Indian Institute of Remote Sensing

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3‐band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time‐series for 1997–1999, (b) Système pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50 km monthly time series for 1961–2000, (d) Global 30 Arc‐Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite‐1 Synthetic Aperture Radar (JERS‐1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992–1993. A single mega‐file data‐cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re‐sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space–time spiral curve (ST‐SC) plots, (b) brightness–greenness–wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very‐high‐resolution imagery (VHRI) ‘zoom‐in views’ in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high‐resolution Landsat‐ETM+ Geocover 150 m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re‐classify the MDFC, and the class identification and labelling protocol repeated. The sub‐pixel area (SPA) calculations were performed by multiplying full‐pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAM was produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year‐round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but ‘equipped for irrigation’ at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467 million hectares (Mha), which is sum of the non‐overlapping areas of: (a) 252 Mha from season one, (b) 174 Mha from season two and (c) 41 Mha from continuous year‐round crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79–91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org).

Owing to its severe effect on productivity of rain-fed crops and indirect effect on employment as well as per capita income, agricultural drought has become a prime concern worldwide. The occurrence of drought is mainly a climatic phenomenon which cannot be eliminated. However, its effects can be reduced if actual spatio-temporal information related to crop status is available to the decision makers. The present study attempts to assess the efficiency of remote sensing and GIS techniques for monitoring the spatio-temporal extent of agricultural drought. In the present study, NOAA-AVHRR NDVI data were used for monitoring agricultural drought through NDVI based Vegetation Condition Index. VCI was calculated for whole Rajasthan using the long term NDVI images which reveals the occurrence of drought related crop stress during the year 2002. The VCI values of normal (2003) and drought (2002) year were compared with meteorological based Standardized Precipitation Index (SPI), Rainfall Anomaly Index and Yield Anomaly Index and a good agreement was found among them. The correlation coefficient between VCI and yield of major rain-fed crops ( r > 0.75) also supports the efficiency of this remote sensing derived index for assessing agricultural drought.

Spectrally-resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes current status and future prospects in the field of ocean colour focussing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focussing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean are described and finally a summary of future perspectives is provided.

The Joint Research Centre of the European Commission (JRC), in partnership with 30 institutions, has produced a global land cover map for the year 2000, the GLC 2000 map. The validation of the GLC2000 product has now been completed. The accuracy assessment relied on two methods: a confidence-building method (quality control based on a comparison with ancillary data) and a quantitative accuracy assessment based on a stratified random sampling of reference data. The sample site stratification used an underlying grid of Landsat data and was based on the proportion of priority land cover classes and on the landscape complexity. A total of 1265 sample sites have been interpreted. The first results indicate an overall accuracy of 68.6%. The GLC2000 validation exercise has provided important experiences. The design-based inference conforms to the CEOS Cal-Val recommendations and has proven to be successful. Both the GLC2000 legend development and reference data interpretations used the FAO Land Cover Classification System (LCCS). Problems in the validation process were identified for areas with heterogeneous land cover. This issue appears in both in the GLC2000 (neighborhood pixel variations) and in the reference data (cartographic and thematic mixed units). Another interesting outcome of the GLC2000 validation is the accuracy reporting. Error statistics are provided from both the producer and user perspective and incorporates measures of thematic similarity between land cover classes derived from LCCS

India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study.

The present study highlights the importance of Digital Elevation Model (DEM) and satellite images for assessment of drainage and extraction of their relative parameters for the Orr watershed Ashok Nagar district, M.P., India. Hydrological parameters such as drainage analysis, topographic parameters and land use pattern were evaluated and interpreted for watershed management of the area. Hydrological module of ARC GIS software was utilized for calculation and delineation of the watershed and morphometric analysis of the watershed using SRTM DEM. The stream order of watershed ranges from first to sixth order showing dendritic type drainage network which is a sign of the homogeneity in texture and lack of structural control of the watershed. The drainage density in the area has been found to be low to medium which indicates that the area possesses highly permeable soils and low relief. The bifurcation ratio varies from 4.74 to 5 and the elongation ratio is 0.58 which reveals that the basin belongs to the elongated shaped basin category. The mean Rb of the entire basin is 4.62 which indicates that the drainage pattern is not much influenced by geological structures. Land use map of the watershed was generated from latest available multispectral satellite data and whole watershed covers under agricultural land, settlement, fallow land, forest, mining areas and water body. The present study reveals that SRTM DEM based hydrological evaluation at watershed scale is more applied and precise compared to other available techniques.

