attained in DSNP.
9 Acharya, P.; Barik, G.; Gayen, B. K.; Bar, S.; Maiti, A.; Sarkar, A.; Ghosh, Surajit; De, S. K.; Sreekesh, S. 2021. Revisiting the levels of aerosol optical depth in South-Southeast Asia, Europe and USA amid the COVID-19 pandemic using satellite observations. Environmental Research, 193:110514. [doi: https://doi.org/10.1016/j.envres.2020.110514]
Air pollution ; Air quality ; Aerosols ; COVID-19 ; Nitrogen dioxide ; Sulphur dioxide ; Emission ; Weather data ; Wind speed ; Humidity ; Satellite observation ; Moderate resolution imaging spectroradiometer / South Asia / South East Asia / Europe / USA
(Location: IWMI HQ Call no: e-copy only Record No: H050797)
(12.20 MB)
The countries around the world are dealing with air quality issues for decades due to their mode of production and energy usages. The outbreak of COVID-19 as a pandemic and consequent global economic shutdown, for the first time, provided a base for the real-time experiment of the effect of reduced emissions across the globe in abetting the air pollution issue. The present study dealt with the changes in Aerosol Optical Depth (AOD), a marker of air pollution, because of global economic shutdown due to the coronavirus pandemic. The study considered the countries in south and south-east Asia (SSEA), Europe and the USA for their extended period of lockdown due to coronavirus pandemic. Daily Aerosol Optical Depth (AOD) from Moderate-resolution imaging spectroradiometer (MODIS) and tropospheric column density of NO2 and SO2 from Ozone monitoring instrument (OMI) sensors, including meteorological data such as wind speed (WS) and relative humidity (RH) were analyzed during the pre-lockdown (2017–2019) and lockdown periods (2020). The average AOD, NO2 and SO2 during the lockdown period were statistically compared with their pre-lockdown average using Wilcoxon-signed-paired-rank test. The accuracy of the MODIS-derived AOD, including the changing pattern of AOD due to lockdown was estimated using AERONET data. The weekly anomaly of AOD, NO2 and SO2 was used for analyzing the space-time variation of aerosol load as restrictions were imposed by the concerned countries at the different points of time. Additionally, a random forest-based regression (RF) model was used to examine the effects of meteorological and emission parameters on the spatial variation of AOD. A significant reduction of AOD (- 20%) was obtained for majority of the areas in SSEA, Europe and USA during the lockdown period. Yet, the clusters of increased AOD (30–60%) was obtained in the south-east part of SSEA, the western part of Europe and US regions. NO2 reductions were measured up to 20–40%, while SO2 emission increased up to 30% for a majority of areas in these regions. A notable space-time variation was observed in weekly anomaly. We found the evidence of the formation of new particles for causing high AOD under high RH and low WS, aided by the downward vertical wind flow. The RF model showed a distinguishable relative importance of emission and meteorological factors among these regions to account for the spatial variability of AOD. Our findings suggest that the continued lockdown might provide a temporary solution to air pollution; however, to combat persistent air quality issues, it needs switching over to the cleaner mode of production and energy. The findings of this study, thus, advocated for alternative energy policy at the global scale.
