HS2.3.8

The cumulative impacts of future climatic and socio-economic change threaten the ability of freshwater catchments to provide valuable socio-ecological services. Stakeholders who manage freshwater resources require decision-support tools that increase their understanding of catchment system resilience and support the appraisal of adaptive management options. Our research aims to address the following question: Can a Bayesian Network (BN) model support stakeholders in the identification and testing of adaptive management options that help increase catchment system resilience to the impacts of cumulative future change? Using the predominantly arable Eden catchment (320km²), in eastern Scotland as a case study, we invited stakeholders from multiple sectors to participate in a series of workshops aimed at addressing water resource issues and achieving good ecological status in the catchment both now and in the future. Outputs of a BN model that simulates both current and future catchment resilience were presented to stakeholders. Outputs informed the identification of adaptive management options which were grouped into five management scenarios. The effectiveness of each management scenario in increasing catchment system resilience was tested using the BN model to support the appraisal of each management scenario by participating stakeholders. Two optimal adaptive management scenarios were identified; the first optimal management scenario focussed on predominantly nature-based management options such as wetland wastewater treatment methods and rural sustainable drainage systems. The second optimal scenario focussed on resource recovery, including phosphorus recovery from wastewater treatment works and constructed lagoons for crop irrigation. Outputs of the model describing the resilience of the catchment initiated conversations about feasible management options that could be applied across sectors to reduce risk and increase catchment resilience. The ability of the BN model to test and compare adaptive management scenarios in a time-effective manner was seen as an advantage in comparison to conventional methods.

How to cite: Adams, K., Macleod, C. (. A. J., Metzger, M. J., Melville, N., Helliwell, R., Pritchard, J., Edwards, K., and Glendell, M.: Identifying and testing adaptive management options to increase catchment resilience using a Bayesian Network., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5652, https://doi.org/10.5194/egusphere-egu22-5652, 2022.

Plastic clean-up technologies deployed in rivers and estuaries can be fundamental to assist in plastic litter management and collection and to mitigate plastic pollution. However, it is vital to supply stakeholders with tools to monitor and minimize possible bycatch, as organic debris and biota provide essential functions to riverine and estuarine environments. Currently, even though some of the clean-up technologies companies perform environmental impact assessments, an independent and objective tool is still missing to assist stakeholders in deploying clean-up mechanisms with a minimal impact on biota. To support stakeholders in making informed decisions about which clean-up technology is best deployed under specific conditions, we suggest using Bayesian Belief Networks (BBNs) as a support tool that would ensure an effective plastic clean-up removal and minimum unintentional bycatch. We have identified four clusters of parameters that account for multiple conditions influencing the chances of bycatch and will form the basis of the BBN. To feed the model, we will acquire data from scientific and grey literature, expert knowledge, and experimental work. The data will include information on (i) the environmental conditions of the river (e.g., river flow), (ii) plastic debris characteristics such as size or buoyancy, (iii) biota traits (e.g., size, buoyancy, adhesiveness), and (iv) mechanism of clean-up technologies (e.g., river booms with conveyor belts, curtains of air bubbles). After the training and validating stages, the model can then be used in different river systems to suggest what type of plastic clean-up mechanism is best suited for the local parameters. This model will enable stakeholders, such as river managers and policymakers, to obtain information on the optimal trade-off between plastic removal and minimal collateral bycatch. 

How to cite: Leone, G., Catarino, A. I., Pauwels, I., Mani, T., Tishler, M., Egger, M., Forio, M. A. E., Goethals, P. L. M., and Everaert, G.: Assisting stakeholders in their choice of riverine and estuarine plastic clean-up technologies with the aid of Bayesian Belief Networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4092, https://doi.org/10.5194/egusphere-egu22-4092, 2022.

