Predicting water quality in unmonitored watersheds using artificial neural networks

Enhancing machine learning models for total organic carbon prediction by integrating geospatial parameters in river watersheds

This study utilizes machine learning (ML) algorithms to develop a robust total organic carbon (TOC) prediction model for river waters in the Geumho River sub-basins, South Korea, considering both non-rain and rain events. The model incorporates geospatial parameters such as land use, slope, flow rate, and basic water quality metrics including biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and suspended solids (SS). A key aspect of this research is examining how land use information enhances the model's predictive accuracy. We compared two ML algorithms-extreme gradient boosting (XGBoost) and deep neural networks (DNN)-with a traditional multiple linear regression (MLR) approach. XGBoost outperformed the others, achieving an R2 value between 0.61 and 0.68 in the test dataset and demonstrating significant improvement during rain events with an R2 of 0.77 when including land use data. In contrast, this enhancement was not observed with the MLR model. Feature importance analysis using Shapley values highlighted COD as the primary predictor for non-rain events, while during rain events, COD, TP, TN, SS and agricultural land collectively influenced TOC levels. This study significantly advances understanding of TOC variability across different land use scenarios in river systems and underscores the importance of integrating geospatial and water quality parameters to enhance TOC prediction, particularly during rain events. This methodology provides a valuable framework for developing river management strategies and monitoring long-term TOC trends, especially in scenarios with gaps in essential monitoring data.

Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approach

In order to enhance the simulation of BMPs (Best Management Practices) reduction effects in unmonitored watersheds, in this study, we combined the physically-based hydrological model Soil & Water Assessment Tool (SWAT) and the data-driven model Bi-directional Long Short-Term Memory (Bi-LSTM), using the very-high-resolution (VHR) Land Use and Land Cover (LULC) dataset SinoLC-1 as data input, to evaluate the feasibility of constructing a water environment model for the Ba-River Basin (BRB) in central China and improving streamflow prediction performance. In the SWAT-BiLSTM model, we calibrated the top five SWAT parameters sorted by P-Value, allowing SWAT to act as a transfer function to convert meteorological data into base flow and storm flow, serving as the data input for the Bi-LSTM model. This optimization improved the Bi-LSTM's learning process for the relationship between the target and explanatory variables. The daily streamflow prediction results showed that the hybrid model had 9 regions rated as "Very good," 2 as "Good," 2 as "Satisfactory," and 1 as "Unsatisfactory" among the 14 regions. The model achieved an NSE of 0.86, R2 of 0.85, and PBIAS of -2.71% for the overall daily streamflow prediction performance during the verification period of the BRB. This indicates that the hybrid model has high predictive accuracy and no significant systematic bias, providing a sound hydrodynamic environment for water quality simulation. The simulation results of different BMPs scenarios showed that in the scenarios with only one BMP measure, stubble mulch had the best reduction effect, with average reductions of 17.83% for TN and 36.17% for TP. In the scenarios with a combination of multiple BMP measures, the combination of stubble mulch, soil testing and formula fertilization, and vegetative filter strip performed the best, achieving average reductions of 42.71% for TN and 50.40% for TP. The hybrid model provides a novel approach to simulate BMPs' reduction effects in regions without measured hydrological data and has the potential for wide application in BMP-related decision-making.

A comparative study of data-driven models for runoff, sediment, and nitrate forecasting

Effective prediction of qualitative and quantitative indicators for runoff is quite essential in water resources planning and management. However, although several data-driven and model-driven forecasting approaches have been employed in the literature for streamflow forecasting, to our knowledge, the literature lacks a comprehensive comparison of well-known data-driven and model-driven forecasting techniques for runoff evaluation in terms of quality and quantity. This study filled this knowledge gap by comparing the accuracy of runoff, sediment, and nitrate forecasting using four robust data-driven techniques: artificial neural network (ANN), long short-term memory (LSTM), wavelet artificial neural network (WANN), and wavelet long short-term memory (WLSTM) models. These comparisons were performed in two main tiers: (1) Comparing the machine learning algorithms' results with the model-driven approach; In order to simulate the runoff, sediment, and nitrate loads, the Soil and Water Assessment Tool (SWAT) model was employed, and (2) Comparing the machine learning algorithms with each other; The wavelet function was utilized in the ANN and LSTM algorithms. These comparisons were assessed based on the substantial statistical indices of coefficient of determination (R-Squared), Nash-Sutcliff efficiency coefficient (NSE), mean absolute error (MAE), and root mean square error (RMSE). Finally, to prove the applicability and efficiency of the proposed novel framework, it was successfully applied to Eagle Creek Watershed (ECW), Indiana, U.S. Results demonstrated that the data-driven algorithms significantly outperformed the model-driven models for both the calibration/training and validation/testing phases. Furthermore, it was found that the coupled ANN and LSTM models with wavelet function led to more accurate results than those without this function.

