Long-term Climate Change Trends: 43-Year Analysis of Climatic Data in Gangotri National Park, Western Himalaya

Long-term Climate Change Trends: 43-Year Analysis of Climatic Data in Gangotri National Park, Western Himalaya | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-term Climate Change Trends: 43-Year Analysis of Climatic Data in Gangotri National Park, Western Himalaya Deepali Bansal, Gautam Talukdar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5401487/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding long-term climatic patterns is essential for assessing climate change impacts and informing adaptation strategies. This study aims to examine the temporal variations in climate from 1981 to 2023 in Gangotri National Park, Western Himalaya. Using NASA’s Modern-Era Retrospective analysis for Research and Applications (MERRA-2), we analysed daily, monthly, and annual agroclimatology data for maximum (T max ) and minimum (T min ) temperatures (°C), corrected precipitation (mm/day), and relative humidity (RH, %) at 2 meters. The datasets were processed using R statistical software (version 4.4.1), and seasonal trends were evaluated with linear regression models to quantify the rate of change and statistical significance (p < 0.05). The analysis revealed that T max significantly decreased during the monsoon season, with an average decline of 0.01°C per year (p < 0.005), while T min increased during both the monsoon and post-monsoon seasons by 0.04°C per year and by 0.02°C per year in summer (p < 0.05). Precipitation trends indicated a substantial rise during the monsoon (0.057 mm per year) and winter (0.016 mm per year), indicating more intense rainfall. RH also increased across all seasons, with the higher rises in summer (0.31% per year) and post-monsoon (0.30% per year). These findings suggest that the observed shifts in these parameters may have a substantial influence on park's distinctive ecosystems. Validation using HOBO fine-scale microclimate loggers confirmed consistent seasonal trends between observational data and NASA POWER estimates, with no significant differences in trendlines (p > 0.05), demonstrating the reliability of NASA POWER for long-term climate studies in this region. Climate change Indian Himalayan Region MERRA-2 Temporal variations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The Indian Himalayan Region (IHR), known for its diverse biodiversity and unique landscapes, is increasingly recognized as a focal point for the effects of climate change (Poornima et al., 2024 ; Tewari et al., 2017 ). This region is highly susceptible to fluctuations in temperature and precipitation due to its high altitude and intricate climate patterns (Poornima et al., 2024 ; Aryal & Pokharel, 2024 ). The socio-economic status of the local communities is also significantly impacted by the effects of these climatic variations, which disrupt the ecological equilibrium. Recent research emphasizes that climate change is profoundly altering glacier melting (Bajracharya et al., 2008 ; Pramanik & Bhaduri, 2016 ), causing shifts in vegetation (Manish et al., 2016 ; Yadav et al., 2021 ), changes in land use (Rathore et al., 2022 ), and loss of biodiversity (Dahal et al., 2021 ). The comprehension of these changes and the development of effective mitigation and adaptation strategies are contingent upon the availability of long-term climate data (Ali & Thakkar, 2023 ). However, the scarcity of long-term monitoring stations and the complex terrain that affects local climate patterns make it difficult to obtain such data in mountainous regions like the Himalaya. The absence of exhaustive, high-resolution climate data impedes the accurate modelling and prediction of climatic trends and their ecological and socio-economic consequences. Reanalysis datasets, which integrate climate models with observations from a variety of sources, are a valuable alternative for regions with limited observational data (Halimi et al., 2023 ; Wang et al., 2024 ). These datasets, including those from the Climate Forecast System Reanalysis (CFSR), National Centres for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR), Japanese 55-year Reanalysis (JRA-55), Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), European Centre for Medium-Range Weather Forecasts (ERA), and others, provide consistent and comprehensive climate information (Chen et al., 2019 ; Pinheiro et al., 2020 ; Arshad et al., 2021 ). NASA's POWER (Prediction of Worldwide Energy Resources) is an extensive data initiative that incorporates solar and meteorological data obtained from satellite observations and reanalysis models. This dataset is particularly noteworthy for its extensive global coverage, accessibility, and high temporal resolution (Halimi et al., 2023 ). Various regions have been validated by prior investigations regarding the accuracy of NASA POWER data. Rodrigues and Braga ( 2021 ) investigated the feasibility of NASA POWER's maximum and minimum air temperature, relative humidity, wind speed, and solar radiation in Portugal. The variables' accuracy was recommended for all parameters, except for wind speed, based on the observation data. Similarly, Aboelkhair et al. ( 2019 ) assessed the POWER satellite and model datasets in Egypt and found a strong relationship between POWER and observed data for all temperature variables, with an RMSE of 5°C. However, the estimated relative humidity had an RMSE of 11.6%. Additionally, Halimi et al. ( 2023 ) conducted a study to compare NASA POWER climatic data with ground-based observations in the Mediterranean and continental regions of Turkey. The research revealed that the NASA POWER dataset was capable of accurately predicting temperature and relative humidity, which is a promising finding for agricultural decision-making, water management, and research in regions that lack observational data. The applicability of NASA POWER data to the IHR has not yet been explored despite its demonstrated accuracy in numerous regions worldwide. This gap in research is significant because utilizing reliable datasets like NASA POWER could enhance our understanding of long-term climate trends in this critical and vulnerable area to climate change. In this investigation, we evaluate the NASA POWER data for the Gangotri National Park to determine its suitability for long-term climate analysis. Addressing data gaps through the application of reanalysis datasets such as NASA POWER is a promising strategy needed to improve our comprehension of climatic changes and their impacts in the Himalayan region. 