No abstract

Rainfall is critical to agricultural and drinking water supply in the Thamirabharani river basin. The upper catchment areas of the Thamirabharani basin are located in high-elevated forest regions, and rainfall variability affects dam inflow and outflow. The well-known methods for rainfall analysis such as the coefficient of variation (CV), the precipitation concentration index (PCI), and trend analysis by Mann-Kendall and Sen’s slope test, as well as the Sen’s graphical innovative trend method (ITA) recently reported in several studies, were used. Rainfall data from gauge stations and the satellite-gridded Multisource Weighted Ensemble Precipitation (MSWEP) dataset were chosen for analysis at the annual and four-season time scales, namely, the Southwest Monsoon, Northeast Monsoon, winter, and summer seasons from 1991 to 2020. The mean annual PCI value reflects irregular monthly rainfall distribution (PCI > 20) in all gauge stations. The spatial monthly rainfall distribution of PCI values remarkedly shows a moderate distribution in the western and an anomalous distribution in the eastern part of the basin. The annual mean rainfall ranges from 718.4 to 2268.6 mm/year, decreasing from the high altitude zone in the west to the low plains and coastal regions in the east. Seasonal rainfall contributes about 42% from the NEM, 30.6% from the SWM, 22.8% from summer, and 3.9% from winter, with moderate variability (CV less than 30%). Ground stations experienced extremely high interannual variability in rainfall (more than 60%). Trend analysis by the MK, TFPW-MK, and ITA methods shows increasing annual rainfall in the plains and coastal regions of the basin; particularly, more variations among the seasons were observed in the Lower Thamirabharani sub-basin. The NEM and summer season rainfall are statistically significant and contribute to the increasing trend in annual rainfall. The ITA method performed better in the annual and seasonal scale for detecting the rainfall trend than the MK and TFPW-MK test. The Lower Thamirabharani sub-basin in the eastern part of the basin receives more rain during the NEM than in other areas. To summarize, the low plains in the central and coastal regions in the southeast part experience an increase in rainfall with irregular monthly distribution. This study helps farmers, governments, and policymakers in effective agricultural crop planning and water management.

In India, drip irrigation with plastic mulch is a common practise for irrigation that conserves water. For the design and administration of irrigation regimes, a thorough understanding of the distribution and flow of soil water in the root zone is required. It has been demonstrated that simulation models are effective tools for this purpose. In this work, an automated drip-irrigated Okra field with seven treatments namely T1- Soil moisture-based drip irrigation to 100% FC, T2- Soil moisture-based drip irrigation to 80% FC, T3- Soil moisture-based drip irrigation to 60% FC, T4- Timer based drip irrigation to 100% CWR, T5- Timer based drip irrigation to 80% CWR, T6- Timer based drip irrigation to 60% CWR and T7- Conventional drip irrigation at 100% CWR were utilised to mimic the temporal fluctuations in soil water content using the numerical model HYDRUS- 2D. With the help of the observed data, the inverse solution was used to optimise the soil hydraulic parameters. The model was used to forecast soil water content for seven field treatments at optimal conditions. Root mean square error (RMSE) and coefficient of determination (R2) were used to assess the congruences between the predictions and data. With RMSE ranging from 0.036 to 0.067 cm3 cm- 3, MAE ranging from 0.020 to 0.059, and R2 ranging from 0.848 to 0.959, the findings showed that the model fairly represented the differences in soil water content at all sites in seven treatments.

Increased anthropogenic activity in recent decades has resulted in major global climate change. This paper mainly focuses on the assessment of changes occurring in the spatio -temporal distribution of rainfall with 40-years database of monthly precipitation for Seasonal Precipitation Concentration Index (SPCI) and trends in Tamil Nadu. The hydro-meteorological time series rainfall data over a period of 40 years (1981–2020) was collected from Tamil Nadu Agricultural University and India Meteorological Department and subsequently analysed using various statistical methods for Tamil Nadu. The SPCI was analysed for both southwest and northeast monsoon. SPCI values (< 10) revealed that the rainfall was uniformly distributed in southwest and SPCI values (> 10) showed that more weather extremes were observed during northeast monsoon. Mann–Kendall, non-parametric test was done using trend software for both the monsoon. During southwest, significant increasing trend in rainfall was observed at Coimbatore (1.8mm/season/year), Erode (2.1mm/season/year), Perambular (2.1mm/season/year), Theni(2.0mm/season/year) and Tirunelveli (2.4 mm/season/year) while significant decreasing trend in rainfall was observed at Namakkal(2.5 mm/season/year). During northeast, significant increasing trend in rainfall was observed Kancheepuram (2.4 mm/season/year), Tutucorin(2.6 mm/season/year) and Villupuram(2.0 mm/season/year).

Climate change is often linked with record-breaking heavy or poor rainfall events, unprecedented storms, extreme day and night time temperatures, etc. It may have a marked impact on climate-sensitive sectors and associated livelihoods. Block-level weather forecasting is a new-fangled dimension of agrometeorological services (AAS) in the country and is getting popularized as a climate-smart farming strategy. Studies on the economic impact of these microlevel advisories are uncommon. Agromet advisory services (AAS) play a critical role as an early warning service and preparedness among the maize farmers in the Parambikulam–Aliyar Basin, as this area still needs to widen and deepen its AWS network to reach the village level. In this article, the responses of the maize farmers of Parambikulam–Aliyar Basin on AAS were analyzed. AAS were provided to early and late Rabi farmers during the year 2020–2022. An automatic weather station was installed at the farmers’ field to understand the real-time weather. Forecast data from the India Meteorological Department (IMD) were used to provide agromet advisory services. Therefore, the present study deserves special focus. Social media and other ICT tools were used for AAS dissemination purposes. A crop simulation model (CSM), DSSAT4.7cereal maize, was used for assessing maize yield in the present scenario and under the elevated GHGs scenario under climate change. Our findings suggest that the AAS significantly supported the farmers in sustaining production. The AAS were helpful for the farmers during the dry spells in the late samba (2021–2022) to provide critical irrigation and during heavy rainfall events at the events of harvest during early and late Rabi (2021–22). Published research articles on the verification of weather forecasts from South India are scanty. This article also tries to understand the reliability of forecasts. Findings from the verification suggest that rainfall represented a fairly good forecast for the season, though erratic, with an accuracy score or HI score of 0.77 and an HK score of 0.60, and the probability of detection (PoD) of hits was found to be 0.91. Verification shows that the forecasted relative humidity observed showed a fairly good correlation, with an R2 value of 0.52. These findings suggest that enhancing model forecast accuracy can enhance the reliability and utility of AAS as a climate-smart adaptation option. This study recommends that AAS can act as a valuable input to alleviate the impacts of hydrometeorological disasters on maize crop production in the basin. There is a huge demand for quality weather forecasts with respect to accuracy, resolution, and lead time, which is increasing across the country. Externally funded research studies such as ours are an added advantage to bridge the gap in AAS dissemination to a great extent.