10 Thakur, P. K.; Garg, V.; Kalura, P.; Agrawal, B.; Sharma, V.; Mohapatra, M.; Kalia, M.; Aggarwal, S. P.; Calmant, S.; Ghosh, Surajit; Dhote, P. R.; Sharma, R.; Chauhan, P. 2021. Water level status of Indian reservoirs: a synoptic view from altimeter observations. Advances in Space Research, 68(2):619-640. [doi: https://doi.org/10.1016/j.asr.2020.06.015]
Water levels ; Estimation ; Reservoirs ; Lakes ; Inland waters ; Water resources ; Satellite observation ; Altimeters ; Time series analysis / India
(Location: IWMI HQ Call no: e-copy only Record No: H050798)
(7.37 MB)
Most of the part of India is already under water-stressed condition. In this regard, the continuous monitoring of the water levels (WL) and storage capacity of reservoirs, lakes, and rivers is very important for the estimation and utilization of water resources effectively. The long term ground observed WL of many of the water bodies is not easily available, which may be very critical for proper water resources management. Satellite radar altimetry is the remote sensing technique, which is being used to study sea surface height for the last three decades. The advancement in radar technology with time has provided the opportunity to exploit the technique to retrieve the WL of inland water bodies. In the current study, an attempt has been made to generate long term time series on WL of around 29 geometrically complicated inland water bodies in India. These water bodies are mainly large reservoirs namely Ban Sagar, Balimela, Bargi, Bhakra, Gandhi Sagar, Hasdeo, Indravati, Jalaput, Kadana, Kolab, Mahi Bajaj, Maithon, Massanjore, Pong, Ramganga, Ranapratap Sagar, Rihand, Sardar Sarovar, Shivaji Sagar, Tilaiya, Ujjani, and Ukai. The WL of these water bodies was retrieved for around two decades using the European Remote-Sensing Satellite – 2 (ERS-2), ENVISAT Radar Altimeter – 2 (ENVISAT RA-2), and Saral-AltiKa altimeters data through Ice-1 retracking algorithm. Further, an attempt has also been made to estimate the WL of gauged/ungauged lakes namely Mansarovar, Pangong, Chilika, Bhopal, and Rann of Kutch over which Saral-AltiKa pass was there. As after July 2016, the SARAL-AltiKa is operating in the drifting orbit, systematic repeated observation of WL data of all reservoirs was not possible. The data of drifted tracks of Saral-AltiKa were tested for WL estimation of Ban Sagar reservoir. As the ERS-2, ENVISAT RA-2 and Saral-AltiKa all were having almost the same passing tracks, a long term WL series of these lakes could be generated from 1997 to 2016. However, at present only Sentinel – 3 is in orbit, the continuous altimeter based WL monitoring of some of these reservoirs (Gandhi Sagar, Nathsagar, Ranapratap, Ujjani, and Ukai) was attempted through Sentinel-3A satellite data from 2016 to 2018. The accuracy of the retrieved WL was than validated against the observed WL. In most of the reservoirs, a systematic bias was found due to the different characteristics and geoid height of each reservoir. The coefficient of determination, R2 , value for a majority of reser voirs was as good as 0.9. In the case of ERS-2, the values of R2 varied for 0.44–0.97 with root mean square error (RMSE) in the range of 0.63–2.72 m. These statistics improved with the ENVISAT RA-2 data analysis, the R2 value reached more than 0.90 for around 11 reservoirs. The highest, 0.99, for Hasdeo and Shivaji Sagar Reservoirs with RMSE of 0.44 and 0.56, respectively. Further, the accuracy improved with the analysis of Saral-AltiKa data. The R2 was always more than 0.9 for each reservoir and the lowest RMSE reduced to 0.03. Therefore, it can be said that the accuracy and consistency of WL retrieval through satellite altimetry has improved with time. Furthermore, the altimeter based retrieved WL may be used in hydrological studies and can contribute to better water resources management.
11 Pandey, S. K.; Chand, N.; Nandy, S.; Muminov, A.; Sharma, A.; Ghosh, Surajit; Srinet, R. 2020. High-resolution mapping of forest carbon stock using Object-Based Image Analysis (OBIA) technique. Journal of the Indian Society of Remote Sensing, 48(6):865-875. [doi: https://doi.org/10.1007/s12524-020-01121-8]
Forests ; Carbon stock assessments ; Mapping ; Satellite imagery ; Image analysis ; Techniques ; Estimation / India / Uttarakhand / Barkot Forest
(Location: IWMI HQ Call no: e-copy only Record No: H050799)
(6.21 MB)
This study assessed and mapped the aboveground tree carbon stock using very high-resolution satellite imagery (VHRS)—WorldView-2 in Barkot forest of Uttarakhand, India. The image was pan-sharpened to get the spectrally and spatially good-quality image. High-pass filter technique of pan-sharpening was found to be the best in this study. Object-based image analysis (OBIA) was carried out for image segmentation and classification. Multi-resolution image segmentation yielded 74% accuracy. The segmented image was classified into sal (Shorea robusta), teak (Tectona grandis) and shadow. The classification accuracy was found to be 83%. The relationship between crown projection area (CPA) and carbon was established in the field for both sal and teak trees. Using the relationship between CPA and carbon, the classified CPA map was converted to carbon stock of individual trees. Mean value of carbon stock per tree for sal was found to be 621 kg, whereas for teak it was 703 kg per tree. The study highlighted the utility of OBIA and VHRS imagery for mapping high-resolution carbon stock of forest.
