Next Article in Journal
Predicting Suspended Sediment Transport in Urbanised Streams: A Case Study of Dry Creek, South Australia
Previous Article in Journal
The Development of a Hydrological Method for Computing Extreme Hydrographs in Engineering Dam Projects
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Enhancing Groundwater Recharge Through Nature-Based Solutions: Benefits and Barriers

Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL 35487, USA
*
Author to whom correspondence should be addressed.
Hydrology 2024, 11(11), 195; https://doi.org/10.3390/hydrology11110195
Submission received: 14 October 2024 / Revised: 8 November 2024 / Accepted: 13 November 2024 / Published: 16 November 2024

Abstract

Nature-based solutions (NbSs) for water involve using or mimicking natural processes to contribute to the improved management of water. Although NbSs are gaining a significant amount of scientific attention, to ensure their wide usage for enhancing groundwater recharge, there is a need for clear documentation outlining their benefits and barriers. In this study, a systematic literature review was carried out to evaluate the application of NbSs for managing groundwater recharge. First, NbS approaches were classified into two broad groups: managed aquifer recharge (MAR) and ancillary recharge methods (ARMs). MAR includes all activities that intentionally enhance the recharge of an aquifer for later recovery, while ARMs include all the remaining NbSs wherein recharge enhancement is a secondary goal. In 50 out of 61 reviewed studies, MAR was reported to be successful in increasing recharge. However, in the remaining studies, reductions in recharge rates were reported. Most of the NbSs that failed to improve groundwater recharge were from the ARMs group. This group had little consensus among studies regarding the effectiveness of NbSs on groundwater recharge. In this study, we also identified opportunities and challenges, such as gaps in our knowledge of NbSs’ effectiveness, their assessment in long-term, cost–benefit analysis and scalability. Addressing these challenges will further enhance the efficiency of NbSs, which indeed is a promising alternative for enhancing groundwater resources.

1. Introduction

Groundwater recharge is the downward movement of water that reaches the water table and replenishes groundwater storage [1]. Recharge occurs through diffuse and focused mechanisms [1]. Diffuse recharge occurs over large areas, often due to rainfall or melt, as soil water input infiltrates the soil and percolates into the water table. In contrast, focused recharge involves leakage from surface water bodies to an aquifer and tends to be localized and more spatially variable. In both these mechanisms, the recharge flux rate is largely driven by the head gradient and substrate hydraulic properties. Overall, groundwater recharge is one of the most important flux components determining the sustainability of aquifers. Estimation of contaminant transport into the subsurface is also critically dependent on reliable estimates of groundwater recharge. Despite its crucial importance, recharge estimates remain highly uncertain [2,3,4,5,6], both due to the challenges with measurements over large areas [1,7,8,9] at high spatiotemporal resolutions and due to parameterization and model structure errors inherent in subsurface components of hydrologic models [10,11,12,13,14]. With the continued depletion of groundwater in different regions of the world [15,16,17], there has been a dash for approaches to replenish groundwater recharge [18,19,20,21,22,23,24].
Nature-based solutions (NbSs) are one of the promising approaches for enhancing and/or sustaining groundwater recharge. According to Cohen-Shacham et al. [25], NbSs are interventions to protect, sustainably manage and restore natural or modified ecosystems. NbSs encompass many previously known interventions and emerging solutions. For recharge, NbSs include methods that use or mimic natural processes for the management of recharge or mitigating risks to it. They have been gaining increased attention, in part due to their perceived environmental sustainability, climate resilience and, in some instances, also for the ancillary benefits that they can potentially provide. While NbSs for enhancing recharge have been studied in different parts of the world [26,27], there is a need to comprehensively study their effectiveness and understand the challenges and opportunities associated with them, especially with the goal of upscaling them for widespread use. This paper presents a systematic review of the effectiveness of NbSs for enhancing and/or sustaining groundwater recharge. This study aims to address three key questions: (1) How effective are different NbSs in recharging groundwater? (2) What are the key challenges that limit the widespread application of NbSs for groundwater recharge? (3) What opportunities exist to tackle the identified challenges?
This paper is organized into the following sections: Section 2 provides the review protocol followed by the comprehensive literature review of NbSs to improve groundwater recharge. A description of NbS concepts, their types and reported effectiveness are presented in Section 3. A summary and analysis of the reviewed papers, grouped by the types of NbSs they focus on, is also included. Section 4 presents key issues and challenges associated with different NbSs. It also outlines the current trends in NbSs for groundwater recharge improvement and gives perspectives on its future use. Finally, Section 5 details the conclusions and the path forward.

2. Literature Review Protocol

We performed a systematic literature review to identify, screen and filter peer-reviewed articles from the Scopus database. The search terms used in the database are as follows:
(((KEY (“groundwater recharge” OR “groundwater level”) AND TITLE-ABS KEY (“Nature based solution” OR “Nature based solutions” OR “nature-based solutions” OR “infiltration basins” OR “infiltration canal” OR “infiltration trenches” OR “sand dam” OR “sand storage dams” OR “wetlands” OR “forest” OR “trees” OR “terraces” OR “utfi” OR “field recharge” OR “ditch” OR “induced filtration” OR “spate irrigation” OR “crop rotation” OR “rotational cultivation” OR “tillage” OR “grasslands” OR “natural vegetation” OR “paddy fields” OR “hillslope trenching” OR “managed aquifer recharge” OR “Conservation” OR “restoration” OR “Enhancement through sustainable management” OR “low intensity grazing” OR “forest management” OR “vegetation management” OR “green infrastructure” OR “permeable surface” OR “riparian buffer” OR “floodplain restoration” OR “Agroforestry” OR “rainwater harvesting” OR “grazing management”) AND NOT TITLE-ABS-KEY (“quality” OR “Soil invertebrates” OR “recharge sites” OR “suitable sites” OR “linings” OR “nutrient” OR “land use change” OR “spectroscopy” OR “solvent” OR “dissolved oxygen” OR “potential sites” OR “chemical” OR “carbon dioxide” OR “sulphate” OR “hydrogen” OR “machine learning” OR “chloride” OR “phosphate” OR “valuing” OR “mineralization” OR “hindcasting” OR “LULC”)))).
Here, KEY field stands for keywords. It allows for searching for specific terms in the keywords of a document. The keywords are usually assigned by authors or generated by the publisher. The TITLE-ABS-KEY field searches for terms in the title, abstract and keywords of documents. This broadens the search but keeps it targeted to the aforementioned sections of a paper. OR, AND and NOT are the logical operators to broaden and/or narrow down the search domain defined based on the keywords.
A search based on the aforementioned query resulted in 699 peer-reviewed articles and gray papers. The trends of the most common words in the title, abstract and keywords of the identified articles were processed using the Biblioshiny web page [28], which was accessed through RStudio (version 2022.02.3)and the Bibliometrix library. As shown in Figure 1, the usage rate of most terms significantly increased, starting in the late 2000s. This is unsurprising, as several studies in recent decades have performed detailed assessments of groundwater drought and depletion hot spots [15,16,29]. Additionally, there has been a growing emphasis on finding sustainable solutions [22,30,31,32,33].
After gathering the 699 articles, those that were directly related to our study objective were manually selected. The result of the selection is shown in the PRISMA flow diagram below. Figure 2 shows the number of articles removed and/or included while screening the identified articles. We reviewed the articles’ keywords, titles and abstracts and found 61 articles to be eligible for full-text review. Some of the eligibility criteria for inclusion of articles in this review included the following: the article considers a conservation measure that fulfills the definition of NbSs set by our study, the effectiveness must be measured quantitively with sound methods and only already implemented NbSs, not mere proposals of such, should be considered.
A full-text review of each of the 61 articles was carried out to extract data. Studies that quantified the change in recharge rate due to NbSs application were thoroughly examined. Each paper was reviewed individually to extract specific features such as type of NbS deployment status, description of study area, size of intervention, main objective of the NbS, geographic area, scale of study (e.g., continental or basin scale, or a pilot study), source of water, effect on groundwater recharge and methods for response measurement/performance assessment. The 35 countries covered by the reviewed papers are shown in Figure 3. Despite our efforts to ensure comprehensiveness by utilizing the global Scopus database and considering a wide range of NbSs, it should be noted that certain regions, including Russia, Scandinavia and large areas of Africa and South America, remain under-represented in this study.

3. NbSs for Groundwater Recharge: Approaches and Their Effectiveness

As defined by the European Commission [34], NbSs are solutions inspired and supported by nature that are cost-effective and capable of simultaneously providing environmental, social and economic benefits and helping build resilience. In this review, we focus on two broad approaches of NbSs for aquifer recharge: managed aquifer recharge (MAR) and ancillary recharge methods (ARMs). MAR includes solutions specifically designed to intentionally enhance groundwater recharge as its primary goal [35]. In contrast, ARMs include all the remaining NbSs that are not primarily intended to improve groundwater recharge but still offer this benefit nonetheless. Previous studies [36,37] have used different approaches to quantitatively estimate the contribution of NbSs to groundwater recharge. Methods of assessment include long-term monitoring of groundwater table/recovered water volume, water balance analysis, stable isotopes and chloride balance, field investigation such as lysimetry and modeling. In the following sections, we briefly describe examples of the two NbS approaches for aquifer recharge, discuss how they lead to recharge improvement and highlight the methods used to assess their effectiveness.
This section has multiple subsections and provides a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.

3.1. Managed Aquifer Recharge (MAR)

MAR is the type of NbS with a deliberate target of recharging aquifers. The recharged water could be recovered to fulfill water demand or serve as an environmental benefit [30,38,39]. Even though the major objective of MAR is to increase groundwater storage and minimize the temporal imbalance of water availability and water demand, it may also be used to mitigate saltwater intrusion in coastal aquifers [38,40], improve treated wastewater quality through soil aquifer treatment (SAT) and manage large-scale issues like land subsidence [41]. Globally, MAR projects are increasing in number by around 5% each year and currently, MAR has reached an estimated capacity of 10 km3/year [30].
Types of MAR solutions that not only rely on built structures but also on natural systems fall under the vast umbrella of NbSs. These types of MAR typically make use of one big natural infrastructure, the aquifer. Even though NbSs are a relatively newer concept, MAR has been practiced for a long time, and different types and modifications of it exist. According to Dillon et al. [30], MAR can be classified into four major groups based on their objectives and the type of method used to achieve the objective. These are the spreading method, induced bank filtration, in-channel modification and artificial well/shafts/borehole recharge. Only the first three are considered in our study since those are the ones that heavily rely on natural processes and natural infrastructure. More details on these three different techniques, their advantages, limitations and overall effectiveness are summarized below.

3.1.1. Spreading Method

This method refers to the MAR application wherein water is infiltrated from the land surface to the underlying aquifers. There are different schemes of spreading the water such as infiltration basins with diverted water (infiltration ponds), buried trenches in the unsaturated zone to enhance infiltration (infiltration ditch), crops irrigated in excess or diffuse land infiltration by diverting flood water to specific areas (field infiltration). The spreading technique of MAR is the most versatile method. The water can either be retained in infiltration ponds or released to lands that are primarily used for other purposes. The spreading method is characterized by high area requirements, cost-effectiveness and high operational efficiency. It is the most commonly used type of MAR [30]. Using the spreading method, dune infiltration basins in the Netherlands managed to push back a seawater wedge, which helped the city to improve the water quality [42]. Although no quantitative assessments were reported, according to the operators of the scheme, this mitigation effort can serve for the coming centuries. This estimate aligns with other studies that have used physical and numerical models to show the reliable efficiency of the spreading method. When it comes to large-scale NbS applications, there are other alternative implementations of the spreading method, such as underground taming of floods for irrigation (UTFI). UTFI recharge rate is two to nine times higher than the performance of infiltration ponds alone [43]. Strategically located UTFI schemes, such as upstream of major flood-prone areas, can not only readily recharge aquifers but also control downstream flooding [44,45].
Long-term measurement of recovered water volume has shown the high performance of the spreading method in sustaining the city’s water supply demand over the last two decades [46]. The source for this recharge scheme is treated wastewater, which has helped to close water and other nutrient cycles. Groundwater level analysis before and after MAR scheme at installation in Iran showed that infiltration ponds helped to slow the rate of groundwater level decline [47], which further confirms the success of this technique in enhancing groundwater recharge. In another study, using monitoring well data, Iwasaki et al. [48] showed the positive impacts of field recharge on groundwater during the irrigation period. Mass balance around boundaries of the spreading system indicated more than 80% of inflow infiltrating in the subsurface [49]. In addition, model complemented mass balance analysis of the field recharge method has shown that declining groundwater level trends of up to 2.5 m/yr could be reversed through the spreading method of MAR. The study was carried out in Arizona and California using multi-decadal data from monitoring wells [50].
From all 61 implementations considered in this study, it was found that the spreading method failed only once in enhancing recharge [51]. A basin in Jordan showed no effect on recharge after two years of operation due to the clogging of ponds at earlier operation stages. Even though the success story of infiltration basins in increasing recharge is ubiquitous, clogging in the ponds has also been reported as a barrier to infiltration even at the early stage of the spreading system. While the latter stage of clogging is related to the design life of the ponds, early stage deterioration of infiltration rate is the result of poor characterization of hydrogeological structure and lateral heterogeneities [52]. This shows that the success of MAR can be highly site-specific. For instance, a previous study highlighted the need for comprehensive analysis before implementing an MAR system in a karst aquifer using the rechargeability index [53].