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.

Fate and transport in environmental quality

Changes in pollutant concentrations in environmental media occur both from pollutant transport in water or air and from local processes, such as adsorption, degradation, precipitation, straining, and so on. The terms "fate and transport" and "transport and fate" reflect the coupling of moving with the carrier media and biogeochemical processes describing local transformations or interactions. The Journal of Environmental Quality (JEQ) was one of the first to publish papers on fate and transport (F&T). This paper is a minireview written to commemorate the 50th anniversary of JEQ and show how the research interests, methodology, and public attention have been reflected in fate and transport publications in JEQ during the last 40 years. We report the statistics showing how the representation of different pollutant groups in papers changed with time. Major focus areas have included the effect of solution composition on F&T and concurrent F&T, the role of organic matter, and the relative role of different F&T pathways. The role of temporal and spatial heterogeneity has been studied at different scales. The value of long-term F&T studies and developments in modeling as the F&T research approach was amply demonstrated. Fate and transport studies have been an essential part of conservation measure evaluation and comparison and ecological risk assessment. For 50 years, JEQ has delivered new insights, methods, and applications related to F&T science. The importance of its service to society is recognized, and we look forward to new generations of F&T researchers presenting their contributions in JEQ.

Contribution of wastewater- versus non-wastewater-derived sources to haloacetonitriles formation potential in a wastewater-impacted river

Population growth and urbanization have led to the increasing presence of treated wastewater effluents in downstream drinking water sources. Drinking water sources influenced by organic matter from upstream wastewater treatment plant (WWTP) effluents are thought prone to the formation of haloacetonitriles (HANs), a group of nitrogenous disinfection by-products (DBPs) that can exhibit higher toxicity than currently regulated carbonaceous DBPs. We develop a framework for studying the HAN formation potential (HAN-FP) considering the WWTP and non-WWTP related sources of HAN precursors, and apply this framework to a representative WWTP-impacted river, the Illinois River, USA. A spatiotemporally-resolved river hydrodynamic and water quality model is developed using HEC-RAS to quantify the contribution of WWTP versus non-WWTP sources of HAN-FP precursors. Results show that non-WWTP sources of HAN-FP are considerable, accounting for up to 78% of HAN-FP concentration. Moreover, the contribution of the two sources varies due to streamflow discharge variability. During lower flows, the contribution of WWTPs drives the high concentration of HAN-FP and during higher flows, the contribution of non-WWTP sources becomes dominant. As a result, a high risk of HAN-FP may exist persistently (HAN-FP concentration is always larger than 9.7 μg/L in this study), not only during low flows but also during high flows due to both wastewater- and non-wastewater-derived HAN-FP sources.

A Comparison of In-Sample and Out-of-Sample Model Selection Approaches for Artificial Neural Network (ANN) Daily Streamflow Simulation

Artificial Neural Networks (ANN) have been widely applied in hydrologic and water quality (H/WQ) modeling in the past three decades. Many studies have demonstrated an ANN’s capability to successfully estimate daily streamflow from meteorological data on the watershed level. One major challenge of ANN streamflow modeling is finding the optimal network structure with good generalization capability while ameliorating model overfitting. This study empirically examines two types of model selection approaches for simulating streamflow time series: the out-of-sample approach using blocked cross-validation (BlockedCV) and an in-sample approach that is based on Akaike’s information criterion (AIC) and Bayesian information criterion (BIC). A three-layer feed-forward neural network using a back-propagation algorithm is utilized to create the streamflow models in this study. The rainfall–streamflow relationship of two adjacent, small watersheds in the San Antonio region in south-central Texas are modeled on a daily time scale. The model selection results of the two approaches are compared, and some commonly used performance measures (PMs) are generated on the stand-alone testing datasets to evaluate the models selected by the two approaches. This study finds that, in general, the out-of-sample and in-sample approaches do not converge to the same model selection results, with AIC and BIC selecting simpler models than BlockedCV. The ANNs were found to have good performance in both study watersheds, with BlockedCV selected models having a Nash–Sutcliffe coefficient of efficiency (NSE) of 0.581 and 0.658, and AIC/BIC selected models having a poorer NSE of 0.574 and 0.310, for the two study watersheds. Overall, out-of-sample BlockedCV selected models with better predictive ability and is preferable to model streamflow time series.

