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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]

This paper presents a composite approach for drought characterization and monitoring using in situ and remote sensing-based drought indicators. The study was carried out on one of the most drought-prone areas of India, i.e., the Marathwada region of Maharashtra. Meteorological, hydrological, and agricultural drought indices, namely standardized precipitation index (SPI), streamflow drought index (SDI), and vegetation condition index (VCI), respectively, were integrated to develop the composite drought index (CDI) using principal component analysis (PCA). SPI and SDI were computed using in situ precipitation and streamflow data, respectively, for 35 years (1980–2014), whereas VCI was computed using MODIS satellite data (500-m resolution) for 15 years (2000–2014) at 1-, 3-, and 5-month time scales. The time scales of drought indices were evaluated using historical drought years and foodgrain production of the region. The drought areas observed by SPI, SDI, and VCI at different time scales were correlated with foodgrain production during the kharif crop growing season for 15 years (2000–2014). The maximum correlation of foodgrain production was observed with 3-month SPI (r = − 0.72), 5-month SDI (r = − 0.40), and 5-month VCI (r = − 0.81) for meteorological, hydrological, and agricultural drought, respectively. Three-month SPI, 5-month SDI, and 5-month VCI were selected from each drought category to develop CDI. These drought indices were combined using weights derived through the PCA technique. The maximum weight was obtained for 3-month SPI (45.4%) followed by 5-month VCI (42.8%) and 5-month SDI (11.8%). The developed CDI products showed a strong relationship (r = − 0.85) with foodgrain production. The drought years observed by CDI were also closest to drought years declared by the State Government. The time series trend of the drought-affected area observed by CDI, 3-month SPI, and 5-month VCI resembled the drought patterns very closely, especially during the drought years. The spatio-temporal analysis of individual drought indices and CDI with foodgrain production deviation showed that CDI was better for capturing drought conditions than individual indicators. The study suggested that an individual or single indicator is not sufficient to capture the actual drought severity and its magnitudes; therefore, using a composite approach could be a good choice for effective drought assessment and monitoring in the region. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET), one of the key components of the hydrological system, plays a major role in the water availability assessment in a region. ET is a complex process, and calculating ET from reference evapotranspiration (ET0) is one of the popular methods, where pan evaporation (EPan) value is converted to ET0 by multiplying the EPan value with the pan coefficient (Kp) value. The Kp value depends on the climatic factors; hence, the determination of Kp varies for different regions. This paper investigates the spatial and temporal variation of the Kp values for the northeastern region (NER) of India having diverse topography and climatic conditions. Observed EPan data and ET0 calculated for ten selected stations of the eco-sensitive region NER of India were used for calculating the Kp values in monthly, seasonal, and annual timescales. Spatial variation of the Kp was identified by interpolation method, and temporal trends in the Kp values were identified by using the parametric method (Linear regression test) and non-parametric (Mann-Kendall test (MK)) after the pre-whitening of the time series. The average monthly Kp values in the NER of India varied from 0.788 in March at Umiam to 2.462 in July at Gangtok, whereas the annual Kp varied from 0.978 at Imphal to 1.822 at Gangtok. The seasonal Kp was found to be high during monsoon (1.401) and low during post-monsoon (1.017). Trend analysis shows that Kp was found to be increasing in annual, pre-monsoon, and monsoon seasons, whereas decreasing trends in Kp were found during winter and post-monsoon in the NER. The spatial distribution of Kp depicted that the values were found high in the northwest region and low in the remaining parts of the NER of India. The spatial distribution of trends and their magnitudes were found to be decreasing in the northwest and some parts of the northeast and southwest, whereas increasing trends were found in the central and southern parts of the NER. Results of Pettit's test for change point analysis observed that all but four stations of the NER witnessed the change point after 1990. Assessment of the Kp could be helpful for the estimation of actual evapotranspiration in the region, which further could be useful for water management in agriculture and other water sectors. [ABSTRACT FROM AUTHOR]

This paper presents a detailed spatiotemporal analysis of the rainfall variability, seasonality, and the extreme characteristics of Tehri catchment located in the lower Himalayan region in India. To this end, the daily rainfall data is extracted from 22 grids for 117 years (1901–2017) from the high-resolution (0.25° × 0.25°) gridded observation dataset. Monthly rainfall distribution is evaluated using precipitation concentration index (PCI) and seasonality index. The extreme rainfall indices, viz., maximum 1-day rainfall (Rx1Day), maximum 5-day rainfall (Rx5Day), number of rainy days (NxRainy), total precipitation in rainy days (PRCPTOT), number of heavy rainfall events (NxHeavy), maximum consecutive wet days (CWD), and simple daily intensity index (SDII) are computed for each year considering the thresholds suggested by India Meteorological Department (IMD). The Mann–Whitney-Pettitt test when applied to the annual rainfall time series revealed the year 1958 to be the statistically significant change point. The non-parametric modified Mann–Kendall and Sen's slope tests are employed to detect the trend in monthly, seasonal, annual rainfall time series, extreme precipitation indices, and seasonality indices for both the pre- and post-1958 periods. The annual rainfall over the grids mostly possessed higher negative trends during 1959–2017 than those during 1901–1958, mainly due to the decreasing trends in post-monsoon and winter seasons. Compared to 1901–1958, NxRainy, CWD, and PRCPTOT exhibited a remarkable decreasing trend whereas NxHeavy, Rx1Day, Rx5Day, and SDII exhibited higher positive trends during 1959–2017, indicating intensification of precipitation. The precipitation over the catchment has been more concentrated in the latter epochs of monsoon season and annual rainfall and it is also evident from the increasing trends of the seasonality indices. There is no such study dealing comprehensively with identification of extreme characteristics, seasonality/concentration characteristics, and various categorical trends of precipitation in a Himalayan region reported in literature. This study will be useful in understanding the decreasing trend of precipitation volume coupled with increasing intensity and concentration and it is quite critical for a Himalayan catchment. [ABSTRACT FROM AUTHOR]

B Correction to: Theoretical and Applied Climatology b https://doi.org/10.1007/s00704-022-04168-4 The affiliation of one of the authors (Pranaya Diwate) has been wrongly mentioned in the paper. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]