3.1.2. Induced Bank Filtration

In this method, water infiltrates from rivers and lakes to aquifers using hydraulic structures that induce vertical flow. The surface water loss is usually induced by a pumping well or weirs located a few meters away from the riverbanks. Natural filtration and attenuation occur as water moves in the subsurface. Induced bank filtration is usually used to improve water quality. There are several different studies regarding water quality improvement of induced bank filtration [51,54,55,56,57,58].
However, only a few of these studies performed a quantitative estimation of the contribution of bank infiltration to aquifer recharge [59,60]. This could perhaps be because the driving factor of induced bank filtration processes is water quality improvement rather than water storage [61].
Several factors control the success of induced bank filtration when they are intended as aquifer recharging schemes. Permeability of the river bed and bank formations, aquifer thickness, suspended matter of the surface water that could potentially accumulate and clog the riverbanks and bed [37] are some of the major factors that need to be considered to prevent rapid clogging. Due to the locally dependent efficiency of induced bank filtration and its potential for rapid clogging, previous studies have noted the restricted popularity [62] of this method despite being one of the oldest types of NbSs. Our review findings also align with this, as out of the 61 implementations we have reviewed (see Table 1), only two of them can be categorized as induced bank filtration. One additional study reported bank filtrate reduction immediately after flooding in induced bank filtration in Australia. However, the major objective of the study was to investigate the filter efficiency of the river bank [63].

3.1.3. In-Channel Modifications

Unlike the first two types, these types of MAR use interception techniques. A structure such as a dam is built in channels to intercept and retain water. In-channel modifications are hybrid systems with natural and built structures. Recharge dams, sub-surface dams, sand dams or check dams are commonly used types of dams. In our review, we included methods that highly rely on natural processes, such as infiltration. As the source for subsurface dams is groundwater itself, those are not included here.
The effectiveness of in-channel modifications on groundwater recharge has been assessed using numerical models and groundwater level field data [113]. In Kenya, hydrological processes around sand dams of arid regions have been modeled to assess their long-term impact on groundwater levels. Besides the approximation of the model used and assumptions made in the study, recharge improvement was highlighted [113]. The scope of their study was only assessing the relative improvement of recharge rather than quantifying it. Dashora et al. [114] estimated the mean annual recharge volume of structures with known capacities, such as sand dams, to be one to two times its detention capacity. Their studies were based on long-term farmer’s water level records. All reviewed studies regarding in-channel modification reported groundwater table rise, although the extent of influence was variable based on seasons [115] and the site hydrogeology [116]. Indirect evaluation of sand dams’ impact on recharge has been performed using vegetation cover as an indicator. In Kenya, areas with higher sand dam density have shown higher vegetation cover based on Normalized Difference Vegetative Index (NDVI) values [117]. Although check dams are not widely discussed in published articles [118], a recent review found that most check dams are effective in improving groundwater recharge [119].

3.2. Ancillary Recharge Methods (ARMs)

Alternative NbS approaches whose main goal is not to enhance the recharge, but may still provide ancillary benefits of enhancing recharge, include source water protection, watershed management, wetlands restoration, protection, construction, water harvesting, agricultural best management practices, afforestation, sustainable drainage systems and protecting mangroves [120]. Although most of the aforementioned NbSs may fall under the ARMs category, only those with enough groundwater recharge studies are included in this review. These are afforestation, wetlands restoration and regenerative agricultural practices that improve soil moisture. The selected ARMs and groundwater recharge responses to them are discussed below.

3.2.1. Afforestation

Afforestation has been used as a large-scale NbS not only for the water sector but also for land, biodiversity and agriculture disciplines. Trees can increase or decrease groundwater recharge according to their type, density, location, size and age [35]. For instance, Ilstedt et al. [121] showed how intermediate tree cover can maximize groundwater recharge in the seasonally dry tropical country in West Africa. The main method used in this study was groundwater budget modeling. However, the negative impact of eucalyptus trees on aquifer recharge has been repeatedly reported [76,92,122,123,124]. Chausson et al. [125] recently also highlighted that the eucalyptus tree plantation, used as an NbS in Portugal, has been uprooted due to its negative impacts on groundwater. In another case study that assessed the effect of heavy selective logging on the water resources, it was found that the water level rose immediately after logging, remained at an elevated but fairly constant level for three years and then declined again as the forest regenerated [82]. Silveira et al. [91] estimated change in groundwater recharge due to the substitution of natural grassland with forest plantation, by summing change in groundwater and stream base flow. By assuming that the variation in unconfined aquifer levels is only due to the groundwater recharge, change in groundwater storage was estimated using water table fluctuation data. Stream baseflow was found using the hydrograph separation method. Although stream discharge was reported to be less in the forested watershed, groundwater recharge was almost similar in both natural and forested watersheds. These results show the dynamic and non-linear response of the natural systems.

3.2.2. Wetlands

Wetlands have previously shown their potential to be an effective NbS by regulating flooding and securing water quantity and quality. However, studies about the relationship between wetlands and groundwater recharge have shown contradicting results.
Large-scale restoration and desilting of abandoned wetlands in the Godavari River basin has been found to have successfully stored water and recharged the aquifer to meet the agricultural demand of the community [126]. Similarly, Staes et al. [127] highlighted, using distributed hydrological models, how the restoration of wetlands promotes groundwater recharge. In the Mississippi alluvial plain, strong evidence from field measurements has highlighted that an Oxbow Lake wetland system was the primary source of groundwater recharge [128]. Another recent example is the application of wetlands for flood reduction and groundwater recharge in the Sponge City Construction (SPCC) of China. The SPCC in Jinan uses wetlands in the city as temporary stormwater storage space and later recharges groundwater through grass swales [129]. While these success stories and reports of wetlands acting as a “sponge” exist, depending on the different hydrological properties, location and type, sometimes these wetlands may act as a barrier to aquifer recharge [130]. In particular, headwater wetlands can saturate and rapidly convey stormwater to surface water. Moreover, many wetlands exist since they are on impermeable soil. Even if wetlands interact with groundwater, the direction of water movement between wetlands and aquifers was found to be interchangeable in different seasons [31,130]. However, for areas where wetlands are proven to be focal points for groundwater recharge, they have a significant economic value. Acharya and Barbier [131] highlighted that the reduction in recharge from wetlands could lead to substantial welfare losses for farmers.
The conflicting results of previous studies show that wetlands may not always recharge groundwater. Even in cases wherein successful groundwater recharge from wetlands is reported, most of the groundwater recharge could likely flow to the moist margins surrounding the wetlands rather than the regional groundwater levels [132]. These contrary results call for further studies that consider large-scale water fluxes and balance to understand these interactions better [132,133].

3.2.3. Regenerative Agricultural Practices

Land use and land cover change due to agricultural management practices alter groundwater recharge in both quantity and quality. Irrigation areas have a higher recharge rate than watersheds dominated by rainfed agriculture [134]. In addition to irrigation, other agricultural practices such as crop rotation and conservation tillage that improve soil moisture have been proven to be effective in recharging aquifers of different depths. Furthermore, traditional agricultural practices utilize physical storage units, such as tank cascade systems, to store water during the monsoon season, which has been found to enhance groundwater recharge [112,135].
Groundwater recharge increased from soil moisture improvement through the restoration of grassland and shrubs [61] or better management of irrigated fields such as using spate irrigation [81]. The effect of crop rotation on recharge rate has been studied using a semi-distributed model and it was found that the continuous rotation of crops with high irrigation rates and shallow roots increases recharge [136]. An isotopic fractionation study showed that the change of native grassland to wheat reduced groundwater recharge [64]. Recharge rate improvement due to cultivation and conservative tillage application has been reported as one explanation for long-term groundwater table rise in arid regions [137]. Conservation tillage changed the hydraulic properties of the top of the soil of the area which consequently affected the recharge rate. These variations show that generalization and assumptions about the ARMs’ response on recharge should be avoided as it is highly site-specific and needs knowledge of hydrology and hydrogeology of the field to be deployed on.

4. Challenges and Opportunities

While several NbSs have shown a potential to improve groundwater recharge, their implementations still face challenges. The following subsections explore a few of these challenges and present opportunities to alleviate them.

4.1. Quantification of the Effectiveness of NbSs in Short and Long Term

Previous studies have mostly used groundwater levels and water budgets to monitor the effectiveness of NbSs for groundwater recharge [44,49,70,81,82,90,92,93,96,98,99,105,112]. However, significant uncertainties abound in the usage of these methods for quantifying the effectiveness of NbSs. For example, the water table fluctuation (WTF) method, which is often used to evaluate groundwater recharge, requires data on specific yield, a variable whose estimates remain highly uncertain [6,138,139,140]. In addition, factors that affect groundwater levels, other than recharge, such as pumping, tides and surface loads are often neglected in recharge estimation using the WTF method. This limitation significantly affects results for NbSs with active groundwater pumping, such as cascade tanks. A few other studies [88,117,141,142,143] have also used remote sensing tools, models and chemical tracers. In addition to models, indirect methods such as electrical resistivity analysis have also been used to complement the direct methods [52]. Depending on the remote sensing data choice and the scale of impact, remote sensing data fail to measure the effectiveness of NbSs on groundwater recharge. According to Eisma and Merwade [117], impact assessment using Gravity Recovery and Climate Experiment (GRACE) data could not detect any significant impacts of dams on water storage, likely due to the limitation of the spatial resolution of GRACE and/or the small magnitude of the sand dams’ impact on groundwater storage.
Both data-based and physically based models have been used in the past to study wetland dynamics [144,145,146,147,148,149,150], which can be used to estimate dynamic groundwater recharge from wetlands. However, these models are often affected by uncertainties in model structure and parameterization [151,152,153,154]. Spatially explicit estimation is also often limited by computational demand [155,156], which can be significant especially while obtaining estimates for smaller-sized wetlands. Model results are highly sensitive to assumed boundary conditions. For example, ill-matched assumptions during model construction resulted in exaggerated water levels and impacts of sand dams [113]. Chemical tracer methods have limitations regarding input tracer and tracer transport process estimation [134,157,158]. Shamsuddin et al. [111] highlighted the advantage of integrating different methods while assessing the effectiveness of aquifer recharge by combining the result of stable isotopes and chloride mass balance.
The relationship between groundwater recharge and afforestation has been studied using methods such as water budget [71], groundwater fluctuation [91,94,98,99], hydrochemistry (stable isotope) [69,72,86] and mathematical models [89,95,101]. Other studies used a solute mass balance [76,159,160] and aquifer water budget [82,92]-based approaches to estimate recharge. Groundwater recharge, as the “residual” of water budget, is one of the most commonly used methods in the reviewed articles. Parameters in these methods, such as specific yield, are one of the major uncertainties of the approach. Individual components of the water balance are estimated through models or field measurements, such as using hydraulic structures and lysimeters [121,161].
Another challenge in the evaluation of the effectiveness of NbSs over the long term is the cost and effort associated with measuring these impacts over the analysis period. The fact that recharge varies temporally, and its pattern is affected by seasonal and long-term trends of climate, adds to the challenge. Performing such evaluations over large scales, at resolutions at which NbSs are applied, makes the endeavor even more challenging. Most studies we reviewed reported observations at plot scale and for the short term (<1 year). Although hydrodynamics models developed for catchment scale are less prone to plot scale measurement limitations, Kumar et al. [162] recently highlighted that these models hardly capture spatially discrete changes in the hydrological and energy balance that resulted from NbS implementation.
Opportunities exist to reduce the uncertainties of measurement methods’ by using multiple methods of estimation [163,164,165,166,167] and scaling them over larger areas. Robustness of estimates of recharge using hydrologic models can be improved by constraining parameter uncertainties by using and/or assimilating data from various sources including in situ products and remote sensing [141,142], improving the representation of physiographic data [168] and through the reduction in structural constraints [169,170].