A novel machine learning application: Water quality resilience prediction Model

Resilience-informed water quality management embraces the growing environmental challenges and provides greater accuracy by unpacking the systems' characteristics in response to failure conditions in order to identify more effective opportunities for intervention. Assessing the resilience of water quality requires complex analysis of influential parameters which can be challenging, time consuming and costly to compute. It may also require building detailed conceptual and/or physically process-based models that are difficult to build, calibrate and validate. This study utilises Artificial Neural Network (ANN) to develop a novel application to predict water quality resilience to simplify resilience evaluation. The Fuzzy Analytic Hierarchy Process method is used to rank water basins based on their level of resilience and to identify the ones that demand prompt restoration strategies. The commonly used 'magnitude * duration of being in failure state' quantification method has been used to formulate and evaluate resilience. A 17-years long water quality dataset from the 22 water basins in the State of São Paulo, Brazil, was used to train and test the ANN model. The overall agreement between the measured and simulated WQI resilience values is satisfactory and hence, can be used by planners and decision makers for improved water management. Moreover, comparative analyses show similarities and differences between the 'level of criticalities' reported in each zone by Environment Agency of the state of São Paulo (CETESB) and by the resilience model in this study.

Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds

This paper proposes a regionalization method for streamflow prediction in ungauged watersheds in the 7461 km² area above the Gharehsoo Hydrometry Station in the Ardabil Province, in the north of Iran. First, the Fuzzy c-means clustering method (FCM) was used to divide 46 gauged (19) and ungauged (27) watersheds into homogenous groups based on a variety of topographical and climatic factors. After identifying the homogenous watersheds, the Soil and Water Assessment Tool (SWAT) was calibrated and validated using data from the gauged watersheds in each group. The calibrated parameters were then tested in another gauged watershed that we considered as a pseudo ungauged watershed in each group. Values of R-Squared and Nash–Sutcliffe efficiency (NSE) were both ≥ 0.70 during the calibration and validation phases; and ≥ 0.80 and ≥ 0.74, respectively, during the testing in the pseudo ungauged watersheds. Based on these metrics, the validated regional models demonstrated a satisfactory result for predicting streamflow in the ungauged watersheds within each group. These models are important for managing stream quantity and quality in the intensive agriculture study area.

Assessment of future hydrologic alteration due to climate change in the Aracthos River basin (NW Greece)

Climate change is a worldwide reality with significant effects on hydrological processes. It has already produce alterations in streamflow regime and is expected to continue in the future. To counteract the climate change impact, a better understanding of its effects is necessary. Hydrological models in combination with Indicators of Hydrologic Alteration (IHA) suppose an up-to-date approach to analyze in detail the impacts of climate change on rivers. In this study, the Soil and Water Assessment Tool (SWAT) model and Indicators of Hydrologic Alteration in Rivers (IAHRIS) software were successfully applied in Aracthos River basin, an agricultural watershed located in the north-western area of Greece. Statistical indices showed an acceptable performance of the SWAT model in both calibration (R² = 0.74, NSE = 0.54, PBIAS = 17.06%) and validation (R² = 0.64, NSE = 0.36, PBIAS = 12.31%) periods on a daily basis. To assess the future hydrologic alteration due to climate change in Aracthos River basin, five Global Climate Models (GFDL-ESM2, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M) were selected and analyzed under two different emission scenarios (RCP 4.5 and RCP 8.5) for a long-term period (2070-2099). Results indicate that precipitation and flow is expected to be reduced and maximum and minimum temperature to be increased, compared to the historical period (1970-1999). IHA, obtained from IAHRIS software, revealed that flow regime can undergo a severe alteration, mainly on droughts that are expected to be more significant and longer. All these future hydrologic alterations could have negative consequences on the Aracthos River and its surroundings. The increase of droughts duration in combination with the reduction of flows and the alteration of seasonality can affect the resilience of riverine species and it can produce the loss of hydraulic and environmental diversity. Therefore, this study provides a useful tool for decision makers to develop strategies against the impact of climate change.

