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The mountainous areas are vulnerable to climate change and may have many socio-economic and environmental implications. The changing pattern of meteorological variables has deleterious effects on natural resources and livelihood. This paper makes an attempt to analyse trend and forecast metrological variables in Nainital district of India. Monthly, seasonal, and annual trends in rainfall and temperature were examined by Modified Mann–Kendall during 1989–2019. The magnitude of trend in temperature and rainfall was determined using Sen's slope estimator. Ensemble machine learning model was utilized for forecasting the variables for the next 16 years (2020–2035). The effectiveness of the model was examined through statistical performance assessors. The results revealed a significant increasing trend in the rainfall (at the rate of 9.42 mm/year) during 1989–2019. Increasing trend in the mean, minimum, and maximum temperatures on an annual basis was observed in the district. A remarkable increase in the rainfall and temperature was forecasted during various seasons. The findings of the study may help the stakeholders in devising suitable adaptation measures to climate variability. The bagging approach has shown its effectiveness in forecasting meteorological variables. The other geographical regions may find the methodology effective for analyzing climate variability and lessening its impact. [ABSTRACT FROM AUTHOR]

Climate change is a major concern, as it profoundly affects many facets of our lives. It has brought about several issues, including declining water supply, reduced agricultural yields, increased drought occurrences, and increased heat waves. Amidst these challenges, the influence of short-term drought events on plant growth and irrigation schedule emerges as a critical concern. However, despite these evident consequences, a nuanced understanding of the intricate relationship between the severity and duration of short-period drought/deficit events still needs to be explored. This paper analyses fortnightly water deficit periods over different regions of India, which would be more relevant to re-scheduling the irrigation events than monthly or longer duration in preventing crops from reaching the permanent wilting point. Hence, this work considers analyzing 15-day Standardized Precipitation Index (SPI) trends and drought characteristics using conventional methods and Innovative Trend Analysis (ITA) techniques. The analysis uses gridded rainfall data from the India Meteorological Department (IMD), which has a spatial resolution of 0.250-E and 0.250-N. The data spans the period from 1970 to 2021. The ITA and Mann Kendall (MK) displayed nearly identical areas of increasing and decreasing trends, but ITA effectively identified significant trends. While MK and Modified Mann Kendall (MMK) could only indicate significant trends for 12.94%, 9.57%, and 9.9% of grid points for SPI, drought severity, and duration, respectively, ITA was able to identify significant trends at 44.31%, 10.9%, and 10.1% on an annual scale. The ITA method effectively identified the significant trends and magnitudes of fortnightly SPI and drought characteristics. The Tropical monsoon (Am), Tropical savannah (Aw), Arid desert hot (BWh), Arid steppe hot (BSh), and Temperate dry winter warm summer (Cwb) climatic zones have shown a significant increase in annual drought severity. Similarly, a significant increase in monsoon drought severity is observed across several states, including Gujarat and Mizoram, impacting diverse geographic extents. The present study can help policymakers and water resource managers decide on water allocation, irrigation, and crop management practices. [ABSTRACT FROM AUTHOR]

Groundwater is an important resource for a multitude of purposes, and change in groundwater levels is an important factor in determining the increase and decrease of groundwater quantity in aquifers. This study has been carried out for Gomti River basin, where groundwater is the major drinking water source for the entire region. This paper evaluates the behavior of the groundwater levels using Visual MODFLOW under RCP 4.5 and RCP 8.5 scenarios. For modeling of groundwater flow, the entire study area was discretized into 108480 (X-direction) and 207090 (Y-direction) gridal network with each cell size of 1000 m × 1000 m. The model was calibrated for the period 2002 to 2008 and validated for the period 2009 to 2013 using data of 24 observation wells. During the calibration period of Visual MODFLOW, residual mean ranged between 0.658 and 0.268 m, and absolute residual mean ranged between 2.035 and 2.174 m. The calibrated Visual MODFLOW model was employed to evaluate the influence of climate change on future groundwater levels. Future groundwater recharge (2020–2030), obtained after the application of four (MIROC-ESM, MIROC-ESM-CHEM, MIROC MIROC5, and MOHC-HADGEM2-ES) global climate model (GCM) data into prior SWAT model, was applied to Visual MODFLOW. After applying, the projected future (2020–2030) trend in the groundwater levels was found continuously decreasing due to low groundwater recharge and excessive groundwater withdrawal in the basin in future. [ABSTRACT FROM AUTHOR]