4.2. Scalability of NbS

To benefit a large number of people, NbSs must be applied at a larger scale or over a range of settings. However, both science and implementation challenges exist in this regard. For example, NbSs implemented to enhance aquifer recharge may be hindered by unfavorable geological and local environmental conditions within large areas. Moreover, the scalability of NbSs is often complicated by the complexities of planning and implementation. There is also a historical inertia against NbS upscaling, with the prevailing assumption that gray infrastructure is superior and faster. NbSs are still considered by many as a less efficient or risker than a gray system [35]. All of these factors restrict NbSs’ upscaling, preventing them from reaching their full and significant potential. To alleviate the aforementioned challenges, there is a need to not only develop methods that can evaluate the effectiveness and efficiency of NbSs at different scales but also implement specific NbSs at various scales. This would allow for a more comprehensive understanding of the scientific, management, planning and economic challenges therein.

4.3. Lack of Stakeholders’ Engagement and Collaboration

Broader stakeholder involvement and participation are crucial for the successful implementation of NbSs for groundwater recharge. When applied at scale, NbS success requires the involvement of various institutions and stakeholder cooperation. However, this is hard to achieve given that success stories at different scales are few and far between, which makes it difficult to be considered as a preferred option for stakeholders. Stakeholders generally prefer tested and conventional solutions, which makes building consensus among stakeholders hard. The problem is in a way compounded by the fact that the hydrological functions of aquifer-based NbSs are less understood, leading to their neglect in policy appraisal and planning. Capacity and knowledge must be enhanced by promoting research that fills this gap. This may raise stakeholder awareness of NbSs’ potential and help NbSs become a part of policies and regulatory frameworks.

4.4. Cost

Although small-scale NbSs can be implemented for no or at a low cost, aquifer-based NbSs can be costly, especially at scale [171,172]. The general assumption about NbSs is that they are more cost-effective than gray (built) systems. A more efficient recharge scheme could be more sustainable and cost-effective than a built infrastructure such as dams. However, the overall management cost is influenced by the local landscape and its hydrological responses.
NbSs for groundwater recharge are often considered cost-effective, offering multiple co-benefits related to climate resilience, public health and job security. However, there is usually a threshold wherein an NbS reaches its optimum cost efficiency and ceases to return the large investment, particularly for NbSs at scale [173,174]. Before making broad assumptions about the cost-effectiveness of NbSs, a comprehensive assessment is required that takes into account not only the conventional cost–benefit terms but also hydrological and other co-benefits. Monetizing co-benefits has been stated as the greatest uncertainty in previous studies [175]. To reduce uncertainty, all involved stakeholder groups can potentially identify and quantify benefits and costs. Identifying the threshold and optimum mix of gray infrastructure and NbSs also calls for further research. Such research could possibly give common performance indicators and frameworks.

4.5. Lack of Guidance

Although new emerging NbSs are being implemented for groundwater recharge, there is a lack of data, information and documentation. Many new technologies with no documented manuals are being constructed at the pilot scale [35], which limits their adaptive capacity. In addition, methods, guidelines and evidence that justify investment and impacts of NbS schemes on groundwater recharge are rarely available. Tools and approaches that help to determine the optimum mix of gray infrastructures and NbSs for groundwater recharge are still lacking. These situations are the results of insufficient research and development in NbSs. Documenting investment, effectiveness and cost–benefit analysis of already deployed NbSs helps to have a well-established, capable and informed subsequent NbS planning and implementation. Moreover, standard regulations, technical guidelines and tools that are common and based on quantifiable benchmarks must be developed. This would facilitate the easy adaptability of NbSs for groundwater recharge. It is to be acknowledged that there could be cases wherein human-made infrastructure may be better suited than NbSs in enhancing recharge.

4.6. Uncertainties with NbSs

NbSs’s effectiveness relies on the substrate ecosystem [176], which is dynamic in nature with a certain capacity threshold. A natural ecosystem’s response changes over time and location. Due to this, the efficiency of the NbS varies from high to incompatible based on the location, climate and ecosystem type it is implemented in. Additionally, the performance of the NbS varies and is influenced by local hydrology, management and the overall ecosystem [34,35,177]. The box plot in Figure 4 shows the effectiveness and their range for different NbSs. It can be noted that there is little consensus among studies regarding the range of their effectiveness.

5. Conclusions

This study provides a comprehensive review of NbSs in improving groundwater recharge. The goals were to assess the effectiveness of NbSs as recharge enhancement methods, identify key challenges in their widespread applications and finally recommend opportunities to reduce the barriers.
Our review of 61 studies showed that NbSs have been successfully applied to increase groundwater recharge in most of the reviewed studies. The managed aquifer recharge approach had a positive effect on recharge consistently, while ancillary recharge methods had mixed results. However, large variations exist in the performance of the same or similar methods across studies.
Key barriers regarding the widespread implementation of NbSs have been identified, which includes challenges related to performance quantification, scalability and stakeholder engagement. This study underscores the uncertainties in determining the efficacy of NbSs on groundwater recharge, revealing substantial knowledge gaps regarding their long-term and large-scale impacts. In addition, as an emerging concept, NbSs face research gaps in areas such as scalability, cost–benefit assessment and lack of technical guidance and tools. Addressing these issues through further studies is crucial for a full successful use of NbSs in improving groundwater recharge. Despite these challenges, NbSs can be considered as a promising alternative for enhancing groundwater resources. Continued and improved planning and funding to support research on NbSs will enable a more robust quantitative basis regarding their effectiveness and costs across a range of scales, settings and methods. This should eventually lead to NbS design and implementation strategies with sound scientific evidence.

Author Contributions

M.M.K.: Writing—original draft, Visualization, Methodology, Data curation, Conceptualization. M.K.: Writing—review and editing, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. M.M.M.: Writing—review and editing, Methodology. T.P.C.: Writing—review and editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by a grant from the National Science Foundation (OIA RII Track2 Award# 2019561).

