Hydro-climatic Time Series Analysis between Evaporation, Temperature, Relative Humidity and Rainfall: Case Study in Pahang

Hydro-climatic Time Series Analysis between Evaporation, Temperature, Relative Humidity and Rainfall: Case Study in Pahang | 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 Hydro-climatic Time Series Analysis between Evaporation, Temperature, Relative Humidity and Rainfall: Case Study in Pahang Intan Najiha Akhmar Saharuddin, Wan Zunairah Othman, Nurul Nadrah Aqilah Tukimat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8284957/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Hydro-climatic time series analysis is critical in understanding the dynamic interactions between atmospheric and hydrological variables, particularly in regions vulnerable to climate variability. This study investigates the temporal relationships between evaporation, temperature, relative humidity, and rainfall in Pahang, Malaysia, using 13 years (2008–2020) of hydro-climatic data. Regression analysis (RA) was employed to quantify these interactions, revealing that temperature is the dominant driver of evaporation, with a strong correlation at the Temerloh station (R² = 0.748) and moderate correlations at Muadzam Shah (R² = 0.676) and Kuantan (R² = 0.544). Conversely, rainfall and relative humidity exhibited weak correlations with evaporation, with R² values ranging from 0.004 to 0.351, indicating indirect influences. The study highlights the efficacy of time series analysis in detecting long-term climate trends and underscores the necessity of incorporating additional climatic factors, such as wind speed and solar radiation, for improved predictive accuracy. The findings contribute to a deeper understanding of regional hydro-climatic variability, informing sustainable water resource management and climate adaptation strategies. Hydro-climatic hydrological variables and regression analysis Figures Figure 1 Figure 2 Figure 3 1. Introduction The impact of climate changes is increasingly evident through the rising frequency of extreme atmospheric events such as flash flood, drought, and the heightened intensity of the El Nino-Southern Oscillation (ENSO) phenomenon (Carvajal 2008 ). These events not only disrupt ecosystems but also pose significant risks to human livelihood and infrastructure, underlining the urgency of comprehensive climate studies. The Hydro-climatic time series analysis, which integrated the study of hydrological and climatic variables, plays a pivotal role in climate research. By analysing temporal patterns, this method enhances our understanding of the dynamic relationship within the water cycle and climate systems. Hydrological components such as rainfall, streamflow, groundwater level, and evaporation are studies alongside climatic elements like temperature, humidity, wind speed and solar radiation. These variables form the foundation of hydro-climatic studies, as they collectively define the state and behaviour of water within the Earth’s climate system. Key components of the hydrological cycle, including river water levels, rainfall, temperature, and evaporation, are particularly influential in hydro-climatic time series analysis. For instance, rainfall act as output of the meteorological system while simultaneously acting as an input for the hydrological system. This dual role underscores the interdependence of climatic and hydrological processes (Kundzewicz et al. 2008 ). Understanding such interconnection is crucial for modelling and predicting water-related challenges in the context of a changing climate. Climate change has altered rainfall patterns in Malaysia, affecting the hydrological cycle and highlighting the importance of monitoring rainfall, temperature, and evaporation for effective water resource management. (Adilah 2020 ). Hydro-climatic time series offers valuable insight into climate studies, enabling researchers to perform trend analysis, correlation studies, forecasting, and impact assessment. For example, regression and correlation analyses are instrumental in evaluating how climatic conditions influence hydrological processes within a watershed (Huo et al. 2021 ). A range of methods is available for conduction such analyses, including statistical techniques, frequency analyses, advanced time series method, and visualization tools. Advances in these methodologies have expanded the scope of hydroclimatic research, providing deeper insights into long-term climate dynamics. One noteworthy development in the high-resolution rainfall estimation technique known as Artificial Neural Network-Climate Data Record (PERSIANN-CDR). As describe by Tan et al. ( 2017 ), this tool facilitates the analysis of long-term hydroclimatic data. However, its application in different study areas remains a challenge. The analysis by Tan et. Al revealed that PERSIANN-CDR tends to slightly underestimate and overestimate rainfall in different locations, highlighting the need for further refinement and validation to improve its reliability. Trend analysis of hydro-climatic variables is crucial for understanding long-term changes that influence water balance components such as evaporation. In past study (Gocic et al. 2013) applied the Mann-Kendall test and Sen’s slope estimator to detect trends in several meteorological variables (temperature, relative humidity, vapour pressure, etc.) over a 30-year period across 12 stations in Serbia. They found significant upward trends in both minimum and maximum temperatures, while relative humidity showed a decreasing trend in summer and autumn, and vapour pressure exhibited increasing trends in multiple seasons. Importantly, they also used change-point analysis to identify abrupt shifts in the time series, and noted that their results have direct implications for reference evapotranspiration estimations. Their methodology and findings provide a strong precedent for applying non-parametric trend tests in hydro-climatic time series analyses. Climate change also exerts a profound influence on hydroclimatic extreme events, amplifying their frequency and intensity due to shifts in mean climate conditions (Tegegne and Melesse 2020 ). These extreme events, which exceed or fall below observed threshold values, often near the upper or lower limits of the recorded data. They include severe, unusual, or unseasonal occurrences, such prolonged droughts, flash floods or unexpected temperature spikes, which, although brief, can have devastating impacts. The consequences of these events are far-reaching, resulting in damages to infrastructure, disruptions in agriculture, losses in business, and setbacks in manufacturing, among other sectors. This study focusses on critical relationship in hydro-climatic time series analysis, specifically examining the interactions between, i) Evaporation and temperature, ii) Evaporation and relative humidity, and iii) Evaporation and rainfall. Understanding these relationships is essential for advancing our understanding of climate and water dynamics, thereby aiding in the development of sustainable solution to mitigate the impacts of climate change and extreme events. 