Analysis of the Behavior of Groundwater Storage Systems at Different Time Scales in Basins of South Central Chile: A Study Based on Flow Recession Records

Abstract

Understanding the groundwater storage and release (S-Q) process and its contribution to river flows is essential for different hydrological applications, especially in periods of water scarcity. The S-Q process can be characterized based on recession parameter b, which is the slope of the power–law relationship −dQ/dt = aQb of the recession flow analysis, where recession parameter b represents the linearity of the S-Q process. In various studies, it has been found that this parameter can present high variability, which has been associated with the approach or spatial variability of basin characteristics. However, the variability of parameter b and its relationship with geology and the behavior of groundwater storage over time (evolution over time) have not been sufficiently studied. The objective of this study is to analyze the variability of recession parameter b and its relationship with geological and morphological characteristics and climate variability at different time scales. To this end, 72 drainage basins located in south central Chile were examined via recession flow analysis, considering five different time scales (5 years, 10 years, 15 years, 20 years, and 25 years). In addition, to analyze spatial variability patterns and generate groups of basins with similar characteristics, a cluster analysis was carried out. Clusters were obtained using the principal component analysis (PCA) and K-means methods. The results show that in wet periods, the slope of recession parameter b tends to increase (fast drainage process), while in dry periods, the recession slope tends to decrease (slow drainage processes). In general, the results suggest that the variability of recession coefficient b indicates changes in S-Q behavior; therefore, it could be used as an indicator of the sensitivity of a basin to climate variability.

Event‐Based Recession Analysis for Estimation of Basin‐Wide Characteristic Drainage Timescale and Groundwater Storage Trends

“…This aquifer system is the source of water for many industries, municipalities, and rural water users. The major secondary aquifers consist of Silurian and Devonian carbonate rocks and Quaternary sand and gravel (Nicholas et al, 1987). Figure 9, to be discussed later in detail, shows the aquifer map of Rock River basin, while Figure 10 shows the elevation map of two major aquifers in study area.…”

Identifying a Minimum Time Period of Streamflow Recession Records to Analyze the Behavior of Groundwater Storage Systems: A Study in Heterogeneous Chilean Watersheds

“…Recession flow analysis is a simple and valuable tool for characterizing the dynamics of the behavior of the groundwater storage and release process of an aquifer [26][27][28][29], as it provides information on watershed functioning, related to runoff generation, storage retention, and baseflow dynamics [30]. For example, Tashie et al [31] analyzed the recession behavior of 1027 streams in the United States.…”

“…Similarly, Li and Ameli [32] found that the streamflow recession behavior strongly depends on different geological, morphological, and hydrological watershed characteristics. Parra et al [28] recently analyzed recession behavior in several Chilean watersheds through moving-time-window analysis and how this behavior was related to geomorphological and climatic variability characteristics. The authors found that the recession behavior in wet periods tends toward fast drainage, while in dry periods it tends toward slow drainage.…”

Applying Multivariate Analysis and Machine Learning Approaches to Evaluating Groundwater Quality on the Kairouan Plain, Tunisia

“…Statistical analysis and imitative approaches, such as Piper diagrams, Gibbs diagrams, Chadha diagrams, and the ionic ratio are powerful and useful tools for evaluating the chemical characteristics of groundwater and the underlying factors [19][20][21][22]. Geochemical models are valuable in computing chemical reactions occurring in groundwater systems; comprehensive processes like solid dissolution and precipitation; ion exchange; and sorption by clay minerals [23][24][25][26][27][28]. Statistical correlation analysis, which establishes connections between various physicochemical factors, holds promise for advancing groundwater quality management.…”

“…Statistical correlation analysis, which establishes connections between various physicochemical factors, holds promise for advancing groundwater quality management. Moreover, integrated multivariate statistical approaches, such as CA and PCA, are powerful tools in identifying significant physicochemical characteristics and discerning relationships among these variables, contributing to a deeper understanding of the primary drivers influencing the distribution of physicochemical variables [15,24,29].…”