IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i4d10.1007_s11269-022-03081-9.html
   My bibliography  Save this article

Calibration of a Distributed Hydrological Model (VIC-3L) Based on Global Water Resources Reanalysis Datasets

Author

Listed:
  • Sakine Koohi

    (Imam Khomeini International University (IKIU))

  • Asghar Azizian

    (Imam Khomeini International University (IKIU))

  • Luca Brocca

    (Research Institute for Geo-Hydrological Protection, National Research Council)

Abstract

The lack of observed streamflow datasets for calibration of rainfall-runoff models imposes substantial problems for their applicability, especially in poorly gauged or ungauged river basins. Developing satellite technologies and increasing computational powers over the past decades, have provided an environment for researchers to simulate several water balance components globally using these datasets and assimilation techniques. Due to importance of accurate hydrologic modeling, this study aims to investigate the applicability of global water resources reanalysis (GWRR) datasets including surface soil moisture (SSM), evapotranspiration (ET), and surface runoff (SR) components for calibration of the macro-scale hydrological model (VIC-3L) over the SefidRood basin (SRB) in Iran at different calibration scenarios. Results show that in the case of using SSM datasets, the model’s performance in the simulation of streamflow hydrograph, with the NSE value higher than 0.65, is better than using other datasets. Among different datasets, the SSM based on LISFLOOD and HBV are the best ones for calibration of VIC-3L model over SRB. In contrast, using ET datasets aren’t so reliable for hydrological calibration in the study area. Furthermore, in the cases of using SSM and surface runoff datasets, the model tends to overestimation of low-flows, while, ET datasets are more reliable for simulation of such these flows. Also, findings displayed that the combination of ET and SSM datasets for hydrological calibration performed better than using only one dataset. In conclusion, this research gives useful and applied insights in the applicability of GWRR data sources for hydrological modeling and water resources studies, especially in data limited regions.

Suggested Citation

  • Sakine Koohi & Asghar Azizian & Luca Brocca, 2022. "Calibration of a Distributed Hydrological Model (VIC-3L) Based on Global Water Resources Reanalysis Datasets," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1287-1306, March.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:4:d:10.1007_s11269-022-03081-9
    DOI: 10.1007/s11269-022-03081-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03081-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-022-03081-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Prem B. Parajuli & Priyantha Jayakody & Ying Ouyang, 2018. "Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 985-996, February.
    2. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    3. Zhandong Sun & Tom Lotz & Qun Huang, 2021. "An ET-Based Two-Phase Method for the Calibration and Application of Distributed Hydrological Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1065-1077, February.
    4. Santosh Thampi & Kolladi Raneesh & T. Surya, 2010. "Influence of Scale on SWAT Model Calibration for Streamflow in a River Basin in the Humid Tropics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(15), pages 4567-4578, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hongxia Li & Yuanyuan Huang & Yongliang Qi & Yanjia Jiang & Xuan Tang & Elizabeth W. Boyer & Carlos R. Mello & Ping Lan & Li Guo, 2024. "Improved Streamflow Simulation by Assimilating In Situ Soil Moisture in Lumped and Distributed Approaches of a Hydrological Model in a Headwater Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 4933-4953, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hong Xuan Do & Hung T.T. Nguyen & Vinh Ngoc Tran & Manh-Hung Le & Binh Quang Nguyen & Hung T. Pham & Tu Hoang Le & Doan Binh & Thanh Duc Dang & Hoang Tran & Tam V. Nguyen, 2024. "Uncertain Benefits of Using Remotely Sensed Evapotranspiration for Streamflow Estimation—Insights From a Randomized, Large-Sample Experiment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3819-3835, August.
    2. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    3. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    4. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    5. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    6. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    7. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    8. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    9. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
    10. Snyder, Ralph D. & Ord, J. Keith & Koehler, Anne B. & McLaren, Keith R. & Beaumont, Adrian N., 2017. "Forecasting compositional time series: A state space approach," International Journal of Forecasting, Elsevier, vol. 33(2), pages 502-512.
    11. Paulo Júlio & Pedro M. Esperança, 2012. "Evaluating the forecast quality of GDP components: An application to G7," GEE Papers 0047, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Apr 2012.
    12. Rivera, Nilza & Guzmán, Juan Ignacio & Jara, José Joaquín & Lagos, Gustavo, 2021. "Evaluation of econometric models of secondary refined copper supply," Resources Policy, Elsevier, vol. 73(C).
    13. Cameron Roach & Rob Hyndman & Souhaib Ben Taieb, 2021. "Non‐linear mixed‐effects models for time series forecasting of smart meter demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1118-1130, September.
    14. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    15. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    16. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    17. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    18. repec:cup:judgdm:v:14:y:2019:i:4:p:395-411 is not listed on IDEAS
    19. I. Yu. Zolotova & V. V. Dvorkin, 2017. "Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks," Studies on Russian Economic Development, Springer, vol. 28(6), pages 608-615, November.
    20. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    21. Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.
    22. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

    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:spr:waterr:v:36:y:2022:i:4:d:10.1007_s11269-022-03081-9. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.