2. Materials and methods 2.1. Study Site The study was conducted within Gangotri National Park (30°59'40.92"N, 78°56'23.64"E) in the Western Himalaya, a region known for its rugged terrain and high-altitude ecosystems. It is the largest national park in Uttarakhand, India, covering an area of 2,390.2 sq. km (Pusalkar & Singh, 2012 ). The park primarily features arctic-alpine and glacial vegetation and is home to many endangered species of plants and animals. The major vegetation types in the area include forests (Deodar, Pine, Mixed Conifer, Birch), scrubs, riverine bushes, alpine meadows, rocky-mountainous, aquatic, marshy, and glacial environments. 2.2. Data The NASA POWER MERRA-2 daily agroclimatology data was utilized in this study, from January 2, 1981 (the earliest possible date) to November 30, 2023 (the utmost possible date), for a point location at latitude 30.9947 and longitude 78.9399. Variables that were assessed included corrected precipitation (mm/day), relative humidity (RH) (%), maximum temperature (T max ) (°C), and minimum temperature (T min ) (°C). The data was downloaded in CSV format for analysis. The dataset was composed of daily numerical values for each variable, spanning a period of 43 years, with a total of 15,673 days of data for each location. The year 1981, having 364 data points and the year 2023 having 334 data points, were considered a full year for the calculation of yearly averages. To validate this data, HOBO U23 Pro v2 fine-scale data loggers were utilized (observational data) to monitor maximum temperature (T max , °C), minimum temperature (T min , °C), and relative humidity (RH, %) at three locations within the Gangotri valley, as illustrated in Fig. 1 . Due to the sloping terrain and accessibility constraints, actual deployment heights of loggers varied slightly, ranging from approximately 2.5 to 3 meters. From 2016 to 2019, these loggers recorded data hourly, resulting in a total of 13,741 data points for each parameter. 2.3. Method The datasets (NASA POWER climatic data and observational data) were arranged in MS Excel 365 and analysed using R version 4.4.1. The Indian Meteorological Department (IMD) classification was used to categorise the data from both NASA POWER MERRA-2 and HOBO data loggers into four main seasons: Monsoon (June to September), Post-monsoon (October to November), Winter (December to February) and Summer (March to May). The database was examined for any missing values and subsequently removed. The `lm` function in R was employed to apply linear regression models to the yearly averages for each season and parameter to evaluate temporal trends over the 43 years. The rate of change was quantified, and statistical significance was assessed (p < 0.05) using these models. Utilising the `ggplot2` package for visualisation, heatmaps were generated for the last decade to examine recent shifts in each parameter. To validate the NASA POWER data from 2016 to 2019, we employed a linear regression model that included interaction terms between year, data source, and season. The consistency and reliability of the data were assured at the appropriate significance level (p < 0.05) by evaluating the difference between the trendlines. 3. Results 3.1 Trends in Maximum and Minimum temperatures The temporal variations in T max and T min across distinct seasons are revealed by the analysis of climatic data from Gangotri National Park over a 43-year period. During the monsoon season, a substantial decrease in T max was observed, with an average decrease of 0.01°C per year (p < 0.05). However, no significant alterations in T max were observed during the post-monsoon, summer, and winter seasons (Figure). July and August have been the months with the highest recorded T max over the past decade, with June and September following closely (Figure). The T min analysis reveals a substantial increase in both the monsoon and post-monsoon seasons, with an average annual increase of 0.04°C (p < 0.001). The summer season also demonstrated a positive trend, with T min increasing by 0.02°C annually (p < 0.05). Conversely, no notable pattern was identified in T min during the winter season (Figure). The lowest T min values were recorded in January and February over the past decade, with December following closely (Figure). 3.2 Trends in Precipitation and RH The analysis of precipitation data demonstrates a substantial upward trend that extends across multiple seasons (Figure). Precipitation increased by an average of 0.057 mm per year during the monsoon season (p < 0.005). Similarly, the winter season experienced a 0.016 mm increase annually (p < 0.05). The highest precipitation levels were concentrated in July and August over the past decade, which indicates a trend towards more intense rainfall during these months (Figure). RH increased significantly in all seasons (Figure). The most significant increases were observed during the summer (0.31% per year, p < 0.05) and post-monsoon (0.30% per year, p < 0.05) seasons, with the monsoon (0.23% per year, p < 0.001) and winter (0.22% per year, p < 0.05) seasons following the same order. July and August were the months with the highest relative humidity levels over the past decade (Figure). 