12 Amarnath, Giriraj; Ghosh, Surajit; Alahacoon, Niranga; Nakada, Toru; Rao, K. V.; Sikka, Alok. 2021. Regional drought monitoring for managing water security in South Asia. In Amaratunga, D.; Haigh, R.; Dias, N. (Eds.). Multi-hazard early warning and disaster risks. Selected papers presented at the International Symposium on Multi-Hazard Early Warning and Disaster Risk Reduction, Online Symposium, 14-16 December 2020. Cham, Switzerland: Springer. pp.465-481. [doi: https://doi.org/10.1007/978-3-030-73003-1_32]
Drought ; Monitoring ; Water security ; Water management ; Climate change ; Agriculture ; Crop production ; Precipitation ; Remote sensing ; Case studies / South Asia
(Location: IWMI HQ Call no: e-copy only Record No: H050800)
(0.60 MB)
Drought is the most complex climate-related disaster issue in South Asia and has affected 1.46 billion people with an economic loss of over 7 billion USD in the last 56 years. South Asia is challenged with water, food, and energy security due to growing populations, incomes, resource degradation, and vulnerability to climate change. Monitoring of drought and associated agricultural production deficits using meteorological and agricultural indices is an essential component for drought preparedness. Remote sensing offers near real-time monitoring of drought conditions and IWMI’s has implemented South Asia Drought Monitoring System (SADMS) in 2014 as an online platform for drought early warning and support in drought declaration. This chapter explores the use of composite drought indices implemented in Google Earth Engine (GEE) and evaluates the crop yield variability during drought years. The study provides a rapid overview of drought-prone conditions that could enhance the present capabilities of early warning systems and enable science based policies for addressing water security in the agriculture sector and develop a drought response plan between water supply and demand, significantly increasing the vulnerability of regions to damaging impacts of drought events.
13 Maiti, A.; Acharya, P.; Sannigrahi, S.; Zhang, Q.; Bar, S.; Chakraborti, S.; Gayen, B. K.; Barik, G.; Ghosh, Surajit; Punia, M. 2022. Mapping active paddy rice area over monsoon Asia using time-series Sentinel – 2 images in Google Earth engine; a case study over Lower Gangetic Plain. Geocarto International, 37(25):10254-10277. [doi: https://doi.org/10.1080/10106049.2022.2032396]
Rice ; Mapping ; Satellite imagery ; Monsoons ; Time series analysis ; Case studies ; Farmland ; Precipitation ; Models / India / West Bengal / Lower Gangetic Plain
(Location: IWMI HQ Call no: e-copy only Record No: H051089)
(4.00 MB)
We proposed a modification of the existing approach for mapping active paddy rice fields in monsoon-dominated areas. In the existing PPPM approach, LSWI higher than EVI at the transplantation stage enables the identification of rice fields. However, it fails to recognize the fields submerged later due to monsoon floods. In the proposed approach (IPPPM), the submerged fields, at the maximum greenness time, were excluded for better estimation. Sentinel–2A/2B time-series images were used for the year 2018 to map paddy rice over the Lower Gangetic Plain (LGP) using Google earth engine (GEE). The overall accuracy (OA) obtained from IPPPM was 85%. Further comparison with the statistical data reveals the IPPPM underestimates (slope (b1) ¼ 0.77) the total reported paddy rice area, though R2 remains close to 0.9. The findings provide a basis for near real-time mapping of active paddy rice areas for addressing the issues of production and food security.
