Modeling Water Quality in Watersheds: From Here to the Next Generation

Uncertainty analysis and economic value prediction of water environmental capacity based on Copula and Bayesian model: A case study of Yitong River, China

Water environmental capacity (WEC) is an indicator of environment management. The uncertainty analysis of WEC is more closely aligned with the actual conditions of the water body. It is crucial for accurately formulating pollution total emissions control schemes. However, the current WEC uncertainty analysis method ignored the connection between water quality and discharge, and required a large amount of monitoring data. This study analyzed the uncertainty of the WEC and predicted its economic value based on Copula and Bayesian model for the Yitong River in China. The Copula model was employed to calculate joint probabilities of water quality and discharge. And the posterior distribution of WEC with limited data was obtained by the Bayesian formula. The results showed that the WEC-COD in the Yitong River was 9009.67 t/a, while NH3-N had no residual WEC. Wanjinta Highway Bridge-Kaoshan Town reach had the most serious pollution. In order to make it have WEC, the reduction of COD and NH3-N was 5330.47 t and 3017.87 t. The economic value of WEC-COD was 5.97 × 107 CNY, and the treatment cost was 2.04 × 108 CNY to make NH3-N have residual WEC. The economic value distribution of WEC was extremely uneven, which could be utilized by adjusting the sewage outlet. In addition, since the treated water was discharged into the Sihua Bridge-Wanjinta Highway Bridge reach, the WEC-COD and the economic value were 19,488.51 t/a and 8.24 × 107 CNY. Increasing the flow of rivers could effectively improve WEC and economic value. This study provided an evaluation tool for guiding river water environment management.

Meeting the Growing Need for Land-Water System Modelling to Assess Land Management Actions

Elevated contaminant levels and hydrological alterations resulting from land use are degrading aquatic ecosystems on a global scale. A range of land management actions may be used to reduce or prevent this degradation. To select among alternative management actions, decision makers require predictions of their effectiveness, their economic impacts, estimated uncertainty in the predictions, and estimated time lags between management actions and environmental responses. There are multiple methods for generating these predictions, but the most rigorous and transparent methods involve quantitative modelling. The challenge for modellers is two-fold. First, they must employ models that represent complex land-water systems, including the causal chains linking land use to contaminant loss and water use, catchment processes that alter contaminant loads and flow regimes, and ecological responses in aquatic environments. Second, they must ensure that these models meet the needs of endusers in terms of reliability, usefulness, feasibility and transparency. Integrated modelling using coupled models to represent the land-water system can meet both challenges and has advantages over alternative approaches. The need for integrated land-water system modelling is growing as the extent and intensity of human land use increases, and regulatory agencies seek more effective land management actions to counter the adverse effects. Here we present recommendations for modelling teams, to help them improve current practices and meet the growing need for land-water system models. The recommendations address several aspects of integrated modelling: (1) assembling modelling teams; (2) problem framing and conceptual modelling; (3) developing spatial frameworks; (4) integrating economic and biophysical models; (5) selecting and coupling models.

Recent applications of AI to environmental disciplines: A review

The rapid development and efficiency of Artificial Intelligence (AI) tools have made them increasingly popular in various fields and research domains. The environmental discipline is now experiencing an exponential interest in harnessing the potential of AI over the past decade. We have reviewed the latest applications of AI tools in the environmental disciplines, highlighting the opportunities they present and discussing their advantages and disadvantages in this field. After the emergence of deep learning algorithms in 2010, interest in using AI tools for environmental tasks has grown exponentially. Among the studied articles, over 65 % of environmental tasks that demonstrate interest in using AI tools initially relied on conventional statistical and mathematical models. Using AI tools can greatly benefit the areas of environmental science and engineering. One of the main advantages of utilizing AI tools is their ability to analyze and process large amounts of data efficiently. Recently, the European Union established a European supercomputing ecosystem program to advance science and enhance the quality of life for its citizens. Nine of these projects prioritize environmental and sustainable goals. Despite the benefits of AI, it is still in its early stages of development, which comes with environmental concerns. The amount of power consumed and the time required to train an AI model can greatly affect the carbon emissions it produces, exacerbating the challenges posed by climate change. Efforts are currently underway to develop AI technology that is environmentally sustainable, minimizes energy consumption, and has a low carbon footprint. Selecting the appropriate AI model architecture can reduce energy consumption by almost 90 %. The main finding suggests that collaboration between environmental and AI professionals becomes crucial in leveraging the full potential of AI in addressing pressing environmental challenges.

