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The Indian economy and food grain production are intricately tied to the spatial distribution and the amount of rainfall during the summer monsoon period (June to September). Numerous studies have delved into modeling methods for forecasting Indian summer monsoon rainfall. This paper utilizes monthly rainfall data spanning 72 years (1950–2021) to analyze, model, and predict the monsoon over Northwest India using Dynamic Linear Modeling (DLM). In view of this, the well-accepted dynamic linear model with significant components is selected from the parsimonious models of DLM based on Bayesian Information Criteria (BIC), significant tests, and statistical fit. The dynamic linear model comprises a significant linear time trend component, a deterministic trigonometric seasonal component, a deterministic cycle component, and a stochastic irregular component has been selected. The validation of the selected model passed through the normal and correlation diagnostics of model residuals. The analysis reveals a subtle decreasing trend in monsoon rainfall, showcasing consistent behaviour in deterministic seasonality, and highlighting the deterministic nature of cyclical component with a period of approximately 2.5 years, alongside irregular variations. Utilizing this model, future monsoon rainfall over Northwest India has been forecasted for the years 2022–2027, providing a 95% confidence interval. [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]

Indian southwest monsoon rainfall has a huge socioeconomic influence, and any changes in monsoon rainfall patterns will have major consequences for agriculture, water supplies, and other connected sectors of economy. The paper explores the charactering and forecasting of the southwest monsoon rainfall (June to September) patterns over the Central region of India for the period (1901–2021: 121 years) using linear state space modeling. The study period is split into two periods, i.e., earlier period (EP: 1901–1950) and the recent period (RP: 1951–2021). The linear state space model (LSSM) with significant latent components such as trend, seasonal, periodic and random variations or noise is selected for modeling the southwest monsoon rainfall (SWMR) based on Bayesian information criteria (BIC) and statistical fit for the two periods. The result indicates that an increase in linear time trend during the EP and the RP has a decreasing local linear time trend in SWMR, and it is interesting to notice that the EP had no noteworthy periodic variations, whereas the RP exhibits significant periodic variations in SWMR. Further, the selected LSSM forecasts SWMR patterns for the period 2022–2028, and it has been observed that the Central Indian region is expected to receive substantial rainfall in the years 2026, 2027 and 2028. [ABSTRACT FROM AUTHOR]

Better understanding on the phases of Indian summer monsoon i.e., onset, progress and withdrawal helps in the decision making and proper management. In this paper, date of onset of monsoon (DOM) and date of withdrawal of monsoon (DWM) is estimated using the long-term daily rainfall data observed by the India Meteorological Department (IMD) and cumulative rainfall anomaly concept, as the onset/withdrawal represents a signature of rainfall transition. The results are promising with a better match between the estimated DOM and IMD announced DOM over Kerala. The length of monsoon season variability is also analysed. The concept of onset at grid point (OGP) in India is also evaluated and progress of monsoon is monitored by considering the latitudinal variation, persistent & strength of rainfall in the continental India. The zonal classification of the spread of monsoon in terms of the intensity and time (duration) is presented. Zone wise pentad analysis of onset clearly signifies the progress of monsoon and its dependency on various physical factors like SST, OLR, TTG, ITCZ, Energy budget and wind circulation. The impact of ENSO on the OGP is brought out, which clearly shows that DOM, DWM and progress is almost influenced by the largescale processes. The intensity–duration–frequency analysis quantified the actual ISM length, seasonal rainfall and rainy days at all locations in India. This study will help modellers in better understanding of onset and progress of monsoon dynamics in India and can be integrated with NWP for the accurate and advance prediction of DOM and the progress. [ABSTRACT FROM AUTHOR]

Landslides cause significant damage to property and life in the West Jaintia Hills district in Meghalaya, India, due to mountains, slopes, and extreme rainfall. Landslide susceptibility maps are highly efficient and helpful for disaster preparation and management. The determination of this work is to create a landslide susceptibility map (LSM) for West Jaintia Hills using the analytical hierarchy process (AHP) method. The previous landslide data are required for effective management of the current situation, and these data are divided into training samples (75%) and testing samples (25%) for predicting efficiency. The different landslide causative factors are investigated in this study: rainfall, slope, geomorphology, elevation, lineament density, LULC, distance from a road, NDWI, MSAVI, and NDVI. The causal factors are subdivided and weighted according to AHP, and a susceptibility map is prepared. According to the developed map, 19% of the region's total area is low susceptible, 52% moderate, 25% high, and 4% very highly susceptible. Field data and the receiver operating characteristics (ROC) approach are used to validate the produced map. The outcomes indicate that the predicted susceptibility map closely reflects previous and recent landslide events. The created map is accurate and can be applied to future land-use planning. [ABSTRACT FROM AUTHOR]

In a mountainous basin, flash-flood-induced soil erosion and its assessment are of major concern to the researchers. Cloudburst-induced extreme rainfall, undulating terrain, and inadequate water and soil conservation practices aggravate the problem of soil erosion. Alaknanda, a major basin of the upper Ganges in India, has experienced several incidents of flash floods. These have resulted in heavy soil loss, water siltation, and pollution in the basin. This study uses the revised universal soil-loss equation (RUSLE) in the GIS framework to assess the soil loss rate in various sub-basins of the Alaknanda. Further, the study suggests sub-basin prioritisation based on high soil erosion rate and past vulnerability that need attention of the decision-makers. RUSLE, an empirical soil erosion model, is easy to comprehend physically, involves minimum resources, and works on readily available inputs. The parameters used in the assessment include R-Rainfall Erosivity, Cover Management-C, Topographic-LS, Conservation Practice-P, and Soil Erodibility-K. The average R, K, LS, C & P factors for the Alaknanda basis are estimated as 437 MJ mm/ha/yr., 0.07 t ha h MJ-1 ha-1 mm-1, 1.4, 0.7, and 0.62, respectively. The mean annual soil erosion rate in the sub-basins varies from 6 to 27.9 t /ha/year with a mean rate of 13.4 t/ha/year and total erosion of 13.7 Mt/year. The results show that out of the twelve sub-basins, two (WS7 and WS5) fall in the high erosional class (18–30 t/ha/year), nine fall in the moderate erosional class (7–18 t /ha /year) and one basin in the low erosional class (1.0–7.0 t /ha/year). It is hoped that the present study will be helpful to the decision-makers while prioritising the actions to be taken for the water and soil conservation purposes. [ABSTRACT FROM AUTHOR]

The Indo-Ganga–Brahmaputra plain is one of the world's most vulnerable areas to catastrophic flood events. The severity and frequency of floods in this region have grown dramatically during the last few decades. As a result, an authentic map providing detailed spatial information on floods is required for reducing future flood disasters. This sort of map can be an invaluable tool for displaying flood-prone areas and determining the magnitude of flood risk. The Ramganga river (~ 200 km length) flowing in the western Gangetic plain was chosen for this investigation. This river is well known for its channel movement and flood hazards. The analytical hierarchy process model in GIS ambiance was used in this work to quantify and map flood risk in the lower Ramganga river basin. Six hazard-forming factors, namely relief, distance from active channel, slope, geomorphology, rainfall, and vegetation, and seven vulnerability factors, namely population density, household density, female density, road density, land use/land cover, female literacy, and total literacy, were chosen to determine flood risk zone (FRZ). According to the findings, about 35.29 per cent of the basin's total area is in a high to very high flood risk zone, where flooding occurs at one- to three-year recurring intervals. The flood risk map has been validated using ground truth data, and the Kappa index has been computed, indicating a high degree of accuracy for the FRZ thematic classes. [ABSTRACT FROM AUTHOR]