Data Availability Statement

All data used in this study are included within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Healy, R.W. Estimating Groundwater Recharge; Cambridge University Press: Cambridge, UK, 2010; ISBN 1-139-49139-3. [Google Scholar]
  2. Berghuijs, W.R.; Luijendijk, E.; Moeck, C.; van der Velde, Y.; Allen, S.T. Global Recharge Data Set Indicates Strengthened Groundwater Connection to Surface Fluxes. Geophys. Res. Lett. 2022, 49, e2022GL099010. [Google Scholar] [CrossRef]
  3. Fan, Y.; Clark, M.; Lawrence, D.M.; Swenson, S.; Band, L.E.; Brantley, S.L.; Brooks, P.D.; Dietrich, W.E.; Flores, A.; Grant, G.; et al. Hillslope Hydrology in Global Change Research and Earth System Modeling. Water Resour. Res. 2019, 55, 1737–1772. [Google Scholar] [CrossRef]
  4. Gnann, S.; Reinecke, R.; Stein, L.; Wada, Y.; Thiery, W.; Müller Schmied, H.; Satoh, Y.; Pokhrel, Y.; Ostberg, S.; Koutroulis, A.; et al. Functional Relationships Reveal Differences in the Water Cycle Representation of Global Water Models. Nat. Water 2023, 1, 1079–1090. [Google Scholar] [CrossRef]
  5. Gonzalez, M.O.; Preetha, P.; Kumar, M.; Clement, T.P. Comparison of Data-Driven Groundwater Recharge Estimates with a Process-Based Model for a River Basin in the Southeastern USA. J. Hydrol. Eng. 2023, 28, 04023019. [Google Scholar] [CrossRef]
  6. Malakar, P.; Anshuman, A.; Kumar, M.; Boumis, G.; Clement, T.P.; Tashie, A.; Thakur, H.; Bhat, N.; Rathore, L. An In-Situ Daily Dataset for Benchmarking Temporal Variability of Groundwater Recharge. Earth Syst. Sci. Data Discuss. 2024, 2024, 1–19. [Google Scholar]
  7. Scanlon, B.; Mukherjee, A.; Gates, J.B.; Reedy, R.C.; Sinha, A.K. Groundwater Recharge in Natural Dune Systems and Agricultural Ecosystems in the Thar Desert Region, Rajasthan, India. Hydrogeol. J. 2010, 18, 959–972. [Google Scholar] [CrossRef]
  8. Lapworth, D.J.; MacDonald, A.M.; Krishan, G.; Rao, M.S.; Gooddy, D.C.; Darling, W.G. Groundwater Recharge and Age-Depth Profiles of Intensively Exploited Groundwater Resources in Northwest India. Geophys. Res. Lett. 2015, 42, 7554–7562. [Google Scholar] [CrossRef]
  9. Moeck, C.; Grech-Cumbo, N.; Podgorski, J.; Bretzler, A.; Gurdak, J.J.; Berg, M.; Schirmer, M. A Global-Scale Dataset of Direct Natural Groundwater Recharge Rates: A Review of Variables, Processes and Relationships. Sci. Total Environ. 2020, 717, 137042. [Google Scholar] [CrossRef]
  10. Condon, L.E.; Maxwell, R.M. Simulating the Sensitivity of Evapotranspiration and Streamflow to Large-Scale Groundwater Depletion. Sci. Adv. 2019, 5, eaav4574. [Google Scholar] [CrossRef]
  11. Gong, C.; Cook, P.G.; Therrien, R.; Wang, W.; Brunner, P. On Groundwater Recharge in Variably Saturated Subsurface Flow Models. Water Resour. Res. 2023, 59, e2023WR034920. [Google Scholar] [CrossRef]
  12. Carrera-Hernández, J.J.; Mendoza, C.A.; Devito, K.J.; Petrone, R.M.; Smerdon, B.D. Effects of Aspen Harvesting on Groundwater Recharge and Water Table Dynamics in a Subhumid Climate. Water Resour. Res. 2011, 47. [Google Scholar] [CrossRef]
  13. Chen, X.; Kumar, M.; deB Richter, D.; Mau, Y. Impact of Gully Incision on Hillslope Hydrology. Hydrol. Process. 2020, 34, 3848–3866. [Google Scholar] [CrossRef]
  14. Kumar, M.; Duffy, C.J.; Salvage, K.M. A Second-Order Accurate, Finite Volume–Based, Integrated Hydrologic Modeling (FIHM) Framework for Simulation of Surface and Subsurface Flow. Vadose Zone J. 2009, 8, 873–890. [Google Scholar] [CrossRef]
  15. Konikow, L.F. Groundwater Depletion in the United States (1900−2008); US Geological Survey: Reston, VA, USA, 2013. [Google Scholar]
  16. Wada, Y.; van Beek, L.P.H.; van Kempen, C.M.; Reckman, J.W.T.M.; Vasak, S.; Bierkens, M.F.P. Global Depletion of Groundwater Resources. Geophys. Res. Lett. 2010, 37. [Google Scholar] [CrossRef]
  17. Liu, P.-W.; Famiglietti, J.S.; Purdy, A.J.; Adams, K.H.; McEvoy, A.L.; Reager, J.T.; Bindlish, R.; Wiese, D.N.; David, C.H.; Rodell, M. Groundwater Depletion in California’s Central Valley Accelerates during Megadrought. Nat. Commun. 2022, 13, 7825. [Google Scholar] [CrossRef]
  18. Moench, M. When the Well Runs Dry but Livelihood Continues: Adaptive Responses to Groundwater Depletion and Strategies for Mitigating the Associated Impacts. In The agricultural Groundwater Revolution: Opportunities and Threats to Development; CABI Head Office: Oxford, UK, 2007; Volume 3, pp. 173–192. [Google Scholar]
  19. Wendt, D.E.; Loon, A.F.V.; Scanlon, B.R.; Hannah, D.M. Managed Aquifer Recharge as a Drought Mitigation Strategy in Heavily-Stressed Aquifers. Environ. Res. Lett. 2021, 16, 014046. [Google Scholar] [CrossRef]
  20. Feng, G.; Jin, W.; Ouyang, Y.; Huang, Y. The Role of Changing Land Use and Irrigation Scheduling in Groundwater Depletion Mitigation in a Humid Region. Agric. Water Manag. 2024, 291, 108606. [Google Scholar] [CrossRef]
  21. Singh, R.; Garg, K.K.; Anantha, K.H.; Akuraju, V.; Dev, I.; Dixit, S.; Dhyani, S.K. Building Resilient Agricultural System through Groundwater Management Interventions in Degraded Landscapes of Bundelkhand Region, Central India. J. Hydrol. Reg. Stud. 2021, 37, 100929. [Google Scholar] [CrossRef]
  22. Alam, S.; Gebremichael, M.; Li, R.; Dozier, J.; Lettenmaier, D.P. Can Managed Aquifer Recharge Mitigate the Groundwater Overdraft in California’s Central Valley? Water Resour. Res. 2020, 56, e2020WR027244. [Google Scholar] [CrossRef]
  23. Bachand, P.A.M.; Roy, S.B.; Choperena, J.; Cameron, D.; Horwath, W.R. Implications of Using On-Farm Flood Flow Capture To Recharge Groundwater and Mitigate Flood Risks Along the Kings River, CA. Environ. Sci. Technol. 2014, 48, 13601–13609. [Google Scholar] [CrossRef]
  24. Baptista, V.S.G.; Coelho, V.H.R.; Bertrand, G.F.; da Silva, G.B.L.; Caicedo, N.O.L.; Montenegro, S.M.G.L.; Stefan, C.; Glass, J.; Heim, R.; Conrad, A.; et al. Rooftop Water Harvesting for Managed Aquifer Recharge and Flood Mitigation in Tropical Cities: Towards a Strategy of Co-Benefit Evaluations in João Pessoa, Northeast Brazil. J. Environ. Manag. 2023, 342, 118034. [Google Scholar] [CrossRef] [PubMed]
  25. Cohen-Shacham, E.; Andrade, A.; Dalton, J.; Dudley, N.; Jones, M.; Kumar, C.; Maginnis, S.; Maynard, S.; Nelson, C.R.; Renaud, F.G.; et al. Core Principles for Successfully Implementing and Upscaling Nature-Based Solutions. Environ. Sci. Policy 2019, 98, 20–29. [Google Scholar] [CrossRef]
  26. Qiu, Y.; Da Silva Rocha Paz, I.; Chen, F.; Versini, P.A.; Schertzer, D.; Tchiguirinskaia, I. Space Variability Impacts on Hydrological Responses of Nature-Based Solutions and the Resulting Uncertainty: A Case Study of Guyancourt (France). Hydrol. Earth Syst. Sci. 2021, 25, 3137–3162. [Google Scholar] [CrossRef]
  27. Ribeiro, L. Revisiting Ancestral Groundwater Techniques as Nature Based Solutions for Managing Water. In Advances in Science, Technology and Innovation; Springer: Berlin/Heidelberger, Germany, 2021; pp. 483–487. [Google Scholar]
  28. Aria, M.; Cuccurullo, C. Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
  29. Link, A.; El-Hokayem, L.; Usman, M.; Conrad, C.; Reinecke, R.; Berger, M.; Wada, Y.; Coroama, V.; Finkbeiner, M. Groundwater-Dependent Ecosystems at Risk—Global Hotspot Analysis and Implications. Environ. Res. Lett. 2023, 18, 094026. [Google Scholar] [CrossRef]
  30. Dillon, P.; Stuyfzand, P.; Grischek, T.; Lluria, M.; Pyne, R.D.G.; Jain, R.C.; Bear, J.; Schwarz, J.; Wang, W.; Fernandez, E.; et al. Sixty Years of Global Progress in Managed Aquifer Recharge. Hydrogeol. J. 2019, 27, 1–30. [Google Scholar] [CrossRef]
  31. Acreman, M.; Smith, A.; Charters, L.; Tickner, D.; Opperman, J.; Acreman, S.; Edwards, F.; Sayers, P.; Chivava, F. Evidence for the Effectiveness of Nature-Based Solutions to Water Issues in Africa. Environ. Res. Lett. 2021, 16, 063007. [Google Scholar] [CrossRef]
  32. Liu, M.; Nie, Z.; Cao, L.; Wang, L.; Lu, H. Nature-Based Solutions for the Restoration of Groundwater Level and Groundwater-Dependent Ecosystems in a Typical Inland Region in China. Water 2024, 16, 33. [Google Scholar] [CrossRef]
  33. Kourakos, G.; Dahlke, H.E.; Harter, T. Increasing Groundwater Availability and Seasonal Base Flow Through Agricultural Managed Aquifer Recharge in an Irrigated Basin. Water Resour. Res. 2019, 55, 7464–7492. [Google Scholar] [CrossRef]
  34. European Commission: Directorate-General for Research and Innovation. Towards an EU Research and Innovation Policy Agenda for Nature-Based Solutions & Re-Naturing Cities–Final Report of the Horizon 2020 Expert Group. on “Nature-Based Solutions and Re-Naturing Cities”–(Full Version); Publications Office: Hong Kong, 2015. [Google Scholar]
  35. WWAP (United Nations World Water Assessment Programme); UN-Water. The United Nations World Water Development Report 2018: Nature-Based Solutions for Water; UNESCO: Paris, France, 2018; ISBN 978-92-3-100264-9. [Google Scholar]
  36. Han, D.; Currell, M.J.; Cao, G.; Hall, B. Alterations to Groundwater Recharge Due to Anthropogenic Landscape Change. J. Hydrol. 2017, 554, 545–557. [Google Scholar] [CrossRef]
  37. Gale, I.; Neumann, I.; Calow, R.; Moench, M. The Effectiveness of Artificial Recharge of Groundwater: A Review; British Geological Survey: Nottingham, UK, 2002. [Google Scholar]
  38. Ringleb, J.; Sallwey, J.; Stefan, C. Assessment of Managed Aquifer Recharge through Modeling-A Review. Water 2016, 8, 579. [Google Scholar] [CrossRef]
  39. Zhang, H.; Xu, Y.; Kanyerere, T. A Review of the Managed Aquifer Recharge: Historical Development, Current Situation and Perspectives. Phys. Chem. Earth Parts A/B/C 2020, 118–119, 102887. [Google Scholar] [CrossRef]
  40. Bouwer, H. Artificial Recharge of Groundwater: Hydrogeology and Engineering. Hydrogeol. J. 2002, 10, 121–142. [Google Scholar] [CrossRef]
  41. Dillon, P. Future Management of Aquifer Recharge. Hydrogeol. J. 2005, 13, 313–316. [Google Scholar] [CrossRef]
  42. van Steenbergen, F. The Dune Water Machine—The Water Channel. Available online: https://thewaterchannel.tv/thewaterblog/the-dune-water-machine/ (accessed on 29 November 2021).
  43. Alam, M.F.; Pavelic, P.; Sharma, N.; Sikka, A. Managed Aquifer Recharge of Monsoon Runoff Using Village Ponds: Performance Assessment of a Pilot Trial in the Ramganga Basin, India. Water 2020, 12, 1028. [Google Scholar] [CrossRef]
  44. Pavelic, P.; Srisuk, K.; Saraphirom, P.; Nadee, S.; Pholkern, K.; Chusanathas, S.; Munyou, S.; Tangsutthinon, T.; Intarasut, T.; Smakhtin, V. Balancing-out Floods and Droughts: Opportunities to Utilize Floodwater Harvesting and Groundwater Storage for Agricultural Development in Thailand. J. Hydrol. 2012, 470–471, 55–64. [Google Scholar] [CrossRef]