A novel RF-CEEMD-LSTM model for predicting water pollution

Accurate water pollution prediction is an important basis for water environment prevention and control. The uncertainty of input variables and the nonstationary and nonlinear characteristics of water pollution series hinder the accuracy and reliability of water pollution prediction. This study proposed a novel water pollution prediction model (RF-CEEMD-LSTM) to improve the performance of water pollution prediction by combining advantages of the random forest (RF) and Long short-term memory (LSTM) models and Complementary ensemble empirical mode decomposition (CEEMD). The experimental results based on measured data show that the proposed RF-CEEMD-LSTM model can accurately predict water pollution trends, with a mean ab-solute percentage error (MAPE) of less than 8%. The RMSE of the RF-CEEMD-LSTM model is reduced by 62.6%, 39.9%, and 15.5% compared to those of the LSTM, RF-LSTM, and CEEMD-LSTM models, respectively, proving that the proposed method has good advantages in predicting non-linear and nonstationary water pollution sequences. The driving force analysis results showed that TN has the most significant impact on water pollution prediction. The research results could provide references for identifying and explaining water pollution variables and improving water pollution prediction method.

Combining spectral analysis and geochemical tracers to investigate surface water-groundwater interactions: A case study in an intensive agricultural setting (southern Guatemala)

An increase in the frequency of severe hydrological events has highlighted the importance of sustainable water management in intensive agricultural regions. In a warming climate, improved understanding and stewardship of water resources are needed to guarantee water supply, ensure food security, and build resilience against extreme events. In this study, we evaluate a framework that combines spectral analysis and geochemical tracers as a potential tool for (1) gaining valuable insights into surface water (SW)-groundwater (GW) interactions, and (2) providing guidance for improved water management in an intensive agricultural basin in southern Guatemala. The framework proves to be useful in revealing important water dynamics, exposing key feedback mechanisms for water availability and quality. With the use of power density functions and hydrochemistry (T, pH, EC, and major ions), two specific interaction regimes (influent and effluent) were identified and delimited for the main watercourse. These segments are estimated to interact at high rates with the shallow aquifer in the river channel proximities and would lose influence towards the basin flanks. Furthermore, the δ2H and δ18O values indicate that regional groundwater flow systems play an essential role in the basin groundwater recharge. Lastly, we established three influence zones that depict the spatial extent of the SW-GW interactions within the basin. With these zones, we provide recommendations that will allow for further investigation and application into better water management strategies regulating groundwater development and land use activities within the agricultural context of the area.

Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty

Two fundamental problems have inhibited progress in the simulation of river water quality under climate (and other) uncertainty: 1) insufficient data, and 2) the inability of existing models to account for the complexity of factors (e.g., hydro-climatic, basin characteristics, land use features) affecting river water quality. To address these concerns this study presents a technique for augmenting limited ground-based observations of water quality variables with remote-sensed surface reflectance data by leveraging a machine learning model capable of accommodating the multidimensionality of water quality influences. Total Suspended Solids (TSS) can serve as a surrogate for chemical and biological pollutants of concern in surface water bodies. Historically, TSS data collection in the United States has been limited to the location of water treatment plants where state or federal agencies conduct regularly-scheduled water sampling. Mathematical models relating riverine TSS concentration to the explanatory factors have therefore been limited and the relationships between climate extremes and water contamination events have not been effectively diagnosed. This paper presents a method to identify these issues by utilizing a Long Short-Term Memory Network (LSTM) model trained on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite reflectance data, which is calibrated to TSS data collected by the Ohio River Valley Water Sanitation Commission (ORSANCO). The methodology developed enables a thorough empirical analysis and data-driven algorithms able to account for spatial variability within the watershed and provide effective water quality prediction under uncertainty.

Enhancing the Soil Health-Watershed Health Nexus: Introduction

Scientific concepts and measurements that relate soil and water resources are lacking in several areas, limiting our development of a framework or nexus to assess soil-watershed health. Current research designs rely on land management practices as a proxy for soil condition. Yet, conservation practices are often studied in isolation of each other, and adoption may be driven by state and federal farm programs that can incentivize a given management practice without accounting for current, novel farmer-driven adoption of conservation systems. Despite the value of conservation management, its ability to predict soil health is often limited if based solely on land management because chemical, physical, and biological processes vary across time, discipline, and terrain. Similarly, connections between soil health and water quality are constrained due to several "grand challenges" that include dissimilar scales and the number of metrics required to correlate soil and water systems. Equally important is soil sampling within the critical flow path(s) that determines sediment/contaminant loading. In some instances, most of the sediment/contaminant loading during a portion or entire year results from channel and bank erosion and not overland flow that may not be within conservation management hectares. Additional challenges include legacy effects of prior land management, climate variability, and varying turnover rates of soil and water systems. This special section aims to frame research issues that inspire new approaches and collaborations for tackling the challenge of leveraging soil health to strengthen water management across plot, field, and watershed scales, using models, statistics, and other novel methodologies.