14 Kumar, S.; Amarnath, Giriraj; Ghosh, Surajit; Park, E.; Baghel, T.; Wang, J.; Pramanik, M.; Belbase, D. 2022. Assessing the performance of the Satellite-Based Precipitation Products (SPP) in the data-sparse Himalayan terrain. Remote Sensing, 14(19):4810. (Special issue: Remote Sensing Monitoring of Natural Disasters and Human Impacts in Asian Rivers) [doi: https://doi.org/10.3390/rs14194810]
Satellite observation ; Precipitation ; River basins ; Hydrological modelling ; Datasets ; Hydrometeorology ; Indicators ; Discharge ; Rain ; Temperature / Nepal / Himalayan Region / Simat Khola River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H051444)
https://www.mdpi.com/2072-4292/14/19/4810/pdf?version=1664270105
https://vlibrary.iwmi.org/pdf/H051444.pdf
https://vlibrary.iwmi.org/pdf/H051444.pdf
(3.42 MB) (3.42 MB)
Located on the south-facing slope of the Himalayas, Nepal receives intense, long-lasting precipitation during the Asian summer monsoon, making Nepal one of the most susceptible countries to flood and landslide hazards in the region. However, sparse gauging and irregular measurement constrain the vulnerability assessments of floods and landslides, which rely highly on the accuracy of precipitation. Therefore, this study evaluates the performance of Satellite-based Precipitation Products (SPPs) in the Himalayas region by comparing different datasets and identifying the best alternative of gauge-based precipitation for hydro-meteorological applications. We compared eight SPPs using statistical metrics and then used the Multi-Criteria Decision-Making (MCDM) technique to rank them. Secondly, we assessed the hydrological utility of SPPs by simulating them through the GR4J hydrological model. We found a high POD (0.60–0.80) for all SPPs except CHIRPS and PERSIANN; however, a high CC (0.20–0.40) only for CHIRPS, IMERG_Final, and CMORPH. Based on MCDM, CMORPH and IMERG_Final rank first and second. While SPPs could not simulate daily discharge (NSE < 0.28), they performed better for monthly streamflow (NSE > 0.54). Overall, this study recommends CMORPH and IMERG_Final and improves the understanding of data quality to better manage hydrological disasters in the data-sparse Himalayas. This study framework can also be used in other Himalayan regions to systematically rank and identify the most suitable datasets for hydro-meteorological applications.
15 Bhattacharya, S.; Ghosh, Surajit; Bhattacharyya, S. 2022. Analytical hierarchy process tool in Google Earth Engine platform: a case study of a tropical landfill site suitability. Environmental Monitoring and Assessment, 194(4):276. [doi: https://doi.org/10.1007/s10661-022-09878-w]
(Location: IWMI HQ Call no: e-copy only Record No: H051499)
(2.01 MB)
Kolkata being a metropolitan city in India has its main municipal solid waste dumpsite situated at Dhapa just adjacent to the East Kolkata Wetlands (Ramsar site). The current prevalent situation at Dhapa is open dumping leading to various contaminations and hazards putting forth the need to look for alternative sites where the landfiilling operation can be shifted to using scientific methods. A user interface (UI)–based analytical hierarchy process (AHP) tool has been developed within the Google Earth Engine (GEE) cloud platform to find out the alternative dumping sites using geospatial layers. AHP function is not available as a native algorithm or developed by any researcher in GEE. The tool has three major functionalities, of which the first one handles the UI elements. The AHP procedure is within another function, and the last function integrates the AHP coefficients to the layers generating the final suitability layer. Users can also upload comparison matrix as GEE asset in the form of CSV file which gets automatically integrated into the AHP to calculate the coefficients and consistency ratio to generate the spatial suitability layers. This approach showcases a generalized AHP function within the GEE environment, which has been done for the first time. The tool is designed in the cloud platform which is dynamic, robust and suitable for use in various AHP-based suitability analysis in environmental monitoring and assessment.
16 Ghosh, Surajit; Mukherjee, J. 2023. Earth observation data to strengthen flood resilience: a recent experience from the Irrawaddy River. Natural Hazards, 115(3):2749-2754. [doi: https://doi.org/10.1007/s11069-022-05644-w]
(Location: IWMI HQ Call no: e-copy only Record No: H051500)
(3.22 MB)
The improvement of Earth Observation (EO) sensors and modern computational efficiency in the form of cloud analytics platform has made monitoring and interpretation of floods much more efficient. In this study, we present the recently occurred floods in the north-central section of the Irrawaddy River, inundating the adjoining farmlands on the active floodplains along a stretch of 228 km. The amount of rainfall was observed to have gradually risen from early June 2022 captured through GPM data. Similarly, the water levels in the study stretch were observed to have increased from 98.08 m to 104.08 m (from Sentinel-3 altimetry) due to torrential rains on the northern hilly tracts of Myanmar. High-resolution Sentinel-1 SAR datasets have been used to estimate flood progression in the GEE platform. The total inundated area had risen from 196 to 989 sq. km. throughout June till the first week of July. Thus, EO data associated with accessible computing on cloud platforms help monitor flood progression, warn the community well in advance and support the development of crop insurance strategies, anticipatory actions and many more to strengthen evidence-based flood policy.