  45. Chinnasamy, P.; Muthuwatta, L.; Eriyagama, N.; Pavelic, P.; Lagudu, S. Modeling the Potential for Floodwater Recharge to Offset Groundwater Depletion: A Case Study from the Ramganga Basin, India. Sustain. Water Resour. Manag. 2018, 4, 331–344. [Google Scholar] [CrossRef]
  46. Tuinhof, A.; Heederik, J.P. Management of Aquifer Recharge and Subsurface Storage: Making Better Use of Our Largest Reservoir; Netherlands National Committee for the IAH: Wageningen, The Netherlands, 2003; ISBN 90-808258-1-6. [Google Scholar]
  47. Shojaeian, M.R.; Karimidastenaei, Z.; Rahmati, O.; Haghighi, A.T. Assessing Morphological Changes in a Human-Impacted Alluvial System Using Hydro-Sediment Modeling and Remote Sensing. Int. J. Sediment. Res. 2021, 36, 439–448. [Google Scholar] [CrossRef]
  48. Iwasaki, Y.; Ozaki, M.; Nakamura, K.; Horino, H.; Kawashima, S. Relationship between Increment of Groundwater Level at the Beginning of Irrigation Period and Paddy Filed Area in the Tedori River Alluvial Fan Area, Japan. Paddy Water Environ. 2013, 11, 551–558. [Google Scholar] [CrossRef]
  49. Esfandiari-Baiat, M. Rahbar Gale Monitoring of Inflow and Outflow Rate from Kaftari Artificial Recharge of Groundwater System in Dorz Sayban Region in South Eastern Iran. In Proceedings of the Proceedings of the Management of Aquifer Recharge and Water Harvesting in Arid and Semi-Arid Region of Asia, Yazd, Iran, 27 November–1 December 2004. [Google Scholar]
  50. Scanlon, B.R.; Reedy, R.C.; Faunt, C.C.; Pool, D.; Uhlman, K. Enhancing Drought Resilience with Conjunctive Use and Managed Aquifer Recharge in California and Arizona. Environ. Res. Lett. 2016, 11, 049501. [Google Scholar] [CrossRef]
  51. Ascott, M.J.; Lapworth, D.J.; Gooddy, D.C.; Sage, R.C.; Karapanos, I. Impacts of Extreme Flooding on Riverbank Filtration Water Quality. Sci. Total Environ. 2016, 554–555, 89–101. [Google Scholar] [CrossRef] [PubMed]
  52. Sendrós, A.; Himi, M.; Lovera, R.; Rivero, L.; Garcia-Artigas, R.; Urruela, A.; Casas, A. Electrical Resistivity Tomography Monitoring of Two Managed Aquifer Recharge Ponds in the Alluvial Aquifer of the Llobregat River (Barcelona, Spain). Near Surf. Geophys. 2020, 18, 353–368. [Google Scholar] [CrossRef]
  53. Daher, W.; Pistre, S.; Kneppers, A.; Bakalowicz, M.; Najem, W. Karst and Artificial Recharge: Theoretical and Practical Problems. A Preliminary Approach to Artificial Recharge Assessment. J. Hydrol. 2011, 408, 189–202. [Google Scholar] [CrossRef]
  54. Hoppe-Jones, C.; Oldham, G.; Drewes, J.E. Attenuation of Total Organic Carbon and Unregulated Trace Organic Chemicals in U.S. Riverbank Filtration Systems. Water Res. 2010, 44, 4643–4659. [Google Scholar] [CrossRef] [PubMed]
  55. Nagy-Kovács, Z.; Davidesz, J.; Czihat-Mártonné, K.; Till, G.; Fleit, E.; Grischek, T. Water Quality Changes during Riverbank Filtration in Budapest, Hungary. Water 2019, 11, 302. [Google Scholar] [CrossRef]
  56. Singh, P.; Kumar, P.; Mehrotra, I.; Grischek, T. Impact of Riverbank Filtration on Treatment of Polluted River Water. J. Environ. Manag. 2010, 91, 1055–1062. [Google Scholar] [CrossRef]
  57. Abd-Elaty, I.; Saleh, O.K.; Ghanayem, H.M.; Zeleňáková, M.; Kuriqi, A. Numerical Assessment of Riverbank Filtration Using Gravel Back Filter to Improve Water Quality in Arid Regions. Front. Earth Sci. 2022, 10, 1006930. [Google Scholar] [CrossRef]
  58. Ray, C.; Jasperse, J.; Grischek, T. Bank Filtration as Natural Filtration. In Drinking Water Treatment: Focusing on Appropriate Technology and Sustainability; Springer: Dordrecht, The Netherlands, 2011; pp. 93–158. [Google Scholar]
  59. Kopač, I.; Vremec, M. Induced Riverbank Filtration (IRBF) for Managed Artificial Groundwater Recharge (MAR) in Slovenia. In Water Resources Management in Balkan Countries; Springer: Cham, Switzerland, 2020. [Google Scholar]
  60. Rossetto, R.; Barbagli, A.; De Filippis, G.; Marchina, C.; Vienken, T.; Mazzanti, G. Importance of the Induced Recharge Term in Riverbank Filtration: Hydrodynamics, Hydrochemical, and Numerical Modelling Investigations. Hydrology 2020, 7, 1–20. [Google Scholar] [CrossRef]
  61. Gale, I. Dillon. In Strategies for Managed Aquifer Recharge (MAR) in Semi-Arid Areas; United Nations Educational, Scientific and Cultural Organization (UNESCO): Paris, France, 2005. [Google Scholar]
  62. Umar, D.A.; Ramli, M.F.; Aris, A.Z.; Sulaiman, W.N.A.; Kura, N.U.; Tukur, A.I. An Overview Assessment of the Effectiveness and Global Popularity of Some Methods Used in Measuring Riverbank Filtration. J. Hydrol. 2017, 550, 497–515. [Google Scholar] [CrossRef]
  63. Wett, B.; Jarosch, H.; Ingerle, K. Flood Induced Infiltration Affecting a Bank Filtrate Well at the River Enns, Austria. J. Hydrol. 2002, 266, 222–234. [Google Scholar] [CrossRef]
  64. Huang, T.; Pang, Z.; Edmunds, W.M. Soil Profile Evolution Following Land-Use Change: Implications for Groundwater Quantity and Quality. Hydrol. Process. 2013, 27, 1238–1252. [Google Scholar] [CrossRef]
  65. Ochoa-Tocachi, B.F.; Bardales, J.D.; Antiporta, J.; Pérez, K.; Acosta, L.; Mao, F.; Zulkafli, Z.; Gil-Ríos, J.; Angulo, O.; Grainger, S.; et al. Potential Contributions of Pre-Inca Infiltration Infrastructure to Andean Water Security. Nat. Sustain. 2019, 2, 584–593. [Google Scholar] [CrossRef]
  66. Kristanto, Y.; Tarigan, S.; June, T.; Wahjunie, E.D.; Sulistyantara, B. Water Regulation Ecosystem Services of Multifunctional Landscape Dominated by Monoculture Plantations. Land 2022, 11, 818. [Google Scholar] [CrossRef]
  67. Zhang, Z.; Wang, W.; Gong, C.; Zhao, M.; Franssen, H.J.H.; Brunner, P. Salix Psammophila Afforestations Can Cause a Decline of the Water Table, Prevent Groundwater Recharge and Reduce Effective Infiltration. Sci. Total Environ. 2021, 780, 146336. [Google Scholar] [CrossRef]
  68. Liu, X.; He, Y.; Sun, S.; Zhang, T.; Luo, Y.; Zhang, L.; Wang, M.; Cheng, L.; Hu, H.; Xu, Y. Restoration of Sand-Stabilizing Vegetation Reduces Deep Percolation of Precipitation in Semi-Arid Sandy Lands, Northern China. Catena 2022, 208, 105728. [Google Scholar] [CrossRef]
  69. Benegas, L.; Hasselquist, N.; Bargués-Tobella, A.; Malmer, A.; Ilstedt, U. Positive Effects of Scattered Trees on Soil Water Dynamics in a Pasture Landscape in the Tropics. Front. Water 2021, 3, 736824. [Google Scholar] [CrossRef]
  70. Nagdeve, M.; Paul, P.K.; Zhang, Y.; Singh, R. Continuous Contour Trench (CCT): Understandings of Hydrological Processes after Standardisation of Dimensions and Development of a User-Friendly Software. Soil Tillage Res. 2021, 205, 104792. [Google Scholar] [CrossRef]
  71. Bremer, L.L.; Wada, C.A.; Medoff, S.; Page, J.; Falinski, K.; Burnett, K.M. Contributions of Native Forest Protection to Local Water Supplies in East Maui. Sci. Total Environ. 2019, 688, 1422–1432. [Google Scholar] [CrossRef]
  72. Bargués-Tobella, A.; Hasselquist, N.J.; Bazié, H.R.; Bayala, J.; Laudon, H.; Ilstedt, U. Trees in African Drylands Can Promote Deep Soil and Groundwater Recharge in a Future Climate with More Intense Rainfall. Land Degrad. Dev. 2020, 31, 81–95. [Google Scholar] [CrossRef]
  73. Jiménez-Martínez, J.; Candela, L.; Molinero, J.; Tamoh, K. Groundwater Recharge in Irrigated Semi-Arid Areas: Quantitative Hydrological Modelling and Sensitivity Analysis. Hydrogeol. J. 2010, 18, 1811–1824. [Google Scholar] [CrossRef]
  74. Riley, D.; Mieno, T.; Schoengold, K.; Brozović, N. The Impact of Land Cover on Groundwater Recharge in the High Plains: An Application to the Conservation Reserve Program. Sci. Total Environ. 2019, 696, 133871. [Google Scholar] [CrossRef] [PubMed]
  75. Rains, M.C. Water Sources and Hydrodynamics of Closed-Basin Depressions, Cook Inlet Region, Alaska. Wetlands 2011, 31, 377–387. [Google Scholar] [CrossRef]
  76. Adane, Z.A.; Gates, J.B. Determining the Impacts of Experimental Forest Plantation on Groundwater Recharge in the Nebraska Sand Hills (USA) Using Chloride and Sulfate. Hydrogeol. J. 2015, 23, 81–94. [Google Scholar] [CrossRef]
  77. Tuswa, N.; Bugan, R.D.H.; Mapeto, T.; Jovanovic, N.; Gush, M.; Kapangaziwiri, E.; Dzikiti, S.; Kanyerere, T.; Xu, Y. The Impacts of Commercial Plantation Forests on Groundwater Recharge: A Case Study from George (Western Cape, South Africa). Phys. Chem. Earth 2019, 112, 187–199. [Google Scholar] [CrossRef]
  78. Iwasaki, Y.; Nakamura, K.; Horino, H.; Kawashima, S. Assessment of Factors Influencing Groundwater-Level Change Using Groundwater Flow Simulation, Considering Vertical Infiltration from Rice-Planted and Crop-Rotated Paddy Fields in Japan. Hydrogeol. J. 2014, 22, 1841–1855. [Google Scholar] [CrossRef]
  79. Sudmeyer, R.A.; Goodreid, A. Short-Rotation Woody Crops: A Prospective Method for Phytoremediation of Agricultural Land at Risk of Salinisation in Southern Australia? Ecol. Eng. 2007, 29, 350–361. [Google Scholar] [CrossRef]
  80. Clark, B.; DeFries, R.; Krishnaswamy, J. India’s Commitments to Increase Tree and Forest Cover: Consequences for Water Supply and Agriculture Production within the Central Indian Highlands. Water 2021, 13, 959. [Google Scholar] [CrossRef]
  81. Steenbergen, F.; Lawrence, P.; Wallingford, H.R.; Salman, M.; Faurès, J.-M.; Anderson, I.M.; Nawaz, K.; Ratsey, J. Guidelines on Spate Irrigation FAO Irrigation and Drainage; FAO Irrigation and Drainage: Rome, Italy, 2010. [Google Scholar]
  82. Borg, H.; Stoneman, G.; Ward, C. The Effect of Logging and Regeneration on Groundwater, Streamflow and Stream Salinity in the Southern Forest of Western Australia. J. Hydrol. 1988, 99, 253–270. [Google Scholar] [CrossRef]
  83. Schulz, S.; Becker, R.; Richard-Cerda, J.C.; Usman, M.; aus der Beek, T.; Merz, R.; Schüth, C. Estimating Water Balance Components in Irrigated Agriculture Using a Combined Approach of Soil Moisture and Energy Balance Monitoring, and Numerical Modelling. Hydrol. Process. 2021, 35, e14077. [Google Scholar] [CrossRef]
  84. Yifru, B.; Kim, M.G.; Chang, S.W.; Lee, J.; Chung, I.M. Assessment of the Effect of Sand Dam on Groundwater Level: A Case Study in Chuncheon, South Korea. J. Eng. Geol. 2020, 30, 119–129. [Google Scholar] [CrossRef]
  85. Patel, P.M.; Saha, D.; Shah, T. Sustainability of Groundwater through Community-Driven Distributed Recharge: An Analysis of Arguments for Water Scarce Regions of Semi-Arid India. J. Hydrol. Reg. Stud. 2020, 29, 100680. [Google Scholar] [CrossRef]
  86. Cardella Dammeyer, H.; Schwinning, S.; Schwartz, B.F.; Moore, G.W. Effects of Juniper Removal and Rainfall Variation on Tree Transpiration in a Semi-Arid Karst: Evidence of Complex Water Storage Dynamics. Hydrol. Process. 2016, 30, 4568–4581. [Google Scholar] [CrossRef]
