Ten serwis zostanie wyłączony 2025-02-11.
Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2024 | Vol. 25, iss. 4 | 241--251
Tytuł artykułu

Evaluation of the Impact of Gap Filling Technology in Precipitation Series on the Estimation of Climate Trends, the Case of the Souss Massa Watershed

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Accurate climatic data, especially precipitation measurements, play a critical role in various studies concerning the water cycle, particularly in modeling flood and drought risks. Unfortunately, these datasets often suffer from temporary gaps that are randomly dispersed over time. This study aims to assess the effectiveness of three imputation methods: KNN, MICE, and missForest, in impute missing values in climate series. The evaluation is conducted in two distinct rainfall regimes: the Moulouya basin and the Sous Massa basin. The performance analysis considers the percentage of missing data across the entire dataset. The imputed datasets are used to estimate annual precipitation, which are then subjected to statistical tests to identify potential trends and detect changepoints. The analysis focuses on the precipitation series within the Souss Massa watershed, encompassing 27 rainfall stations. Results indicate that data imputation has a highly positive impact on the study of rainfall series trends and change point detection. The study found that studying trends without data imputation could lead to questionable conclusions. The most significant breakpoints detected in the analyzed rainfall series were in the years 1988, 1991, 1997, 2007, and 2010. The decrease in precipitation at stations showing a downward trend varies between -60 mm and -137 mm using the MICE method, and between -40 mm and 186 mm using the missForest method.
Wydawca

Rocznik
Strony
241--251
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
  • Solidarity Fund Against Catastrophic Events, Department of Studies and Risk Management
autor
  • Department of Geology, University FS Rabat, Rabat, Morocco
  • Solidarity Fund Against Catastrophic Events, Department of Studies and Risk Management
  • Solidarity Fund Against Catastrophic Events, Department of Studies and Risk Management
  • Solidarity Fund Against Catastrophic Events, Department of Studies and Risk Management
  • Solidarity Fund Against Catastrophic Events, Department of Studies and Risk Management
  • Department of Geology, University FS Meknes, Meknes, Morocco
  • Sultan Moulay Slimane University, Faculty of Science and Technology, Beni Mellal, Morocco
  • Marine Geosciences and Soil Sciences Laboratory, Faculty of Sciences, Chouaib Doukkali University Jabran Khalil, Jabran Avenue B.P 299-24000 El Jadida Grand-Casablanca Morocco
Bibliografia
  • 1. Acharki, S., Amharref, M., El Halimi, R., Bernoussi, A.S. 2019. Assessment by statistical approach of climate change impact on water resources: Application to the Gharb perimeter (Morocco). Water Science Review, 32(3), 291–315. https://doi.org/10.7202/1067310ar
  • 2. Aissia, M.A.B. 2014. Étude des variables hydrologiques dans un cadre multivarié et dans un contexte de changement. Ph.D. Thesis, Québec University, Québec.
  • 3. Bousri, I., Salah, S.A., Arab, B.M. 2021. Validation d’une méthode d’imputation de données manquantes pour la reconstitution des séries de température. JAMA, 5, 28–32.
  • 4. Driouech, F. 2010. Distribution of winter precipitation over Morocco in the context of climate change: downscaling and uncertainties. Ph.D. Thesis, Toulouse University, Toulouse.
  • 5. Evin, H., Suetsugu, F., Kagabu, M. 2021. The Importance of Filling Missing Data in Hydrological Modeling: A Review of Methods and Impacts.
  • 6. Imbert, A., Vialaneix, N. 2018. Describing, accounting for, imputing and evaluating missing values in statistical studies: a review of existing approaches. Journal de la société française de statistique, 159(2), 1–55.
  • 7. Zhao L., Hu X., et al. 2018. Importance of Filling Missing Rainfall Data in Streamflow Forecasting in Ungauged Catchments.
  • 8. Mann, H.B., Whitney, D.R. 1947. On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics, 50–60.
  • 9. Marlinda, Uyun, A.S., Miyazaki, T., Ueda, Y., Akisawa, A. 2010. Performance analysis of a double-effect adsorption refrigeration cycle with a silica gel/water working pair. Energies, 3(11), 1704–1720.
  • 10. Melki S.S., Kariuki S.M. 2020. Impact of Missing Rainfall Data on Hydrological Modeling.
  • 11. Nejjari, I., Abdelhai, S., Lebzar, B. 2020. Measuring human capital: dimensions and alternative methods. La Revue de Publicité et de Communication Marketing, 1(2).
  • 12. Niass, O., Diongue, A.K., Touré, A. 2015. Analysis of missing data in sereo-epidemiologic studies. African Journal of Applied Statistics, 2(1), 29–37.
  • 13. Paturel, J.E., Servat, E., Delattre, M.O., Lubes-Niel, H. 1998. Analysis of rainfall long series in non-Sahelian West and Central Africa within a context of climate variability. Hydrol. Sci. J., 43(6), 937–946.
  • 14. Paturel, J.-E., Ibrahim, B. 2004. Sahelian Paradox View project Monthly rainfall gridded data set for Africa View project.
  • 15. Pettitt, A.N. 1979. A non‐parametric approach to the change‐point problem. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(2), 126–135.
  • 16. Rousseau, M., Simon, M., Bertrand, R., Hachey, K. 2012. Reporting missing data: a study of selected articles published from 2003–2007. Quality & Quantity, 46, 1393–1406.
  • 17. Sapriza M.P., Azuri, J., Buytaert, B., Timbe, et al. 2019. implications of missing rainfall data on hydrological modelling: a case study in the Tropical Andes.
  • 18. Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Altman, R.B. 2001. Missing value estimation methods for DNA microarrays. Bioinformatics, 17(6), 520–525.
  • 19. Van Buuren, S., Groothuis-Oudshoorn, K. 2011. Journal of Statistical Software mice: Multivariate Imputation by Chained Equations in R, 45, 1–67.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-ccb6f75c-3764-47dc-a011-193608c2a9f5
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.