3.3 Data validation A comprehensive analysis of T max , T min , and RH between NASA POWER data and observational data from 2016 to 2019 was conducted to validate climatic data. All three climatic parameters exhibited consistent seasonal trends in the comparative assessment, despite the significant differences in absolute readings. Although NASA POWER mostly recorded lower T max and T min than the observational data, which may be attributed to the varying heights of monitoring linear regression analysis, did not reveal any significant differences in trendlines (p > 0.05) for either parameter. Similarly, NASA POWER reported a higher average RH than the observational data—e.g., during the 2016 monsoon season, NASA POWER data with a RH of 85.2% compared to 67.4% of observational dataset. However, the trendlines did not exhibit any significant differences (p > 0.05). 4. Discussion The analysis of temperature variations at Gangotri National Park over the past 43 years reveals interesting insights into the region's climatic dynamics. There is significant decrease in T max during the monsoon season, with an average decline of 0.01°C per year (p < 0.005). This finding contrasts with the generally expected trend of rising temperatures attributed to climate warming (Wallace et al., 2014 ). The decrease in T max during the monsoon could suggest local climatic influences, such as increased cloud cover or shifts in atmospheric circulation patterns, which may be mitigating daytime heating (Zhou et al., 2012 ). The implication of this trend is important for local ecosystems and water resources, as changes in temperature during the monsoon season can affect river flows and soil moisture levels, critical for hydrology (Sahastrabuddhe et al., 2023 ). The analysis indicates an increase in T min during the monsoon and post-monsoon seasons, with annual increases of 0.04°C and 0.02°C, respectively. The observed trends indicate a warming pattern that aligns with findings in other areas, where minimum temperatures are increasing at a faster rate than maximum temperatures (Gil-Alana, 2018 ). This phenomenon, commonly known as "warming nights" (Vose et al., 2005 ), has the potential to disrupt ecological balances and influence the behaviour of both flora and fauna during vital growth and breeding phases (Cox et al., 2020 ). RH was found to be increasing significantly across all seasons and could exacerbate the effects of warmer nights. Such shifts not only influence individual species but can also disrupt entire ecosystems and their functions. Interestingly, the analysis revealed no significant changes in T max across the summer, winter, and post-monsoon seasons. The consistency in T max , alongside the increase in T min , underscores the importance of investigating the fundamental elements driving these temperature variations. Understanding the reasons behind the lack of change in T max during these seasons is crucial, especially when considering how these temperature dynamics correlate with trends in precipitation (Sahastrabuddhe et al., 2023 ). For instance, the observed increase in precipitation during the monsoon season, averaging 0.057 mm per year (p < 0.005), may partly explain the decline in T max , as increased rainfall can lead to greater cloud cover and thus reduce solar insolation. 5. Conclusion The validation of NASA POWER climatic data against fine-scale data loggers in Gangotri National Park highlights the reliability and relevance of these datasets for climate study in the area. The reliability of NASA POWER data is underscored by the consistency of trends between the two data sources, rendering it a valuable proxy for understanding climatic alterations, particularly in cases where ground-based microclimate data is scarce. An extensive analysis of meteorological data from the past 43 years has revealed significant patterns and changes, including the reduction in temperature ranges and increased precipitation along with relative humidity, which could potentially affect the park's distinct ecosystems. These findings validate the use of NASA POWER data for comprehensive climatic evaluations and ecological modelling, offering critical insights for policymakers and academics focused to the long-term environmental sustainability of Gangotri National Park. Declarations Funding This research work was funded by the Department of Science and Technology (DST), Govt. of India, under the National Mission for Sustaining the Himalayan Ecosystem (NMSHE-Phase II) project (Grant no. DST/CCP/TF-4/Phase-2/WII/2021(G)). Competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Authors’ Contribution Both authors contributed to the study's conception and design, with supervision and guidance provided by GT. Material preparation, data access, and analysis were performed by DB. The first draft of the manuscript was written by DB, with both authors collaboratively revising subsequent versions. Both authors have reviewed and approved the final manuscript. Data availability The NASA POWER data utilized in this study is freely accessible at https://power.larc.nasa.gov/data-access-viewer/. The observational datasets will be made freely available to the public once the manuscript is accepted. AI Disclosure Statement: In this study, we utilized artificial intelligence tools to assist with grammar and syntax error corrections using Grammarly and QuillBot. Additionally, ChatGPT was employed to enhance clarity during the writing process. We acknowledge that while these AI tools contributed to improving the manuscript, the final conclusions and interpretations presented in this work are solely the responsibility of the authors. References Ali, A. H., & Thakkar, R. (2023). Climate changes through data science: understanding and mitigating environmental crisis. Mesopotamian Journal of Big Data , 2023 , 125-137. Arshad, M., Ma, X., Yin, J., Ullah, W., Liu, M., & Ullah, I. (2021). Performance evaluation of ERA-5, JRA-55, MERRA-2, and CFS-2 reanalysis datasets, over diverse climate regions of Pakistan. Weather and Climate Extremes , 33 , 100373. Aryal, D., & Pokharel, B. (2024). 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C., & Braga, R. P. (2021). Evaluation of NASA POWER reanalysis products to estimate daily weather variables in a hot summer mediterranean climate. Agronomy , 11 (6), 1207. Aboelkhair, H., Morsy, M., & El Afandi, G. (2019). Assessment of agroclimatology NASA POWER reanalysis datasets for temperature types and relative humidity at 2 m against ground observations over Egypt. Advances in Space Research , 64 (1), 129-142. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5401487","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379286039,"identity":"d87e78a5-b214-4d1b-9f86-eb3cb0b2d9eb","order_by":0,"name":"Deepali Bansal","email":"","orcid":"","institution":"Wildlife Institute of India","correspondingAuthor":false,"prefix":"","firstName":"Deepali","middleName":"","lastName":"Bansal","suffix":""},{"id":379286042,"identity":"89b83e99-73e6-447c-ace3-06dd8b85c49b","order_by":1,"name":"Gautam Talukdar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDADPgY2BgkGBhsgk7HxAAHFjA0MDAZADWAtaWABkrQcBgvh1SLvfsb8wc+cP/JsEmmJN37uOW+3tv0w0JYam2hcWgzP5Bg29m4zMGyTSDts2fPsdvK2M4lALcfSchtwaWnIMWzg3WbA2CaR3ibBc+B2stkBoBbGhsO4tfS/MWz8u83AHqRF8s+Bc8lm5x/i1yIvkWPYDLQlEeiwY9I8Bw7Ymd0gYIuBxLPC2bLbjJPbeJ4lW8scSE4wuwG0JQGPX+T7kzd8fLtNzrafPc3w5psDdvZm59MfPvhQY4PblgNoAolglQk4lINtQTfLHo/iUTAKRsEoGKEAAIpwZUUXwOLmAAAAAElFTkSuQmCC","orcid":"","institution":"Wildlife Institute of India","correspondingAuthor":true,"prefix":"","firstName":"Gautam","middleName":"","lastName":"Talukdar","suffix":""}],"badges":[],"createdAt":"2024-11-06 09:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5401487/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5401487/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69882267,"identity":"5ab458d5-634a-4bd6-a48a-751e7beb7660","added_by":"auto","created_at":"2024-11-26 09:17:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1220154,"visible":true,"origin":"","legend":"\u003cp\u003eA study area map of Gangotri National Park, showing data logger placements within the Gangotri Valley for NASA POWER data validation.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5401487/v1/bf617eb11ab29d5bd514adba.jpg"},{"id":69883570,"identity":"b542d34e-f876-45ff-aeac-7d483fe8bc7d","added_by":"auto","created_at":"2024-11-26 09:25:19","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":402076,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal and interannual trends in average maximum and minimum temperatures (°C) in Gangotri National Park, from 1981 to 2023 across four seasons: Monsoon, Post-monsoon, Summer, and Winter. Each panel shows the linear regression equation and p-value for the trend line.\u003c/p\u003e","description":"","filename":"Fig.2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5401487/v1/a43cc861cacae0dae5049364.jpeg"},{"id":69883571,"identity":"456a6730-8b3a-434c-9a05-81dc27c93df0","added_by":"auto","created_at":"2024-11-26 09:25:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":530214,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of monthly average maximum temperatures (°C) in Gangotri National Park, from 2014 to 2023. Darker shades signify higher temperatures, highlighting seasonal trends and interannual variations.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-5401487/v1/f86f5992b5857ef55cc7e56a.png"},{"id":69882271,"identity":"14cf096e-cc82-49f9-af15-cdd937f3de97","added_by":"auto","created_at":"2024-11-26 09:17:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":529152,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of monthly average minimum temperatures (°C) in Gangotri National Park, from 2014 to 2023 by month. Darker shades signify lower temperatures, highlighting seasonal trends and interannual variations.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-5401487/v1/5a986092d9dd7295ba6bd38b.png"},{"id":69882268,"identity":"6eb640df-1bd0-47ec-8c2a-88be4dfe3201","added_by":"auto","created_at":"2024-11-26 09:17:19","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":425566,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal and interannual trends in average precipitation (mm/day) and relative humidity (%) in Gangotri National Park, from 1981 to 2023 across four seasons: Monsoon, Post-monsoon, Summer, and Winter. Each panel shows the linear regression equation and p-value for the trend line.\u003c/p\u003e","description":"","filename":"Fig.5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5401487/v1/875bb508f7917487128a15b5.jpeg"},{"id":69882272,"identity":"c80c11e7-6b30-48e7-bcff-8dfe3946ef24","added_by":"auto","created_at":"2024-11-26 09:17:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":534585,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of monthly average precipitation (mm/day) in Gangotri National Park, from 2014 to 2023 by month. Darker shades signify higher precipitation, highlighting seasonal trends and interannual variations.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-5401487/v1/c7f2ed5013243744c95cb7e8.png"},{"id":69883569,"identity":"a2f4c60e-1770-4f59-b534-fc67142da7d3","added_by":"auto","created_at":"2024-11-26 09:25:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":533357,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of monthly average relative humidity (%) in Gangotri National Park, from 2014 to 2023 by month. Darker shades signify higher relative humidity, highlighting seasonal trends and interannual variations.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-5401487/v1/62f2d7ac27eaed6c9139998c.png"},{"id":69882273,"identity":"00aeadaf-cd8c-4b29-8854-aba4e06ffb63","added_by":"auto","created_at":"2024-11-26 09:17:20","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":337120,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal and interannual trends in average maximum and minimum temperatures (°C), and relative humidity (%) from 2016 to 2019 in Gangotri valley, utilizing NASA POWER and observational data\u003c/p\u003e","description":"","filename":"Fig.8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5401487/v1/c3daf355d4921e071e86f706.jpeg"},{"id":73205445,"identity":"cba19af9-e3fe-4b67-a317-16a0092abdf1","added_by":"auto","created_at":"2025-01-07 17:17:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4987477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5401487/v1/eba6a247-cbe1-49e5-a17f-d5534c058da0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term