Forested watersheds provide the highest water quality among all land cover types, but the benefit of this ecosystem service depends on landscape context

Conversion of natural land cover can degrade water quality in water supply watersheds and increase treatment costs for Public Water Systems (PWSs), but there are few studies that have fully evaluated land cover and water quality relationships in mixed use watersheds across broad hydroclimatic settings. We related upstream land cover (forest, other natural land covers, development, and agriculture) to observed and modeled water quality across the southeastern US and specifically at 1746 PWS drinking water intake facilities. While there was considerable complexity and variability in the relationship between land cover and water quality, results suggest that Total Nitrogen (TN), Total Phosphorus (TP) and Suspended Sediment (SS) concentrations decrease significantly with increasing forest cover, and increase with increasing developed or agricultural cover. Catchments with dominant (>90 %) agricultural land cover had the greatest export rates for TN, TP, and SS based on SPARROW model estimates, followed by developed-dominant, then forest- and other-natural-dominant catchments. Variability in modeled TN, TP, and SS export rates by land cover type was driven by variability in natural background sources and catchment characteristics that affected water quality even in forest-dominated catchments. Both intake setting (i.e., run-of-river or reservoir) and upstream land cover were important determinants of water quality at PWS intakes. Of all PWS intakes, 15 % had high raw water quality, and 85 % of those were on reservoirs. Of the run-of-river intakes with high raw water quality, 75 % had at least 50 % forest land cover upstream. In addition, PWS intakes obtaining surface water supply from smaller upstream catchments may experience the largest losses of natural land cover based on projections of land cover in 2070. These results illustrate the complexity and variability in the relationship between land cover and water quality at broad scales, but also suggest that forest conservation can enhance the resilience of drinking water supplies.

Prediction of water quality extremes with composite quantile regression neural network

Water quality extremes, which water quality models often struggle to predict, are a grave concern to water supply facilities. Most existing water quality models use mean error functions to maximize the predictability of water quality mean value. This paper describes a composite quantile regression neural network (CQRNN) model, which simultaneously estimates non-crossing regression quantiles by minimizing the composite quantile regression error function. This method can improve the prediction of extremes. This paper evaluates the performance of CQRNN for predicting extreme values of turbidity and total organic carbon (TOC) and compares with quantile regression (QR), linear regression (LR), and k-nearest neighbors (KNN) in an application to the Hetch Hetchy Regional Water System, which is the primary water supply for San Francisco, CA. CQRNN is superior to QR, LR, and KNN for predicting the mean trend and extremes of turbidity and TOC, especially for the non-Gaussian turbidity data. The performance of CQRNN is the most stable relative to other methods over different training sample sizes.

Numerical modeling of water pollution by products of chemical reactions from the activities of industrial facilities at variable and constant temperatures of the environment

The work focuses on the behavior of heated effluents discharged at elevated temperatures into the Ilek River, which is located in the city of Aktobe, Kazakhstan, as a result of industrial activities. This study is aimed at studying the dispersion characteristics of heated effluents in the near and far fields at different flow rates and dynamic conditions of the river. The chemical reaction, which is formed as a result of the combination of the ejected substance and the substance in water, is numerically investigated. The work took into account the variable temperature of the river, which changes during the day, and the values were compared with the results of modeling at a constant river temperature. It was found that, although the emitted element HNO₂ does not exceed the maximum permissible value (MPC), but the resulting products (HNO₃,HCl) exceed the MPC several times and cause significant damage to the aquatic environment.

Developing a Decision Support System for Regional Agricultural Nonpoint Salinity Pollution Management: Application to the San Joaquin River, California

Environmental problems and production losses associated with irrigated agriculture, such as salinity, degradation of receiving waters, such as rivers, and deep percolation of saline water to aquifers, highlight water-quality concerns that require a paradigm shift in resource-management policy. New tools are needed to assist environmental managers in developing sustainable solutions to these problems, given the nonpoint source nature of salt loads to surface water and groundwater from irrigated agriculture. Equity issues arise in distributing responsibility and costs to the generators of this source of pollution. This paper describes an alternative approach to salt regulation and control using the concept of “Real-Time Water Quality management”. The approach relies on a continually updateable WARMF (Watershed Analysis Risk Management Framework) forecasting model to provide daily estimates of salt load assimilative capacity in the San Joaquin River and assessments of compliance with salinity concentration objectives at key monitoring sites on the river. The results of the study showed that the policy combination of well-crafted river salinity objectives by the regulator and the application of an easy-to use and maintain decision support tool by stakeholders have succeeded in minimizing water quality (salinity) exceedances over a 20-year study period.