The present study majorly focused on the degree of rainfall alteration (DIRA) using an indicator-based novel approach. Most of the previous works majorly focused on only magnitude parameters for measuring monthly rainfall change; however, alteration may also come from other relevant aspects like frequency, timing, rate of change, and duration of rainfall. The present paper considered 33 indicators to assess alterations in rainfall, encompassing various such aspects. Subsequently, it computed the degrees of rainfall alteration categorized as low, moderate, and high range of variability (RVA). This computation was based on the gridded rainfall data endorsed by the Indian Meteorological Department (IMD). Getis-ord was applied to compute the significance of rainfall patterns. The study revealed that middle RVA-based DIRA is low (< 0.33) in the case of magnitude indicators across IMD-defined meteorological sub-divisions, but moderate DIRA (0.33–0.67) was detected in some parts in the case of rate of change, frequency, duration, and timing indicators. This change was detected rather high (moderate to high) over wider parts of India (six and sixteen sub-divisions) in the case of low and high DIRA. In monsoon and post-monsoon seasons, north-eastern hilly hot spots (95–99% level) were weakened, while it was found enhanced in north-western India. A new rainfall hot spot (95%) was identified in Odisha, Telangana, and Vidarbha transition in 1991–2021. Taking into account the notable success achieved through the IRA approach, the study strongly recommends its application across various related domains. [ABSTRACT FROM AUTHOR]

Under global warming and climate change (CC), the vulnerable piece of land is India's Eastern-Himalayan-Region (EHR), where millions' livelihood activities are at risk. Quantifying the degree of CC-induced vulnerability over the highly climate-sensitive mountain zone is challenging due to data inadequacy. This paper attempted to assess the CC perception of kiwifruit growers under EHR and the impact of CC on its cultivation. Perceived and scientific data were used for accurate decision-support information. The climatic variables of two locations were analyzed for trend using Mann–Kendall's test. Results suggested that the rainfall trend was found non-significant for both locations, but there was a negative trend during winter for Pasighat and during all seasons for Itanagar. Maximum temperature exhibited an increasing trend for annual, and minimum temperature showed an increasing trend across seasons for the Pasighat region. For the Itanagar region, the maximum temperature was found to have a significantly increasing trend in all seasons, while the minimum temperature showed an increasing trend during winter and annual seasons. Morning relative humidity showed an increasing trend for annual data for Pasighat, while annual evening relative humidity was significantly increased for Itanagar. The study shows that the indigenous farming communities were following shifting cultivation and diversifying their traditional agriculture to kiwifruit cultivation as an adaptation strategy. The community had a clear perception of CC trends and an inadequate perception of the impact on agriculture, therefore, engaging indigenous communities and other stakeholders to postulate a comprehensive adaptation and mitigation strategy for CC. [ABSTRACT FROM AUTHOR]

Drought depends upon many factors such as lack of precipitation, sunshine duration, and altitude. In this study, future rainfall trend pattern was studied, and an attempt was made to quantify the amount of rainfall deficit to cause drought occurrence and corresponding required water for the recovery of future drought. The non-standardized anomaly index (AI) was used to quantify the water deficit for future drought event, which is based on CMIP 6 climatic model generated rainfall data under the four different shared socioeconomic pathways' (SSPs) scenarios (SSP 1–2.6, SSP 2–4.5, SSP 3–7.0, and SSP 5–8.5) for the chosen study area (the upper Tapti River basin in central India). Our study found out that the future rainfall pattern would be an increasing trend for all the SSPs scenarios up to the end of the twenty-first century. Particularly, SSP 5–8.5 scenario projected increasing rainfall pattern and more number of drought events than other SSPs. In specific season, the future monsoon season showed more number of drought events. The water required for drought recovery was found increased from SSP 1–2.6 to SSP 5–8.5 scenario. The results presented in this study are helpful to adopt drought management activity in response to changing rainfall pattern under climate change aspect. [ABSTRACT FROM AUTHOR]