  87. Bam, E.K.P.; Ireson, A.M.; van der Kamp, G.; Hendry, J.M. Ephemeral Ponds: Are They the Dominant Source of Depression-Focused Groundwater Recharge? Water Resour. Res. 2020, 56, e2019WR026640. [Google Scholar] [CrossRef]
  88. Xu, Q.; Zhao, K.; Liu, F.; Peng, D.; Chen, W. Effects of Land Use on Groundwater Recharge of a Loess Terrace under Long-Term Irrigation. Sci. Total Environ. 2021, 751, 142340. [Google Scholar] [CrossRef] [PubMed]
  89. Ouyang, Y.; Leininger, T.D.; Panda, S.S.; Zipperer, W.C.; Stroope, T.L. Contributions to Groundwater from National Forest Lands in the Mississippi Embayment: A Century-Long Simulation. Water Pract. Technol. 2021, 16, 83–95. [Google Scholar] [CrossRef]
  90. Soriano, M.A.; Herath, S. Quantifying the Role of Traditional Rice Terraces in Regulating Water Resources: Implications for Management and Conservation Efforts. Agroecol. Sustain. Food Syst. 2018, 42, 885–910. [Google Scholar] [CrossRef]
  91. Silveira, L.; Gamazo, P.; Alonso, J.; Martínez, L. Effects of Afforestation on Groundwater Recharge and Water Budgets in the Western Region of Uruguay. Hydrol. Process. 2016, 30, 3596–3608. [Google Scholar] [CrossRef]
  92. Mattos, T.S.; de Oliveira, P.T.S.; Lucas, M.C.; Wendland, E. Groundwater Recharge Decrease Replacing Pasture by Eucalyptus Plantation. Water 2019, 11, 1213. [Google Scholar] [CrossRef]
  93. Eisma, J.A.; Merwade, V.M. Investigating the Environmental Response to Water Harvesting Structures: A Field Study in Tanzania. Hydrol. Earth Syst. Sci. 2020, 24, 1891–1906. [Google Scholar] [CrossRef]
  94. Grajewski, S.; Miler, A.T.; Okoński, B. Seasonal Variability of Ground Water Levels in the Puszcza Zielonka Forest. J. Water Land Dev. 2014, 21, 55–62. [Google Scholar] [CrossRef]
  95. Schenk, E.R.; O’Donnell, F.; Springer, A.E.; Stevens, L.E. The Impacts of Tree Stand Thinning on Groundwater Recharge in Aridland Forests. Ecol. Eng. 2020, 145, 105701. [Google Scholar] [CrossRef]
  96. Anzai, T.; Kitamura, Y.; Shimizu, K. The Influence of Seepage from Canals and Paddy Fields on the Groundwater Level of Neighboring Rotation Cropping Fields: A Case Study from the Lower Ili River Basin, Kazakhstan. Paddy Water Environ. 2014, 12, 387–392. [Google Scholar] [CrossRef]
  97. Somers, L.D.; McKenzie, J.M.; Zipper, S.C.; Mark, B.G.; Lagos, P.; Baraer, M. Does Hillslope Trenching Enhance Groundwater Recharge and Baseflow in the Peruvian Andes? Hydrol. Process. 2018, 32, 318–331. [Google Scholar] [CrossRef]
  98. Dzikiti, S.; Schachtschneider, K.; Naiken, V.; Gush, M.; Moses, G.; Le Maitre, D.C. Water Relations and the Effects of Clearing Invasive Prosopis Trees on Groundwater in an Arid Environment in the Northern Cape, South Africa. J. Arid. Environ. 2013, 90, 103–113. [Google Scholar] [CrossRef]
  99. Jiao, P.; Hu, S.-J. Estimation of Evapotranspiration in the Desert–Oasis Transition Zone Using the Water Balance Method and Groundwater Level Fluctuation Method—Taking the Haloxylon Ammodendron Forest at the Edge of the Gurbantunggut Desert as an Example. Water 2023, 15, 1210. [Google Scholar] [CrossRef]
  100. Nan, T.; Cao, W. Effect of Ecological Water Supplement on Groundwater Restoration in the Yongding River Based on Multi-Model Linkage. Water 2023, 15, 374. [Google Scholar] [CrossRef]
  101. Benyon, R.G.; Doody, T.M.; Lawson, J.; Hay, A.; Myers, B. Effects of Climate Variability and Change on Groundwater Impacts of Forestry Plantations. Hydrol. Process. 2024, 38, e15213. [Google Scholar] [CrossRef]
  102. Sappa, G.; Vitale, S.; Ferranti, F.; Barbieri, M. Limpopo National Park (Mozambico): Groundwater Assessment as a Tool for a Sustainable Management of the Area. Env. Earth Sci. 2023, 82, 461. [Google Scholar] [CrossRef]
  103. Bali, K.M.; Mohamed, A.Z.; Begna, S.; Wang, D.; Putnam, D.; Dahlke, H.E.; Eltarabily, M.G. The Use of HYDRUS-2D to Simulate Intermittent Agricultural Managed Aquifer Recharge (Ag-MAR) in Alfalfa in the San Joaquin Valley. Agric. Water Manag. 2023, 282, 108296. [Google Scholar] [CrossRef]
  104. Fennell, J.; Soulsby, C.; Wilkinson, M.E.; Daalmans, R.; Geris, J. Assessing the Role of Location and Scale of Nature Based Solutions for the Enhancement of Low Flows. Int. J. River Basin Manag. 2023, 21, 743–758. [Google Scholar] [CrossRef]
  105. Khardi, Y.; Lacombe, G.; Dewandel, B.; Hammani, A.; Taky, A.; Bouarfa, S. Conjunctive Use of Floodwater Harvesting for Managed Aquifer Recharge and Irrigation on a Date Farm in Morocco. Irrig. Drain. 2024, 73, 1424–1436. [Google Scholar] [CrossRef]
  106. Sahin, Y.; Tayfur, G. 3D Modelling of Surface Spreading and Underground Dam Groundwater Recharge: Egri Creek Subbasin, Turkey. Env. Monit. Assess. 2023, 195, 688. [Google Scholar] [CrossRef] [PubMed]
  107. Nawale, S.; Mategaonkar, M. Groundwater Flow Simulation for Artificial Recharge: A GIS, Remote Sensing, and MODFLOW Integration. Water Pract. Technol. 2024, 19, 2136–2157. [Google Scholar] [CrossRef]
  108. Blango, M.M.; Cooke, R.A.C.; Moiwo, J.P.; Sawyerr, P.A.; Kangoma, E. Rainwater Harvesting for Supplemental Irrigation under Tropical Inland Valley Swamp Conditions. Irrig. Drain. 2020, 69, 1095–1105. [Google Scholar] [CrossRef]
  109. Yadav, B.; Patidar, N.; Sharma, A.; Panigrahi, N.; Sharma, R.K.; Loganathan, V.; Krishan, G.; Singh, J.; Kumar, S.; Parker, A. Assessment of Traditional Rainwater Harvesting System in Barren Lands of a Semi-Arid Region: A Case Study of Rajasthan (India). J. Hydrol. Reg. Stud. 2022, 42, 101149. [Google Scholar] [CrossRef]
  110. Wang, W.; Zhao, J.; Duan, L. Simulation of Irrigation-Induced Groundwater Recharge in an Arid Area of China. Hydrogeol. J. 2021, 29, 525–540. [Google Scholar] [CrossRef]
  111. Shamsuddin, M.K.N.; Sulaiman, W.N.A.; Ramli, M.F.; Mohd Kusin, F.; Samuding, K. Assessments of Seasonal Groundwater Recharge and Discharge Using Environmental Stable Isotopes at Lower Muda River Basin, Malaysia. Appl. Water Sci. 2018, 8, 120. [Google Scholar] [CrossRef]
  112. Perera, M.P. Shallow Groundwater Behavior of Tank Cascade Areas in Sri Lanka: A Study Based on Geo-Spatial Technology. In Proceedings of the In Proceedings of the Seventh UGIT International Conference on “Climate Change, Disaster Risk Reduction, and Sustainable Development through Geospatial Technologies” (CDSGeo-2018), Kandy, Sri Lanka, 24 November 2018. [Google Scholar]
  113. Hut, R.; Ertsen, M.; Joeman, N.; Vergeer, N.; Winsemius, H.; van de Giesen, N. Effects of Sand Storage Dams on Groundwater Levels with Examples from Kenya. Phys. Chem. Earth 2008, 33, 56–66. [Google Scholar] [CrossRef]
  114. Dashora, Y.; Dillon, P.; Maheshwari, B.; Soni, P.; Dashora, R.; Davande, S.; Purohit, R.C.; Mittal, H.K. A Simple Method Using Farmers’ Measurements Applied to Estimate Check Dam Recharge in Rajasthan, India. Sustain. Water Resour. Manag. 2018, 4, 301–316. [Google Scholar] [CrossRef]
  115. Ritchie, H.; Eisma, J.A.; Parker, A. Sand Dams as a Potential Solution to Rural Water Security in Drylands: Existing Research and Future Opportunities. Front. Water 2021, 3, 651954. [Google Scholar] [CrossRef]
  116. Yifru, B.A.; Kim, M.G.; Lee, J.W.; Kim, I.H.; Chang, S.W.; Chung, I.M. Water Storage in Dry Riverbeds of Arid and Semi-arid Regions: Overview, Challenges, and Prospects of Sand Dam Technology. Sustainability 2021, 13, 5905. [Google Scholar] [CrossRef]
  117. Eisma, J.A.; Merwade, V. A Data-Driven Approach to Assessing the Impact of Water Harvesting Structures on Regional Water Storage in East Africa. J. Hydroinformatics 2021, 23, 352–367. [Google Scholar] [CrossRef]
  118. Agoramoorthy, G.; Chaudhary, S.; Chinnasamy, P.; Hsu, M.J. Harvesting River Water through Small Dams Promote Positive Environmental Impact. Env. Monit. Assess. 2016, 188, 645. [Google Scholar] [CrossRef]
  119. Lucas-Borja, M.E.; Piton, G.; Yu, Y.; Castillo, C.; Antonio Zema, D. Check Dams Worldwide: Objectives, Functions, Effectiveness and Undesired Effects. CATENA 2021, 204, 105390. [Google Scholar] [CrossRef]
  120. Cooper, R. Nature-Based Solutions and Water Security; Institute of Development Studies: Brighton, UK, 2020. [Google Scholar]
  121. Ilstedt, U.; Bargués Tobella, A.; Bazié, H.R.; Bayala, J.; Verbeeten, E.; Nyberg, G.; Sanou, J.; Benegas, L.; Murdiyarso, D.; Laudon, H.; et al. Intermediate Tree Cover Can Maximize Groundwater Recharge in the Seasonally Dry Tropics. Sci. Rep. 2016, 6, 21930. [Google Scholar] [CrossRef]
  122. Adelana, S.M.; Dresel, P.E.; Hekmeijer, P.; Zydor, H.; Webb, J.A.; Reynolds, M.; Ryan, M. A Comparison of Streamflow, Salt and Water Balances in Adjacent Farmland and Forest Catchments in South-Western Victoria, Australia. Hydrol. Process. 2015, 29, 1630–1643. [Google Scholar] [CrossRef]
  123. Fan, J.; Oestergaard, K.T.; Guyot, A.; Lockington, D.A. Estimating Groundwater Recharge and Evapotranspiration from Water Table Fluctuations under Three Vegetation Covers in a Coastal Sandy Aquifer of Subtropical Australia. J. Hydrol. 2014, 519, 1120–1129. [Google Scholar] [CrossRef]
  124. van Dijk, A.I.J.M.; Hairsine, P.B.; Arancibia, J.P.; Dowling, T.I. Reforestation, Water Availability and Stream Salinity: A Multi-Scale Analysis in the Murray-Darling Basin, Australia. For. Ecol. Manag. 2007, 251, 94–109. [Google Scholar] [CrossRef]
  125. Chausson, A.; Turner, B.; Seddon, D.; Chabaneix, N.; Girardin, C.A.J.; Kapos, V.; Key, I.; Roe, D.; Smith, A.; Woroniecki, S.; et al. Mapping the Effectiveness of Nature-Based Solutions for Climate Change Adaptation. Glob. Change Biol. 2020, 26, 6134–6155. [Google Scholar] [CrossRef]
  126. Gujja, B.; Dalai, S.; Shaik, H.; Goud, V. Adapting to Climate Change in the Godavari River Basin of India by Restoring Traditional Water Storage Systems. Clim. Dev. 2009, 1, 229–240. [Google Scholar] [CrossRef]
  127. Staes, J.; Rubarenzya, M.H.; Meire, P.; Willems, P. Modelling Hydrological Effects of Wetland Restoration: A Differentiated View. Water Sci. Technol. 2009, 59, 433–441. [Google Scholar] [CrossRef] [PubMed]
  128. Gratzer, M.C.; Davidson, G.R.; O’Reilly, A.M.; Rigby, J.R. Groundwater Recharge from an Oxbow Lake-Wetland System in the Mississippi Alluvial Plain. Hydrol. Process. 2020, 34, 1359–1370. [Google Scholar] [CrossRef]
  129. Yin, D.; Chen, Y.; Jia, H.; Wang, Q.; Chen, Z.; Xu, C.; Li, Q.; Wang, W.; Yang, Y.; Fu, G.; et al. Sponge City Practice in China: A Review of Construction, Assessment, Operational and Maintenance. J. Clean. Prod. 2021, 280, 124963. [Google Scholar] [CrossRef]
  130. Bullock, A.; Acreman, M. The Role of Wetlands in the Hydrological Cycle. Hydrol. Earth Syst. Sci. 2003, 7, 358–389. [Google Scholar] [CrossRef]