Significant amounts of pollutant are measured in surface water, their presence due in part to the use of pesticides in agriculture. One solution to limit pesticide transfer by surface runoff is to implement vegetative filter strips (VFS) along rivers. The sizing of these strips is a major issue, with influencing factors that include local conditions (climate, soil, etc.). The BUVARD modeling toolkit was developed to design VFSs throughout France according to these properties. This toolkit includes the numerical model VFSMOD, which quantifies dynamic effects of VFS site-specific pesticide mitigation efficiency. However, the toolkit is quite complex to use with many input uncertain parameters (quantitative - such as the slope, the Curve Number - or qualitative -such as the soil type or the rainfall event), making it not easy to use for risk management.

In this study, a metamodeling (or model dimension reduction) approach is proposed to ease the use of BUVARD and to help users design VFSs that are adapted to specific contexts. Different reduced models, or surrogates, are compared, based on Bayesian learning approaches or not: Polynomial Chaos Expansions, Mixed-kriging, and Deep-GP. Mixed-kriging is a kriging method that was implemented with a covariance kernel for a mixture of qualitative and quantitative inputs. Kriging and Deep-GP are built by couple of modalities and PCE and Mixed-kriging are built considering mixed quantitative and qualitative variables. As a last step, Finally, we perform a global sensitivity analysis with the help of the two surrogate models with the best accuracy. The results show that they give the same ranking of the importance of the input parameters.

The metamodel is a simple way to provide a relevant first guess to help design the pollution reduction device. In addition, the surrogate model is a relevant uncertainty tool, to visualize the impact that lack of knowledge of some parameters of filter efficiency can have when performing risk analysis and management.

How to cite: Lauvernet, C., Helbert, C., Xujia, Z., and Sudret, B.: Metamodeling approaches to help designing vegetative filter strips and improve the water quality., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10539, https://doi.org/10.5194/egusphere-egu22-10539, 2022.

In Korea, algal blooms are repeated every year in the four major rivers. Especially, the frequency and duration of algal blooms have increased due to climate change (increase in the atmosphere and water temperature) and environmental changes (river development projects and weir construction), which has led to the development of related research. However, it is still difficult to elucidate the cause of algal blooms because of a complicated mechanism. In this study, the concentration of Chlorophyll-a was selected as an indicator of algae occurrence, and representative hydrometeorological factors affecting the algal phenomenon were selected. Then, the optimal marginal distribution for each variable was found. The risk of algal blooms was analyzed through bivariate copula analysis by identifying the relationship between the influencing factors and the concentration of Chlorophyll-a. As a result, it was possible to identify the factors that had the most significant influence on the occurrence of algal blooms. Further, this study will employ the Vine Copula function to improve the complex relationship between variables in the context of multivariate modeling.

How to cite: Cho, H., Yu, J.-U., Kim, J., and Kwon, H.-H.: Risk Analysis of Algal Blooms Using the Conditional Copula Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11862, https://doi.org/10.5194/egusphere-egu22-11862, 2022.

Surface water bodies serve a critical role in preserving ecological systems and maintaining biodiversity. Anthropogenic eutrophication of fresh water ecosystems is one of the main causes of surface water quality degradation. Excessive nutrient loading to freshwater bodies is a driving cause of water quality impairments worldwide. Accurately estimating riverine nutrient loads remains an imperative step towards mitigating and managing impairments. Yet, load estimation is often hindered by the sporadic and infrequent monitoring of nutrient concentrations. Several modelling approaches have been proposed and implemented over the years to estimate pollutant loads; yet most suffer from biases and/or from their capabilities to transparently quantify uncertainties. In this work, we propose a spatio-temporal Bayesian hierarchical ratio-estimator model to predict the annual total phosphorus loads between 2005 and 2020 for six intensively monitored watersheds discharging in Lake Erie and the Ohio River-USA. The integration of higher-level Land-Use-Land-Cover predictors proved successful in capturing inter-station variabilities in phosphorus loading. Meanwhile, accounting for annual climatic variability partially helped explain temporal changes in the flow-weighted nutrient concentrations across the six watersheds. The performance of the model was tested against different levels of data censorship. Results showed that under a weekly sampling program, the load estimates from the proposed Bayesian Hierarchical spatio-temporal model were within -19 and 31 % (mean difference of 0.3% across stations and years) from the true loads calculated for years with uninterrupted concentration measurements. Predictions from traditional load estimation methods were found to vary between -56% and 73% from the true loads. Meanwhile, failing to account for the spatio-temporal hierarchical structure of the proposed model, either by adopting a completely pooled or an unpooled model, resulted in a significant drop in the accuracy of the predicted loads and inflated the associated uncertainties.