2. Study Area Pahang is one of the states in Peninsular Malaysia, located on the eastern region of peninsula. The Pahang River Basin (PRB) lies between latitudes 2˚ 48’ 45’’ N and 3˚ 40’’ 24’N, and longitudes 101˚ 16’31’’E and 103˚29’34’’E. The catchment area spans a maximum length of 205 km and a breadth of 236 km. Pahang, situated on the east coast Peninsular Malaysia, is significantly affected by annual flooding due to the Northeast Monsoon (Sulaiman et al. 2010 ). Notably, December marked the worst flood disaster in Pahang, with substantial losses, particularly in Kuantan and Temerloh, Pahang (Kamarudin et al. 2023 ). This study is essential to deepen the understanding of hydrological cycle and climate to develop effective mitigation plans. Such plans aim to reduce the impact of extreme climatic events, like flooding, and enhance community resilience against these disasters. Figure 1 shows the location of Pahang in Peninsular Malaysia. 3. Methodology The methodology of this research study begins with the collection of hydrological data, including evaporation and rainfall and climatic data, such as temperature and relative humidity. These variables are crucial for understanding the dynamic interactions within the hydro-climatic system. Data was sourced from Department of Irrigation and Drainage Malaysia (DID) and Department of Meteorological Malaysia (MET). For this study, data was gathered from six (6) stations of evaporation data, twelve (12) stations of rainfall data, nine (9) stations of temperature data, and nine (9) stations of relative humidity data, strategically distributed across the study area. The variables analyzed include daily evaporation, temperature, relative humidity, and rainfall recorded over a 13-year period from 2008 to 2020. This temporal range was chosen to provide a robust dataset for trend analysis, allowing for a better understanding of long-term patterns and variations in the region’s hydro-climatic conditions. Table 1 summarized the hydro-climatic data collected in the Pahang. To analyse temporal trends in temperature, we applied a hybrid non-parametric method combining the Mann–Kendall test (MK) (Mann, 1945; Kendall, 1975) for statistical significance and Sen’s slope estimator (Sen, 1968) for trend magnitude. This approach is widely used in climate and hydrological studies (Gocic et al. 2013; Bolbasova et al. 2023; Kliengchuay et al. 2024) to assess long-term climatic variations in Pahang. Table 1. The list of hydro-climatic data in Pahang. Evaporation Data Station Period Cameron Highlands January 2008 – December 2022 Batu Embun January 2008 – December 2022 Kuantan January 2008 – December 2022 Temerloh January 2008 – December 2022 Muadzam Shah January 2008 – December 2022 Rainfall Data Station Period Cameron Highlands January 2008 – December 2022 Batu Embun January 2008 – December 2022 Kuantan January 2008 – December 2022 Temerloh January 2008 – December 2022 Muadzam Shah January 2008 – December 2022 Temperature Data Station Period Cameron Highlands January 2008 – December 2022 Batu Embun January 2008 – December 2022 Kuantan January 2008 – December 2022 Temerloh January 2008 – December 2022 Muadzam Shah January 2008 – December 2022 Relative Humidity Data Station Period Cameron Highlands January 2008 – December 2022 Batu Embun January 2008 – December 2022 Kuantan January 2008 – December 2022 Temerloh January 2008 – December 2022 Muadzam Shah January 2008 – December 2022 The collected data was preprocessed to ensure its quality and reliability. The process included missing data treatment using the Inversed Distance Weighted (IDW) method, which has been demonstrated as an effective approach in a recent study by (Azman et al. 2021). The IDW method was found to outperform other techniques, such as Expectation Maximization (EM) and Multiple Imputation (MI), in handling missing data. These quality control stages were essential in order to maintain the integrity of analysis and minimize the potential biases in the results. Next, for the statistical analysis, the study proceeds with demonstrating a Regression Analysis (RA) for each of the following relationships: Evaporation-Temperature, Evaporation-Rainfall, and Evaporation-Relative Humidity. Each of these relationships have been evaluated the strength of relationship using R2 values. Huo et al. (2021), demonstrated the application of RA in evaluating the influence strong correlations that allowed for effective modeling of water resource management scenarios. Studies such as those by Tan et al. (2017) have shown that regression models achieve high accuracy in long-term hydro-climatic predictions, making them reliable for environmental. Hydro-climatic systems are complex, with many interacting variables. RA simplifies these interactions by identifying dominant relationships. During the validation stages, the findings from RA analysis were compared with the previous research study to ensure consistency and reliability. This step involved a detailed examination and interpretation of relationships among the hydro-climatic variables, such as evaporation, temperature, rainfall and relative humidity, as revealed by the analysis. The observed patterns and correlations were carefully interpreted. Figure 2 summarized the methodology of hydro-climatic time series analysis between evaporation, rainfall, temperature, and relative humidity. 