Groundwater vulnerability assessment: A review including new statistical and hybrid methods

The concept of groundwater vulnerability was first introduced in the 1970s in France to recognize sensitive areas in which surface pollution could affect groundwater, and to enable others to develop management methods for groundwater protection against surface pollutants. Since this time, numerous methods have been developed for groundwater vulnerability assessment (GVA). These can be categorized into four groups: (i) overlay and index-based methods, (ii) process-based simulation models, (iii) statistical methods, and (iv) hybrid methods. This work provides a comprehensive review of modern GVA methods, which in contrast to previous reviews, examines the last two categories in detail. First, the concept of groundwater vulnerability is defined, then the major GVA methods are introduced and classified. This includes detailed accounts of statistical methods, which can be subdivided into orthodox statistical, data-driven and Bayesian methods, and their advantages and disadvantages, as well as modern hybrid methods. It is concluded that Bayesian inference offers many advantages compared with other GVA methods. It combines theory and data to give the posterior probabilities of different models, which can be continually updated with new data. Furthermore, using the Bayesian approach, it is possible to calculate the probability of a proposition, which is exactly what is needed to make decisions. However, despite the advantages of Bayesian inference, its applications to date have been very limited.

The Influence of Freshwater Discharge and Wind Forcing on the Dispersal of River Plumes Using a Three-Dimensional Circulation Model

Tidal estuaries provide crucial pathways for contaminant transport. The salinity levels in estuaries and coasts are conserved substances that function as natural tracers to easily understand the offshore transport of substances that are subject to environmental factors. A three-dimensional (3D) circulation and mass transport model were utilized to delineate the salinity plume in a tidal estuary and continental shelf. The numerical modeling results were compared with the tidal amplitudes and phases, velocities, and salinities at different gauging stations in 2017. Quantitatively, the simulation and measurement results are in reasonably good agreement. Furthermore, the validated model was adopted to estimate the recovery times in tidal estuaries that are subjected to extreme freshwater discharges that come from the upstream reaches during typhoon events and to analyze the influences of freshwater discharge and wind stress on the river plume around the continental shelf. The simulated results revealed that the salinity recovery time at the river mouth due to Typhoon Saola in 2012 was less than 8 days. Increased inputs from freshwater discharge resulted in changes in the distances and areas of the river plumes. Linear regression relationships between the plume distance/plume area and the total freshwater discharge inputs were established. Neap and high slack tides were associated with the maximum plume distances and areas. Excluding tidal forcing resulted in larger plume distances and areas compared to the case in which tidal forcing was considered. The southward-favorable and northward-favorable plumes were controlled by northeasterly winds and southwesterly winds, respectively. The relative importance of freshwater discharges and wind forcing was explored. The results indicate that freshwater discharges frequently dominated the river plume, except when strong southwesterly or northeasterly winds prevailed.

Control-oriented quality modelling approach of sewer networks

A control-oriented quality modeling approach is proposed for sewer networks, which can represent quality dynamics using simple equations in order to optimize pollution load from combined sewer overflows in large scale sewer network in real time. Total suspended solid has been selected as the quality indicator, regarding it is easy to be estimated through measuring turbidity and correlated with other quality indicators. The model equations are independent for different elements in sewer network, which allows a scalable usage. In order to ensure accuracy of the proposed models, a calibration procedure and a sensitivity analysis have been presented using data generated by virtual reality simulation. Afterwards, a quality-based model predictive control has been developed based on the proposed models. To validate effectiveness and efficiency of the modelling and optimization approaches, a pilot case, based on the Badalona sewer network in Spain is used. Application results under different scenarios show that the control-oriented modelling approach works properly to cope with quality dynamics in sewers. The quality-based optimization approach can provide strategies in reducing pollution loads in real time.

Advances in Environmental Hydraulics

Environmental Hydraulics (EH) is the scientific study of environmental water flows and their related transport and transformation processes affecting the environmental quality of natural water systems, such as rivers, lakes, and aquifers, on our planet Earth [...]