How to cite: Alameddine, I., Sawma, J., and Khalife, H.: A Bayesian hierarchical spatio-temporal ratio-estimator approach to model phosphorus loading in six Ohio watersheds: the importance of accounting for inter-annual and inter-basin variabilities, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8907, https://doi.org/10.5194/egusphere-egu22-8907, 2022.

Investigations of concentration (C) and discharge (Q) relationships (C–Q relationships) at the catchment scale are commonly used to characterize export regimes of instream particulates and solutes. C–Q relationships also provide insights on spatial and temporal variability in pollutant export, allowing identification of the sources and transfer pathways of pollutants. Previous studies have shown that several key catchment attributes control the export of sediment and dissolved nutrients within catchments. These catchment attributes include land use, topography, geology and soils. However, only few studies have investigated the relative importance of multiple catchment attributes over large spatial scales (e.g., at the continental scale) and between different climate zones. This is mostly due to either a limited number of catchments that have been monitored or a strong focus on temperate catchments. Therefore, our current understanding of key controls on spatial variability and export regimes across different climates is still limited. In this study, we investigated spatial differences and the C–Q relationships of six commonly monitored constituents (i.e., total suspended solid – TSS, total nitrogen – TN, sum of nitrate and nitrite – NO_x, total phosphorus – TP, soluble reactive phosphorus – SRP and electrical conductivity – EC) from 507 catchments across the Australian continent. These catchments represent five main climate zones in Australia (i.e., arid, Mediterranean, temperate, subtropical and tropical). We used a hierarchical Bayesian multi-model averaging approach to 1) identify key catchment attributes (e.g., land use, topography, geology and hydrology) driving the spatial variability of mean concentration and export regimes (C–Q relationship) for individual constituents; 2) understand the role of climatic gradients in determining the magnitude and direction of the key controls, and 3) use the key controls identified to predict the mean concentration and C–Q relationship in multiple catchments across Australia.

The proposed Bayesian modelling framework provided a higher predictive capability for mean concentrations (Nash-Sutcliffe efficiency (NSE) ranging from 0.58 for SRP to 0.86 for EC), compared to log(C) – log(Q) slopes (NSE ranging from 0.25 for NO_x to 0.39 for TP). For mean concentrations, land use (e.g., agriculture and urban) has a significantly positive effect on nutrients (i.e., TN, NO_x, TP and SRP), particularly in the Mediterranean, subtropical and tropical regions, indicating that land use is a key driver for these constituents. For log(C) – log(Q) slopes, catchment topographical characteristics (e.g., slope and maximum flow pathway) have relatively high impacts on TSS, TP and EC, indicating export of sediments and solutes in catchments largely controlled by mobilization (sediment) and surface-subsurface flow interaction (solutes). Findings from our study provide a data-driven understanding of key controls on riverine water quality across multiple climate types and can inform future water quality management strategies.

How to cite: Liu, S., Guo, D., Minaudo, C., Lintern, A., Dupas, R., Bende-Michl, U., Zhang, K., and Duvert, C.: Key controls of catchment attribute on spatial differences and export regimes in riverine water quality: a study across the Australian continent using a Bayesian approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11003, https://doi.org/10.5194/egusphere-egu22-11003, 2022.