MIDAS: A New Integrated Flood Early Warning System for the Miño River

Early warning systems have become an essential tool to mitigate the impact of river floods, whose frequency and magnitude have increased during the last few decades as a consequence of climate change. In this context, the Miño River Flood Alert System (MIDAS) early warning system has been developed for the Miño River (Galicia, NW Spain), whose flood events have historically caused severe damage in urban areas and are expected to increase in intensity in the next decades. MIDAS is integrated by a hydrologic (HEC-HMS) and a hydraulic (Iber+) model using precipitation forecast as input data. The system runs automatically and is governed by a set of Python scripts. When any hazard is detected, an alert is issued by the system, including detailed hazards maps, to help decision makers to take precise and effective mitigation measures. Statistical analysis supports the accuracy of hydrologic and hydraulic modules implemented to forecast river flow and flooded critical areas during the analyzed period of time, including some of the most extreme events registered in the Miño River. In fact, MIDAS has proven to be capable of predicting most of the alert situations occurred during the study period, showing its capability to anticipate risk situations.

Assessment of Ecological and Hydro-Geomorphological Alterations under Climate Change Using SWAT and IAHRIS in the Eo River in Northern Spain

Magnitude and temporal variability of streamflow is essential for natural biodiversity and the stability of aquatic environments. In this study, a comparative analysis between historical data (1971–2013) and future climate change scenarios (2010–2039, 2040–2069 and 2070–2099) of the hydrological regime in the Eo river, in the north of Spain, is carried out in order to assess the ecological and hydro-geomorphological risks over the short-, medium- and long-term. The Soil and Water Assessment Tool (SWAT) model was applied on a daily basis to assess climate-induced hydrological changes in the river under five general circulation models and two representative concentration pathways. Statistical results, both in calibration (Nash-Sutcliffe efficiency coefficient (NSE): 0.73, percent bias (PBIAS): 3.52, R2: 0.74) and validation (NSE: 0.62, PBIAS: 6.62, R2: 0.65), are indicative of the SWAT model’s good performance. The ten climate scenarios pointed out a reduction in rainfall (up to −22%) and an increase in temperatures, both maximum (from +1 to +7 °C) and minimum ones (from +1 to +4 °C). Predicted flow rates resulted in an incrementally greater decrease the longer the term is, varying between −5% (in short-term) and −53% (in long-term). The free software IAHRIS (Indicators of Hydrologic Alteration in Rivers) determined that alteration for usual values remains between excellent and good status and from good to moderate in drought values, but flood values showed a deficient regime in most scenarios, which implies an instability of river morphology, a progressive reduction in the section of the river and an advance of aging of riparian habitat, endangering the renewal of the species.

A Hybrid Approach Combining Conceptual Hydrological Models, Support Vector Machines and Remote Sensing Data for Rainfall-Runoff Modeling

Understanding catchment response to rainfall events is important for accurate runoff estimation in many water-related applications, including water resources management. This study introduced a hybrid model, the Tank-least squared support vector machine (LSSVM), that incorporated intermediate state variables from a conceptual tank model within the least squared support vector machine (LSSVM) framework in order to describe aspects of the rainfall-runoff (RR) process. The efficacy of the Tank-LSSVM model was demonstrated with hydro-meteorological data measured in the Yongdam Catchment between 2007 and 2016, South Korea. We first explored the role of satellite soil moisture (SM) data (i.e., European Space Agency (ESA) CCI) in the rainfall-runoff modeling. The results indicated that the SM states inferred from the ESA CCISWI provided an effective means of describing the temporal dynamics of SM. Further, the Tank-LSSVM model’s ability to simulate daily runoff was assessed by using goodness of fit measures (i.e., root mean square error, Nash Sutcliffe coefficient (NSE), and coefficient of determination). The Tank-LSSVM models’ NSE were all classified as “very good” based on their performance during the training and testing periods. Compared to individual LSSVM and Tank models, improved daily runoff simulations were seen in the proposed Tank-LSSVM model. In particular, low flow simulations demonstrated the improvement of the Tank-LSSVM model compared to the conventional tank model.