Climate Change Trends: 43-Year Analysis of Climatic Data in Gangotri National Park, Western Himalaya","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Indian Himalayan Region (IHR), known for its diverse biodiversity and unique landscapes, is increasingly recognized as a focal point for the effects of climate change (Poornima et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tewari et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This region is highly susceptible to fluctuations in temperature and precipitation due to its high altitude and intricate climate patterns (Poornima et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Aryal \u0026amp; Pokharel, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The socio-economic status of the local communities is also significantly impacted by the effects of these climatic variations, which disrupt the ecological equilibrium. Recent research emphasizes that climate change is profoundly altering glacier melting (Bajracharya et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pramanik \u0026amp; Bhaduri, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), causing shifts in vegetation (Manish et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), changes in land use (Rathore et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and loss of biodiversity (Dahal et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe comprehension of these changes and the development of effective mitigation and adaptation strategies are contingent upon the availability of long-term climate data (Ali \u0026amp; Thakkar, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the scarcity of long-term monitoring stations and the complex terrain that affects local climate patterns make it difficult to obtain such data in mountainous regions like the Himalaya. The absence of exhaustive, high-resolution climate data impedes the accurate modelling and prediction of climatic trends and their ecological and socio-economic consequences.\u003c/p\u003e \u003cp\u003eReanalysis datasets, which integrate climate models with observations from a variety of sources, are a valuable alternative for regions with limited observational data (Halimi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These datasets, including those from the Climate Forecast System Reanalysis (CFSR), National Centres for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR), Japanese 55-year Reanalysis (JRA-55), Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), European Centre for Medium-Range Weather Forecasts (ERA), and others, provide consistent and comprehensive climate information (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pinheiro et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Arshad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). NASA's POWER (Prediction of Worldwide Energy Resources) is an extensive data initiative that incorporates solar and meteorological data obtained from satellite observations and reanalysis models. This dataset is particularly noteworthy for its extensive global coverage, accessibility, and high temporal resolution (Halimi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVarious regions have been validated by prior investigations regarding the accuracy of NASA POWER data. Rodrigues and Braga (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) investigated the feasibility of NASA POWER's maximum and minimum air temperature, relative humidity, wind speed, and solar radiation in Portugal. The variables' accuracy was recommended for all parameters, except for wind speed, based on the observation data. Similarly, Aboelkhair et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) assessed the POWER satellite and model datasets in Egypt and found a strong relationship between POWER and observed data for all temperature variables, with an RMSE of 5\u0026deg;C. However, the estimated relative humidity had an RMSE of 11.6%. Additionally, Halimi et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) conducted a study to compare NASA POWER climatic data with ground-based observations in the Mediterranean and continental regions of Turkey. The research revealed that the NASA POWER dataset was capable of accurately predicting temperature and relative humidity, which is a promising finding for agricultural decision-making, water management, and research in regions that lack observational data.\u003c/p\u003e \u003cp\u003eThe applicability of NASA POWER data to the IHR has not yet been explored despite its demonstrated accuracy in numerous regions worldwide. This gap in research is significant because utilizing reliable datasets like NASA POWER could enhance our understanding of long-term climate trends in this critical and vulnerable area to climate change. In this investigation, we evaluate the NASA POWER data for the Gangotri National Park to determine its suitability for long-term climate analysis. Addressing data gaps through the application of reanalysis datasets such as NASA POWER is a promising strategy needed to improve our comprehension of climatic changes and their impacts in the Himalayan region.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Site\u003c/h2\u003e \u003cp\u003eThe study was conducted within Gangotri National Park (30\u0026deg;59'40.92\"N, 78\u0026deg;56'23.64\"E) in the Western Himalaya, a region known for its rugged terrain and high-altitude ecosystems. It is the largest national park in Uttarakhand, India, covering an area of 2,390.2 sq. km (Pusalkar \u0026amp; Singh, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The park primarily features arctic-alpine and glacial vegetation and is home to many endangered species of plants and animals. The major vegetation types in the area include forests (Deodar, Pine, Mixed Conifer, Birch), scrubs, riverine bushes, alpine meadows, rocky-mountainous, aquatic, marshy, and glacial environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data\u003c/h2\u003e \u003cp\u003eThe NASA POWER MERRA-2 daily agroclimatology data was utilized in this study, from January 2, 1981 (the earliest possible date) to November 30, 2023 (the utmost possible date), for a point location at latitude 30.9947 and longitude 78.9399. Variables that were assessed included corrected precipitation (mm/day), relative humidity (RH) (%), maximum temperature (T\u003csub\u003emax\u003c/sub\u003e) (\u0026deg;C), and minimum temperature (T\u003csub\u003emin\u003c/sub\u003e) (\u0026deg;C). The data was downloaded in CSV format for analysis. The dataset was composed of daily numerical values for each variable, spanning a period of 43 years, with a total of 15,673 days of data for each location. The year 1981, having 364 data points and the year 2023 having 334 data points, were considered a full year for the calculation of yearly averages.