Understanding concentration-discharge (C-Q) relationships is critical to inform catchment export processes for solute and particulates. The contribution of baseflow to streamflow has been found to affect C-Q relationships in some catchments in previous studies. Current understanding on the effects of baseflow contribution in shaping the C-Q patterns is largely limited to temperate catchments, but we still lack quantitative understanding of these effects across a wide range of climates (e.g., arid, tropical and subtropical). The study aims to assess how baseflow contributions within individual catchments influence C-Q slopes across Australia. The wide range of hydro-climatic regimes and land use/land cover conditions in Australian catchments make this continent the ideal experimental field to gain such an understanding. We analyzed 157 catchments in Australia spanning five climate zones, for six water quality variables: electrical conductivity (EC), total phosphorus (TP), soluble reactive phosphorus (SRP), total suspended solids (TSS), the sum of nitrate and nitrite (NO_x) and total nitrogen (TN). The impact of baseflow contributions was defined by the median and the range of daily baseflow indices (BFI_m and BFI_range, respectively) for each catchment. A novel Bayesian hierarchical model was developed to synthesize these effects for individual catchments across the continent.  

Sediments and nutrient species (TSS, NO_x, TN and TP) generally show positive C-Q slopes for most catchments, suggesting a dominance of mobilization export patterns. Further, TSS, NO_x and TP show stronger mobilization (i.e., steeper positive C-Q slopes) in catchments with higher values in both the BFI_m and BFI_range, while these two metrics are also positively correlated for most catchments. The enhanced mobilization in catchments with higher BFI_m or BFI_range might be explained by more variable flow pathways in catchments with higher baseflow contributions. In such catchments, the more variable flow pathways can lead to higher concentration gradients between low flows and high flows. These gradients are due to  different dominant flow pathways and contributions of groundwater/slow subsurface flow and surface water sources. Our results highlight the need for further studies focusing on identifying and quantifying: a) the influences of temporal variations of baseflow contributions on flow pathways, and b) the impacts of variable flow pathways on catchment C-Q relationships.

How to cite: Guo, D., Minaudo, C., Lintern, A., Bende-Michl, U., Liu, S., Zhang, K., and Duvert, C.: How does baseflow contribution affect catchment C-Q relationships? A continental synthesis using a Bayesian Hierarchical Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6847, https://doi.org/10.5194/egusphere-egu22-6847, 2022.

Bayesian mechanistic modeling often suffers from overconfident and biased posterior distributions for parameters and predictions. This phenomenon arises because the fundamental assumption of Bayesian Model Analysis is violated: the underlying model is assumed to be true, but in fact, it is a simplification of reality with structural errors that show at least during some periods of the modeled time span. As a result, a compromise solution in parameter space is identified that can formally fit the full data set best, but this parameter set will not be representative of the true system state. Neither will it be representative of the “compensation mode” in which the model is whenever structural error kicks in. As a logical consequence, predictions will be biased and their intervals too narrow. The longer the data set used for calibration, the stronger the misleading effect. Typical sources of severe structural deficits that produce dynamically occurring errors are missing or misspecified processes in the model. 

We propose a formal time-windowed Bayesian analysis to overcome this general problem. When performing Bayesian updating on shorter time windows, the assumption of a (quasi-) true model becomes more plausible, and by sliding this window through the calibration time series, we let the model adjust its posterior parameter distributions according to the current strength of error. These time-shifting parameter distributions allow us to (1) identify periods of statistically significant model error occurrence via measuring time-varying Bayesian model evidence, (2) diagnose potential sources of model error by understanding the time-varying parameter compensation mechanisms, and (3) predict with more realistic uncertainty intervals by distribution averaging. 

We demonstrate the proposed method on a set of synthetic and real-world scenarios of soil moisture modeling. With this example, we also highlight its usefulness to analyze dynamic systems in a wide range of disciplines, such as water quality modeling, decision support, and risk assessment. Results show that the time sequence of posterior parameter distributions (and dependent model mechanisms such as water retention curves and unsaturated hydraulic conductivity functions) provides valuable insights into the model’s weaknesses, and it also provides guidance for model improvement.