A Preliminary Assessment of the “Undercatching” and the Precipitation Pattern in an Alpine Basin

Gauges modify wind fields, producing important systematic errors (undercatching) in the measurement of solid precipitation (Ps), especially under windy conditions. A methodology that combines geostatistical techniques and hydrological models to perform a preliminary assessment of global undercatch and precipitation patterns in alpine regions is proposed. An assessment of temperature and precipitation fields is performed by applying geostatistical approaches assuming different hypothesis about the relationship between climatic fields and altitude. Several experiments using different approximations of climatic fields in different approaches to a hydrological model are evaluated. A new hydrological model, the Snow-Témez Model (STM), is developed including two parameters to correct the solid (Cs) and liquid precipitation (Cr). The procedure allows identifying the best combination of geostatistical approach and hydrological model for estimating streamflow in the Canales Basin, an alpine catchment of the Sierra Nevada (Spain). The sensitivity of the results to the correction of the precipitation fields is analyzed, revealing that the results of the streamflow simulation are improved when the precipitation is corrected considerably. High values of solid Cs are obtained, while Cr values, although smaller than the solid one, are also significant.

A Comparative Study of Statistical Methods for Daily Streamflow Estimation at Ungauged Basins in Turkey

In this study, a comparative evaluation of the statistical methods for daily streamflow estimation at ungauged basins is presented. The single donor station drainage area ratio (DAR) method, the multiple-donor stations drainage area ratio (MDAR) method, the inverse similarity weighted (ISW) method, and its variations with three different power parameters (1, 2, and 3) are applied to the two main subbasins of the Euphrates Basin in Turkey to estimate daily streamflow data. Each station in each basin is considered in turn as the target station where there are no streamflow data. The donor stations are selected based on the physical similarities between the donor and target stations. Then, streamflow data from the most physically similar donor station(s) is transferred to the target station using the statistical methods. In addition, the effect of data preprocessing on the estimation performance of the statistical methods is investigated. The preprocessing discussed in this study is streamflow data smoothing using the two-sided moving average (MA). Three statistical methods using the smoothed data by the MA, named as DAR-MA, MDAR-MA, and ISW-MA, are proposed. The estimation performance of the statistical methods is compared by using daily streamflow data with preprocessing and without preprocessing. The Nash–Sutcliffe efficiency (NSE), the ratio of the root mean square error (RMSE) to the standard deviation of the observed data (RSR), the percent bias (PBIAS), and the coefficient of determination (R2) are used to evaluate the performance of the statistical methods. The results show that MDAR and ISW give improved performances compared to DAR to estimate daily streamflow for 7 out of 8 target stations in the Middle Euphrates Basin and for 4 out of 7 target stations in the Upper Euphrates Basin. Higher NSE values for both MDAR and ISW are mostly obtained with the three most physically similar donor stations in the Middle Euphrates Basin and with the two most physically similar donor stations in the Upper Euphrates Basin. The best statistical method for each target station exhibits slightly greater NSE when the smoothed data by the MA is used for all target stations in the Middle Euphrates Basin and for 6 out of 7 target stations in the Upper Euphrates Basin.

Challenges Calibrating Hydrology for Groundwater-Fed Wetlands: a Headwater Wetland Case Study

This study aims to adapt the Soil and Watershed Assessment Tool (SWAT), a ubiquitously used watershed model, for ground-water dominated surface waterbodies by accounting for recharge from the aquifers. Using measured flow to a headwater slope wetland in Alabama's coastal plain region as a case study, we present challenges and relatively simple approaches in using the SWAT model to predict flows from the draining watershed and relatively simple approaches to model groundwater upwelling. SWAT-simulated flow at the study watershed was limited by precipitation, and consequently, simulated flows were several times smaller in magnitude than observed flows. Thus, our first approach involved a separate stormflow and baseflow calibration which included the use of a regression relationship between observed and simulated baseflow (E NASH = 0.67). Our next approach involved adapting SWAT to simulate upwelling groundwater discharge instead of deep aquifer losses by constraining the range of deep losses, β deep parameter, to negative values (E NASH = 0.75). Finally, we also investigated the use of artificial neural networks (ANN) in conjunction with SWAT to further improve calibration performance. This approach used SWAT-calibrated flow, evapotranspiration, and precipitation as inputs to ANN (E NASH = 0.88). The methods investigated in this study can be used to navigate similar flow calibration challenges in other groundwater dominant watersheds which can be very useful tool for managers and modelers alike.

