Groundwater discharge drives water quality and greenhouse gas emissions in a tidal wetland

Wetlands play an important role in the global carbon cycle as they can be sources or sinks for greenhouse gases. Groundwater discharge into wetlands can affect the water chemistry and act as a source of dissolved greenhouse gases, including CO2 and CH4. In this study, surface water quality parameters and CO2 and CH4 concentrations were evaluated in a tidal wetland (Hunter Wetlands National Park, Australia) using time series measurements. Radon (222Rn), a natural groundwater tracer, was used to investigate the role of groundwater as a pathway for transporting dissolved CO2 and CH4 into the wetland. In addition, water-to-air CO2 and CH4 fluxes from the wetland were also estimated. The results showed a high concentration of radon in wetland surface water, indicating the occurrence of groundwater discharge. Radon concentration had a strong negative relationship with water depth with a determination coefficient (R2) of 0.7, indicating that tidal pumping was the main driver of groundwater discharge to the wetland. Radon concentration also showed a positive relationship with CO2 and CH4 concentrations (R2 = 0.4 and 0.5, respectively), while the time series data revealed that radon, CO2, and CH4 concentrations peaked concurrently during low tides. This implied that groundwater discharge was a source of CO2 and CH4 to the wetland. The wetland had an average water-to-air CO2 flux of 99.1 mmol/(m2·d), twice higher than the global average CO2 flux from wetlands. The average CH4 flux from the wetland was estimated to be 0.3 mmol/(m2·d), which is at the higher end of the global CH4 flux range for wetlands. The results showed that groundwater discharge could be an important, yet unaccounted source of CO2 and CH4 to tidal wetlands. This work has implications for tidal wetland carbon budgets and emphasizes the role of groundwater as a subsurface pathway for carbon transport

Blue carbon ecosystem monitoring using remote sensing reveals wetland restoration pathways

In an era of climate and biodiversity crises, ecosystem rehabilitation is critical to the ongoing wellbeing of humans and the environment. Coastal ecosystem rehabilitation is particularly important, as these ecosystems sequester large quantities of carbon (known in marine ecosystems as “blue carbon”) thereby mitigating climate change effects while also providing ecosystem services and biodiversity benefits. The recent formal accreditation of blue carbon services is producing a proliferation of rehabilitation projects, which must be monitored and quantified over time and space to assess on-ground outcomes. Consequently, remote sensing techniques such as drone surveys, and machine learning techniques such as image classification, are increasingly being employed to monitor wetlands. However, few projects, if any, have tracked blue carbon restoration across temporal and spatial scales at an accuracy that could be used to adequately map species establishment with low-cost methods. This study presents an open-source, user-friendly workflow, using object-based image classification and a random forest classifier in Google Earth Engine, to accurately classify 4 years of multispectral and photogrammetrically derived digital elevation model drone data at a saltmarsh rehabilitation site on the east coast of Australia (Hunter River estuary, NSW). High classification accuracies were achieved, with >90% accuracy at 0.1 m resolution. At the study site, saltmarsh colonised most suitable areas, increasing by 142% and resulting in 56 tonnes of carbon sequestered, within a 4-year period, providing insight into blue carbon regeneration trajectories. Saltmarsh growth patterns were species-specific, influenced by species’ reproductive and dispersal strategies. Our findings suggested that biotic factors and interactions were important in influencing species’ distributions and succession trajectories. This work can help improve the efficiency and effectiveness of restoration planning and monitoring at coastal wetlands and similar ecosystems worldwide, with the potential to apply this approach to other types of remote sensing imagery and to calculate other rehabilitation co-benefits. Importantly, the method can be used to calculate blue carbon habitat creation following tidal restoration of coastal wetlands

Optimal reservoir operation using Nash bargaining solution and evolutionary algorithms

Optimizing reservoir operation is critical to ongoing sustainable water resources management. However, different stakeholders in reservoir management often have different interests and resource competition may provoke conflicts. Resource competition warrants the use of bargaining solution approaches to develop an optimal operational scheme. In this study, the Nash bargaining solution method was used to formulate an objective function for water allocation in a reservoir. Additionally, the genetic and ant colony optimization algorithms were used to achieve optimal solutions of the objective function. The Mahabad Dam in West Azerbaijan, Iran, was used as a case study site due to its complex water allocation requirements for multiple stakeholders, including agricultural, domestic, industrial, and environmental sectors. The relative weights of different sectors in the objective function were determined using a discrete kernel based on the priorities stipulated by the government (the Lake Urmia National Restoration Program). According to the policies for the agricultural sector, water allocation optimization for different sectors was carried out using three scenarios: (1) the current situation, (2) optimization of the cultivation pattern, and (3) changes to the irrigation system. The results showed that the objective function and the Nash bargaining solution method led to a water utility for all stakeholders of 98%. Furthermore, the two optimization algorithms were used to achieve the global optimal solution of the objective function, and reduced the failure of the domestic sector by 10% while meeting the required objective in water-limited periods. As the conflicts among stakeholders may become more common with a changing climate and an increase in water demand, these results have implications for reservoir operation and associated policies

