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The present experiment was carried out to assess influence and inter-relationships of soil properties in grasslands of three national parks in Madhya Pradesh, Central India. Above ground biomass samples of grass and soil samples were collected during different seasons based on specified measurement protocol by the state forest department. Aboveground biomass of grass showed a characteristic growth pattern throughout the year, increased and achieved maximum biomass at the end of rainy season (September), while started decreasing and attained minimum biomass during pre-summer (March) season. Mean aboveground biomass in the study regions varied from 2.03 (Bandhavgadh National Park) to 5.44 Mg/ha (Kanha National Park) with an average biomass of 3.37 Mg/ha. Biomass showed strong correlation with soil moisture (r=0.69), organic carbon (r=0.77) and electric conductivity (r=0.80) at p=0.05. Correlation between six soil properties showed significant correlation (r=0.90; p=0.01). From this analysis it can be conclude that variation in soil moisture, electric conductivity and organic carbon have direct effect on biophysical properties of grass. [ABSTRACT FROM AUTHOR]

Rainforests play an important role in hydrological and carbon cycles, both at regional and global scales. They pump large quantities of moisture from the soil to the atmosphere and are major rainfall hotspots of the world. Satellite-observed stable water isotope ratios have played an essential role in determining sources of moisture in the atmosphere. Satellites provide information about the processes involving vapour transport in different zones of the world, identifying sources of rainfall and distinguishing moisture transport in monsoonal systems. This paper focuses on major rainforests of the world (Southern Amazon, Congo and Northeast India) to understand the role of continental evapotranspiration in influencing tropospheric water vapour. We have used satellite measurements of 1H2H16O/1H216O from Atmospheric InfraRed Sounder (AIRS), evapotranspiration (ET), solar-induced fluorescence (SIF), precipitation (P), atmospheric reanalysis-derived moisture flux convergence (MFC) and wind to discern the role of ET in influencing water vapour isotopes. A global map of the correlation between δ2Hv and ET-P flux indicates that densely vegetated regions in the tropics show the highest positive correlation (r > 0.5). Using mixing models and observations of specific humidity and isotopic ratio over these forested regions, we discern the source of moisture in pre-wet and wet seasons. [ABSTRACT FROM AUTHOR]

This paper reports results on the LAI Retrieval and Validation Experiment (LRVE) that was conducted for two agricultural areas in Central India during the winter season of 2001–2002. The study aimed at relating field measurements of leaf area index (LAI) to spaceborne Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor-III (LISS-III) data, preparation of site-level LAI maps, and validation of Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km LAI global fields. Measurements of field-level LAI, aerosol optical thickness and water vapor were carried out on the day of LISS-III overpasses. Empirical models based on the site-specific LAI-vegetation index relation were developed and used to generate 23-m resolution LAI maps for two sites (Indore and Bhopal) covering 30 km × 30 km. These LAI images were degraded to 1-km spatial resolution and used for validation of the version 3 and 4 MODIS LAI products (MOD15A2). The results indicate a positive correlation (r = 0.78) between LAI derived from LISS-III data and MODIS data. However an overestimate by a factor of 1.6 to 2.5 in the version 3 MODIS product is observed with root mean square error (RMSE) ranging from 0.20 to 1.26. The factor of overestimation reduces significantly by 50% and RMSE by 40% when version 4 MODIS LAI was analyzed. The improvement in accuracy was observed to be associated with the change in algorithm path adopted for retrieving version 3 and 4 MODIS LAI. Analysis of the MODIS land cover product that is an input in the MODIS LAI retrieval algorithm indicated errors in assigning land cover classes for the study sites, which could be one of the sources of error in MODIS LAI product. [ABSTRACT FROM AUTHOR]

Presents information on the bandpass solar exoatmospheric irradiance and Rayleigh optical thickness for several categories of sensors. Categories of sensors; Characteristics of ground surface that are being quantified through the use of remote sensing data; Constituents of the atmosphere which affect radiance in the visible and near infrared wavebands; Information on the relative spectral measurement.