4. Results and Discussion This research study aims to understand the relationship between hydro-climatic variables, including evaporation, temperature, rainfall, and relative humidity. This section presents the results of the hydro-climatic time series analysis and discusses the relationship between those variables. The analysis employed Regression Analysis (RA) to evaluate the relationship between variables, with the coefficient of determination (R2) used to assess model fit. The interpretation of R2 values is as follows: R2 = 1 represents a perfect fit, indicating that the variables are fully dependent on each other, R2 greater than 0.7 indicated a strong fit, with a high proportion of variance explained by the model, R2 value lies between 0.5 to 0.7 considered as moderate fit, providing a reasonable explanation of the relationship, and R2 value is less than 0.5 considered as a weak fit model, where the model explains only a small portion of the variance (Shiru et al. 2021). The results provide valuable insight into the dynamics of hydro-climatic variables within the study area, contributing to a deeper understanding of their interactions and potential implications for hydrological and climate-related studies. The findings of the present study can be contextualized with projections of hydro-climatic extremes. Tegegne et al. (2020) used multimodel ensemble approaches to assess future changes in temperature, precipitation, and other hydro-climatic variables, showing that climate change can significantly intensify extreme events and alter water availability. This implies that the observed trends in evaporation, temperature, humidity, and rainfall in Pahang could translate into more pronounced seasonal water deficits, affecting water resources management and planning strategies. Table 2 summarize the findings of hydro-climatic time series analysis conducted in Pahang, focusing on selected locations such as Batu Embun, Kuantan, Temerloh, Muadzam Shah, and Cameron Highlands. Table 2 Result of R2 between hydro-climatic time series variables in Pahang Location Evaporation - Temperature Evaporation - Rainfall Evaporation - Relative Humidity Cameron Highlands 0.680 0.004 0.362 Batu Embun 0.544 0.135 0.152 Kuantan 0.748 0.005 0.223 Temerloh 0.676 0.351 0.308 Muadzam Shah 0.132 0.165 0.075 The analysis of the relationship between evaporation and temperature shows a strong correlation at the Temerloh station, with an R2 value of 0.748. In contrast, the Muadzam Shah and Kuantan stations exhibit moderate correlation, with R2 values of 0.676 and 0.544 respectively. The Cameron Highlands station, however, demonstrates a weak correlation between evaporation and temperature, with an R2 value of 0.132. The weakness of the dataset contributed to the unreliability of the model. The relationship between evaporation and rainfall reveals a weak correlation across all five (5) stations in Pahang. The R2 values are 0.004 at Batu Embun, 0.135 at Kuantan, 0.005 at Temerloh, 0.351 at Muadzam Shah, and 0.165 at Cameron Highland. Similarly, the relationship between evaporation and relative humidity indicates a weak correlation at all five (5) stations. The R2 values are 0.362 at Batu Embun, 0.152 at Kuantan, 0.223 at Temerloh, 0.308 at Muadzam Shah, and 0.075 at Cameron Highland. Figure 3 presents the relationship between evaporation and temperature. As shown, there is a positive correlation between the two variables, indicating that higher temperatures tend to result in increased evaporation rates. 5. Conclusion The hydro-climatic time series analysis in Pahang revealed significant temporal and spatial relationships between evaporation, temperature, relative humidity, and rainfall. Regression analysis (RA) demonstrated that temperature is the primary driver of evaporation, with a strong correlation observed at the Temerloh station (R² = 0.748). Moderate correlations were found at Muadzam Shah (R² = 0.676) and Kuantan (R² = 0.544), while Cameron Highlands showed a weak correlation (R² = 0.132), potentially due to its high-altitude, cooler climate. These findings align with prior studies highlighting temperature as a dominant variable in evaporation processes (Huo C. et al., 2021 ). Rainfall and relative humidity were found to have weaker correlations with evaporation, as indicated by R² values ranging from 0.004 to 0.351 for rainfall and 0.075 to 0.362 for relative humidity. These results suggest that while these variables influence evaporation, their effects are less direct compared to temperature. Similar weak correlations were reported by Shiru and Chung ( 2021 ) in hydro-climatic systems, emphasizing the need to consider additional factors such as wind speed and solar radiation to fully understand evaporation dynamics. The time series approach adopted in this study provided a robust framework for analyzing long-term trends and variability in hydro-climatic interactions over the 13-year period (2008–2020). The use of methods such as the Inverse Distance Weighted (IDW) technique for handling missing data ensured the reliability of the dataset, as demonstrated by Azman et al. ( 2021 ). This methodological rigor reinforces the value of time series analysis in addressing water resource management challenges under changing climatic conditions. However, the study is limited by its geographical scope and the exclusion of variables such as wind speed and solar radiation. Future research should explore more advanced methodologies, including machine learning techniques, to improve predictive accuracy, as suggested by Tan et al. ( 2017 ). Expanding this analysis to other regions would further validate the findings and enhance their applicability to diverse climatic settings. (Cai 2025 ) suggests that heterogeneous effects of climate change across countries and climate-zones indicate the value of broader regional coverage in future studies, which would help in validating results and improving relevance across diverse climatic contexts. In conclusion, this study underscores the critical role of temperature in driving evaporation and highlights the utility of time series analysis in understanding hydro-climatic interactions. These insights are instrumental for developing sustainable water management strategies and mitigating the impacts of climate variability, particularly in regions like Pahang that are susceptible to hydro-climatic extremes. Declarations 6. Acknowledgement This research is supported by Universiti Malaysia Pahang Al-Sultan Abdullah under grant number RDU230112 (FRGS/1/2023/TK06/UMP/02/1), Tabung Persidangan Dalam Negeri (TPDN-UMPSA) entitled Hydro-climatic Time Series Analysis between Evaporation, Temperature, Relative Humidity and Rainfall: Case Study in Pahang. Acknowledgements Not applicable. Funding