How to cite: Guthke, A., Hsueh, H.-F., Wöhling, T., and Nowak, W.: Bayesian updating despite model errors? A sliding time-window approach to rescue, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12525, https://doi.org/10.5194/egusphere-egu22-12525, 2022.

The source identification of surface water pollution is the key issue in environmental management and has important practical needs. Model-based numerical inversion methods have received widespread attention that there are many research reports. However, most of the existing research on the pollution source identification (PSI) problem focuses on the combination innovation and theoretical analysis of the inversion algorithm, and does not consider the urgent time constraints of the emergency response process, which has become an important technical bottleneck. To this end, this study focuses on the timeliness and operability of numerical inversion methods in the emergency response process, and makes full use of multi-source information of pollutants to explore robust and fast sampling and numerical inversion methods in practical operations. The study adopts the Adative Metroplis Monte Carlo (AM-MCMC) Bayesian method as the basic source identification inversion framework, and takes the USGS tracer test in Truckee River from 2006 to 2007 as the basic scenario to carry out numerical experiments. Through the data assimilation method, the pollution source information is dynamically updated. With the input of new monitoring data, the accuracy of the inversion results is gradually improved; By integrating multiple pollutants information, greatly improves the robustness and practical ability of the numerical source identification technology. The study establishes the best practice method of parallel sampling, which can achieve reliable numerical inversion accuracy in the early stage of sampling. The quantitative design criteria of the minimum sampling cost required for inversion to meet a certain error limit under different river hydrological conditions are discussed, and the relative critical time Λ of sampling and Peclet number (Pe) that characterizes river hydrodynamics are found as follow relation: Λ=-0.816×Pe^-1/2-0.978×Pe^-1/2lnPe^-1/2+0.554, R²=0.938, and has been proved by information entropy theory. The precise design process of the emergency PSI monitoring scheme with the timeliness as the optimization goal is further proposed. This study provides important theoretical guidance for the innovative application of new monitoring methods in scenarios such as leakage detection and emergency monitoring under the background of environmental Internet of Things.

How to cite: Yang, R., Jiang, J., Pang, T., Men, Y., and Zheng, Y.: New insights on the practical significance of numerical methods for surface water pollution source identification, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11975, https://doi.org/10.5194/egusphere-egu22-11975, 2022.

Water quality modeling plays an important role in better understanding the magnitude and impact of water quality issues and in providing evidence for policy-making and implementing measures to mitigate water pollution. Process-based nutrient models are very complex, requiring a lot of input parameters and computationally expensive calibration. Often there is also a lack of high spatial and temporal resolution water quality data because water sampling is expensive and river water quality can’t be measured using remote sensing. Machine learning approaches have been shown to achieve similar accuracy to the physically-based models and even outperform them when describing nonlinear relationships. We used 242 observation sites located at 139 streams in Estonia, amounting to 469 yearly total nitrogen (TN) and 470 total phosphorus (TP) measurements covering the period 2016–2020 to train random forest models for predicting N and P concentrations. We used a total of 82 predictor variables, including land cover, soil, climate, and topography parameters, and applied a feature selection strategy to reduce the number of dependent features in the model. The models resulted in an accuracy of 82% in the case of TN and 54% for TP. The SHAP (SHapley Additive exPlanations) values used to explain the models showed that the most important features for predicting TN were arable land proportion, soil rock content, and hydraulic conductivity, while the main features affecting TP concentration were the urban and grassland proportion in the catchment. The results indicate that the TN model is a viable alternative to process-based models in Estonia. In the case of TP, the derived feature importances and feature interactions can potentially help improve the corresponding model in the future. 

How to cite: Virro, H., Kmoch, A., Vainu, M., and Uuemaa, E.: Machine learning-based water quality modeling at national level in data-scarce region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6062, https://doi.org/10.5194/egusphere-egu22-6062, 2022.