Search

Accurate and timely information on evapotranspiration (ET0) is essential for multiple agricultural applications, including irrigation scheduling, studying crop-specific water loss at different growth stages, predicting crop yields, and forecasting drought conditions. This study aims to examine the spatiotemporal patterns of ET0 and facilitate the monitoring of crop water demand, optimize irrigation water usage, and enhance agricultural advisory services. This paper estimates regional-level daily ET0 gridded data with a spatial resolution of 12.5 km by integrating observed weather data, IMD GFS-T1534 reanalysis data, and INSAT-3D satellite-based insolation product using the standard FAO56 Penman–Monteith empirical equation. The estimated monthly mean of ET0 across India ranged from 10 to 400 mm. ET0 exhibited an increasing trend from January to May, reaching its maximum values in May. In June, ET0 significantly decreased as the monsoon arrived, coinciding with the movement of rainfall patterns. The month of December exhibited the lowest ET0 values. The estimated daily gridded ET0 was compared with station-based ET0, resulting in daily correlation coefficient R2 and daily maximum absolute percentage errors ranging from 0.34 to 0.90 and 10% to 27% respectively. However, these errors decreased to a large extent when considering multiday accumulated values. A comparison was conducted between the GLDAS model ET0 and the station-estimated values, revealing an overestimation of ET0 by the GLDAS model. Additionally, significant variations were observed among the meteorological subdivisions. This highlights the necessity for proper calibration of the GLDAS model ET0 or its effective agricultural application. [ABSTRACT FROM AUTHOR]

Accurate information of crop water requirements is essential for optimal crop growth and yield. Assessing this information at the appropriate time, particularly during the vegetative and reproductive stages when water demand is highest, is crucial for successful crop production. Our study centered on the drought-prone Marathwada region, specifically targeting the years 2015 to 2020, encompassing the challenging drought year of 2015 and the favourable year of 2020. The crop water stress was detected using crop water stress (CWSI) index and compared with normalized difference vegetation index (NDVI) and normalized difference wetness index (NDWI) derived from satellite data. Our findings reveal a negative correlation between the CWSI and satellite derived vegetation indices NDVI and NDWI. Notably, the NDWI index exhibits stronger alignment with CWSI compared to NDVI. The correlation demonstrates particular robustness during drought or deficient rainfall years such as 2015, 2017, and 2019, while weaker correlations are observed in 2016, 2018, and 2020. Moreover, these correlations display variations across different areas within distinct rainfall zones. [ABSTRACT FROM AUTHOR]

India Meteorological Department (IMD), Ministry of Earth Sciences (MoES) in collaboration with Indian Council of Agriculture Research (ICAR), State Agriculture Universities (SAUs), Indian Institute of Technology (IITs) and other organizations is rendering weather forecast based District level Agrometeorological Advisory Service (AAS) for benefits of farmers in the country under the centrally sponsored scheme 'Atmosphere & Climate Research-Modelling Observing Systems & Services (ACROSS) ' of MoES. AAS, popularly known as Gramin Krishi Mausam Sewa (GKMS) provides advance weather information along, with crop specific agromet advisories to the farming community by using state of the art instruments and technology through efficient delivering mechanism of the information which ultimately enables farmers to take appropriate actions at farm level. The various components of GKMS viz. observing weather, its monitoring and forecast; crop specific advisory bulletin generation and dissemination; outreach and feedback have been/are being digitized to support integrating all the components of information generation and action suggested linked to these information. An Information and Communication Technology (ICT) based Agromet Decision Support System is developed for automation of the services provided under GKMS. This includes a dynamic framework to link the information of weather forecast, real time weather observation, crop-weather calendar etc. to translate weather forecast into actionable farm advisories for efficient farm level decision making in India. Apart from this, effort is being made to develop recent technology driven tools to estimate future yield of crops and prepare an irrigation schedule without a need of multiple parameters. [ABSTRACT FROM AUTHOR]

The sub-seasonal variation of Indian summer monsoon rainfall highly impacts Kharif crop production in comparison with seasonal total rainfall. The rainfall frequency and intensity corresponding to various rainfall events are found to be highly related to crop production and therefore, the predictability of such events are considered to be diagnosed. Daily rainfall predictions are made available by one of the coupled dynamical model National Centers for Environmental Prediction Climate Forecast System (NCEPCFS). A large error in the simulation of daily rainfall sequence influences to take up a bias correction and for that reason, two approaches are used. The bias-corrected GCM is able to capture the inter-annual variability in rainfall events. Maximum prediction skill of frequency of less rainfall (LR) event is observed during the month of September and a similar result is also noticed for moderate rainfall event with maximum skill over the central parts of the country. On the other hand, the impact of rainfall weekly rainfall intensity is evaluated against the Kharif rice production. It is found that weekly rainfall intensity during July is having a significant impact on Kharif rice production, but the corresponding skill was found very low in GCM. The GCM are able to simulate the less and moderate rainfall frequency with significant skill. [ABSTRACT FROM AUTHOR]

ABSTRACT India gets the maximum amount of rainfall during the months of June to September ( JJAS) which is known as the summer monsoon season. The erratic nature of Indian summer monsoon rainfall ( ISMR), in terms of both rainfall amount and distribution, is highly responsible for the interannual variability in agricultural production as well as occurrence of floods and droughts. Accurate seasonal predictions of ISMR are required for appropriate hydrological planning and disaster management systems. Studies have revealed that probabilistic prediction, based on the products of General Circulation Models ( GCMs), can be generated in a parametric as well as non-parametric manner. The present paper discusses the enhancement of probabilistic prediction by improving the potential predictable signal obtained from these GCMs. A Singular- Value- Decomposition based multiple linear regression method ( SVD-MLR) has been applied to improve the signal and a simple average of all GCMs ( EM) has been used as the benchmark to examine the skill of the SVD-MLR method. The potential of the proposed method has been assessed through Brier Skill Score ( BSS) and Rank Probability Skill Score ( RPSS). A rigorous analysis has finally revealed that SVD-MLR method has better skill than EM in predicting the typical nature of observed monsoon rainfall in extreme years. [ABSTRACT FROM AUTHOR]