This research is supported by Universiti Malaysia Pahang Al-Sultan Abdullah under grant number RDU230112 (FRGS/1/2023/TK06/UMP/02/1), Tabung Persidangan Dalam Negeri (TPDN-UMPSA) entitled Hydro-climatic Time Series Analysis between Evaporation, Temperature, Relative Humidity and Rainfall: Case Study in Pahang. Authors’ Contributions: All authors read and approved the final manuscript. Intan Najiha Akhmar Saharuddin: Conceptualization, methodology, data curation, formal analysis, writing original draft. Wan Zunairah Othman: Data analysis support, validation, review of analytical methods and writing original draft. Nurul Nadrah Aqilah Tukimat: Supervision, project administration, writing – review & editing. Ethical Approval: All ethical standards have been followed during this research. Consent to Participate: Not applicable. Consent to Publish: Not applicable. Competing Interests: The authors declare that they have no competing interests. Data Availability Statement: The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. References Adilah N, Zarif MMU (2020) IOP Publishing Ltd Mater Sci Eng 712:1–8. https://doi.org/10.1088/1757-899X/712/1/012021 . Rainfall Trend Analysis using Box Plot Method Case Study: UMP Campus Gambang and Pekan. Azman AZ, Tukimat NNA, Malek MA (2021) Mater Sci Eng 1144:1–10. https://doi.org/10.1088/1757-899X/1144/1/012046 . Comparison of Missing Rainfall Data Treatment Analysis at Kenyir Lake. Bolbasova L, Lukin VP (2023) Possibilities of Adaptive Optical Correction of the Global Wavefront Tilt Using Signals from Traditional and Polychromatic Laser Guide Stars. Atmos Ocean Opt 35(1):165–170. https://doi.org/10.1134/S1024856023010037 Cai Y (2025) How does climate change affect regional sustainable development? Empirical evidence from 186 countries around the world. Int Rev Econ Finance 99:1–17. https://doi.org/10.1016/j.iref.2025.104047 Carvajal E (2008) Environmental flow regime in the framework of integrated water resources management strategy. Ecohydrol Hydrobiol 8(2–4):307–315. https://doi.org/10.2478/v10104-009-0024-x Gocic & Trajkovic (2013) Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Page 1–10. https://doi.org/10.13140/RG.2.1.4264.4322 Huo C, Hameed J, Sadiq MW, Albasher G, Alqahtani W (2021) Tourism, environment and hotel management: an innovative perspective toaddress modern trends in contemporary tourism management. Bus Process Manage J. https://doi.org/10.1108/BPMJ-12-2020 Kamarudin MZ, Mohd Syafiq AMN, Romarzila O (2023) A scoping review of the effects of a technology-integrated. Res Sci Technological Educ 1–18. https://doi.org/10.1080/02635143.2022.2138847 Kliengchuay W, Mingkhwan, Kiangkoo N, Suwanmanee N, Sahanavin N, Kongpran J, WaiAung H, Tantrakarnapa K (2024) Analyzing temperature, humidity, and precipitation trends in six regions of Thailand using innovative trend analysis. 14 (780 0) Scientific Reports. 1–9. https://doi.org/10.1038/s41598-024-57980-5 Kundzewicz ZW, Mata LJ, Arnell NW, Doll P, Jimenez B, Miller K, Oki T, Sen Z, Shiklomanov I (2008) The Implications of Projected Climate Change for Freshwater Resources and Their Management. Hydrol Sciences–Journal–des Sci Hydrol 53(1):3–10. https://doi.org/10.1623/hysj.53.1.3 Shiru MS, Chung ES (2021) Empirical Model for the Assessment of Climate Change Impacts on Spatial Pattern of Water Availability in Nigeria. Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation, page 405–427. https://doi.org/10.1007/978-981-15-5772-9_19 Sulaiman WNA, Heshmatpoor A, Rosli MH (2010) Identification of Flood Source Areas in Pahang River Basin. Peninsular Malaysia Environ Asia 3:73–78. https://doi.org/10.14456/ea.2010.43 Tan ML, Gassman PW, Cracknell AP (2017) Assessment of three long-term gridded climate products for hydro-climatic simulations in tropical river basins. 9(3), 229, Water, page 1–24. https://doi.org/10.3390/w9030229 Tegegne G, Melesse A (2020) Multimodel Ensemble Projection of Hydro-climatic Extremes for Climate Change Impact Assessment on Water Resources. Water Resour Manage 34(8):3019–3035. https://doi.org/10.1007/s11269-020-02601-9 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 09 Mar, 2026 Editor assigned by journal 08 Dec, 2025 First submitted to journal 04 Dec, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8284957","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603158812,"identity":"f4f517d6-14af-4bdb-86d6-7aa186cdfdf7","order_by":0,"name":"Intan Najiha Akhmar Saharuddin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBACNijNzCbBwP7hQwWIydxAQAszXAsb44wzICYjfi0MUC0MDEAtzLxtIBYBLXzS/Qc/F+6wY+eTbn72cOa82mj+dqCWHxXbcDtM5jCz9MwzycxsMsfMDT5uO5474zBjA2PPmdu4tUgkM0jztjED/ZJgIDlz27HcBqAWZsY2vFqYf/O21QO1pH+Q5p1zLHc+EVrYgLYcBmrJMZPmbajJ3UCEFjNr3rbjIC3FhjOOHcjdCNRyEJ9f5GckPr7N21adLD8jfeODDzV1ufPOHz744EcFbi0wkAylD4PJAwTVA4EdlK4jRvEoGAWjYBSMMAAA34pSj5jWXSwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0006-0689-1432","institution":"Universiti Malaysia Pahang Al-Sultan Abdullah","correspondingAuthor":true,"prefix":"","firstName":"Intan","middleName":"Najiha Akhmar","lastName":"Saharuddin","suffix":""},{"id":603158813,"identity":"f7ba9d28-2726-4bf1-be6a-02315fbfc849","order_by":1,"name":"Wan Zunairah Othman","email":"","orcid":"","institution":"UMPSA FTKA: Universiti Malaysia Pahang Al-Sultan Abdullah Fakulti Teknologi Kejuruteraan Awam","correspondingAuthor":false,"prefix":"","firstName":"Wan","middleName":"Zunairah","lastName":"Othman","suffix":""},{"id":603158814,"identity":"30adc01e-3be9-41ce-8445-5886dcacc2fe","order_by":2,"name":"Nurul Nadrah Aqilah Tukimat","email":"","orcid":"","institution":"UMPSA FTKA: Universiti Malaysia Pahang Al-Sultan Abdullah Fakulti Teknologi Kejuruteraan Awam","correspondingAuthor":false,"prefix":"","firstName":"Nurul","middleName":"Nadrah Aqilah","lastName":"Tukimat","suffix":""}],"badges":[],"createdAt":"2025-12-05 06:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8284957/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8284957/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104572191,"identity":"9d27f7c3-2748-4f30-a69a-d6caf3c42ea9","added_by":"auto","created_at":"2026-03-13 13:00:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":609086,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Pahang showing the river basins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e Department of Irrigation and Drainage (DID) Malaysia.