IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13051-d939985.html
   My bibliography  Save this article

Evaluation of Satellite-Based and Reanalysis Precipitation Datasets with Gauge-Observed Data over Haraz-Gharehsoo Basin, Iran

Author

Listed:
  • Mohammad Reza Goodarzi

    (Department of Civil Engineering, Yazd University, Yazd 8915813135, Iran)

  • Roxana Pooladi

    (Department of Civil Engineering, Water and Hydraulic Structures, Ayatollah Ozma Borujerdi University, Borujerd, Iran)

  • Majid Niazkar

    (Department of Agricultural and Environmental Sciences, University of Milan, Via Celoria 2, 20133 Milan, Italy)

Abstract

Evaluating satellite-based products is vital for precipitation estimation for sustainable water resources management. The current study evaluates the accuracy of predicting precipitation using four remotely sensed rainfall datasets—Tropical Rainfall Measuring Mission products (TRMM-3B42V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Records (PERSIANN-CDR), Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR), and National Centers for Environmental Prediction (NCEP)-Climate Forecast System Reanalysis (CFSR)—over the Haraz-Gharehsoo basin during 2008–2016. The benchmark values for the assessment are gauge-observed data gathered without missing precipitation data at nine ground-based measuring stations over the basin. The results indicate that the TRMM and CCS-CDR satellites provide more robust precipitation estimations in 75% of high-altitude stations at daily, monthly, and annual time scales. Furthermore, the comparative analysis reveals some precipitation underestimations for each satellite. The underestimation values obtained by TRMM CDR, CCS-CDR, and CFSR are 8.93 mm, 20.34 mm, 9.77 mm, and 17.23 mm annually, respectively. The results obtained are compared to previous studies conducted over other basins. It is concluded that considering the accuracy of each satellite product for estimating remotely sensed precipitation is valuable and essential for sustainable hydrological modelling.

Suggested Citation

  • Mohammad Reza Goodarzi & Roxana Pooladi & Majid Niazkar, 2022. "Evaluation of Satellite-Based and Reanalysis Precipitation Datasets with Gauge-Observed Data over Haraz-Gharehsoo Basin, Iran," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13051-:d:939985
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13051/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/20/13051/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13051-:d:939985. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.