Hypothesis-Driven Conceptual Model for Groundwater–Surface Water Interaction at Aguieira Dam Reservoir (Central Portugal) Based on Principal Component Analysis and Hierarchical Clustering

The interaction between groundwater and surface water can be significant in lakes or irrigation channels, as well as in large dam reservoirs or along portions of them. To evaluate this interaction at a sampling location directly controlled by a large dam equipped with reversible pump-turbines, data from Rn-222 and physicochemical parameters at specific depths and times were obtained and studied using Principal Component Analysis and Hierarchical Clustering. Dimension 1 explains 45.3% of the total variability in the original data, which can be interpreted as the result of external factors related to seasonal variability (e.g., temperature, turbulent flow, and precipitation), while Dimension 2 explains up to 31.2% and can be interpreted as the variability related to groundwater inputs. Five hierarchical clusters based on these dimensions were considered and were related to the temporal variability observed in the water column throughout the year, as well as the depth relationships observed between successive surveys. A hypothesis-driven conceptual piston-like effect model is proposed for groundwater–surface water interactions, considering the identified relationships between variables, including higher Rn-222 concentrations in surface water after heavy rain. According to this simplified conceptual model, water infiltrates in a weathered granitic recharging area; during heavy rain, it is forced through the fracture systems of a lesser-weathered granite. Thus, an overall increase in pressure over the hydrological system forces the older radon-enriched water to discharge into the Mondego River. This work highlights the importance of exploratory techniques such as PCA and Hierarchical Clustering, in addition to underlying knowledge of the geological setting, for the proposal of simplified conceptual models that help in the management of important reservoirs. This work also demonstrates the utility of Rn-222 as a simple tracer of groundwater discharge into surface water.

Authors

Institutions

Publication Details

Journal
Water
Published
2025-10-11
DOI
https://doi.org/10.3390/w17202933
Primary Topic
Hydrological Forecasting Using AI
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Hypothesis-Driven Conceptual Model for Groundwater–Surface Water Interaction at Aguieira Dam Reservoir (Central Portugal) Based on Principal Component Analysis and Hierarchical Clustering

Alcides Pereira, Luís Neves, Gustavo Santiago Luís
Water
Hydrological Forecasting Using AI
article

Hypothesis-Driven Conceptual Model for Groundwater–Surface Water Interaction at Aguieira Dam Reservoir (Central Portugal) Based on Principal Component Analysis and Hierarchical Clustering

Alcides Pereira, Luís Neves, Gustavo Santiago Luís
article en

Abstract

The interaction between groundwater and surface water can be significant in lakes or irrigation channels, as well as in large dam reservoirs or along portions of them. To evaluate this interaction at a sampling location directly controlled by a large dam equipped with reversible pump-turbines, data from Rn-222 and physicochemical parameters at specific depths and times were obtained and studied using Principal Component Analysis and Hierarchical Clustering. Dimension 1 explains 45.3% of the total variability in the original data, which can be interpreted as the result of external factors related to seasonal variability (e.g., temperature, turbulent flow, and precipitation), while Dimension 2 explains up to 31.2% and can be interpreted as the variability related to groundwater inputs. Five hierarchical clusters based on these dimensions were considered and were related to the temporal variability observed in the water column throughout the year, as well as the depth relationships observed between successive surveys. A hypothesis-driven conceptual piston-like effect model is proposed for groundwater–surface water interactions, considering the identified relationships between variables, including higher Rn-222 concentrations in surface water after heavy rain. According to this simplified conceptual model, water infiltrates in a weathered granitic recharging area; during heavy rain, it is forced through the fracture systems of a lesser-weathered granite. Thus, an overall increase in pressure over the hydrological system forces the older radon-enriched water to discharge into the Mondego River. This work highlights the importance of exploratory techniques such as PCA and Hierarchical Clustering, in addition to underlying knowledge of the geological setting, for the proposal of simplified conceptual models that help in the management of important reservoirs. This work also demonstrates the utility of Rn-222 as a simple tracer of groundwater discharge into surface water.

WaterVol. 17(20)
University of Coimbra (PT)
Universidade de Coimbra, Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 74%
Hydrological Forecasting Using AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.