Integrating Geochemical and Regression Analyses to Increase Understanding of Groundwater Evolution in a Semiarid Basin: A Systematic Review

Introduction

Understanding the evolution of groundwater in semiarid regions is essential for sustainable water resources management, especially under increasing pressure from climate variability and anthropo- genic use [1,2]. Groundwater composition is defined by various processes, e.g., mineral precipitation/dissolution, evaporation, weathering, recharge dynamics and human impacts. Geochemical modelling elucidates these mechanisms, though regression and multivariate analyses establish predictive relationships between hydro-chemical parameters [3,4].

This review synthesises 13 peer-reviewed articles that integrate geochemical modelling and/or regression analysis-based statistical analyses to dissect groundwater evolution in semiarid contexts. It addresses: (i) the dominant hydrogeochemical processes identified, (ii) how regression or statistical models quantify relationships, (iii) insights into spatiotemporal groundwater chemistry changes and (iv) implications for management under semiarid conditions. The aim is to spotlight methodological integration, reveal common themes and propose future directions for a holistic understanding of groundwater evolution. Groundwater in semiarid regions is in- creasingly threatened by declining recharge, over extraction and pollution, yet its evolutionary pathways remain complex due to the interplay of anthropogenic and natural processes [5,6]. Tradi- tional hydro chemical assessments offer useful snapshots but often fall short in unravelling the coupled mechanisms and long-term dynamics influencing groundwater composition. Geochemical modelling offers a mechanistic understanding of mineral-water interactions, while regression and multivariate analyses reveal sta- tistically significant patterns and predictive relationships between hydro-chemical parameters [7,8]. Integrating these approaches is therefore critical for moving beyond descriptive assessments to- ward holistic interpretations of groundwater evolution. Despite growing applications, there remains a lack of consolidated knowl- edge on how these methods complement each other in semiarid contexts. This review fills that gap by synthesising evidence from thirteen peer-reviewed studies, offering a methodological and con- ceptual framework to guide sustainable water resource manage- ment and policy formation in vulnerable semiarid basins.

Methodology (Prisma Framework)

A PRISMA-style systematic search was performed across databas- es, e.g., WoS, Scopus, MDPI, PubMed, and SpringerLink. Search term included 'geochemical modelling groundwater semiarid', 're- gression analysis groundwater chemistry', 'groundwater evolution semiarid basin geochemical', etc. Inclusion criteria: (i) empirical studies in semiarid or arid basins, (ii) incorporating geochemical modelling (e.g., PFREEQC, NETPATH, saturation indices), (iii) employing statistical or regression techniques (MRA, PCA, Mul- tivariate Analysis), published in peer-reviewed journals between 2015-2025. Thus, from an initial set of forty (40), thirteen (13) studies met criteria (Figure 1). These were categorised and extract- ed for the key study area, methods, main geochemical drivers' sta- tistical models and findings.

Figure 1: PRISMA Flow Chart

Results and Discussion

Dominant Geochemical Processes

Based on the summarised studies, groundwater evolution in semi- arid basins is controlled by (i) weathering and mineral dissolution (carbonate, silicate, evaporite minerals)- such as Ghar Boumaa- za (carbonate dissolution); Kaduna Basin (Silicate weathering); Longwanggou (dissolution of halite and gypsum), (ii) Ion ex- change mechanisms, zoning of hydro chemical evolution, and (iii) recharge origin and mixing, such as isotopic insights in Qaidam and Sulaimani basins.

Regression and Statistical Modelling

Regression modelling was performed in Algeria's Ghar Boumaaa- za, a third-degree polynomial regression related TDS to discharge (Q), yielding predictive accuracy (R2 = 0.95) [9]. In the Kaduna basin, multiple regression analysis (MRA) tied hydro chemical variation to rock-weathering processes (Na+/Cl- ratios), with sta- tistical significance (R-Sq 63%, p<0.01) [10,13]. Multivariate sta- tistical application (PCA, cluster analysis, factor analysis) in Gha- na Pru Basin, Northern Ethiopia, Great Artesian Basin were used to classify water types and interpret controlling processes [14,19].