  131. Acharya, G.; Barbier, E.B. Valuing Groundwater Recharge through Agricultural Production in the Hadejia-Nguru Wetlands in Northern Nigeria. Agric. Econ. 2000, 22, 247–259. [Google Scholar] [CrossRef]
  132. van der Kamp, G.; Hayashi, M. The Groundwater Recharge Function of Small Wetlands in the Semi-Arid Northern Prairies. Great Plains Res. 1998, 8, 39–56. [Google Scholar]
  133. Thorslund, J.; Jarsjo, J.; Jaramillo, F.; Jawitz, J.W.; Manzoni, S.; Basu, N.B.; Chalov, S.R.; Cohen, M.J.; Creed, I.F.; Goldenberg, R.; et al. Wetlands as Large-Scale Nature-Based Solutions: Status and Challenges for Research, Engineering and Management. Ecol. Eng. 2017, 108, 489–497. [Google Scholar] [CrossRef]
  134. Scanlon, B.R.; Reedy, R.C.; Stonestrom, D.A.; Prudic, D.E.; Dennehy, K.F. Impact of Land Use and Land Cover Change on Groundwater Recharge and Quality in the Southwestern US. Glob. Change Biol. 2005, 11, 1577–1593. [Google Scholar] [CrossRef]
  135. Chinnasamy, P.; Srivastava, A. Revival of Traditional Cascade Tanks for Achieving Climate Resilience in Drylands of South India. Front. Water 2021, 3, 639637. [Google Scholar] [CrossRef]
  136. Dakhlalla, A.O.; Parajuli, P.B.; Ouyang, Y.; Schmitz, D.W. Evaluating the Impacts of Crop Rotations on Groundwater Storage and Recharge in an Agricultural Watershed. Agric. Water Manag. 2016, 163, 332–343. [Google Scholar] [CrossRef]
  137. Leduc, C.; Favreau, G.; Schroeter, P. Long-Term Rise in a Sahelian Water-Table: The Continental Terminal in South-West Niger. J. Hydrol. 2001, 243, 43–54. [Google Scholar] [CrossRef]
  138. Boumis, G.; Kumar, M.; Nimmo, J.R.; Clement, T.P. Influence of Shallow Groundwater Evapotranspiration on Recharge Estimation Using the Water Table Fluctuation Method. Water Resour. Res. 2022, 58, e2022WR032073. [Google Scholar] [CrossRef]
  139. Crosbie, R.S.; Doble, R.C.; Turnadge, C.; Taylor, A.R. Constraining the Magnitude and Uncertainty of Specific Yield for Use in the Water Table Fluctuation Method of Estimating Recharge. Water Resour. Res. 2019, 55, 7343–7361. [Google Scholar] [CrossRef]
  140. Liu, G.; Wilson, B.B.; Bohling, G.C.; Whittemore, D.O.; Butler Jr, J.J. Estimation of Specific Yield for Regional Groundwater Models: Pitfalls, Ramifications, and a Promising Path Forward. Water Resour. Res. 2022, 58, e2021WR030761. [Google Scholar] [CrossRef]
  141. Silvestro, F.; Gabellani, S.; Rudari, R.; Delogu, F.; Laiolo, P.; Boni, G. Uncertainty Reduction and Parameter Estimation of a Distributed Hydrological Model with Ground and Remote-Sensing Data. Hydrol. Earth Syst. Sci. 2015, 19, 1727–1751. [Google Scholar] [CrossRef]
  142. Stisen, S.; McCabe, M.F.; Refsgaard, J.C.; Lerer, S.; Butts, M.B. Model Parameter Analysis Using Remotely Sensed Pattern Information in a Multi-Constraint Framework. J. Hydrol. 2011, 409, 337–349. [Google Scholar] [CrossRef]
  143. Cerlini, P.B.; Silvestri, L.; Meniconi, S.; Brunone, B. Simulation of the Water Table Elevation in Shallow Unconfined Aquifers by Means of the ERA5 Soil Moisture Dataset: The Umbria Region Case Study. Earth Interact. 2021, 25, 15–32. [Google Scholar] [CrossRef]
  144. Rossman, N.R.; Zlotnik, V.A.; Rowe, C.M. Simulating Lake and Wetland Areal Coverage under Future Groundwater Recharge Projections: The Nebraska Sand Hills System. J. Hydrol. 2019, 576, 185–196. [Google Scholar] [CrossRef]
  145. Liu, Y.; Kumar, M. Role of Meteorological Controls on Interannual Variations in Wet-Period Characteristics of Wetlands. Water Resour. Res. 2016, 52, 5056–5074. [Google Scholar] [CrossRef]
  146. Wang, D.; Liu, Y.; Kumar, M. Using Nested Discretization for a Detailed yet Computationally Efficient Simulation of Local Hydrology in a Distributed Hydrologic Model. Sci. Rep. 2018, 8, 5785. [Google Scholar] [CrossRef]
  147. Park, J.; Kumar, M.; Lane, C.R.; Basu, N.B. Seasonality of Inundation in Geographically Isolated Wetlands across the United States. Environ. Res. Lett. 2022, 17, 054005. [Google Scholar] [CrossRef] [PubMed]
  148. Havril, T.; Tóth, Á.; Molson, J.W.; Galsa, A.; Mádl-Szőnyi, J. Impacts of Predicted Climate Change on Groundwater Flow Systems: Can Wetlands Disappear Due to Recharge Reduction? J. Hydrol. 2018, 563, 1169–1180. [Google Scholar] [CrossRef]
  149. Zhu, J.; Sun, G.; Li, W.; Zhang, Y.; Miao, G.; Noormets, A.; McNulty, S.G.; King, J.S.; Kumar, M.; Wang, X. Modeling the Potential Impacts of Climate Change on the Water Table Level of Selected Forested Wetlands in the Southeastern United States. Hydrol. Earth Syst. Sci. 2017, 21, 6289–6305. [Google Scholar] [CrossRef]
  150. Wang, M.; Wang, H.; Qin, D.; Lu, C.; Li, Y. Modelling the Artificial Recharge of a Wetland and Its Influence on Regional Hydrological Process in China: A Case Study. Ecohydrology 2011, 4, 589–596. [Google Scholar] [CrossRef]
  151. Haan, C. Parametric Uncertainty in Hydrologic Modeling. Trans. ASAE 1989, 32, 137–0146. [Google Scholar] [CrossRef]
  152. Gupta, A.; Govindaraju, R.S. Propagation of Structural Uncertainty in Watershed Hydrologic Models. J. Hydrol. 2019, 575, 66–81. [Google Scholar] [CrossRef]
  153. Parasuraman, K.; Elshorbagy, A. Toward Improving the Reliability of Hydrologic Prediction: Model Structure Uncertainty and Its Quantification Using Ensemble-Based Genetic Programming Framework. Water Resour. Res. 2008, 44. [Google Scholar] [CrossRef]
  154. McMillan, H.K.; Westerberg, I.K.; Krueger, T. Hydrological Data Uncertainty and Its Implications. WIREs Water 2018, 5, e1319. [Google Scholar] [CrossRef]
  155. Kumar, M.; Duffy, C. Exploring the Role of Domain Partitioning on Efficiency of Parallel Distributed Hydrologic Model Simulations. J. Hydrogeol. Hydrol. Eng. 2015, 12, 2. [Google Scholar]
  156. Kollet, S.J.; Maxwell, R.M.; Woodward, C.S.; Smith, S.; Vanderborght, J.; Vereecken, H.; Simmer, C. Proof of Concept of Regional Scale Hydrologic Simulations at Hydrologic Resolution Utilizing Massively Parallel Computer Resources. Water Resour. Res. 2010, 46. [Google Scholar] [CrossRef]
  157. Scanlon, B.R.; Healy, R.W.; Cook, P.G. Choosing Appropriate Techniques for Quantifying Groundwater Recharge. Hydrogeol. J. 2002, 10, 18–39. [Google Scholar] [CrossRef]
  158. Allison, G.B.; Hughes, M.W. The Use of Environmental Chloride and Tritium to Estimate Total Recharge to an Unconfined Aquifer. Soil Res. 1978, 16, 181–195. [Google Scholar] [CrossRef]
  159. Reichert, J.M.; Rodrigues, M.F.; Peláez, J.J.Z.; Lanza, R.; Minella, J.P.G.; Arnold, J.G.; Cavalcante, R.B.L. Water Balance in Paired Watersheds with Eucalyptus and Degraded Grassland in Pampa Biome. Agric. For. Meteorol. 2017, 237–238, 282–295. [Google Scholar] [CrossRef]
  160. Rodrigues Capítulo, L.; Carretero, S.C.; Kruse, E.E. Impact of Afforestation on Coastal Aquifer Recharge. Case Study: Eastern Coast of the Province of Buenos Aires, Argentina. Environ. Earth Sci. 2018, 77, 74. [Google Scholar] [CrossRef]
  161. Krishnaswamy, J.; Bonell, M.; Venkatesh, B.; Purandara, B.K.; Rakesh, K.N.; Lele, S.; Kiran, M.C.; Reddy, V.; Badiger, S. The Groundwater Recharge Response and Hydrologic Services of Tropical Humid Forest Ecosystems to Use and Reforestation: Support for the “Infiltration-Evapotranspiration Trade-off Hypothesis”. J. Hydrol. 2013, 498, 191–209. [Google Scholar] [CrossRef]
  162. Kumar, P.; Debele, S.E.; Sahani, J.; Rawat, N.; Marti-Cardona, B.; Alfieri, S.M.; Basu, B.; Basu, A.S.; Bowyer, P.; Charizopoulos, N.; et al. An Overview of Monitoring Methods for Assessing the Performance of Nature-Based Solutions against Natural Hazards. Earth-Sci. Rev. 2021, 217, 103603. [Google Scholar] [CrossRef]
  163. Jaafarzadeh, M.S.; Tahmasebipour, N.; Haghizadeh, A.; Pourghasemi, H.R.; Rouhani, H. Groundwater Recharge Potential Zonation Using an Ensemble of Machine Learning and Bivariate Statistical Models. Sci. Rep. 2021, 11, 5587. [Google Scholar] [CrossRef]
  164. Crosbie, R.S.; Pickett, T.; Mpelasoka, F.S.; Hodgson, G.; Charles, S.P.; Barron, O.V. An Assessment of the Climate Change Impacts on Groundwater Recharge at a Continental Scale Using a Probabilistic Approach with an Ensemble of GCMs. Clim. Change 2013, 117, 41–53. [Google Scholar] [CrossRef]
  165. Reinecke, R.; Müller Schmied, H.; Trautmann, T.; Andersen, L.S.; Burek, P.; Flörke, M.; Gosling, S.N.; Grillakis, M.; Hanasaki, N.; Koutroulis, A.; et al. Uncertainty of Simulated Groundwater Recharge at Different Global Warming Levels: A Global-Scale Multi-Model Ensemble Study. Hydrol. Earth Syst. Sci. 2021, 25, 787–810. [Google Scholar] [CrossRef]
  166. Molina, J.-L.; Pulido-Velázquez, D.; García-Aróstegui, J.L.; Pulido-Velázquez, M. Dynamic Bayesian Networks as a Decision Support Tool for Assessing Climate Change Impacts on Highly Stressed Groundwater Systems. J. Hydrol. 2013, 479, 113–129. [Google Scholar] [CrossRef]
  167. Manzione, R.L.; Castrignanò, A. A Geostatistical Approach for Multi-Source Data Fusion to Predict Water Table Depth. Sci. Total Environ. 2019, 696, 133763. [Google Scholar] [CrossRef] [PubMed]
  168. Kumar, M.; Bhatt, G.; Duffy, C.J. An Efficient Domain Decomposition Framework for Accurate Representation of Geodata in Distributed Hydrologic Models. Int. J. Geogr. Inf. Sci. 2009, 23, 1569–1596. [Google Scholar] [CrossRef]
  169. Hublart, P.; Ruelland, D.; Dezetter, A.; Jourde, H. Reducing Structural Uncertainty in Conceptual Hydrological Modelling in the Semi-Arid Andes. Hydrol. Earth Syst. Sci. 2015, 19, 2295–2314. [Google Scholar] [CrossRef]
  170. Saavedra, D.; Mendoza, P.A.; Addor, N.; Llauca, H.; Vargas, X. A Multi-Objective Approach to Select Hydrological Models and Constrain Structural Uncertainties for Climate Impact Assessments. Hydrol. Process. 2022, 36, e14446. [Google Scholar] [CrossRef]
  171. Panagopoulos, Y.; Dimitriou, E. A Large-Scale Nature-Based Solution in Agriculture for Sustainable Water Management: The Lake Karla Case. Sustainability 2020, 12, 6761. [Google Scholar] [CrossRef]
  172. Martire, S.; Enyedi, E.; Breil, M.; Budding-Polo, M.; Ballinas, D.Z.; Tonks, E.; Vikstrom, S.; Turunen, V. Understanding the Scaling Potential of Nature-Based Solutions; PublisherThe European Topic Centre on Climate change adaptation and LULUCF (ETC-CA): Amsterdam, The Netherlands, 2022. [Google Scholar]
  173. Fennell, J.; Soulsby, C.; Wilkinson, M.E.; Daalmans, R.; Geris, J. Time Variable Effectiveness and Cost-Benefits of Different Nature-Based Solution Types and Design for Drought and Flood Management. Nat.-Based Solut. 2023, 3, 100050. [Google Scholar] [CrossRef]
  174. Qiu, Y.; Schertzer, D.; Tchiguirinskaia, I. Assessing Cost-Effectiveness of Nature-Based Solutions Scenarios: Integrating Hydrological Impacts and Life Cycle Costs. J. Clean. Prod. 2021, 329, 129740. [Google Scholar] [CrossRef]
