IDEAS home Printed from https://ideas.repec.org/a/gam/jresou/v11y2022i12p111-d989566.html
   My bibliography  Save this article

Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions

Author

Listed:
  • Renata Graf

    (Department of Hydrology and Water Management, Institute of Physical Geography and Environmental Planning, Adam Mickiewicz University, Bogumiła Krygowskiego Street 10, 61-680 Poznań, Poland)

  • Viktor Vyshnevskyi

    (Faculty of International Relations, National Aviation University, Liubomyra Huzara Avenue 1, 03058 Kyiv, Ukraine)

Abstract

River-flow forecasts are important for the management and planning of water resources and their rational use. The present study, based on direct multistep-ahead forecasting with multiple time series specific to the XGBoost algorithm, estimates the long-term changes and forecast monthly flows of selected rivers in Ukraine. In a new, applied approach, a single multioutput model was proposed that forecasts over both short- and long-term horizons using grouped or hierarchical data series. Three forecast stages were considered: using train and test subsets, using a model with train-test data, and training with all data. The historical period included the measurements of the monthly flows, precipitation, and air temperature in the period 1961–2020. The forecast horizons of 12, 60, and 120 months into the future were selected for this dataset, i.e., December 2021, December 2025, and December 2030. The research was conducted for diverse hydrological systems: the Prut, a mountain river; the Styr, an upland river; and the Sula, a lowland river in relation to the variability and forecasts of precipitation and air temperature. The results of the analyses showed a varying degree of sensitivity among rivers to changes in precipitation and air temperature and different projections for future time horizons of 12, 60, and 120 months. For all studied rivers, variable dynamics of flow was observed in the years 1961–2020, yet with a clearly marked decrease in monthly flows during in the final, 2010–2020 decade. The last decade of low flows on the Prut and Styr rivers was preceded by their noticeable increase in the earlier decade (2000–2010). In the case of the Sula River, a continuous decrease in monthly flows has been observed since the end of the 1990s, with a global minimum in the decade 2010–2020. Two patterns were obtained in the forecasts: a decrease in flow for the rivers Prut (6%) and the Styr (12–14%), accompanied by a decrease in precipitation and an increase in air temperature until 2030, and for the Sula River, an increase in flow (16–23%), with a slight increase in precipitation and an increase in air temperature. The predicted changes in the flows of the Prut, the Styr, and the Sula rivers correspond to forecasts in other regions of Ukraine and Europe. The performance of the models over a variety of available datasets over time was assessed and hyperparameters, which minimize the forecast error over the relevant forecast horizons, were selected. The obtained RMSE parameter values indicate high variability in hydrological and meteorological data in the catchment areas and not very good fit of retrospective data regardless of the selected horizon length. The advantages of this model, which was used in the work for forecasting monthly river flows in Ukraine, include modelling multiple time series simultaneously with a single model, the simplicity of the modelling, potentially more-robust results because of pooling data across time series, and solving the “cold start” problem when few data points were available for a given time series. The model, because of its universality, can be used in forecasting hydrological and meteorological parameters in other catchments, irrespective of their geographic location.

Suggested Citation

  • Renata Graf & Viktor Vyshnevskyi, 2022. "Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions," Resources, MDPI, vol. 11(12), pages 1-24, November.
  • Handle: RePEc:gam:jresou:v:11:y:2022:i:12:p:111-:d:989566
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2079-9276/11/12/111/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2079-9276/11/12/111/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    2. Chantal Donnelly & Wouter Greuell & Jafet Andersson & Dieter Gerten & Giovanna Pisacane & Philippe Roudier & Fulco Ludwig, 2017. "Erratum to: Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level," Climatic Change, Springer, vol. 143(3), pages 535-535, August.
    3. Chantal Donnelly & Wouter Greuell & Jafet Andersson & Dieter Gerten & Giovanna Pisacane & Philippe Roudier & Fulco Ludwig, 2017. "Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level," Climatic Change, Springer, vol. 143(1), pages 13-26, July.
    4. Firat, Mahmut & Güngör, Mahmud, 2007. "River flow estimation using adaptive neuro fuzzy inference system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 75(3), pages 87-96.
    5. Salam A. Abbas & Yunqing Xuan, 2019. "Development of a New Quantile-Based Method for the Assessment of Regional Water Resources in a Highly-Regulated River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3187-3210, July.
    6. Fiaz Hussain & Ray-Shyan Wu & Jing-Xue Wang, 2021. "Comparative study of very short-term flood forecasting using physics-based numerical model and data-driven prediction model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 249-284, May.
    7. Renata Graf & Tomasz Kolerski & Senlin Zhu, 2022. "Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting," Resources, MDPI, vol. 11(2), pages 1-26, January.
    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. Diego Copetti, 2023. "Integration of Water Quantity/Quality Needs with Socio-Economical Issues: A Focus on Monitoring and Modelling," Resources, MDPI, vol. 12(5), pages 1-4, May.