Zoning approaches used in the Western Jilin Model, hydro chemical zoning coupled with PHREEQC modelling, distinguish recharge –run-off-discharge zones (Figure 2).

                                                                                           Table 1: Literature Summary

Figure 2: Integrating Geochemical and Regression Analyses.

Integration Insights and Comparative Themes

The literature indicates transferable frameworks. For instance, Ghar Boumaaza and Kaduna studies show how combining modelling with regression/ statistical techniques enables both mechanistic understanding and predictive capacity [9,10]. Likewise, zonal differentiation in multiple basins (Jilin, Ghana, Qaidam) revealed spatial hydro chemical variations along flow paths or recharge- discharge gradients. Additionally, anthropogenic/human influence in Haolebaoji and others underscores the over-exploitation or land use change, which accelerates chemically distinct evolution pathways [18]. The literature further elucidates recharge dynamics via isotopes in Qaidam and Soleimani basins, which highlights how isotopic data reveal Paleo water vs modern recharge contributions (Figure 3).

Figure 3: Integration of Methods for Understanding Groundwater Evolution

Methodological Reflections

Most studies effectively couple geochemical modelling (saturation indices, mineral equilibria, thermodynamic modelling) with sta- tistical or regression analysis, which improves both quantification and explanation. Regression models (e.g., polynomial MRA) of- fer tangible predilection and validation capacity, but they are less common than descriptive multivariate techniques [22]. Spatial tools (GIS, zoning models) are increasingly important for map- ping vulnerability (e.g., Wadi Fatimah) and Hydro chemical dif- ferentiation [12]. Few studies link directly statistical predictors to management outcomes; future research could prioritise actionable modelling.

Conclusion

Although this review summarises only thirteen (13) peer-reviewed studies, it reveals:

I. Geochemical drivers of groundwater evolution (mineral dis- solution, weathering, evaporation and ion exchange are con- sistently pivotal in semiarid groundwater composition.

II. Modelling integration, which combines geochemical model- ling with regression/statistical methods (e.g., polynomial re- gression, MRA, PCA), deepens mechanistic insight and pre- dictive power.

III. Spatial variability of hydro-chemical evolution shows zona- tion influenced by geological conditions and hydrodynamics; zoning and GIS models effectively capture this.

IV. Recharge characterisation using isotopic and modelling ap- proaches clarifies the sources and timing of groundwater re- charge.

V. Human impacts through intensive abstraction and land use change considerably alter geochemical evolution paths.

Recommendations and Future Research Direction

I. Expand regression-based modelling to integrate with geo- chemical modelling, for example, multivariate regression pre- dicting major hydro-chemical parameters based on land use, recharge and climate parameters.

II. Longitudinal monitoring to evaluate temporal changes, partic- ularly under exploitation or climate change.

III. Comparative cross-basin studies to benchmark semiarid aqui- fers' behaviour and modelling techniques

IV. Decision-support integration via developing frameworks that link hydro-chemical modelling outputs to actionable water management strategies.

V. Leveraging machine learning (e.g., GP-DNN) for improved prediction in data-sparse settings; mostly applied to levels, these approaches extend to chemistry.

Therefore, integrating geochemical modelling with regression and statistical techniques offers a robust, multidisciplinary framework for unravelling groundwater evolution in semiarid basins. The studies reviewed here show both scientific insight and practical potential. Hence, future work should build on this synergy toward predictive, spatially resolved and management-oriented ground- water chemistry modelling.

Acknowledgements

This research was supported by Federal University Birnin kebbi through the National Research Fund (NRF, 2023) TETFES/ DR&D- CENRF-2023/SETI/WAS/00156/VOL.6.

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