  175. Maliva, R.G. Economics of Managed Aquifer Recharge. Water 2014, 6, 1257–1279. [Google Scholar] [CrossRef]
  176. Gómez Martín, E.; Máñez Costa, M.; Egerer, S.; Schneider, U.A. Assessing the Long-Term Effectiveness of Nature-Based Solutions under Different Climate Change Scenarios. Sci. Total Environ. 2021, 794, 148515. [Google Scholar] [CrossRef]
  177. Nika, C.E.; Gusmaroli, L.; Ghafourian, M.; Atanasova, N.; Buttiglieri, G.; Katsou, E. Nature-Based Solutions as Enablers of Circularity in Water Systems: A Review on Assessment Methodologies, Tools and Indicators. Water Res. 2020, 183, 115988. [Google Scholar] [CrossRef]
Figure 1. Cumulative occurrence of phrases in keyword, title and abstract of the total identified articles. The most frequently used search terms are plotted here.
Figure 1. Cumulative occurrence of phrases in keyword, title and abstract of the total identified articles. The most frequently used search terms are plotted here.
Hydrology 11 00195 g001
Figure 2. PRISMA flow diagram describing the different phases of the literature review.
Figure 2. PRISMA flow diagram describing the different phases of the literature review.
Hydrology 11 00195 g002
Figure 3. Spatial map of the study areas with the number of reviewed papers per country.
Figure 3. Spatial map of the study areas with the number of reviewed papers per country.
Hydrology 11 00195 g003
Figure 4. Mean effectiveness measurements of groundwater recharge from NbSs: (a) Annual groundwater recharge rate; (b) groundwater recharge to precipitation proportion in percentage. The x-axis labels in (a,b) correspond to the NbS considered in the reviewed studies. Forest category includes afforestation, forest restoration, forest management and tree cutting and thinning. Trench includes hillslope and contour trenching methods. The vegetation category corresponds to sand-stabilizing vegetation implementation. The labels of the data points in the box plots correspond to the citation of the studies being considered [64,65,66,68,69,71,72,77,81,85,89,93,98,117,122,125].
Figure 4. Mean effectiveness measurements of groundwater recharge from NbSs: (a) Annual groundwater recharge rate; (b) groundwater recharge to precipitation proportion in percentage. The x-axis labels in (a,b) correspond to the NbS considered in the reviewed studies. Forest category includes afforestation, forest restoration, forest management and tree cutting and thinning. Trench includes hillslope and contour trenching methods. The vegetation category corresponds to sand-stabilizing vegetation implementation. The labels of the data points in the box plots correspond to the citation of the studies being considered [64,65,66,68,69,71,72,77,81,85,89,93,98,117,122,125].
Hydrology 11 00195 g004
Table 1. Papers reviewed in this study. Also noted are the NbS types (as discussed in Section 3), their impact on recharge and how the effectiveness was quantified.
Table 1. Papers reviewed in this study. Also noted are the NbS types (as discussed in Section 3), their impact on recharge and how the effectiveness was quantified.
NbS TypeCountryHydrogeology/HydrologyNbS’s Main TargetRechargeEffect Measurement MethodYearSource
Sand damKenyaArid with erratic rainfalllivestock, irrigation ImprovedGroundwater level comparison2013[64]
Induced bank filtration SloveniaRiver with stable flow throughout the yearGroundwater rechargeImproved Groundwater level comparison2020[59]
UTFIIndiaAlluvial aquiferFlood controlImprovedRelative mound height 2020[43]
Infiltration pondIranOverexploited aquiferMitigating drawdownImprovedMass balance/field measurement 2004[49]
Infiltration systemPeruArid coastal regionRunoff harvestingImprovedTracer experiments 2019[65]
UTFIIndia Flood control and rechargeImprovedHydrologic simulation 2018[45]
Forest patchesIndonesiaTropical lowland/sandy clay loam soil Ecosystem regulationImprovedHydrological model (SWAT)2022[66]
TreesChinaSemi-aridDesertification preventionReducedField measurement/lysimeters2021[67]
Dune vegetationChina Semi-arid region with deep aquiferErosion controlReducedField measurement/lysimeters2022[68]
TreesGuatemalaTropical forestry pasture/scattered native treesErosion controlImprovedIsotopic measurement2021[69]
Infiltration pondIranAlluvial aquifer in arid regionFlood controlImprovedObserved data from wells2021[47]
Paddy fieldJapanConfined alluvial aquiferImproving farming systemImprovedField measurement2013[48]
Infiltration pondNetherlandsCoastal aquifer in dune areaSalt intrusion reductionImprovedInsights of the scheme operators2021[42]
Continuous contour trenchIndiaSemi-arid watershedSoil conservationImprovedHydrological modeling 2021[70]
TreesUSA Conserving natural forestImprovedWater balance2019[71]
TreesBurkina FasoSemi-arid dryland with high rainfall conditionAgroforestryImprovedIsotropic analysis2020[72]
Crop rotationSpainSemi-arid regionSoil fertilityImprovedModeling/Water balance 2010[73]
GrasslandUSAUnconfined aquiferSoil erosion controlReducedWater table fluctuation (WTF)2019[74]
Ponds USAIntrusive rocks with moraine deposits Conservation measureImprovedWater balance2011[75]
TreeUSA AfforestationDeclinedChemical tracers2015[76]
TreeSouth AfricaSandstone/shale rock aquifer in subtropics Conserving natural forestsImprovedModeling using Hydrus 2D2019[77]
Crop rotationJapanUpland crop-rotated paddy fields/sandy aquiferImproving farming systemDeclinedModeling using Hydrus 1D2014[78]
Phase farming with treesAustraliaMediterranean/precipitation in growing seasonsImproving farming systemDeclinedNumerical model2006[79]
UTFIThailandShallow alluvial aquiferFlood controlImprovedLong-term measurement2012[44]
Trees IndiaHighlands agro-ecological zoneAfforestationVariableField measurement/modeling2021[80]
Infiltration pondSpainAlluvial aquifer prone to salt intrusionGroundwater rechargeLow rateERT survey2020[52]
Bank filtrationItalySand/gravel aquifer Groundwater rechargeImprovedNumerical modeling2020[60]
Spate irrigationYemenUnconfined, coastal quaternary aquifersIrrigationImprovedWater balance2010[81]
TreesAustralia Logging and regeneration VariableGroundwater level1988[82]
Crop rotationPakistanArid regionRegenerative farmingImprovedEnergy balance/modeling2021[83]
Sand damsSouth Korea Storage of ephemeral riverImprovedNumerical model (MODFLOW)2020[84]
MARIndiaSemi-arid region with hard rock basalt aquiferGroundwater rechargeImprovedWater table fluctuation 2020[85]
ForestUSAKarst aquiferForest managementImprovedIsotopic analysis2016[86]
WetlandsCanadaPrairie regionsEco-change trackingImprovedIsotropic analysis 2020[87]
Terraces flood irrigationChinaLoess area in a semi-arid region Erosion controlImprovedERT survey, field measurement 2021[88]
National forestsUSAMixed hydrogeological unitConservation measureVariable Hydrologic simulations2021[89]
Traditional rice terracesPhilippinesWet region with high annual precipitationSustainable farmingImprovedLong-term field measurements2018[90]
AfforestationUruguayTemperate region with shallow aquiferForest expansionVariableHydrograph separation/WTF2016[91]
Eucalyptus PlantationBrazilUnconfined aquifer with sandstone layersForest expansionDeclinedWater table fluctuation2019[92]
Sand damsTanzaniaSemi-arid regionGroundwater rechargeImprovedGroundwater level2020[93]
ForestPolandClayey sand aquifer in a compacted forest areaConservation measureVariableWater table fluctuation2014[94]
Forest managementUSAArid land forestsWildfire suppressionImprovedProcess and statistical modeling2020[95]
Paddy fields/crop rotationKazakhstanArid regionImproved irrigation ImprovedGroundwater table fluctuation2014[96]
Hillslope trenchPeruSteep alpine grassland with seasonal rainfallGlacier retreat adaptationImprovedHydrological models2018[97]
TreesSouth AfricaAlluvium aquifer in arid regionConservation measuresDeclinedGroundwater level2013[98]
ForestChinaTemperate desert, desert–oasis transition zoneConservation measuresDeclinedWater balance/measurement 2023[99]
MARChinaPhreatic aquifer with coarse gravel soilGroundwater rechargeImprovedProcess- and data-based models2023[100]
Forestry plantation AustraliaUndulating coastal plain of marine originConservation measuresReducedEmpirical models2024[101]
Biodiversity conservation MozambiqueGently undulating terrain with tributary streamsConservation measuresImprovedHydrogeological inverse budget2023[102]
MARUSAAlluvium floodplain with well-drained soilGroundwater rechargeImprovedNumerical modeling (Hydrus)2023[103]
Flow path interception UKPoorly draining soil with lower water storage Runoff attenuationImprovedHydrological mode (MIKE-SHE)2022[104]
MARMoroccoAlluvium and underlying fractured aquifers with rare but intense rainfallGroundwater recharge and floodwater harvestingImprovedMeasurement of piezometric variations in groundwater table2024[105]
Underground damTurkeyAlluvium aquiferGroundwater rechargeImprovedNumerical modeling 2023[106]
MARIndiaBasaltic and alluvium aquiferGroundwater rechargeImprovedNumerical modeling 2024[107]
In-channel modificationSierra LeoneInland valley swampRainwater harvestingImprovedField measurement2020[108]
MARIndiaSemi-arid region with sandy/loamy soilGroundwater rechargeImprovedNumerical modeling 2022[109]
Flood irrigationChinaClosed-drainage depression in arid regionGroundwater rechargeImprovedNumerical modeling 2021[110]
WetlandAustraliaSemi-arid and suburban areaStormwater managementImprovedIsotopic measurements2017[36]
Riverbank filtrationMalaysiaShallow alluvial aquiferGroundwater rechargeImprovedIsotopic measurement 2018[111]
Cascade tanks Sri LankaAlluvium aquifer on weathered bedrock Storing runoffImprovedWater table fluctuation2018[112]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kebede, M.M.; Kumar, M.; Mekonnen, M.M.; Clement, T.P. Enhancing Groundwater Recharge Through Nature-Based Solutions: Benefits and Barriers. Hydrology 2024, 11, 195. https://doi.org/10.3390/hydrology11110195

AMA Style

Kebede MM, Kumar M, Mekonnen MM, Clement TP. Enhancing Groundwater Recharge Through Nature-Based Solutions: Benefits and Barriers. Hydrology. 2024; 11(11):195. https://doi.org/10.3390/hydrology11110195

Chicago/Turabian Style

Kebede, Mahlet M., Mukesh Kumar, Mesfin M. Mekonnen, and T. Prabhakar Clement. 2024. "Enhancing Groundwater Recharge Through Nature-Based Solutions: Benefits and Barriers" Hydrology 11, no. 11: 195. https://doi.org/10.3390/hydrology11110195

APA Style

Kebede, M. M., Kumar, M., Mekonnen, M. M., & Clement, T. P. (2024). Enhancing Groundwater Recharge Through Nature-Based Solutions: Benefits and Barriers. Hydrology, 11(11), 195. https://doi.org/10.3390/hydrology11110195

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop