Regional power duration curve model for ungauged intermittent river basins

被引:0
|
作者
Alakbar, Turan [1 ]
Burgan, Halil Ibrahim [1 ]
机构
[1] Akdeniz Univ, Dept Civil Engn, Antalya, Turkiye
关键词
hydropower; intermittent river; power duration curve; regional model; FLOW-DURATION; HYDROPOWER; WATER; STREAMFLOW;
D O I
10.2166/wcc.2024.207
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Hydropower is a sustainable and renewable energy source that can serve as a practical and economically viable solution to the future possible energy crisis and climate change scenarios. Moreover, they possess a higher energy density compared to other alternative energy sources as renewable energy sources such as solar, wind energy, etc. In order to determine the potential of hydropower, long-term observed hydrometeorological data of streamflow, precipitation, etc. are crucial. This study investigates a new power duration curve (PDC) methodology. Basin characteristics such as drainage area and basin relief with meteorological data as precipitation are used for regional models in the application. A basic classification based on geographical locations is made for regional models. Six models based on equation type were utilized to determine the optimal regional model. Absolute errors of cease-to-flow point estimates ranging from 0.01 to 11.49% were observed. The model provided successful results according to Nash-Sutcliffe efficiency which is widely used in hydrological studies very close to 1 and higher than 0.87 except for one streamflow gauging station therewithal all other calculated performance metrics. As a result, it is observed that power and cease-to-flow point estimates of intermittent rivers can be obtained with a new PDC model based on basin characteristics<bold>.</bold>
引用
收藏
页码:4596 / 4612
页数:17
相关论文
共 50 条
  • [41] A Power Law Model for River Flow Velocity in Iowa Basins
    Ghimire, Ganesh Raj
    Krajewski, Witold F.
    Mantilla, Ricardo
    JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 2018, 54 (05): : 1055 - 1067
  • [42] Process-based design flood estimation in ungauged basins by conditioning model parameters on regional hydrological signatures
    Biondi, Daniela
    De Luca, Davide Luciano
    NATURAL HAZARDS, 2015, 79 (02) : 1015 - 1038
  • [43] Process-based design flood estimation in ungauged basins by conditioning model parameters on regional hydrological signatures
    Daniela Biondi
    Davide Luciano De Luca
    Natural Hazards, 2015, 79 : 1015 - 1038
  • [44] Toward the estimation of river discharge variations using MODIS data in ungauged basins
    Tarpanelli, Angelica
    Brocca, Luca
    Lacava, Teodosio
    Melone, Florisa
    Moramarco, Tommaso
    Faruolo, Mariapia
    Pergola, Nicola
    Tramutoli, Valerio
    REMOTE SENSING OF ENVIRONMENT, 2013, 136 : 47 - 55
  • [45] Low-Altitude Remote Sensing Inversion of River Flow in Ungauged Basins
    Zhou, Mingtong
    Guo, Yuchuan
    Wang, Ning
    Wei, Xuan
    Bai, Yunbao
    Wang, Huijing
    SUSTAINABILITY, 2022, 14 (19)
  • [46] Rainfall-runoff model parameter conditioning on regional hydrological signatures: application to ungauged basins in southern Italy
    Biondi, Daniela
    De Luca, Davide Luciano
    HYDROLOGY RESEARCH, 2017, 48 (03): : 714 - 725
  • [47] Predicting annual and long-term flow-duration curves in ungauged basins
    Castellarin, Attilio
    Camorani, Giorgio
    Brath, Armando
    ADVANCES IN WATER RESOURCES, 2007, 30 (04) : 937 - 953
  • [48] Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
    Kratzert, Frederik
    Klotz, Daniel
    Herrnegger, Mathew
    Sampson, Alden K.
    Hochreiter, Sepp
    Nearing, Grey S.
    WATER RESOURCES RESEARCH, 2019, 55 (12) : 11344 - 11354
  • [49] A methodology for discharge estimation and rating curve development at ungauged river sites
    Perumal, Muthiah
    Moramarco, Tommaso
    Sahoo, Bhabagrahi
    Barbetta, Silvia
    WATER RESOURCES RESEARCH, 2007, 43 (02)
  • [50] Prediction of flow duration curve in ungauged catchments using genetic expression programming
    Razaq, Salaudeen Abdul
    Shahid, Shamsuddin
    Ismail, Tarmizi
    Chung, Eun-Sung
    Mohsenipour, Morteza
    Wang, Xiao-jun
    12TH INTERNATIONAL CONFERENCE ON HYDROINFORMATICS (HIC 2016) - SMART WATER FOR THE FUTURE, 2016, 154 : 1431 - 1438