Flood forecasting (FF) is one the most challenging and difficult problems in hydrology. However, it is also one of the most important problems in hydrology due to its critical contribution in reducing economic and life losses. In many regions of the world, flood forecasting is one among the few feasible options to manage floods. Reliability of forecasts has increased in the recent years due to the integration of meteorological and hydrological modelling capabilities, improvements in data collection through satellite observations, and advancements in knowledge and algorithms for analysis and communication of uncertainties. The present paper reviews different aspects of flood forecasting, including the models being used, emerging techniques of collecting inputs and displaying results, uncertainties, and warnings. In the end, future directions for research and development are identified.

Abstract The exploration for groundwater in hard rock terrains is a complex task. To overcome this complexity, the integrated approach based on advanced applications of remote sensing and geographical information systems (GIS) lends itself as an efficient and effective result-oriented method for studying the development and management of water resources. Chittoor area, comprised of a hard rock terrain, is located in the drought-prone Rayalaseema region of Andhra Pradesh, India. Using remote sensing and GIS technology, groundwater potential zones, along with zones of water quality suitable for domestic purposes, were delineated and classified. Results indicated that, for the town of Chittoor, 1.64% of the area was classified to have very high groundwater potential, with groundwater quality suitable or moderately suitable for domestic purposes; and 31.68% of the area was classified as high potential, with over 31% being suitable or moderately suitable. Most (62.05%) of the area is of moderate groundwater potential, with groundwater quality mostly suitable or moderately suitable for domestic purposes. Résumé La recherche d'eaux souterraines dans des sites à roches dures est une tâche complexe. Face à cette complexité, l'approche intégrée basée sur des applications avancées de télédétection et de système d'information géographique (SIG) permet de proposer une méthode efficace et efficiente pour étudier le développement et la gestion des ressources en eau. La zone de Chittoor, présentant un substrat de roches dures, se situe dans la région sensible aux sécheresses de Rayalaseema en Andhra Pradesh, en Inde. Les zones de potentiel en eaux souterraines ainsi que les zones où la qualité d'eau est compatible avec les usages domestiques ont été délimitées et classifiées en utilisant la télédétection et la technologie SIG. Les résultats indiquent que, pour la ville de Chittoor, un peu moins de 2% de la zone ont été classifiés avec un très fort potentiel en eaux souterraines, bien que moins de 1% seulement soit valable ou presque valable pour les usages domestiques; et que 31.68% de la zone aient été classifiés avec un fort potentiel, avec plus de 31% valables ou presque valables. La plupart (62.05%) de la zone présente un potentiel en eaux souterraines modéré, avec une qualité d'eau essentiellement valable et modérément valable pour les usages domestiques. Keywords: groundwater potential zonesgroundwater qualityremote sensingGISAndhra PradeshIndiaMots clefs: zones de potentiel en eaux souterrainesqualité d'eaux souterrainestélédétectionSIGAndhra PradeshInde

Abstract Drought is a slow‐onset, creeping natural hazard and a recurrent phenomenon in the arid and semi‐arid regions of Gujarat (India). In Asia, the standardized precipitation index (SPI) has gained wider acceptance in the detection and the estimation of the intensity, magnitude and spatial extent of droughts. The main advantage of the SPI, in comparison with other indices, is that the SPI enables both determination of drought conditions at different time scales and monitoring of different drought types. This index captures the accumulated deficit (SPI < 0) or surplus (SPI > 0) of precipitation over a specified period, and provides a normalized measure (i.e. spatially invariant Z score) of relative precipitation anomalies at multiple time scales. In the present study, monthly time series of rainfall data (1981–2003) from 160 stations were used to derive SPI, particularly at 3‐month time scales. This 3‐month SPI was interpolated to depict spatial patterns of meteorological drought and its severity during typical drought and wet years. Correlation analysis was also done to evaluate usefulness of SPI to quantify effects of drought on food grain productivity. Further, time series of SPI were exploited to assess the drought risk in Gujarat. Copyright © 2007 Royal Meteorological Society

