Mahardika Wira Aji Bayu Sutera, S. B. Soeryamassoeka, Eko Yulianto
{"title":"DETERMINATION OF REPRESENTATIVE MOCK MODEL PARAMETERS FOR MONTHLY DISCHARGE CURVE DEVELOPMENT IN THE UPPER KAPUAS RIVER BASIN","authors":"Mahardika Wira Aji Bayu Sutera, S. B. Soeryamassoeka, Eko Yulianto","doi":"10.26418/jts.v24i2.69008","DOIUrl":null,"url":null,"abstract":"Representative Mock Model Parameters for Generating Monthly Discharge Curves in the Upper Kapuas River Basin provide valuable insights into hydrological processes influenced by climatic factors. Potential evapotranspiration peaks in August due to elevated temperatures and intensified sunshine during the 2005 dry season, leading to increased water demand from soil and vegetation. This results in heightened water loss to the atmosphere, reducing available water for river flow and decreasing monthly discharge, which is crucial during dry periods. Effective water resource management strategies are essential to mitigate potential water scarcity. High rainfall in the upstream Kapuas watershed significantly impacts monthly discharge, with increased surface flow directly boosting river discharge. The monthly discharge varies widely between rainy and dry seasons, notably rising during heavy rainfall, potentially causing flooding. Effective watershed management, including runoff management, reforestation, and infrastructure development, is critical to mitigate these impacts and optimize water resources for irrigation and supply, ensuring efficient utilization of increased rainfall. Correlation and RSR test results underscore the model's ability to capture variable relationships and predict outcomes accurately. Strong correlations between 0.8 to 1 and RSR values ranging from 0.5 to 0.7 demonstrate the model's reliability in various scenarios. Models with lower RSR values below 0.5 exhibit exceptional prediction accuracy, emphasizing their utility in diverse applications. These findings highlight the importance of refining models to enhance accuracy and reliability in predictive hydrological applications within the Upper Kapuas River Basin, ensuring adequate water resource management and flood risk mitigation.","PeriodicalId":52838,"journal":{"name":"Jurnal Teknik Sipil","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Teknik Sipil","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26418/jts.v24i2.69008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Representative Mock Model Parameters for Generating Monthly Discharge Curves in the Upper Kapuas River Basin provide valuable insights into hydrological processes influenced by climatic factors. Potential evapotranspiration peaks in August due to elevated temperatures and intensified sunshine during the 2005 dry season, leading to increased water demand from soil and vegetation. This results in heightened water loss to the atmosphere, reducing available water for river flow and decreasing monthly discharge, which is crucial during dry periods. Effective water resource management strategies are essential to mitigate potential water scarcity. High rainfall in the upstream Kapuas watershed significantly impacts monthly discharge, with increased surface flow directly boosting river discharge. The monthly discharge varies widely between rainy and dry seasons, notably rising during heavy rainfall, potentially causing flooding. Effective watershed management, including runoff management, reforestation, and infrastructure development, is critical to mitigate these impacts and optimize water resources for irrigation and supply, ensuring efficient utilization of increased rainfall. Correlation and RSR test results underscore the model's ability to capture variable relationships and predict outcomes accurately. Strong correlations between 0.8 to 1 and RSR values ranging from 0.5 to 0.7 demonstrate the model's reliability in various scenarios. Models with lower RSR values below 0.5 exhibit exceptional prediction accuracy, emphasizing their utility in diverse applications. These findings highlight the importance of refining models to enhance accuracy and reliability in predictive hydrological applications within the Upper Kapuas River Basin, ensuring adequate water resource management and flood risk mitigation.