Isotopic composition of atmospheric water vapor provides information on transport, mixing and phase change of water in the atmosphere. It provides a useful tool for understanding various aspects of the hydrological cycle. SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT-1 was a spectrometer designed to measure the composition of trace gases in troposphere and stratosphere. It provided global measurements of total columnar HDO and H2O concentrations using the spectral window between 2338.5 and 2382.5 nm. Temporal variability of columnar δD was studied over Northeast (NE) India and mean columnar δD for pre-monsoon and monsoon months were correlated with precipitation data obtained from Global System for Mapping of Precipitation (GSMaP). It was observed that δD during the pre-monsoon months of April–May showed good correlation (r > 0.7, p < 0.05) with total precipitation during June–August for the corresponding year over forested regions of Meghalaya and parts of Assam. Analysis was also carried out to understand the relationship between SCIAMACHY derived gridded monthly δD and Multivariate El-Niño Index (MEI) with zero and one month lag periods. Positive correlation was observed between δD and MEI over parts of Central India, Myanmar and Thailand. Isotope ratio of water vapor provides additional information compared to traditional meteorological observations and holds the potential to improve forecasting models. [ABSTRACT FROM AUTHOR]

• An Indian Land Data Assimilation System (ILDAS) is established and ran at 0.1° spatial resolution for a period of 40 years. • The model is able to capture water balance components at monthly and daily scale. • IMD outperformed global precipitation datasets in reproducing streamflow anomalies. 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 India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF's ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region. [ABSTRACT FROM AUTHOR]

Variations in isotopic composition of water vapor in the atmosphere is an important indicator of the processes within the hydrological cycle. Isotopic signature of water vapor and precipitation can be helpful in partitioning evaporation and transpiration fluxes. It is well known that transpiration from forested regions supplies a significant amount of vapor to the atmosphere in monsoon and post-monsoon seasons. Here, we utilize observations from Tropospheric Emission Spectrometer (TES), Atmospheric Infra-Red Sounder (AIRS) and simulation models to ascertain that transpiration is dominant in the forests of Northeast India (NE) during pre-monsoon season. Our results show an increase in δD of 78.0 ± 7.1‰ and in specific humidity of 3.1 ± 0.2 g kg−1 during the pre-monsoon months of April-May compared to January-February. In the monsoon months of July-August, δD reduces by 53.0 ± 6.5‰ albeit the specific humidity increases by 3.4 ± 0.2 g kg−1. Using joint observations of specific humidity and isotope ratio in lower troposphere, we discern the moisture sources over NE India in pre-monsoon and monsoon seasons and posit the role of transpiration in continental recycling during pre-monsoon season. [ABSTRACT FROM AUTHOR]

• Integrated approach towards flood modeling framework for a large transboundary river. • WRF-Hydro model simulation for flood discharge estimation. • River flood-plain inundation and elevation using microwave remote sensing. • Spatio-temporal quantification of flood wave characteristics for a large braided river. The Brahmaputra River, a trans-boundary river between India and Bangladesh experiences large scale floods with frequent inundation events affecting millions of people annually. An integrated approach for quantitative understanding of flood wave characteristics and real time flood modeling framework can be helpful for effective decision making and planning of flood risk management strategies. In this study, we have characterized the flood wave propagation in the Brahmaputra River using gauge observations and model simulations. The spatio-temporal variation of flood lift is estimated using stage hydrograph of different gauge stations along the river. The temporal variation in flood hydrograph is characterized by different flood wave parameters for a gauge station which govern the flood condition in the Brahmaputra River. These flood wave characteristics are used in the categorization of different intensity of flood events. To estimate the river discharge, the WRF-Hydro model was set-up over the Brahmaputra river basin for hourly discharge estimation. The satellite observations form altimeter and Synthetic Aperture Radar (SAR) are used to estimate river flood-plain elevation and inundation. Altimetry data from Jason-3 satellite was used for water level retrieval and compared with gauge and simulated discharge time-series for further validation of model capabilities. Sentinel-1 satellite SAR images are used to assess the flood inundation extent and probability based on flood wave characteristics in the downstream flood-plain region of Guwahati. Finally, we have integrated these data-sets for a flood modeling and forecasting framework for a braided transboundary river system. This integrated system is studied in detail for an extreme event that occurred during July 2019 in the Brahmaputra River. An hourly estimate of each event for the flood period proved the capability of this system for operational deployment in near real-time flood assessment and forecasting purposes for the flood-prone region of South Asia. [ABSTRACT FROM AUTHOR]