\u003c/p\u003e \u003cp\u003eTo validate this data, HOBO U23 Pro v2 fine-scale data loggers were utilized (observational data) to monitor maximum temperature (T\u003csub\u003emax\u003c/sub\u003e, \u0026deg;C), minimum temperature (T\u003csub\u003emin\u003c/sub\u003e, \u0026deg;C), and relative humidity (RH, %) at three locations within the Gangotri valley, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Due to the sloping terrain and accessibility constraints, actual deployment heights of loggers varied slightly, ranging from approximately 2.5 to 3 meters. From 2016 to 2019, these loggers recorded data hourly, resulting in a total of 13,741 data points for each parameter.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Method\u003c/h2\u003e \u003cp\u003eThe datasets (NASA POWER climatic data and observational data) were arranged in MS Excel 365 and analysed using R version 4.4.1. The Indian Meteorological Department (IMD) classification was used to categorise the data from both NASA POWER MERRA-2 and HOBO data loggers into four main seasons: Monsoon (June to September), Post-monsoon (October to November), Winter (December to February) and Summer (March to May). The database was examined for any missing values and subsequently removed. The `lm` function in R was employed to apply linear regression models to the yearly averages for each season and parameter to evaluate temporal trends over the 43 years. The rate of change was quantified, and statistical significance was assessed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) using these models. Utilising the `ggplot2` package for visualisation, heatmaps were generated for the last decade to examine recent shifts in each parameter.\u003c/p\u003e \u003cp\u003eTo validate the NASA POWER data from 2016 to 2019, we employed a linear regression model that included interaction terms between year, data source, and season. The consistency and reliability of the data were assured at the appropriate significance level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) by evaluating the difference between the trendlines.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cem\u003e3.1 Trends in Maximum and Minimum temperatures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe temporal variations in T\u003csub\u003emax\u003c/sub\u003e and T\u003csub\u003emin\u003c/sub\u003e across distinct seasons are revealed by the analysis of climatic data from Gangotri National Park over a 43-year period. During the monsoon season, a substantial decrease in T\u003csub\u003emax\u003c/sub\u003e was observed, with an average decrease of 0.01\u0026deg;C per year (p \u0026lt; 0.05). However, no significant alterations in T\u003csub\u003emax\u003c/sub\u003e were observed during the post-monsoon, summer, and winter seasons (Figure). July and August have been the months with the highest recorded T\u003csub\u003emax\u003c/sub\u003e over the past decade, with June and September following closely (Figure).\u003c/p\u003e\n\u003cp\u003eThe T\u003csub\u003emin\u003c/sub\u003e analysis reveals a substantial increase in both the monsoon and post-monsoon seasons, with an average annual increase of 0.04\u0026deg;C (p \u0026lt; 0.001). The summer season also demonstrated a positive trend, with T\u003csub\u003emin\u003c/sub\u003e increasing by 0.02\u0026deg;C annually (p \u0026lt; 0.05). Conversely, no notable pattern was identified in T\u003csub\u003emin\u003c/sub\u003e during the winter season (Figure). The lowest T\u003csub\u003emin\u003c/sub\u003e values were recorded in January and February over the past decade, with December following closely (Figure).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2 Trends in Precipitation and RH\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of precipitation data demonstrates a substantial upward trend that extends across multiple seasons (Figure). Precipitation increased by an average of 0.057 mm per year during the monsoon season (p \u0026lt; 0.005). Similarly, the winter season experienced a 0.016 mm increase annually (p \u0026lt; 0.05). The highest precipitation levels were concentrated in July and August over the past decade, which indicates a trend towards more intense rainfall during these months (Figure).\u003c/p\u003e\n\u003cp\u003eRH increased significantly in all seasons (Figure). The most significant increases were observed during the summer (0.31% per year, p \u0026lt; 0.05) and post-monsoon (0.30% per year, p \u0026lt; 0.05) seasons, with the monsoon (0.23% per year, p \u0026lt; 0.001) and winter (0.22% per year, p \u0026lt; 0.05) seasons following the same order. July and August were the months with the highest relative humidity levels over the past decade (Figure).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3 Data validation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA comprehensive analysis of T\u003csub\u003emax\u003c/sub\u003e, T\u003csub\u003emin\u003c/sub\u003e, and RH between NASA POWER data and observational data from 2016 to 2019 was conducted to validate climatic data. All three climatic parameters exhibited consistent seasonal trends in the comparative assessment, despite the significant differences in absolute readings.