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8284957/v1/4c64ee043b8fad41f7d87187.png"},{"id":104572194,"identity":"a2a60c49-39d0-4850-a60b-142e1c2c4d9e","added_by":"auto","created_at":"2026-03-13 13:00:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116004,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of methodology of hydro-climatic time-series analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8284957/v1/0439b67bef80ba6516e196f6.png"},{"id":104782270,"identity":"996f8dee-c821-4e0e-93f5-2fa022522d2b","added_by":"auto","created_at":"2026-03-17 07:57:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":315486,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of hydro-climatic time-series analysis in Pahang.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8284957/v1/027f9530065389416066e3ec.png"},{"id":104808408,"identity":"c1c1f270-095d-4cea-9e30-7928b6010911","added_by":"auto","created_at":"2026-03-17 12:37:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1677345,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8284957/v1/f7cae5f1-5b0c-4d1f-b4bd-ec1af2a6720b.pdf"}],"financialInterests":"","formattedTitle":"Hydro-climatic Time Series Analysis between Evaporation, Temperature, Relative Humidity and Rainfall: Case Study in Pahang","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe impact of climate changes is increasingly evident through the rising frequency of extreme atmospheric events such as flash flood, drought, and the heightened intensity of the El Nino-Southern Oscillation (ENSO) phenomenon (Carvajal \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These events not only disrupt ecosystems but also pose significant risks to human livelihood and infrastructure, underlining the urgency of comprehensive climate studies.\u003c/p\u003e \u003cp\u003eThe Hydro-climatic time series analysis, which integrated the study of hydrological and climatic variables, plays a pivotal role in climate research. By analysing temporal patterns, this method enhances our understanding of the dynamic relationship within the water cycle and climate systems. Hydrological components such as rainfall, streamflow, groundwater level, and evaporation are studies alongside climatic elements like temperature, humidity, wind speed and solar radiation. These variables form the foundation of hydro-climatic studies, as they collectively define the state and behaviour of water within the Earth\u0026rsquo;s climate system.\u003c/p\u003e \u003cp\u003eKey components of the hydrological cycle, including river water levels, rainfall, temperature, and evaporation, are particularly influential in hydro-climatic time series analysis. For instance, rainfall act as output of the meteorological system while simultaneously acting as an input for the hydrological system. This dual role underscores the interdependence of climatic and hydrological processes (Kundzewicz et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Understanding such interconnection is crucial for modelling and predicting water-related challenges in the context of a changing climate. Climate change has altered rainfall patterns in Malaysia, affecting the hydrological cycle and highlighting the importance of monitoring rainfall, temperature, and evaporation for effective water resource management. (Adilah \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHydro-climatic time series offers valuable insight into climate studies, enabling researchers to perform trend analysis, correlation studies, forecasting, and impact assessment. For example, regression and correlation analyses are instrumental in evaluating how climatic conditions influence hydrological processes within a watershed (Huo et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A range of methods is available for conduction such analyses, including statistical techniques, frequency analyses, advanced time series method, and visualization tools. Advances in these methodologies have expanded the scope of hydroclimatic research, providing deeper insights into long-term climate dynamics.\u003c/p\u003e \u003cp\u003eOne noteworthy development in the high-resolution rainfall estimation technique known as Artificial Neural Network-Climate Data Record (PERSIANN-CDR). As describe by Tan et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), this tool facilitates the analysis of long-term hydroclimatic data. However, its application in different study areas remains a challenge. The analysis by Tan et. Al revealed that PERSIANN-CDR tends to slightly underestimate and overestimate rainfall in different locations, highlighting the need for further refinement and validation to improve its reliability.\u003c/p\u003e \u003cp\u003eTrend analysis of hydro-climatic variables is crucial for understanding long-term changes that influence water balance components such as evaporation. In past study (Gocic et al. 2013) applied the Mann-Kendall test and Sen\u0026rsquo;s slope estimator to detect trends in several meteorological variables (temperature, relative humidity, vapour pressure, etc.) over a 30-year period across 12 stations in Serbia. They found significant upward trends in both minimum and maximum temperatures, while relative humidity showed a decreasing trend in summer and autumn, and vapour pressure exhibited increasing trends in multiple seasons. Importantly, they also used change-point analysis to identify abrupt shifts in the time series, and noted that their results have direct implications for reference evapotranspiration estimations. Their methodology and findings provide a strong precedent for applying non-parametric trend tests in hydro-climatic time series analyses.\u003c/p\u003e \u003cp\u003eClimate change also exerts a profound influence on hydroclimatic extreme events, amplifying their frequency and intensity due to shifts in mean climate conditions (Tegegne and Melesse \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These extreme events, which exceed or fall below observed threshold values, often near the upper or lower limits of the recorded data. They include severe, unusual, or unseasonal occurrences, such prolonged droughts, flash floods or unexpected temperature spikes, which, although brief, can have devastating impacts. The consequences of these events are far-reaching, resulting in damages to infrastructure, disruptions in agriculture, losses in business, and setbacks in manufacturing, among other sectors. This study focusses on critical relationship in hydro-climatic time series analysis, specifically examining the interactions between, i) Evaporation and temperature, ii) Evaporation and relative humidity, and iii) Evaporation and rainfall. Understanding these relationships is essential for advancing our understanding of climate and water dynamics, thereby aiding in the development of sustainable solution to mitigate the impacts of climate change and extreme events.