Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Groundwater Level Trend Analysis and Prediction in the Upper Crocodile (West) Basin, South Africa

Version 1 : Received: 25 May 2023 / Approved: 29 May 2023 / Online: 29 May 2023 (02:19:56 CEST)

How to cite: Tladi, T.M.; Ndambuki, J.M.; Olwal, T.O.; Rwanga, S.S. Groundwater Level Trend Analysis and Prediction in the Upper Crocodile (West) Basin, South Africa. Preprints 2023, 2023051945. https://doi.org/10.20944/preprints202305.1945.v1 Tladi, T.M.; Ndambuki, J.M.; Olwal, T.O.; Rwanga, S.S. Groundwater Level Trend Analysis and Prediction in the Upper Crocodile (West) Basin, South Africa. Preprints 2023, 2023051945. https://doi.org/10.20944/preprints202305.1945.v1

Abstract

Disasters related to climate change on our water resources are on the rise in terms of scale and severity. Therefore, predicting groundwater levels (GWL) is a crucial means to aid adaptive capacity towards disasters related to climate change in our water resources. In this study Gradient Boosting (GB) regression modelling approach for GWL prediction as a function of rainfall and antecedent GWL is used. Firstly, we sought to demonstrate the effects of rainfall changes on our groundwater resources through a Mann-Kendall trend analysis. Secondly, we evaluated the relationship between the input and response variables and determined the optimal lag times between the variables using autocorrelations and cross-correlations. Lastly a predictive model was developed for eight groundwater stations in the Upper Crocodile. 50 % of the groundwater stations revealed declining trends, while 25% had no trends and the other 25% showed an increasing trend. Generally low cross-correlation maximum (CCmax) were obtained, with the highest CCmax being 0.299 at an optimal lag of 2-month. While the highest autocorrelation was 0.969 at a 1-month lag. The best groundwater predictive model had R2 and MSE of 0.66 and 0.06, respectively. The stations that generally performed better had both high autocorrelation and cross-correlation coefficients. GB model performed satisfactorily in predicting GWL for most of the stations in the study area. Therefore, GB can be used for GWL prediction in the Upper Crocodile.

Keywords

Rainfall; Groundwater; Mann-Kendall; Upper Crocodile; Characterisation; Cross-correlation; Autocorrelation; lag time; Gradient Boosting; Machine Learning

Subject

Environmental and Earth Sciences, Water Science and Technology

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