*Editorial* **Editorial for Special Issue: "Integrated Surface Water and Groundwater Analysis"**

**Il-Moon Chung 1,\* , Sun Woo Chang 1,\* , Yeonsang Hwang <sup>2</sup> and Yeonjoo Kim <sup>3</sup>**


Comprehensive understanding of groundwater—surface water (GW–SW) interaction is essential for effective water resources management. Groundwater (GW) and surface water (SW) are closely connected components that constantly interact with each other within the earth's hydrologic cycle. Many studies utilized observations to explain the GW–SW interactions by carefully analyzing the behavior of surface water features (streams, lakes, reservoirs, wetlands, and estuaries) and the related aquifer environments. Surface water bodies gain water and solutes from groundwater systems, and in other cases surface water bodies recharge groundwater, which causes changes in groundwater quality. The interfaces between GW and SW environments, such as hyporheic—benthic zones and riparian corridors, often function as biogeochemical hotspots and can have significant influences on the entire stream ecology. Furthermore, groundwater is a major source of drinking water supply and irrigation, and hence critical to global food security. Groundwater needs to be wisely managed, protected, and especially sustainably used. However, the aforementioned tasks have become challenging to many hydrologic systems in various areas from arid to even humid regions because of added stress caused by changing environment, climate, land use, and population. The aim of the Special Issue "Integrated Surface Water and Groundwater Analysis" was to elevate integrated understanding of the science in GW–SW systems through healthy discussions in the relevant research communities.

In this Special Issue, researchers have contributed to the study of groundwater–surface water interactions on a variety of subjects and methods, such as analytical and explicit numerical approaches [1], groundwater level prediction via a long short-term memory (LSTM) network [2], the impact of hydraulic fracturing and climate change [3], modification of the SWAT+ watershed model [4], water management in small islands [5], fluctuation of induced aquifer recharge [6,7], response of river to the 2016 seismic sequence [8], hydrological connectivity in permafrost regions [9], groundwater and streamflow interactions during floods [10], heat transport in managed aquifer recharge (MAR) [11], isotope analysis for distinguishing different types of water [12], digital platform to support decision-making [13], and deep percolation in irrigated fields [14,15].

When evaluating SW–GW interactions, the accuracy of calibration or prediction has been demonstrated by new techniques or multidisciplinary techniques applied in sitespecific regional studies. The hydrodynamic surface water module of the STRIVE package (stream river ecosystem) of FEMME (flexible environment for mathematically modelling the environment), combined with analytical/explicit numerical solutions for groundwater flows, successfully investigated the hydraulic GW–SW interaction [1]. Machine learning techniques predicted the groundwater level, revealing that the LSTM (long short-term memory) network approach can be very useful for one-day forecasting of groundwater fluctuations in Jeju Island, Korea [2]. Bailey [4] developed a new module called 'gwflow'

**Citation:** Chung, I.-M.; Chang, S.W.; Hwang, Y.; Kim, Y. Editorial for Special Issue: "Integrated Surface Water and Groundwater Analysis". *Hydrology* **2022**, *9*, 70. https://doi.org/ 10.3390/hydrology9050070

Received: 22 April 2022 Accepted: 25 April 2022 Published: 27 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

for the SWAT+ modeling code and applied this module to simulate both land surface and subsurface hydrological processes of Little River Experimental Watershed (LREW) (327 km<sup>2</sup> ) in southern Georgia, USA. There was also a valuable case study that simultaneously employed water isotopes, dissolved organic carbon, and electrical resistivity tomography to analyze the hydrological connectivity in a permafrost region [9]. Oxygen and hydrogen isotope (δ <sup>18</sup>O–δ <sup>2</sup>H) relationships were characterized by means of various statistical approaches on the Northern Italian Apennines [12].

Important investigations were presented regarding the effects of natural and anthropogenic stress on GW–SW interactions. An integrated hydrologic model (MIKE-SHE and MIKE-11 models) and a cumulative effects landscape simulator (ALCES) were used to assess the impact of hydraulic fracturing on GW–SW interactions in a shale gas and oil play area (23,984.9 km<sup>2</sup> ) of northwestern Alberta, Canada during 2021–2036 under future climate change scenarios [3]. The impact of a 2016 seismic sequence was analyzed with stream discharge data and recession curves in Nera River Basin, Italy [8]. The hydrological–ecological integrated watershed-scale flow model (HEIFLOW) was tested to verify interactions between the groundwater and streamflow during flood events in 2013 in the Miho catchment, Korea [10].

The interaction of GW–SW was also understood by observing or assessing quantitative/qualitative changes in major hydrologic components. First, in the process of managed aquifer recharging (MAR), GW–SW interactions occur as a mechanism of induced recharge. Hydrodynamics, hydrochemical, and numerical modeling methods were used to analyze an induced aquifer recharge in riverbank filtration (RBF) at Serchio River in Italy [6]. Integrated MODFLOW and SWAT modeling quantitatively assessed induced aquifer recharge due to nearby rivers during the seasonal exploitation of groundwater water curtain cultivation sites in Korea, and it predicted that the aquifers were being depleted every year [7]. Groundwater heat and temperature were monitored in shallow aquifers in the alluvial plain of the Cornia River, Italy to detect the mechanism development of recharge in MAR operations [11]. Second, in addition to recharge, the SW–GW interaction can be explained by another component such as deep percolation (DP) from water balance analysis. In addition to recharge as a direct indicator, the SW–GW interaction can be explained by deep percolation. A two-year study on Willamette Valley in western Oregon, USA assessed DP and recharge into the aquifer [14]. Estimation of DP into shallow aquifers characterized the practice of water management of two flood-irrigated fields in northern New Mexico [15].

Development of tools for the decision-making process was also presented. White [5] found large water supply differences between small islands vulnerable to various natural disasters and climate change. The author compared the national Tonga Strategic Development Framework, 2015–2025 (TSDFII) and local community development plans (CDPs) with census and limited hydrological data in the study. Rojas et al. [13] focused on early involvement of stakeholders, and therefore developed a digital platform (SimCopiapo) that combined integrated modelling and participatory modelling to support decision making for water management in the Copiapó River Basin, northern Chile.

We believe that the insights from the latest research outcomes in the areas of SW–GW interaction observations, modeling calibration/analyses, and decision-making support systems presented in the articles published in this Special Issue can serve as a foundation for an integrated water resource management (IWRM) approach in the future.

**Funding:** This research received no external funding.

**Acknowledgments:** We want to thank the authors who contributed to this Special Issue on "Integrated Surface Water and Groundwater Analysis" and their anonymous reviewers who provided the authors with insightful and constructive comments.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