\u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cp\u003ePahang is one of the states in Peninsular Malaysia, located on the eastern region of peninsula. The Pahang River Basin (PRB) lies between latitudes 2˚ 48\u0026rsquo; 45\u0026rsquo;\u0026rsquo; N and 3˚ 40\u0026rsquo;\u0026rsquo; 24\u0026rsquo;N, and longitudes 101˚ 16\u0026rsquo;31\u0026rsquo;\u0026rsquo;E and 103˚29\u0026rsquo;34\u0026rsquo;\u0026rsquo;E. The catchment area spans a maximum length of 205 km and a breadth of 236 km. Pahang, situated on the east coast Peninsular Malaysia, is significantly affected by annual flooding due to the Northeast Monsoon (Sulaiman et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Notably, December marked the worst flood disaster in Pahang, with substantial losses, particularly in Kuantan and Temerloh, Pahang (Kamarudin et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study is essential to deepen the understanding of hydrological cycle and climate to develop effective mitigation plans. Such plans aim to reduce the impact of extreme climatic events, like flooding, and enhance community resilience against these disasters. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the location of Pahang in Peninsular Malaysia.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe methodology of this research study begins with the collection of hydrological data, including evaporation and rainfall and climatic data, such as temperature and relative humidity. These variables are crucial for understanding the dynamic interactions within the hydro-climatic system. Data was sourced from Department of Irrigation and Drainage Malaysia (DID) and Department of Meteorological Malaysia (MET).\u003c/p\u003e\n\u003cp\u003eFor this study, data was gathered from six (6) stations of evaporation data, twelve (12) stations of rainfall data, nine (9) stations of temperature data, and nine (9) stations of relative humidity data, strategically distributed across the study area. The variables analyzed include daily evaporation, temperature, relative humidity, and rainfall recorded over a 13-year period from 2008 to 2020. This temporal range was chosen to provide a robust dataset for trend analysis, allowing for a better understanding of long-term patterns and variations in the region\u0026rsquo;s hydro-climatic conditions. Table 1 summarized the hydro-climatic data collected in the Pahang. To analyse temporal trends in temperature, we applied a hybrid non-parametric method combining the Mann\u0026ndash;Kendall test (MK) (Mann, 1945; Kendall, 1975) for statistical significance and Sen\u0026rsquo;s slope estimator (Sen, 1968) for trend magnitude. This approach is widely used in climate and hydrological studies (Gocic et al. 2013; Bolbasova et al. 2023; Kliengchuay et al. 2024) to assess long-term climatic variations in Pahang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e The list of hydro-climatic data in Pahang.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvaporation Data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeriod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eCameron Highlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eBatu Embun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eKuantan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTemerloh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eMuadzam Shah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRainfall Data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeriod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eCameron Highlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eBatu Embun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eKuantan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTemerloh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eMuadzam Shah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemperature Data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeriod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eCameron Highlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eBatu Embun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eKuantan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTemerloh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eMuadzam Shah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative Humidity Data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeriod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eCameron Highlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eBatu Embun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eKuantan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTemerloh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eMuadzam Shah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eJanuary 2008 \u0026ndash; December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe collected data was preprocessed to ensure its quality and reliability. The process included missing data treatment using the Inversed Distance Weighted (IDW) method, which has been demonstrated as an effective approach in a recent study by (Azman et al. 2021). The IDW method was found to outperform other techniques, such as Expectation Maximization (EM) and Multiple Imputation (MI), in handling missing data. These quality control stages were essential in order to maintain the integrity of analysis and minimize the potential biases in the results.\u003c/p\u003e\n\u003cp\u003eNext, for the statistical analysis, the study proceeds with demonstrating a Regression Analysis (RA) for each of the following relationships: Evaporation-Temperature, Evaporation-Rainfall, and Evaporation-Relative Humidity. Each of these relationships have been evaluated the strength of relationship using R2 values. Huo et al. (2021), demonstrated the application of RA in evaluating the influence strong correlations that allowed for effective modeling of water resource management scenarios. Studies such as those by Tan et al. (2017) have shown that regression models achieve high accuracy in long-term hydro-climatic predictions, making them reliable for environmental. Hydro-climatic systems are complex, with many interacting variables. RA simplifies these interactions by identifying dominant relationships.