17 Patle, P.; Singh, P. K.; Ahmad, I.; Matsuno, Y.; Leh, Mansoor; Ghosh, Surajit. 2023. Spatio-temporal estimation of green and blue water consumptions and water and land productivity using satellite remote sensing datasets and WA+ framework: a case study of the Mahi Basin, India. Agricultural Water Management, 277:108097. [doi: https://doi.org/10.1016/j.agwat.2022.108097]
Water use ; Land productivity ; Water productivity ; Satellite observation ; Remote sensing ; Datasets ; Frameworks ; Estimation ; Evapotranspiration ; Semiarid zones ; Case studies / India / Madhya Pradesh / Gujarat / Rajasthan / Mahi Basin
(Location: IWMI HQ Call no: e-copy only Record No: H051577)
https://www.sciencedirect.com/science/article/pii/S0378377422006448/pdfft?md5=50b09813950cc58134ad605f62d666a9&pid=1-s2.0-S0378377422006448-main.pdf
https://vlibrary.iwmi.org/pdf/H051577.pdf
https://vlibrary.iwmi.org/pdf/H051577.pdf
(15.50 MB) (15.5 MB)
The agricultural activities contribute to the largest share of water consumption in the arid and semi-arid basins. In this study, we demonstrate the application of Water Accounting Plus (WA+) for estimation of the green water consumption (ETGreen) and blue water consumption (ETBlue) for assessing the water productivity (WP) and land productivity (LP) to identify the bright-spots and hot-spots at the district administrative unit level for effectively managing the scarce water resources and sustaining food security in a highly non-resilient semi-arid basin of India. The WA+ framework uses satellite remote sensing datasets from different sources for this purpose and we used the data from 2003 to 2020. The long-term average of ETGreen and ETBlue in the Mahi basin is found to be 15.8 km3 /year and 12.32 km3 /year, respectively. The blue water index (BWI) and green water index (GWI) in the basin vary from 0.282 to 0.598 and 0.40–0.72. We found that the BWI is highest for the districts of Gujarat, whereas, the GWI is highest for the districts of Madhya Pradesh. The long-term average of the LP and WP for both the irrigated and rainfed cereals in the basin is found as 2287.71 kg/ha & 1713.62 kg/ha and 0.721 kg/ m3 & 0.483 kg/m3 , respectively from 2003 to 2020. The WP (rainfed) of all the districts of the Gujarat is comparatively lower (varying from 0.34 kg/m3 to 0.5 kg/m3 ) than the districts of the Madhya Pradesh (varying from 0.59 kg/m3 to 0.70 kg/m3 ) and the Rajasthan (varying from 0.48 kg/m3 to 0.73 kg/m3 ). Based on the results, we found that the Ratlam district of the Madhya Pradesh has both highest LP and WP (irrigated) as 2573.96 kg/ha and 2.14 kg/m3 , respectively among all the districts of the Mahi basin, and hence it is classified as the ‘Bright spot-district’. The Anand district is found to have the lowest WP and LP as 0.44 kg/m3 and 2467.51 kg/ha, respectively and hence it is classified as the ‘hot spot-district’. For rainfed cereals, we found that the Neemuch district of Madhya Pradesh has the highest WP and LP as 0.59 kg/m3 and 1948.13 kg /ha, respectively, and the Anand district with the lowest WP as 0.34 kg/m3 and LP of 1572.21 kg/ha, respectively. Therefore, we classified the Neemach district as the ‘Bright spot-district’ and the Anand district as the hot spot- district for rainfed cereals. These findings will help develop sustainable and actionable agricultural water management plans by the policymakers and stakeholders in the basin.
18 Ghosh, Surajit; Wellington, Michael; Holmatov, Bunyod. 2022. Mekong River Delta crop mapping using a machine learning approach. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Low-Emission Food Systems (Mitigate+). 11p.