\u003c/p\u003e\n\u003cp\u003eAlthough\u0026nbsp;NASA POWER mostly recorded lower T\u003csub\u003emax\u003c/sub\u003e and T\u003csub\u003emin\u003c/sub\u003e than the observational data, which may be attributed to the varying heights of monitoring linear regression analysis, did not reveal any significant differences in trendlines (p \u0026gt; 0.05) for either parameter. Similarly, NASA POWER reported a higher average RH than the observational data\u0026mdash;e.g., during the 2016 monsoon season, NASA POWER data with a RH of 85.2% compared to 67.4% of observational dataset. However, the trendlines did not exhibit any significant differences (p \u0026gt; 0.05).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe analysis of temperature variations at Gangotri National Park over the past 43 years reveals interesting insights into the region's climatic dynamics. There is significant decrease in T\u003csub\u003emax\u003c/sub\u003e during the monsoon season, with an average decline of 0.01\u0026deg;C per year (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005). This finding contrasts with the generally expected trend of rising temperatures attributed to climate warming (Wallace et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The decrease in T\u003csub\u003emax\u003c/sub\u003e during the monsoon could suggest local climatic influences, such as increased cloud cover or shifts in atmospheric circulation patterns, which may be mitigating daytime heating (Zhou et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The implication of this trend is important for local ecosystems and water resources, as changes in temperature during the monsoon season can affect river flows and soil moisture levels, critical for hydrology (Sahastrabuddhe et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analysis indicates an increase in T\u003csub\u003emin\u003c/sub\u003e during the monsoon and post-monsoon seasons, with annual increases of 0.04\u0026deg;C and 0.02\u0026deg;C, respectively. The observed trends indicate a warming pattern that aligns with findings in other areas, where minimum temperatures are increasing at a faster rate than maximum temperatures (Gil-Alana, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This phenomenon, commonly known as \"warming nights\" (Vose et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), has the potential to disrupt ecological balances and influence the behaviour of both flora and fauna during vital growth and breeding phases (Cox et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). RH was found to be increasing significantly across all seasons and could exacerbate the effects of warmer nights. Such shifts not only influence individual species but can also disrupt entire ecosystems and their functions.\u003c/p\u003e \u003cp\u003eInterestingly, the analysis revealed no significant changes in T\u003csub\u003emax\u003c/sub\u003e across the summer, winter, and post-monsoon seasons. The consistency in T\u003csub\u003emax\u003c/sub\u003e, alongside the increase in T\u003csub\u003emin\u003c/sub\u003e, underscores the importance of investigating the fundamental elements driving these temperature variations. Understanding the reasons behind the lack of change in T\u003csub\u003emax\u003c/sub\u003e during these seasons is crucial, especially when considering how these temperature dynamics correlate with trends in precipitation (Sahastrabuddhe et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, the observed increase in precipitation during the monsoon season, averaging 0.057 mm per year (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005), may partly explain the decline in T\u003csub\u003emax\u003c/sub\u003e, as increased rainfall can lead to greater cloud cover and thus reduce solar insolation.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe validation of NASA POWER climatic data against fine-scale data loggers in Gangotri National Park highlights the reliability and relevance of these datasets for climate study in the area. The reliability of NASA POWER data is underscored by the consistency of trends between the two data sources, rendering it a valuable proxy for understanding climatic alterations, particularly in cases where ground-based microclimate data is scarce. An extensive analysis of meteorological data from the past 43 years has revealed significant patterns and changes, including the reduction in temperature ranges and increased precipitation along with relative humidity, which could potentially affect the park's distinct ecosystems. These findings validate the use of NASA POWER data for comprehensive climatic evaluations and ecological modelling, offering critical insights for policymakers and academics focused to the long-term environmental sustainability of Gangotri National Park.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research work was funded by the Department of Science and Technology (DST), Govt. of India, under the National Mission for Sustaining the Himalayan Ecosystem (NMSHE-Phase II) project (Grant no. DST/CCP/TF-4/Phase-2/WII/2021(G)).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; Contribution\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBoth authors contributed to the study\u0026apos;s conception and design, with supervision and guidance provided by GT. Material preparation, data access, and analysis were performed by DB. The first draft of the manuscript was written by DB, with both authors collaboratively revising subsequent versions. Both authors have reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe NASA POWER data utilized in this study is freely accessible at https://power.larc.nasa.gov/data-access-viewer/. The observational datasets will be made freely available to the public once the manuscript is accepted.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAI Disclosure Statement:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we utilized artificial intelligence tools to assist with grammar and syntax error corrections using Grammarly and QuillBot. Additionally, ChatGPT was employed to enhance clarity during the writing process. We acknowledge that while these AI tools contributed to improving the manuscript, the final conclusions and interpretations presented in this work are solely the responsibility of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAli, A. H., \u0026amp; Thakkar, R. (2023). Climate changes through data science: understanding and mitigating environmental crisis. \u003cem\u003eMesopotamian Journal of Big Data\u003c/em\u003e, \u003cem\u003e2023\u003c/em\u003e, 125-137.\u003c/li\u003e\n \u003cli\u003eArshad, M., Ma, X., Yin, J., Ullah, W., Liu, M., \u0026amp; Ullah, I. (2021). Performance evaluation of ERA-5, JRA-55, MERRA-2, and CFS-2 reanalysis datasets, over diverse climate regions of Pakistan. \u003cem\u003eWeather and Climate Extremes\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e, 100373.\u003c/li\u003e\n \u003cli\u003eAryal, D., \u0026amp; Pokharel, B. (2024). \u003cem\u003eHistorical trend and future projection of climate extremes over the southern slope of Himalayas\u003c/em\u003e (No. EGU24-9265). Copernicus Meetings.\u003c/li\u003e\n \u003cli\u003eBajracharya, S. R., Mool, P. K., \u0026amp; Shrestha, B. R. (2008). Global climate change and melting of Himalayan glaciers. \u003cem\u003eMelting glaciers and rising sea levels: Impacts and implications\u003c/em\u003e, 28-46.\u003c/li\u003e\n \u003cli\u003eChen, S., Gan, T. Y., Tan, X., Shao, D., \u0026amp; Zhu, J. (2019). Assessment of CFSR, ERA-Interim, JRA-55, MERRA-2, NCEP-2 reanalysis data for drought analysis over China. \u003cem\u003eClimate Dynamics\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e, 737-757.\u003c/li\u003e\n \u003cli\u003eDahal, N., Lamichhaney, S., \u0026amp; Kumar, S. (2021). Climate change impacts on Himalayan biodiversity: evidence-based perception and current approaches to evaluate threats under climate change. \u003cem\u003eJournal of the Indian Institute of Science\u003c/em\u003e, \u003cem\u003e101\u003c/em\u003e(2), 195-210.\u003c/li\u003e\n \u003cli\u003eHalimi, A. H., Karaca, C., \u0026amp; B\u0026uuml;y\u0026uuml;ktaş, D. (2023). 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S., \u0026amp; Gaston, K. J. (2020). Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(12), 7099-7111.\u003c/li\u003e\n \u003cli\u003eVose, R. S., Easterling, D. R., \u0026amp; Gleason, B. (2005). Maximum and minimum temperature trends for the globe: An update through 2004. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(23).\u003c/li\u003e\n \u003cli\u003eRodrigues, G. C., \u0026amp; Braga, R. P. (2021). Evaluation of NASA POWER reanalysis products to estimate daily weather variables in a hot summer mediterranean climate. \u003cem\u003eAgronomy\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(6), 1207.\u003c/li\u003e\n \u003cli\u003eAboelkhair, H., Morsy, M., \u0026amp; El Afandi, G. (2019). Assessment of agroclimatology NASA POWER reanalysis datasets for temperature types and relative humidity at 2 m against ground observations over Egypt. \u003cem\u003eAdvances in Space Research\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e(1), 129-142.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate change, Indian Himalayan Region, MERRA-2, Temporal variations","lastPublishedDoi":"10.21203/rs.3.rs-5401487/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5401487/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding long-term climatic patterns is essential for assessing climate change impacts and informing adaptation strategies. This study aims to examine the temporal variations in climate from 1981 to 2023 in Gangotri National Park, Western Himalaya. Using NASA\u0026rsquo;s Modern-Era Retrospective analysis for Research and Applications (MERRA-2), we analysed daily, monthly, and annual agroclimatology data for maximum (T\u003csub\u003emax\u003c/sub\u003e) and minimum (T\u003csub\u003emin\u003c/sub\u003e) temperatures (\u0026deg;C), corrected precipitation (mm/day), and relative humidity (RH, %) at 2 meters. The datasets were processed using R statistical software (version 4.4.1), and seasonal trends were evaluated with linear regression models to quantify the rate of change and statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The analysis revealed that T\u003csub\u003emax\u003c/sub\u003e significantly decreased during the monsoon season, with an average decline of 0.01\u0026deg;C per year (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005), while T\u003csub\u003emin\u003c/sub\u003e increased during both the monsoon and post-monsoon seasons by 0.04\u0026deg;C per year and by 0.02\u0026deg;C per year in summer (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Precipitation trends indicated a substantial rise during the monsoon (0.057 mm per year) and winter (0.016 mm per year), indicating more intense rainfall. RH also increased across all seasons, with the higher rises in summer (0.31% per year) and post-monsoon (0.30% per year). These findings suggest that the observed shifts in these parameters may have a substantial influence on park's distinctive ecosystems. Validation using HOBO fine-scale microclimate loggers confirmed consistent seasonal trends between observational data and NASA POWER estimates, with no significant differences in trendlines (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), demonstrating the reliability of NASA POWER for long-term climate studies in this region.\u003c/p\u003e","manuscriptTitle":"Long-term Climate Change Trends: 43-Year Analysis of Climatic Data in Gangotri National Park, Western Himalaya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-26 09:17:14","doi":"10.21203/rs.3.rs-5401487/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c4510dbb-c6ab-4578-8484-7cb3a5acb8ed","owner":[],"postedDate":"November 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-07T17:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-26 09:17:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5401487","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5401487","identity":"rs-5401487","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}