Innovative Tidal Control Successfully Promotes Saltmarsh Restoration

The reduction of saltmarsh habitat at a global scale has seen a concomitant loss of associated ecosystem services. As such, there is a need and a push for habitat rehabilitation. This study examined an innovative saltmarsh restoration project in Australia which sought to address the threats of mangrove encroachment and sea level rise. The project was implemented in 2017, using automated hydraulic control gates, termed“SmartGates,”to lower the tidal regime over one site, effectively reversing sea level rise at a local level. Measured indicators of saltmarsh cover, number of species, seedling counts, and saltmarsh assemblages all showed significant positive development over time, with trends varying based on saltmarsh zone. The saltmarsh, predominantly Sarcocornia quinque flora, developed from remnant supralittoral (previously high) marsh which remained at 45% cover to achieve over 15% coverage across the cleared habitat after 3 years. Slower development in the low marsh (\u3c5%) compared to other zones contrasts with other saltmarsh restoration studies which may be due to the unique nature of the restoration method or the nature of Australian saltmarsh species which favor higher elevations and drier conditions. The development of saltmarsh at the treatment site was found to track toward that at comparison sites over time, becoming similar to some comparison sites by the studies end. This study highlights the usefulness of the novel restoration method used and of the measured indicators for assessing saltmarsh development. This innovative tidal control method could play an important role in the future of saltmarsh restoration worldwide

Coastal wetlands can be saved from sea level rise by recreating past tidal regimes

Climate change driven Sea Level Rise (SLR) is creating a major global environmental crisis in coastal ecosystems, however, limited practical solutions are provided to prevent or mitigate the impacts. Here, we propose a novel eco-engineering solution to protect highly valued vegetated intertidal ecosystems. The new ‘Tidal Replicate Method’ involves the creation of a synthetic tidal regime that mimics the desired hydroperiod for intertidal wetlands. This synthetic tidal regime can then be applied via automated tidal control systems, “SmartGates”, at suitable locations. As a proof of concept study, this method was applied at an intertidal wetland with the aim of restabilising saltmarsh vegetation at a location representative of SLR. Results from aerial drone surveys and on-ground vegetation sampling indicated that the Tidal Replicate Method effectively established saltmarsh onsite over a 3-year period of post-restoration, showing the method is able to protect endangered intertidal ecosystems from submersion. If applied globally, this method can protect high value coastal wetlands with similar environmental settings, including over 1,184,000 ha of Ramsar coastal wetlands. This equates to a saving of US$230 billion in ecosystem services per year. This solution can play an important role in the global effort to conserve coastal wetlands under accelerating SLR

The evolving landscape of sea-level rise science from 1990 to 2021

As sea-level rise (SLR) accelerates due to climate change, its multidisciplinary field of science has similarly expanded, from 41 articles published in 1990 to 1475 articles published in 2021, and nearly 15,000 articles published in the Web of Science over this 32-year period. Here, big-data bibliometric techniques are adopted to systematically analyse this large literature set. Four main research clusters (themes) emerge: (I) geological dimensions and sea-level indicators, (II) impacts, risks, and adaptation, (III) physical components of sea-level change, and (IV) coastal ecosystems and habitats, with 16 associated sub-themes. This analysis provides insights into the evolution of research agendas, the challenges and opportunities for future assessments (e.g. next IPCC reports), and growing focus on adaptation. For example, the relative importance of sub-themes evolves consistently with a relative decline in pure science analysis towards solution-focused topics associated with SLR risks such as high-end rises, declining ecosystem services, flood hazards, and coastal erosion/squeeze

The influence of submarine groundwater discharge on carbon, nutrient and greenhouse gases dynamics in coastal waters

This thesis documents the use of mass balance modelling combined with a multi-tracer approach, to quantify, interpret and manage coastal groundwater discharge. The dynamic characteristics of groundwater discharge requires intensive and multifaceted sampling strategies to constrain its effects on surface water quality. This thesis highlights that dissolved carbon, greenhouse gases and nutrient export from estuaries are strongly coupled to groundwater discharge therefore, should not be neglected in coastal carbon and nutrient budget studies. Results presented here indicate that even small volumetric groundwater discharge fluxes occurring on large scales play a major role in the hydrology and biogeochemistry of coastal ecosystems