    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. Alice Baronetti & Vincent Dubreuil & Antonello Provenzale & Simona Fratianni, 2022. "Future droughts in northern Italy: high-resolution projections using EURO-CORDEX and MED-CORDEX ensembles," Climatic Change, Springer, vol. 172(3), pages 1-22, June.
    2. Gianluigi Busico & Maria Margarita Ntona & Sílvia C. P. Carvalho & Olga Patrikaki & Konstantinos Voudouris & Nerantzis Kazakis, 2021. "Simulating Future Groundwater Recharge in Coastal and Inland Catchments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3617-3632, September.
    3. Yi Yang & Jianping Tang, 2023. "Downscaling and uncertainty analysis of future concurrent long-duration dry and hot events in China," Climatic Change, Springer, vol. 176(2), pages 1-25, February.
    4. Rejani Raghavan & Kondru Venkateswara Rao & Maheshwar Shivashankar Shirahatti & Duvvala Kalyana Srinivas & Kotha Sammi Reddy & Gajjala Ravindra Chary & Kodigal A. Gopinath & Mohammed Osman & Mathyam P, 2022. "Assessment of Spatial and Temporal Variations in Runoff Potential under Changing Climatic Scenarios in Northern Part of Karnataka in India Using Geospatial Techniques," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    5. Thibault Lemaitre-Basset & Ludovic Oudin & Guillaume Thirel, 2022. "Evapotranspiration in hydrological models under rising CO2: a jump into the unknown," Climatic Change, Springer, vol. 172(3), pages 1-19, June.
    6. Muhammad Ishfaque & Qianwei Dai & Nuhman ul Haq & Khanzaib Jadoon & Syed Muzyan Shahzad & Hammad Tariq Janjuhah, 2022. "Use of Recurrent Neural Network with Long Short-Term Memory for Seepage Prediction at Tarbela Dam, KP, Pakistan," Energies, MDPI, vol. 15(9), pages 1-16, April.
    7. Charlotte Poussin & Yaniss Guigoz & Elisa Palazzi & Silvia Terzago & Bruno Chatenoux & Gregory Giuliani, 2019. "Snow Cover Evolution in the Gran Paradiso National Park, Italian Alps, Using the Earth Observation Data Cube," Data, MDPI, vol. 4(4), pages 1-25, October.
    8. Si Xie & Tianshu Li & Ke Cao, 2023. "Analysis of the Impact of Carbon Emission Control on Urban Economic Indicators based on the Concept of Green Economy under Sustainable Development," Sustainability, MDPI, vol. 15(13), pages 1-22, June.
    9. Yeshewatesfa Hundecha & Berit Arheimer & Peter Berg & René Capell & Jude Musuuza & Ilias Pechlivanidis & Christiana Photiadou, 2020. "Effect of model calibration strategy on climate projections of hydrological indicators at a continental scale," Climatic Change, Springer, vol. 163(3), pages 1287-1306, December.
    10. Buddhi Wijesiri & Erick Bandala & An Liu & Ashantha Goonetilleke, 2020. "A Framework for Stormwater Quality Modelling under the Effects of Climate Change to Enhance Reuse," Sustainability, MDPI, vol. 12(24), pages 1-12, December.
    11. Qi Guo & Bruno Remillard & Anatoliy Swishchuk, 2020. "Multivariate General Compound Point Processes in Limit Order Books," Risks, MDPI, vol. 8(3), pages 1-20, September.
    12. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    13. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    14. 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.
    15. Gary S. Anderson & Alena Audzeyeva, 2019. "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series 2019-074, Board of Governors of the Federal Reserve System (U.S.).
    16. Thomas Despois & Catherine Doz, 2022. "Identifying and interpreting the factors in factor models via sparsity : Different approaches," Working Papers halshs-03626503, HAL.
    17. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Short-Term Exuberance and Long-Term Stability: A Simultaneous Optimization of Stock Return Predictions for Short and Long Horizons," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
    18. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    19. Tomáš Plíhal, 2021. "Scheduled macroeconomic news announcements and Forex volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1379-1397, December.
    20. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.

    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:jresou:v:11:y:2022:i:12:p:111-:d:989566. 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: 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.