\u003c/p\u003e\n\u003cp\u003eDuring the validation stages, the findings from RA analysis were compared with the previous research study to ensure consistency and reliability. This step involved a detailed examination and interpretation of relationships among the hydro-climatic variables, such as evaporation, temperature, rainfall and relative humidity, as revealed by the analysis. The observed patterns and correlations were carefully interpreted. Figure 2 summarized the methodology of hydro-climatic time series analysis between evaporation, rainfall, temperature, and relative humidity.\u003c/p\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eThis research study aims to understand the relationship between hydro-climatic variables, including evaporation, temperature, rainfall, and relative humidity. This section presents the results of the hydro-climatic time series analysis and discusses the relationship between those variables. The analysis employed Regression Analysis (RA) to evaluate the relationship between variables, with the coefficient of determination (R2) used to assess model fit.\u003c/p\u003e \u003cp\u003eThe interpretation of R2 values is as follows: R2\u0026thinsp;=\u0026thinsp;1 represents a perfect fit, indicating that the variables are fully dependent on each other, R2 greater than 0.7 indicated a strong fit, with a high proportion of variance explained by the model, R2 value lies between 0.5 to 0.7 considered as moderate fit, providing a reasonable explanation of the relationship, and R2 value is less than 0.5 considered as a weak fit model, where the model explains only a small portion of the variance (Shiru et al. 2021). The results provide valuable insight into the dynamics of hydro-climatic variables within the study area, contributing to a deeper understanding of their interactions and potential implications for hydrological and climate-related studies.\u003c/p\u003e \u003cp\u003eThe findings of the present study can be contextualized with projections of hydro-climatic extremes. Tegegne et al. (2020) used multimodel ensemble approaches to assess future changes in temperature, precipitation, and other hydro-climatic variables, showing that climate change can significantly intensify extreme events and alter water availability. This implies that the observed trends in evaporation, temperature, humidity, and rainfall in Pahang could translate into more pronounced seasonal water deficits, affecting water resources management and planning strategies. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarize the findings of hydro-climatic time series analysis conducted in Pahang, focusing on selected locations such as Batu Embun, Kuantan, Temerloh, Muadzam Shah, and Cameron Highlands.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of R2 between hydro-climatic time series variables in Pahang\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvaporation\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003eTemperature\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvaporation\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003eRainfall\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvaporation\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003eRelative Humidity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCameron Highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatu Embun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKuantan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemerloh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuadzam Shah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis of the relationship between evaporation and temperature shows a strong correlation at the Temerloh station, with an R2 value of 0.748. In contrast, the Muadzam Shah and Kuantan stations exhibit moderate correlation, with R2 values of 0.676 and 0.544 respectively. The Cameron Highlands station, however, demonstrates a weak correlation between evaporation and temperature, with an R2 value of 0.132. The weakness of the dataset contributed to the unreliability of the model.\u003c/p\u003e \u003cp\u003eThe relationship between evaporation and rainfall reveals a weak correlation across all five (5) stations in Pahang. The R2 values are 0.004 at Batu Embun, 0.135 at Kuantan, 0.005 at Temerloh, 0.351 at Muadzam Shah, and 0.165 at Cameron Highland. Similarly, the relationship between evaporation and relative humidity indicates a weak correlation at all five (5) stations. The R2 values are 0.362 at Batu Embun, 0.152 at Kuantan, 0.223 at Temerloh, 0.308 at Muadzam Shah, and 0.075 at Cameron Highland. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the relationship between evaporation and temperature. As shown, there is a positive correlation between the two variables, indicating that higher temperatures tend to result in increased evaporation rates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe hydro-climatic time series analysis in Pahang revealed significant temporal and spatial relationships between evaporation, temperature, relative humidity, and rainfall. Regression analysis (RA) demonstrated that temperature is the primary driver of evaporation, with a strong correlation observed at the Temerloh station (R\u0026sup2; = 0.748). Moderate correlations were found at Muadzam Shah (R\u0026sup2; = 0.676) and Kuantan (R\u0026sup2; = 0.544), while Cameron Highlands showed a weak correlation (R\u0026sup2; = 0.132), potentially due to its high-altitude, cooler climate. These findings align with prior studies highlighting temperature as a dominant variable in evaporation processes (Huo C. et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRainfall and relative humidity were found to have weaker correlations with evaporation, as indicated by R\u0026sup2; values ranging from 0.004 to 0.351 for rainfall and 0.075 to 0.362 for relative humidity. These results suggest that while these variables influence evaporation, their effects are less direct compared to temperature. Similar weak correlations were reported by Shiru and Chung (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in hydro-climatic systems, emphasizing the need to consider additional factors such as wind speed and solar radiation to fully understand evaporation dynamics.