Groundwater vulnerability assessment in agricultural areas using a modified DRASTIC model

Groundwater contamination is a major concern for groundwater resource managers worldwide. We evaluated groundwater pollution potential by producing a vulnerability map of an aquifer using a modified Depth to water, Net recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and Hydraulic conductivity (DRASTIC) model within a Geographic Information System (GIS) environment. The proposed modification which incorporated the use of statistical techniques optimizes the rating function of the DRASTIC model parameters, to obtain a more accurate vulnerability map. The new rates were computed using the relationships between the parameters and point data chloride concentrations in groundwater. The model was applied on Saveh-Nobaran plain in central Iran, and results showed that the coefficient of determination (R2) between the point data and the relevant vulnerability map increased significantly from 0.52 to 0.78 after modification. As compared to the original DRASTIC model, the modified version produced better vulnerability zonation. Additionally, single-parameter and parameter removal sensitivity analyses were performed to evaluate the relative importance of each DRASTIC parameter. The results from both analyses revealed that the vadose zone is the most sensitive parameter influencing the variability of the aquifers’ vulnerability index. Based on the results, for non-point source pollution in agricultural areas, using the modified DRASTIC model is efficient compared to the original model. The proposed method can be effective for future groundwater assessment and plain-land management where agricultural activities are dominant

Groundwater level prediction using genetic programming: the importance of precipitation data and weather station location on model accuracy

Groundwater (GW) level prediction is important for effective GW resource management. It is hypothesized that using precipitation data in GW level modelling will increase the overall accuracy of the results and that the distance of the observation well to the weather station (where precipitation data are obtained) will affect the model outcome. Here, genetic programming (GP) was used to predict GW level fluctuation in multiple observation wells under three scenarios to test these hypotheses. In Scenario 1, GW level and precipitation data were used as input data. Scenarios 2 only had GW level data as inputs to the model, and in Scenarios 3, only precipitation data were used as inputs. Long-term GW level time series data covering a period of 8 years were used to train and test the GP model. Further, to examine the effect of data from previous time periods on the accuracy of GW level prediction, 12 models with input data up to 12 months prior to the current period were investigated. Model performance was evaluated using two criteria, coefficient of determination (R2) and root mean square error (RMSE). Results show that when predicting GW levels through GP, using GW level and precipitation data together (Scenario 1) produces results with higher accuracy compared to only using GW level (Scenario 2) or precipitation data (Scenario 3). Additionally, it was found that model accuracy was highest for the well located closest to the weather station (where precipitation data were collected), demonstrating the importance of weather station location in GW level prediction. It was also found that using data from up to six previous time periods (months) can be the most efficient combination of input data for accurate predictions. The findings from this study are useful for increasing the prediction accuracy of GW level variations in unconfined aquifers for sustainable GW resource management

Groundwater discharge as a source of dissolved carbon and greenhouse gases in a subtropical estuary

Groundwater may be highly enriched in dissolved carbon species, but its role as a source of carbon to coastal waters is still poorly constrained. Exports of deep and shallow groundwater-derived dissolved carbon species from a small subtropical estuary (Korogoro Creek, Australia, latitude −31.0478°, longitude 153.0649°) were quantified using a radium isotope mass balance model (233Ra and 224Ra, natural groundwater tracers) under two hydrological conditions. In addition, air-water exchange of carbon dioxide and methane in the estuary was estimated. The highest carbon inputs to the estuary were from deep fresh groundwater in the wet season. Most of the dissolved carbon delivered by groundwater and exported from the estuary to the coastal ocean was in the form of dissolved inorganic carbon (DIC; 687 mmol m−2 estuary day−1; 20 mmol m−2 catchment day−1, respectively), with a large export of alkalinity (23 mmol m−2 catchment day−1). Average water to air flux of CO2 (869 mmol m−2 day−1) and CH4 (26 mmol m−2 day−1) were 5- and 43-fold higher, respectively, than the average global evasion in estuaries due to the large input of CO2- and CH4-enriched groundwater. The groundwater discharge contribution to carbon exports from the estuary for DIC, dissolved organic carbon (DOC), alkalinity, CO2, and CH4 was 22, 41, 3, 75, and 100 %, respectively. The results show that CO2 and CH4 evasion rates from small subtropical estuaries surrounded by wetlands can be extremely high and that groundwater discharge had a major role in carbon export and evasion from the estuary and therefore should be accounted for in coastal carbon budgets