Indian Rainfall Research Articles

Influences of rainfall characteristics on water saving and stormwater control performances of rainwater harvesting systems.

Rainwater harvesting systems (RHS) are extensively executed to manage stormwater control and water shortage issues in cities. However, the influences of rainfall characteristics on the performances of RHS are still not deeply explored. In this research, a methodology framework is developed to explore the influences of rainfall characteristics on stormwater control and water saving performances of RHS, by using daily precipitation data during 1968-2017at 30 stations across the Beijing region as a testbed. The proposed methodology framework applies a water balance model, which requires daily rainfall and water demands, catchment area, tank size, runoff losses and first flush as input data, to assess water saving efficiency (WSE), reliability (R), and stormwater capture efficiency (SCE) of RHS, and a clustering approach to determine if same rainfall patterns produce similar/equivalent outputs of RHS. Rainfall characteristics are quantified using 10 rainfall indices, and their influences on the performances of RHS are investigated. Results demonstrate that WSE, R, and SCE of RHS emerge less scattered when clustering becomes more accurate. Equivalent outputs of RHS for combined water demand can be obtained by dividing 30 stations into eight clusters. Furthermore, 6 rainfall indices (mean annual rainfall, standard precipitation index, rainfall concentration degree, ratio of dry and rainy days, consecutive number of dry days per year, average duration of dry days) are found significantly influencing the stormwater control and water saving performances of RHS. Therefore, they are recommended to be carefully considered to quantify rainfall characteristics and support the performance evaluation and design of RHS.

Addressing Climate Change in India: Public Policies, Challenges, and Opportunities"

Climate change is a pressing global challenge, and India, as a developing nation with a significant population, faces unique challenges and opportunities in addressing it. This paper explores the public policies implemented by the Indian government to combat climate change, focusing on strategies for mitigation and adaptation. The study examines key policies, such as the National Action Plan on Climate Change (NAPCC), state-level initiatives, and India's commitments under international frameworks like the Paris Agreement. Additionally, it highlights the role of renewable energy, afforestation, and sustainable development practices in India's climate strategy. The paper concludes with an evaluation of policy effectiveness, identifying gaps and recommending measures to strengthen India's response to climate change.

Identification and Variability Analysis of New Homogeneous Summer Monsoon Rainfall Regions Over India by Using K‐Means Clustering Technique

ABSTRACTThe identification of new homogeneous Indian summer monsoon rainfall regions is important for both research and operational forecasting purposes. The homogeneous rainfall regions used by the India Meteorological Department include areas that are actually dissimilar in terms of both the amount of rainfall and its variability. In the present study, we have identified three primary clusters of homogenous summer monsoon rainfall zones (low, medium and high rainfall) of India by using K‐means clustering technique and Calinski–Harabasz (CH) index. The identified clusters are again subdivided based on the distance between the clusters into six homogenous rainfall zones such as Southeast India (SI), West India (WI), Central India (CI), West Coast of India (WC), Northeast India1&2 (NE1 & NE2). Summer monsoon rainfall exhibit a positive trend over the western and southern region of India (SI, WC, and WI) while the eastern part of India (CI, NE1 and NE2) exhibit a negative trend. The increased sea surface temperature of tropical north Atlantic during May is conducive for the enhanced summer monsoon rainfall activity over low rainfall zone (Southeast India and West India). Tropical North Atlantic SST during May is related with the subsequent warming of Indo Pacific Ocean and cooling of Central Pacific Ocean, which induces southeasterlies towards Indian region and strengthen the rainfall activity over SI and WI. The position and intensity of the upper‐level circulations have profound impact on the interannual variability of rainfall over different homogenous regions. Above‐normal rainfall over SI and WI (CI and WC) is enhanced by the upper‐level anticyclonic (cyclonic) circulation in Bay of Bengal (West Asia), which extends north westward (south eastward) to the Indian landmass. Indian Ocean warming during May is associated with above (below) normal rainfall over NE2 region (the eastern part of CI). Southern Ocean SST during May is significantly correlated with the rainfall over southwest coast of India, which is also indicated by the positive correlation between April southern Annular Mode and south west coast rainfall. Sea surface temperature during May in different ocean basins can be used as the potential predictors for improving the long‐range forecast of seasonal rainfall over newly identified homogenous rainfall zones.

Reduced aerosols and intensified summertime rainfall in India during the pandemic suggest potentially more amplified precipitation in the future

Abstract Aerosol pollution is anticipated to decrease in the future, yet the associated effects of reduced aerosol loading on precipitation remain insufficiently explored. Widespread reductions in anthropogenic emission during COVID-19 lockdowns offer a unique opportunity to understand precipitation responses to changes in anthropogenic aerosols. Based on observations and regional and global climate-chemistry coupled model simulations, we attribute unprecedented precipitation in India during the 2021 lockdown to decreased aerosol levels due to emission reductions. Reduced aerosol loading leads to a northward shift of the subtropical westerly jet, which induces a westward movement of the subtropical southern branch trough and negative sea-level pressure anomalies over the eastern Arabian Sea. This shift facilitates greater water vapor transport from surrounding oceans to land, increasing precipitation in India by approximately 24.2% in May according to WRF-Chem simulations and by 28.5% over the entire lockdown period according to CESM2 simulations. Future projections under the lower aerosol emission scenario indicate an additional enhancement in monsoon precipitation in India. Our findings highlight the complex interplay between aerosol emissions and hydrometeorological dynamics, with implications for understanding future precipitation changes and providing theoretical reference for water resource management.

Paradigm Shift in Typhoon Forecasting for the Korean Peninsula: A Case Study on the Applicability of the Typhoon-Ready System

Climate change has significantly increased multi-hazard disasters caused by typhoons, exposing the limitations of conventional forecasting systems that often neglect regional socio-economic vulnerabilities. This study develops and validates the Typhoon-Ready System (TRS) as an effective disaster-management framework for the Korean Peninsula. The TRS integrates hazard data with socio-economic and environmental vulnerability factors to produce region-specific risk indices. The analysis of four representative typhoons—Lingling (2019), Rusa (2002), Maemi (2003), and Mitak (2019)—demonstrates TRS’s applicability in identifying high-risk zones and supporting disaster preparedness strategies. The TRS framework incorporates indices, such as the Strong Wind Index (SWI), Heavy Rainfall Index (HRI), Storm Surge Index (SSI), and Air Quality Index (AQI), effectively combining meteorological modeling with vulnerability analysis. Results demonstrate that the TRS outperforms traditional systems by accurately identifying high-risk zones and correlating them with observed damage patterns. For example, the TRS successfully pinpointed high wind risks in Seoul and Incheon during Typhoon Lingling and forecasted severe flooding in Gangneung and Samcheok during Typhoon Rusa. By integrating vulnerability factors, including population density, infrastructure aging, and urbanization levels, the TRS provides a more holistic and accurate risk assessment. This research highlights the necessity of a multi-dimensional forecasting approach for enhancing disaster preparedness and resilience against climate change-induced typhoon impacts.

Trends and Drivers of Greenhouse Gas Emissions in India: A Decadal Analysis (2010-2020)

This paper analyses trends and determinants of greenhouse gas (GHG) emissions in India in 2010-2020, the decade with some of the fastest economic growth rates and the increased awareness of environmental issues. The research pays attention to India, the world's third-largest emitter of GHG, which will be analysed regarding emissions in four sectors: energy, industry, agriculture, and waste management. A time series analysis of data drawn from national and international agencies, such as the World Bank and India's Ministry of Environment, Forestry, and Climate Change, was applied in this study. Statistical analysis and data visualisation were performed using the R software. The key findings show an increase of 42% in the total emissions of GHG over the last decade, with the energy sector dominating at 68% in 2020. Other significant contributors were industry (20%), agriculture (9%), and waste (3%). Growth in population, along with accompanying urbanisation and industrial expansion, is cited as the main factor responsible for emission rise. However, there are also indications of decoupling between growth in the economy and emissions intensity. The study analyzes the greenhouse gas emission trends for major emitters between 2010 and 2020, in India. The dynamic panel data model analyses indicate signs of decoupling of emissions from economic growth in developed economies and the role of renewable energy and carbon pricing. Research findings further indicate that stronger climate policy and investment in clean energy is the necessity to decrease global emissions.The paper identifies policy issues that require strong activity in enforcing efficiency measures, fastening the shift toward krenewable sources of energy, and effective sustainable planning and waste management practices in urbanisation processes. In that respect, examination of policies like the PAT scheme and promotion of electric vehicles has shown where more aggressive implementation may be seen as necessary to ensure India's climate commitments. This elaborate decadal review finds most of its utility within the framework of policy designing for sustainable development and climate change mitigation by policymakers and stakeholders in the Indian scenario.

Classification of weather conditions based on automatic weather station data using a multi-layer perceptron neural network

Weather is one of the important elements that greatly determines human activities, especially those related to economic factors. Therefore, understanding weather conditions using weather parameters as a reference is important for human life, so a method is needed to classify weather according to its category so that the information produced can be used for various needs. Determining weather conditions in an area will not run well without a reliable method that can analyze existing weather parameters. Therefore, in this study, the weather condition classification process was carried out using the multilayer perceptron algorithm, a type of neural network (NN) algorithm. All data analyzed were weather parameter data collected by mini weather stations placed on land. The weather parameters used were temperature, humidity, air pressure, wind speed, dew point, wind chill, daily rainfall, solar radiation, and UV index. This study was conducted in Palu city, Central Sulawesi Province, Indonesia. The classification process carried out by the multilayer perceptron algorithm was carried out on the Altair AI Studio application and produced an accuracy value of 93.87%, recall of 92.33%, and precision of 91.29%.

Adaptive responses of large-seeded lentils across diverse Indian climates

Adaptive responses of large-seeded lentils across diverse Indian climates

Cloud computing-based estimation of Peninsular India’s long-term climate change impacts on rainfall, surface temperature, and geospatial indices

Realizing the intricate relationships between drought, vegetation dynamics, and climate change is essential for sustainable resource management. Although temperature and rainfall patterns are the primary determinants of these fluctuations, human activity also plays a significant role. Recent decades have witnessed significant climate change events, particularly in peninsular India. Analyzing these year-by-year variations in rainfall and temperature is essential for informed decision-making. This knowledge can guide the development of innovative adaptation strategies to ensure sustainable livelihoods in the region. This study utilizes the Google Earth Engine platform to analyze yearly climate data and relevant geographical indices from 2003 to 2023 across Peninsular India. The analysis reveals a statistically significant increase in mean annual rainfall (0.262 mm/year) alongside a slight rise in regional land surface temperature (LST) trends (0.102 °C/year). However, yearly average anomaly values for LST also show an upward trend, rising from 2.56 in 2003 to 3.23 in 2023. This suggests a potential shift in rainfall patterns, with potential consequences for water availability. Rising temperatures coupled with altered rainfall patterns can lead to water scarcity, especially in regions reliant on rain-fed agriculture. This has a direct impact on crop yields and overall agricultural productivity. Despite rising temperatures, the analysis using drought indices suggests a decline in average annual drought severity across Peninsular India, with values decreasing from 0.33 in 2003 to 2.70 in 2023. Interestingly, we found a strong positive association between rainfall and vegetation indices, while rainfall and LST exhibited a negative correlation. Interestingly, while rainfall and LST exhibited a negative correlation, a strong positive association was found between rainfall and vegetation indices. These comprehensive findings hold significant potential for informing future climate projections and promoting sustainable development in peninsular India through evidence-based applications.

Exploring the driving factors of compound flood severity in coastal cities: a comprehensive analytical approach

Abstract. Coastal cities face severe compound flooding, including both fluvial flooding and pluvial flooding. Currently, there is a lack of comprehensive methods to analyze the driving factors of compound flooding. This study establishes a coupled one-dimensional and two-dimensional hydrodynamic model. Based on historical data, the model constructs joint probability distributions of rainfall and tide levels with different return periods and durations. Using the results from the coupled model under various design scenarios, the study proposes an impact index to quantify the relative contributions of rainfall and tide level to flooding. Additionally, the model quantifies the interactions between fluvial flooding and pluvial flooding. Taking the Shahe River basin in Guangzhou, China, as a case study, the results show that the combination of the copula function and the Kendall return period method is effective for designing hydrological variable combinations. The impact degree index of rainfall on flooding varies between 0.5 and 1, with the minimum at 24 h duration, indicating that the compound flooding is primarily affected by rainfall, and the influence of tide level is most significant at 24 h duration. The pluvial flooding caused by the influence of river water level on the drainage outfalls accounts for up to 19.08 % of the total volume. This shows that fluvial flooding affects the seriousness of compound flooding by influencing the water levels of outfalls. The flood-prone area is divided into different regions based on the main natural factors (rainfall and tide level) and social factors (pipeline network, drainage outfalls, and riverbank defenses) to help decision-makers identify the causes of flooding in each drainage unit and better formulate targeted disaster-reduction strategies to improve flood control capabilities.

Agricultural Insurance Premium Determination Model for Risk Mitigation Based on Rainfall Index: Systematic Literature Review

Rainfall is significantly essential in the agricultural sector to increase productivity. However, rainfall instability serves as a potential source of risk, causing crop failure and negatively impacting the welfare of farmers. To mitigate this risk, rainfall index-based agricultural insurance offers financial protection to farmers. There is no information on how to set a reasonable premium in index-based agricultural insurance. Therefore, this research aimed to systematically explore a model for determining a rainfall index-based agricultural insurance premium, focusing on the methods used and their effectiveness in mitigating the risk of harvest failure in the agricultural sector. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method and a bibliometric analysis were used to collect and analyze articles from Scopus, ScienceDirect, and Dimensions databases. The results showed that there were 15 articles on determining a rainfall index-based agricultural insurance premium, where 4 used the Black–Scholes method and 11 applied other main methods. Meanwhile, no articles applied the fractional Black–Scholes method in determining agricultural insurance premiums based on the rainfall index, providing new opportunities for further research. The results contributed to the development of a model for agricultural insurance premium determination that could generate more diverse and flexible premium estimates as a sustainable method to mitigate the risk of harvest failure. This research is expected to serve as a reference for developing rainfall index-based agricultural insurance in the future and contribute to the Government of the Agriculture Department’s policy formulation regarding insurance programs for farmers.

Men in the making of nations: understanding the nexus between nation, belonging, and complicit masculinity

ABSTRACT The ideal of masculine Hinduism which is currently being employed within the Indian national rhetoric to sustain and build a modern Hindu Rashtra is a direct consequence of India’s tryst with British imperialism. Since it is men who, as real actors of nationalist movement defend their homeland and the honour of women, the current Indian right wing politics ensures that men continue to uphold the ideals of Hindu hegemonic masculinity defined by martial prowess, muscular strength, moral fortitude and a readiness to battle groups to strengthen the nation. However, while the hegemonic notions of Hindu masculinity are achieved by a small minority who become the public face of gender and sexual politics, the majority are those who reap benefits from such gendered arrangements by being complicit in the hegemonic project. This paper makes an attempt to study the complex interdependencies between hegemonic and complicit masculinities in nation building processes in India. The aim is to uncover how complicit masculinities are created to sustain Hindutva nationalism within the current Indian climate and whether there are alternative possibilities and codes of behaviour in which privileges of masculinity and power are confronted and exposed.

Impacts of climate variability on the spatio-temporal dynamics of plant formations in the forest-savannah transition zone: the case of the Lamto Scientific Reserve, Central Côte d’Ivoire

Understanding climatic variability’s effects on land and biodiversity is vital for guiding sustainability, conservation, and climate impact predictions in fragile ecosystems like Côte d’Ivoire’s forest-savanna transition zone. This study aims to analyse the impact of climate variability on the spatio-temporal dynamics of land use in the Lamto Scientific Reserve. To do this, a set of monthly climate data covering the period from 1990 to 2022 was used, including indicators such as rainfall, maximum, minimum and average temperatures, drought and standardised rainfall indices. The study also involved the classification of Landsat images dating from 1990, 2002, 2012 and 2022, enabling changes in land use to be observed. The corresponding areas were correlated with the climatic variables using a Spearman correlation test. The results show a transition from savannah to denser tree cover in the reserve. In addition, an increase in rainfall, varying between 900 and 1687 mm, suggests that Lamto could be classified as a humid region. The analysis highlights the complex interactions between climate change, particularly high temperatures, and land-use dynamics. Gallery and semi-deciduous forests show resilience in the face of rising temperatures, favouring their expansion. On the other hand, pre-forest formations, such as open forests and wooded savannahs, are more affected by these temperatures, which hinders their development. Tree savannahs also show a certain resilience, while shrub savannahs and bare land are often associated with ecological degradation processes in response to high temperatures. Finally, although rainfall plays a role, its influence seems minor, suggesting that other environmental or climatic factors, such as watercourses or microclimate, play a more significant role in land use/land cover dynamics.

Relative humidity prediction across the Indian subcontinent using Kumaraswamy distribution based non-Gaussian model.

Relative humidity (RH) significantly influences various aspects of human life, including agriculture, weather prediction, indoor air quality, and energy consumption. Its intricate non-linear behavior poses a significant challenge for accurate estimation. In the context of Indian climate change, precise forecasting of RH is vital for agriculture and weather prediction. This study proposes a new non-Gaussian approach, the Kumaraswamy seasonal autoregressive moving average (KSARMA) model, for RH prediction across India's five climatic zones (hot-dry, warm-humid, moderate, cold, and composite). Analyzing monthly RH data from 1981 to 2021, model selection was based on diagnostic analysis and Akaike's information criterion (AIC), with parameter estimation utilizing conditional maximum likelihood estimators (CMLE). The KSARMA model demonstrated superior predictive accuracy compared to other models (e.g., multilayer perceptron, SARIMA, Holt-Winter, and SARMA) across all climatic zones, except the hot-dry zone, as supported by error metrics including RMSE (0.04-0.12), MAE (0.03-0.1), SMAPE (0.04-0.2), and MASE (0.5-.08).

Evaluating reproduction traits in a crossbreeding program between indigenous and exotic sheep in semi-arid lands

Reproduction traits are important factors determining the efficiency of any sheep production system. This study evaluates the age at first lambing (AFL), lambing interval (LI), litter weight at birth (LBWT), litter weight at weaning (LWWT), birth weight of ewe (EBWT) and weaning weight of ewes (EWWT) in a crossbreeding program between the Red Maasai (RRRR) and Dorper sheep (DDDD) and their crosses, 75% Dorper (DDDR) and 50% Dorper (DDRR) breeds. All the traits significantly (P<0.05) differed across breeds and season of birth of the ewe. LBWT and LWWT were significantly affected by the sex of the lamb, type of birth of the lamb and parity in which the lambs were born in. AFL and LI had very high environment variances. Overall heritability estimates of AFL (0.09±0.04) and LI (0.00±0.01) were not significant from zero while the heritability estimates for EBWT (0.38±0.04), EWWT (0.23±0.03), LBWT (0.19±0.03) and LWWT (0.09±0.02) were significant (P<0.05). The RRRR had the highest genetic gain for all traits while the DDRR had a higher genetic gain among the crosses. LI had negative genetic correlations with LBWT (-0.53±0.08) and LWWT (-0.28±19.59) while AFL had positive genetic correlations with LBWT (0.27±0.46) and LWWT (0.31±0.34). The phenotypic trends for AFL and LWWT showed a negative and positive association, respectively, with the rainfall index over the years. With proper farm management, improved reproduction performance of ewes is possible by indirect selection using LBWT and LWWT for the Red Maasai, Dorper and their crosses within the semi-arid lands.

Settling-on-the-Move: Birsing Char-scapes in the Brahmaputra Valley

Chars are shifting riverine islands. This article focuses on Birsing Char, part of Birsing Jarua Village Panchayat, in the Brahmaputra River near the Indo-Bangladesh border. Generations of families have migrated across this porous border, settling in the Lower Brahmaputra Valley. This migration has intensified the sociocultural othering of Bengali Muslims amid Assam’s identity politics and India’s rising authoritarianism. Through fieldwork and interpretative mapping, the article uncovers forms of alternative knowledge, including local spatial practices and intangible heritage like songs and poetry, threatened by infrastructural development, policies of the Indian government and climate change. It explores how such knowledge can be harnessed and inspire alternative development policy and design in the context of global warming in the Brahmaputra Valley and in Assam’s sociopolitical climate. The case underscores the urgency of recognizing marginalized chars as vital to the region’s water legacy, as they contribute both to local livelihoods and broader ecological systems.

Optimizing the genetic evaluation criteria for the small herd of Saanen x Beetal crossbred dairy goats of Indian sub-tropic

This study focused on the accurate genetic evaluation of a small dairy flock of crossbred dairy goats, specifically developed for high milk production in the Indian sub-tropical climate. The study was conducted on Saanen x Beetal (SxB) crossbred goats, which utilized 12,660 test day records for first parity. Additionally, complete lactation records (N=1283) across multiple parities and for the first parity (N=659) were analyzed separately for genetic evaluation. The main traits examined were 150-day milk yield (150DMY), days in milk (DIM), peak yield (PY), and total milk yield (TMY), with averages of 198.80±2.83 kg, 227.90±4.18 days, 1.17±0.02 kg, and 262.40±6.12 kg, respectively, highlighting the genetic superiority of SB goats over native Indian goats. Across parities, the estimates for 150DMY, DIM, and TMY were 230±6.16 kg, 210±7.0 days, and 277±11.82 kg, respectively. Given the moderate heritability (0.24±0.08) and repeatability (0.29±0.04) estimates of 150DMY, the study recommends using 150DMY as the primary selection criterion for genetic improvement in SxB goats. The single-trait random regression model (RRM) utilizing various orders of orthogonal Legendre polynomials (LEG) and B-spline (BS) functions with heterogeneous residual variances was also employed. The test day milk yield (TDMY) showed a least squares mean of 1.30±0.01 kg, with moderate heritability estimates across test days (0.26±0.08). The optimal model was identified as a quadratic B-spline function with six knots (BS6Q). Positive genetic correlations were observed between consecutive test-day milk yield values, while correlations decreased for more distant test days. The study demonstrated the superiority of the B-spline model in genetically assessing Saanen x Beetal dairy goats, highlighting its benefits in curve fitting, genetic parameter estimation, and higher breeding value prediction accuracy. Looking into the moderate heritability and desirable genetic correlation of 150 DMY with other lactation traits, we recommend using 150 DMY for further selection programs. As the concordance of ranking between different approaches for breeding value prediction was high, we recommend using the random regression test day model (RR-TDM) when data recording across lactation is costly.

Variability of drinking water for pigs and poutry the southern region of Brazil over a twelve – month period

The increase in global food demand has led to intensified production of pigs and poultry. In this context, water quality on farms needs to be monitored to ensure maximum production and animal welfare. This study aimed to evaluate the quality and variability of drinking water for animals over twelve months in properties in the Southern Region of Brazil. Physical, chemical, microbiological parameters, and the average rainfall index of nine water sources were observed. Variations in pH were noted among the evaluated water sources, as well as within the same source over monthly collections. A relationship between average rainfall and elevated iron levels was also demonstrated. Nitrate and nitrite levels exceeded Brazilian legislation at certain sampling points, posing risks to human and animal health. In 67.59% of samplings, total coliforms were present, and 59.26% showed the presence of Escherichia coli. The study highlighted the variability of water sources in the Southern Region of Brazil, reflecting the need for constant monitoring and treatment measures, such as water treatment stations, chlorination systems, and acidification in rural properties.