A dynamic growth model (CO2FIX) was used for estimating the carbon sequestration potential of sal (Shorea Robusta Gaertn. f.), Eucalyptus (Eucalyptus Tereticornis Sm.), poplar (Populus Deltoides Marsh), and teak (Tectona Grandis Linn. f.) forests in India. The results indicate that long-term total carbon storage ranges from 101 to 156 Mg C ha−1, with the largest carbon stock in the living biomass of long rotation sal forests (82 Mg C ha−1). The net annual carbon sequestration rates were achieved for fast growing short rotation poplar (8 Mg C ha−1 yr−1) and Eucalyptus (6 Mg C ha−1 yr−1) plantations followed by moderate growing teak forests (2 Mg C ha−1 yr−1) and slow growing long rotation sal forests (1 Mg C ha−1 yr−1). Due to fast growth rate and adaptability to a range of environments, short rotation plantations, in addition to carbon storage rapidly produce biomass for energy and contribute to reduced greenhouse gas emissions. We also used the model to evaluate the effect of changing rotation length and thinning regime on carbon stocks of forest ecosystem (trees + soil) and wood products, respectively for sal and teak forests. The carbon stock in soil and products was less sensitive than carbon stock of trees to the change in rotation length. Extending rotation length from the recommended 120 to 150 years increased the average carbon stock of forest ecosystem (trees + soil) by 12%. The net primary productivity was highest (3.7 Mg ha−1 yr−1) when a 60-year rotation length was applied but decreased with increasing rotation length (e.g., 1.7 Mg ha−1 yr−1) at 150 years. Goal of maximum carbon storage and production of more valuable saw logs can be achieved from longer rotation lengths. ‘No thinning’ has the largest biomass, but from an economical perspective, there will be no wood available from thinning operations to replace fossil fuel for bioenergy and to the pulp industry and such patches have high risks of forest fires, insects etc. Extended rotation lengths and reduced thinning intensity could enhance the long-term capacity of forest ecosystems to sequester carbon. While accounting for effects of climate change, a combination of bioenergy and carbon sequestration will be best to mitigation of CO2 emission in the long term.

Abstract Land‐cover/climate changes and their impacts on hydrological processes are of widespread concern and a great challenge to researchers and policy makers. Kejie Watershed in the Salween River Basin in Yunnan, south‐west China, has been reforested extensively during the past two decades. In terms of climate change, there has been a marked increase in temperature. The impact of these changes on hydrological processes required investigation: hence, this paper assesses aspects of changes in land cover and climate. The response of hydrological processes to land‐cover/climate changes was examined using the Soil and Water Assessment Tool (SWAT) and impacts of single factor, land‐use/climate change on hydrological processes were differentiated. Land‐cover maps revealed extensive reforestation at the expense of grassland, cropland, and barren land. A significant monotonic trend and noticeable changes had occurred in annual temperature over the long term. Long‐term changes in annual rainfall and streamflow were weak; and changes in monthly rainfall (May, June, July, and September) were apparent. Hydrological simulations showed that the impact of climate change on surface water, baseflow, and streamflow was offset by the impact of land‐cover change. Seasonal variation in streamflow was influenced by seasonal variation in rainfall. The earlier onset of monsoon and the variability of rainfall resulted in extreme monthly streamflow. Land‐cover change played a dominant role in mean annual values; seasonal variation in surface water and streamflow was influenced mainly by seasonal variation in rainfall; and land‐cover change played a regulating role in this. Surface water is more sensitive to land‐cover change and climate change: an increase in surface water in September and May due to increased rainfall was offset by a decrease in surface water due to land‐cover change. A decrease in baseflow caused by changes in rainfall and temperature was offset by an increase in baseflow due to land‐cover change. Copyright © 2009 John Wiley & Sons, Ltd.

Over the last few decades several vegetation indices were used to map Mangrove forest using satellite images. Difficulty still persists in discrimination of mangroves from non-mangrove vegetation, especially in areas where mangrove species are mixed with other vegetation types. In the present study we have attempted to develop an improved index, which utilizes the information from the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) of Bhitarkanika mangrove forest of Odisha, India. These indices are negatively correlated (r = -0.988; p < 0.01). Further, the NDWI values were subtracted from the NDVI values at the pixel level. As the outputs are negatively related, subtraction increases the upper and lower range of the overall output, also increasing the distinct values of two classes with near-similar spectral signatures. Same algorithm was applied on mangroves of Sundarbans (r = -0.987) and Andaman (r = -0.989). A comparison between four established indices [NDVI, NDWI, Soil Adjusted Vegetation Index (SAVI), Simple Ratio (SR)] and the newly developed index namely Combined Mangrove Recognition Index (CMRI) were performed. Accuracy assessment using Kappa statistics, revealing that CMRI produces better accuracy (73.43%) compared to other indices, followed by NDVI (56.29%) and SR (48.79%).