\u003c/p\u003e \u003cp\u003eThe time series approach adopted in this study provided a robust framework for analyzing long-term trends and variability in hydro-climatic interactions over the 13-year period (2008\u0026ndash;2020). The use of methods such as the Inverse Distance Weighted (IDW) technique for handling missing data ensured the reliability of the dataset, as demonstrated by Azman et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This methodological rigor reinforces the value of time series analysis in addressing water resource management challenges under changing climatic conditions.\u003c/p\u003e \u003cp\u003eHowever, the study is limited by its geographical scope and the exclusion of variables such as wind speed and solar radiation. Future research should explore more advanced methodologies, including machine learning techniques, to improve predictive accuracy, as suggested by Tan et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Expanding this analysis to other regions would further validate the findings and enhance their applicability to diverse climatic settings. (Cai \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) suggests that heterogeneous effects of climate change across countries and climate-zones indicate the value of broader regional coverage in future studies, which would help in validating results and improving relevance across diverse climatic contexts.\u003c/p\u003e \u003cp\u003eIn conclusion, this study underscores the critical role of temperature in driving evaporation and highlights the utility of time series analysis in understanding hydro-climatic interactions. These insights are instrumental for developing sustainable water management strategies and mitigating the impacts of climate variability, particularly in regions like Pahang that are susceptible to hydro-climatic extremes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6. Acknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is supported by Universiti Malaysia Pahang Al-Sultan Abdullah under grant number RDU230112 (FRGS/1/2023/TK06/UMP/02/1), Tabung Persidangan Dalam Negeri (TPDN-UMPSA) entitled Hydro-climatic Time Series Analysis between Evaporation, Temperature, Relative Humidity and Rainfall: Case Study in Pahang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is supported by Universiti Malaysia Pahang Al-Sultan Abdullah under grant number RDU230112 (FRGS/1/2023/TK06/UMP/02/1), Tabung Persidangan Dalam Negeri (TPDN-UMPSA) entitled Hydro-climatic Time Series Analysis between Evaporation, Temperature, Relative Humidity and Rainfall: Case Study in Pahang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u0026nbsp;\u003c/strong\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntan Najiha Akhmar Saharuddin:\u003c/strong\u003e Conceptualization, methodology, data curation, formal analysis, writing original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWan Zunairah Othman:\u003c/strong\u003e Data analysis support, validation, review of analytical methods and writing original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNurul Nadrah Aqilah Tukimat:\u003c/strong\u003e Supervision, project administration, writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eAll ethical standards have been followed during this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdilah N, Zarif MMU (2020) IOP Publishing Ltd Mater Sci Eng 712:1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1757-899X/712/1/012021\u003c/span\u003e\u003cspan address=\"10.1088/1757-899X/712/1/012021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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Water Resour Manage 34(8):3019\u0026ndash;3035. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11269-020-02601-9\u003c/span\u003e\u003cspan address=\"10.1007/s11269-020-02601-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hydro-climatic, hydrological variables and regression analysis","lastPublishedDoi":"10.21203/rs.3.rs-8284957/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8284957/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHydro-climatic time series analysis is critical in understanding the dynamic interactions between atmospheric and hydrological variables, particularly in regions vulnerable to climate variability. This study investigates the temporal relationships between evaporation, temperature, relative humidity, and rainfall in Pahang, Malaysia, using 13 years (2008\u0026ndash;2020) of hydro-climatic data. Regression analysis (RA) was employed to quantify these interactions, revealing that temperature is the dominant driver of evaporation, with a strong correlation at the Temerloh station (R\u0026sup2; = 0.748) and moderate correlations at Muadzam Shah (R\u0026sup2; = 0.676) and Kuantan (R\u0026sup2; = 0.544). Conversely, rainfall and relative humidity exhibited weak correlations with evaporation, with R\u0026sup2; values ranging from 0.004 to 0.351, indicating indirect influences. The study highlights the efficacy of time series analysis in detecting long-term climate trends and underscores the necessity of incorporating additional climatic factors, such as wind speed and solar radiation, for improved predictive accuracy. The findings contribute to a deeper understanding of regional hydro-climatic variability, informing sustainable water resource management and climate adaptation strategies.\u003c/p\u003e","manuscriptTitle":"Hydro-climatic Time Series Analysis between Evaporation, Temperature, Relative Humidity and Rainfall: Case Study in Pahang","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 13:00:09","doi":"10.21203/rs.3.rs-8284957/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-17T06:45:23+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-09T12:26:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-08T05:55:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2025-12-05T01:52:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5af83308-9ff9-4a07-9286-2577f532ae7b","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T13:00:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 13:00:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8284957","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8284957","identity":"rs-8284957","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}