Crops ; Mapping ; Deltas ; Machine learning ; Satellite imagery ; Land use ; Land cover ; Farmland / South East Asia / Vietnam / Mekong River Delta
(Location: IWMI HQ Call no: e-copy only Record No: H051629)
(1.05 MB)
Agricultural land use and practices have important implications for climate change mitigation and adaptation. It is, therefore, important to develop methods of monitoring and quantifying the extent of crop types and cropping practices. A machine learning approach using random forest classification was applied to Sentinel-1 and 2 satellite imagery and satellite-derived phenological statistics to map crop types in the Mekong River Delta, enabling levels of rice intensification to be identified. This initial classification differentiated between broad and prevalent crop types, including perennial tree crops, rice, other vegetation, oil palm and other crops. A two-step classification was used to classify rice seasonality, whereby the areas identified as rice in the initial classification were further classified into single, double, or triple-cropped rice in a subsequent classification with the same input data but different training polygons. Both classifications had an overall accuracy of approximately 96% when cross-validated on test data. Radar bands from Sentinel-1 and Sentinel-2 reflectance bands were important predictors of crop type, perhaps due to their capacity to differentiate between periodically flooded rice fields and perennial tree cover, which were the predominant classes in the Delta. On the other hand, the Start of Season (SoS) and End of Season (EoS) dates were the most important predictors of single, double, or triple-cropped rice, demonstrating the efficacy of the phenological predictors. The accuracy and detail are limited by the availability of reliable training data, especially for tree crops in small-scale orchards. A preliminary result is presented here, and, in the future, efficient collection of ground images may enable cost-effective training data collection for similar mapping exercises.
19 Alaminie, A.; Amarnath, Giriraj; Padhee, Suman; Ghosh, Surajit; Tilahun, S.; Mekonnen, M.; Assefa, G.; Seid, Abdulkarim; Zimale, F.; Jury, M. 2023. Application of advanced Wflow_sbm Model with the CMIP6 climate projection for flood prediction in the data-scarce: Lake-Tana Basin, Ethiopia [Abstract only]. Paper presented at the European Geosciences Union (EGU) General Assembly 2023, Vienna, Austria and Online, 24-28 April 2023. 1p. [doi: https://doi.org/10.5194/egusphere-egu23-1113]
Flood forecasting ; Climate change ; Hydrological modelling ; Climate models / Ethiopia / Lake Tana Basin
(Location: IWMI HQ Call no: e-copy only Record No: H051891)
https://meetingorganizer.copernicus.org/EGU23/EGU23-1113.html?pdf
https://vlibrary.iwmi.org/pdf/H051891.pdf
https://vlibrary.iwmi.org/pdf/H051891.pdf
(0.28 MB) (289 KB)
20 Mukherjee, J.; Ghosh, Surajit. 2023. Decoding the vitality of earth observation for flood monitoring in the Lower Godavari River Basin, India. Journal of the Geological Society of India, 99(6):802-808. [doi: https://doi.org/10.1007/s12594-023-2387-9]
Floods ; Monitoring ; Earth observation satellites ; River basins ; Datasets ; Stream flow ; Forecasting ; Rainfall ; Monsoons ; Climate change ; Satellite imagery / India / Lower Godavari River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H052095)
(2.44 MB)
The entire Indian subcontinent experienced devastating floods in the year 2022. The central section of the Godavari river basin (GRB) received torrential rainfall from the southwest monsoon during the second week of July 2022. This study exhibits how Earth observation (EO) datasets and cloud platforms like Google Earth Engine (GEE) can be used for swift, lucid and accurate decoding of the flood inundation signatures. Geospatial analysts can estimate concurrent floods using high-resolution C-band SAR/Sentinel-1 images, gridded precipitation and streamflow forecast datasets. The GPM (IMERG) precipitation data showed an incremental trend with prime hotspots, rainfall dissemination and retrieval from 01–20 July 2022 in the mid-GRB. The flood inundation layers were derived based on Otsu’s method with selective topographic conditions from Sentinel-1 in GEE. Five significant flood affected case sites were identified in the lower GRB from Kothapalli to Yanam town, where the Godavari river meets the Bay of Bengal. Large stretches of agricultural lands were found to be inundated, resulting in extensive economic losses. Such flooded farmlands surrounding Kothapalli, Bhadrachalam, Kunavaram, Polavaram and Yanam towns were estimated as 60, 91, 86, 170 and 142 km2 on 16 and 21 July 2022, respectively. The results were validated and cross-verified using bulletins and maps issued by various national agencies. Hence, EO, GEE and cloud analytical techniques are modern untapped potential e-assets vital for incorporation in policy frameworks helping disaster managers with comprehensive flood condition analysis.
Powered by DB/Text WebPublisher, from