High‐resolution soil moisture holds the key to improving weather forecast, drought monitoring and hydrological modelling. Therefore, the present study investigates the potential of the temperature/vegetation dryness index (TVDI) from the MODIS to assess soil moisture status in sub‐humid parts of India (western Uttar Pradesh). The TVDI was calculated by parameterizing the normalized difference vegetation index–surface temperature space from 8 day MODIS reflectance and surface temperature products. Correlation and regression analysis was carried out to relate the TVDI against in‐situ measured soil moisture during early (April) and peak (October) stages of growth in sugarcane crop. Spatio‐temporal patterns in the TVDI shows that northern areas had more surface wetness compared to southern areas. The results further reveal that a significantly strong and negative relationship exists between the TVDI and in‐situ soil moisture, particularly when vegetation cover is sparse. The dryness index was also found satisfactory to capture the temporal variation in the surface moisture status in terms of antecedent precipitation index.

Abstract The present study aims to map forest canopy height by integrating ICESat‐2 and Sentinel‐1 data and investigate the effect of integrating forest canopy height information with Sentinel‐2 data‐derived spectral variables on the prediction of spatial distribution of forest aboveground biomass (AGB). Random forest (RF) algorithm was used to develop forest canopy height and AGB models. It was observed that ICESat‐2 and Sentinel‐1 based model was able to predict forest canopy height with R 2 = 0.84 and %RMSE = 4.48%. Two forest AGB models were developed, with only spectral variables and by incorporating forest height information with spectral variables. The results reflected that incorporation of forest canopy height in the forest AGB model improved the accuracy of the AGB predictions ( R 2 = 0.83, %RMSE = 4.64%). The study presents a comprehensive methodology for mapping forest canopy height and AGB.

India's urban population has grown tremendously in the last four decades from 79 million in 1961 to 285 million in 2001. This fast rate of increase in urban population is mainly due to large scale migration of people from rural and smaller towns to bigger cities in search of better employment opportunities and good life style. This rapid population pressure has resulted in unplanned growth in the urban areas to accommodate these migrant people which in turn leads to urban sprawl. It is a growing problematic aspect of metropolitan and bigger city's growth and development in recent years in India. Urban sprawl has resulted in loss of productive agricultural lands, open green spaces, loss of surface water bodies and depletion of ground water. Therefore, there is a need to study, understand and quantify the urban sprawl. In this paper an attempt has been made to use Shannon's entropy model to assess urban sprawl using IRS P-6 data and topographic sheet in GIS environment for one of the fastest growing city of South India and its surrounding area. The built-up area of the city has increased from 135 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in 1971 to 370 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in 2005. The study shows that there is a remarkable urban sprawl in and around the twin city between 1971 and 2005 because 215 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of agricultural land has lost to built-up land during this period. As a result the urban ecosystem has changed in the last four decades.

India had announced the longest ever lockdown from 25 March 2020 to 14 April 2020 amid COVID-19 pandemic. It was reported that the water quality of the Ganga River has improved as compared to regular during this country-wide lockdown. In the present study, an attempt has been made to study the change in water quality of the river in terms of turbidity purely through remote sensing data, in the absence of ground observations, especially during this time period. The change in spectral reflectance of water along the river in the visible region has been analyzed using the Sentinel-2 multispectral remote sensing data at Haridwar, Kanpur, Prayagraj, and Varanasi stretches of the river. In the present study, it was found that the red and NIR bands are most sensitive, and can be used to estimate the turbidity. Further, the temporal variation in turbidity was also analyzed through normalized difference turbidity index at each location. It was observed that the turbidity in the river has reduced drastically at each stretch of the river. The study elicited that the remote sensing approach can be used to make qualitative estimates on turbidity, even in the absence of field observations.

The number of civilian, commercial and military applications are dependant on accurate knowledge of bathymetry of coastal regions. Conventionally, hydrographic surveying methods are used for bathymetric surveys carried by ship-based acoustic systems, but needs high-cost resources. Space technology has provided a cost-effective alternate means for charting near shore and inaccessible waters. The optical satellite data have capabilities to offer alternate solution in near-shore region, which has been researched for past 50 years, using evolving algorithms to estimate Satellite Derived Bathymetry (SDB). However, there is no agreement on use of terms like approach, model, method and techniques, which have been used varyingly and interchangeably as per context of SDB research. This paper suggests a classification scheme for SDB algorithms which is also applicable to other Marine Remote Sensing studies. In this paper, based on literature available on SDB for the past five decades, an insight on SDB classification has been offered grounded in research philosophy. The SDB approaches, models, methods and techniques have been elaborated with chronological development, along with SDB studies based on them, their accuracy and errors in SDB retrieval. We have suggested a matrix of prerequisite satellite data, in-situ data resolution, methods and algorithms of SDB based on level of accuracy needs to be achieved, which will guide future researchers to select one as per their context of research.