Temperature and Precipitation Extremes Over Borneo Island: An Integrated Climate Risk Assessment

ABSTRACTGlobal warming has significantly increased the frequency and intensity of extreme events, which have catastrophic consequences for ecosystems and humans. Despite efforts to assess the impact of climate change on the potential risk of Borneo, most research has focused on partial regions, considering short timescales and a limited number of temperature and precipitation extremes indices to quantify the expected climate risks. This study employed a new method of climate risk assessment of Borneo based on the combined changes in various climate parameters. It estimated 23 climate indices at all grid points covering Borneo for three overlapping sub‐periods (1951–1980, 1961–1990, 1991–2020). The modified Mann‐Kendall test was employed to identify grid points exhibiting significant increasing or decreasing trends of each index for each sub‐period. Finally, significant trends of 23 indices were integrated to estimate the potential climate risk indicator (RI) based on the combined changes in various climate parameters for each grid point and sub‐period. Temperature indices showed a clear warming trend across Borneo Island, particularly in the eastern regions, with absolute temperature indices showing an increase of 0.5°C–2.5°C in 1991–2020 compared to the reference period (1951–1980). However, extreme cold temperatures have become less prevalent over the study period. There is a shift from light consecutive rainfall days towards more heavy and short‐duration rainfall events. Therefore, there are indications of intensifying rainfall events over the island's southern half, counterbalanced by drying trends in the northern regions, especially Brunei. The spatial distribution of RI revealed an overall 184% increase in climate risk on the island in recent years (1991–2020) compared to the reference period. The highest rise in RI was in the central east of the island, mostly due to significant increases in rainfall and temperature indices. The findings can inform adaptation initiatives to manage escalating heat and flood risks while guiding additional research to explain further the complex climatic changes occurring in this ecologically and socially vital region.

Spatial Characteristics and Driving Factors of Net Primary Productivity of Vegetation in the Upper and Middle Yellow River Basin

The Net Primary Productivity （NPP） of vegetation plays a crucial role in terrestrial ecosystems, and a detailed investigation into the annual average NPP and its driving factors is of significant importance for promoting regional ecological construction and sustainable development. This research utilized MOD17A3 annual average NPP data from 2000 to 2020 and employed methods such as trend analysis, Hurst index, random forest model, partial dependence model, geographic weighted regression, and partial least squares-structural equation model （PLS-SEM） to analyze the annual variation characteristics of NPP and its relationship with driving factors in the upper and middle reaches of the Yellow River. The results showed： ① During the period from 2000 to 2020, the annual average NPP in the upper and middle reaches of the Yellow River generally exhibited a year-on-year increasing trend, with 79.25% of the region showing a significant improvement in annual average NPP, while 0.25% of the region exhibited a severe degradation trend. ② The dominant influencing factors of the annual average NPP in the upper and middle reaches of the Yellow River included precipitation, NDVI, drought, and relative humidity. The suitable range for increasing annual average NPP was 400 mm to 650 mm for precipitation and 40% to 70% for relative humidity, and NDVI showed a significantly linear positive correlation with annual average NPP. Drought index was negatively correlated with annual average NPP. The annual average NPP tended to stabilize and did not decline further when the drought index exceeded the threshold of 8. ③ Extreme rainfall indirectly affected the variation in annual average NPP in the basin by influencing the vegetation growth condition. The impact degree of extreme rainfall indices on the annual average NPP in the basin was in the following order： R10&gt;R95P&gt;PRCPTOT&gt;SDII&gt;Rx5day&gt;R99P. ④ The next steps in ecological construction in the upper and middle reaches of the Yellow River should focus on the arid and severely arid regions in the northern part of the Yellow River, adapting measures to local conditions and enhancing the regional ecological environment.