Abstract Accurate precipitation measurement is crucial for weather forecasting and hydrological modeling. Tropical Rainfall Measuring Mission (TRMM) 3B42V7 satellite precipitation product offers an opportunity to monitor precipitation at high spatiotemporal resolution. However, it has several inherent errors related to observation, instrument, and rainfall retrieval algorithms. It is, therefore, essential to validate it with ground‐based measurements. We divide the region into different elevation ranges and compare 3B42V7 with India Meteorological Department gauge‐based measurements, so as to observe the behavior of satellite at different altitudes. This paper evaluates error characteristics of 3B42V7 using continuous and categorical validation schemes. The analysis reveals 3100 m altitude as the breakpoint for the satellite overestimating and underestimating rainfall amount for elevation ranges below and above it, respectively. It gives a poor positive correlation of ~0.23 between individual rainfall events, though the correlation improves (~0.67) for areal‐averaged precipitation values. 3B42V7 also underestimates the frequency of actual rainfall events and is not very good at identifying correct rain and no‐rain events with the overall accuracy of ~66%. Conclusively, the satellite exhibits comparatively better performance for 1000–2000 m elevations but exacerbates over higher‐altitude regions. Further, we assess its capability for very heavy rainfall events using three percentile thresholds. The low‐magnitude bias for 98th and 99th percentiles and high‐magnitude bias for 99.99th percentile imply that 3B42V7 may not be suitable for the study of very heavy rainfall events. On the basis of these findings, it is recommended to improve satellite precipitation retrieval algorithms by incorporating topographical and local climatic factors into consideration.

The LULC change vis-à-vis climate change inherently encompassing human dimensions consequently impact hydrological processes. A slight change in it may affect the water yield, as both are explicitly linked through various hydrological processes. The future availability of water resources largely depends upon planning and management of land use in this changing environment. However, the continuous human interactions keep on modifying the land use land cover (LULC) to fulfil the enhanced demand especially due to significant increase in population and development towards better facilities. These changes consequently impact each and every hydrological process vis-à-vis water availability. It has now become a major concern for water resources planners and managers. Moreover, these changes in LULC pattern along with changing climate put forth a huge challenge in front of them. The present study investigates the capabilities of Variable Infiltration Capacity (VIC) model, which has been developed as soil-vegetation-atmosphere transfer schemes, to assess the runoff potential. Initially, the required model inputs were derived and simulated at the national scale. It was found that the model has assessed the runoff potential of major river basins with high accuracy. The successful implication of the model at the national scale offered an opportunity to carry out a detailed hydrological simulation to study the impact of LULC change on hydrological regime of a basin. For the analysis, the Pennar River Basin, located in Southern part of India, was selected. The LULC change detection analysis showed that there is human induced alteration to LULC pattern, as the area under urban landscape is increasing (0.14%) and natural forest is declining (0.7%). These changes resulted in increase of runoff potential by around 45%. The study on Indian River basin clearly indicates that the LULC change influence and alter the hydrological regime of the river basin. Keywords: Land use land cover change, Runoff potential, Hydrological modeling, Variable infiltration capacity model, Impact assessment

Abstract The Gandak megafan of eastern Uttar Pradesh and northwestern Bihar lies in the Middle Gangetic Plains. The Gandak River has shifted about 80 km to the east due to tilting in the last 5000 years. This has created a soil chronoassociation similar to the chronosequences found on some flights of river terraces. This chronoassociation has five members, QGD1‐5. They are distinguished on the basis of profile development, clay mineralogy and calcium carbonate content. Chlorite transforms to vermiculite on a large scale from QGD1 to QGD3 and decreases drastically in member QGD4. Kaolinite and interstratified kaolinite‐smectite are abundant in the older members of the chronoassociation. The youngest soils (QGD1:? &lt; 500 b.p.) are found on the floodplains of the major rivers. QGD2 soils, like those of the Young Gandak Plain, date from? &gt; 500 b.p., while QGD3 soils, like those on the Older Gandak Plain and Old Rapti Plains date back to 2500 b.p. QGD4 soils, like those on the Oldest Gandak Plain, are dated as? 5000 years b.p., whilst the oldest QGD5 soils, as on the Old Ghaghra Plain and Ganga‐Ghaghra Interfluve, date back to 10000 b.p. These soils, which include pedogenic calcite and a? saline epipedon, indicate a dry climatic spell during the period 9000‐11000 b.p. Faults developed on the megafan are not related to the basement structures.