Multi-Scale Hydrologic Sensitivity to Climatic and Anthropogenic Changes in Northern Morocco

Natural and human-induced impacts on water resources across the globe continue to negatively impact water resources. Characterizing the hydrologic sensitivity to climatic and anthropogenic changes is problematic given the lack of monitoring networks and global-scale model uncertainties. This study presents an integrated methodology combining satellite remote sensing (e.g., GRACE, TRMM), hydrologic modeling (e.g., SWAT), and climate projections (IPCC AR5), to evaluate the impact of climatic and man-made changes on groundwater and surface water resources. The approach was carried out on two scales: regional (Morocco) and watershed (Souss Basin, Morocco) to capture the recent climatic changes in precipitation and total water storage, examine current and projected impacts on total water resources (surface and groundwater), and investigate the link between climate change and groundwater resources. Simulated (1979–2014) potential renewable groundwater resources obtained from SWAT are ~4.3 × 108 m3/yr. GRACE data (2002–2016) indicates a decline in total water storage anomaly of ~0.019m/yr., while precipitation remains relatively constant through the same time period (2002–2016), suggesting human interactions as the major underlying cause of depleting groundwater reserves. Results highlight the need for further conservation of diminishing groundwater resources and a more complete understanding of the links and impacts of climate change on groundwater resources.

Land use impact on the water quality of large tropical river: Mun River Basin, Thailand

Globally, rivers and streams are experiencing declining water quality. Anthropogenic activities largely contribute to surface water pollution. Understanding human-induced influence on river water quality remains a challenge owing to spatiotemporal variations. In this study, we assessed the influence of various land uses (LU) on 16 water quality parameters of the Mun River, a tributary of the Mekong River, at different scales. Water quality was statistically analyzed both spatially and temporally (1995-2010). Seasonal and annual effect of LU on water quality was evaluated at buffer zone scale and sub-basin scale (i.e., catchment scale) using multiple regression analysis. The result showed that urban LU extensively adds to the nutrient concentration [i.e., total phosphorus (TP), ammonia nitrogen (NH₃-N)] followed by agriculture LU at the sub-basin scale. Site-specific variability of TP is explained by urban LU and biological oxygen demand (BOD) by agriculture LU at the 5-km buffer in Upper and Middle Mun whereas at Lower Mun, the 20-km buffer explains the variability of suspended solids (SS) and total suspended solids (TSS), suggesting a more localized effect on the parameters upstream. The high concentration of parameters was noted in the dry season whereas the opposite was true for fecal coliform bacteria (FCB), SS, and TP. The maximum parameter concentration of NH₃-N, FCB, and total coliform bacteria exceeds the permissible surface water quality standards of the Pollution Control Department (PCD) of Thailand in all three sub-basins. The study suggests the need for multi-scale interventions and effective pollution control measures focusing on nutrient, pathogenic bacteria, and solids pollution to improve the river water quality of large river basin.

IPEAT+: A Built-In Optimization and Automatic Calibration Tool of SWAT+

For almost 30 years, the Soil and Water Assessment Tool (SWAT) has been successfully implemented to address issues around various scientific subjects in the world. On the other hand, it has been reaching to the limit of potential flexibility in further development by the current structure. The new generation SWAT, dubbed SWAT+, was released recently with entirely new coding features. SWAT+ is designed to have far more advanced functions and capacities to handle challenging watershed modeling tasks for hydrologic and water quality processes. However, it is still inevitable to conduct model calibration before the SWAT+ model is applied to engineering projects and research programs. The primary goal of this study is to develop an open-source, easy-to-operate automatic calibration tool for SWAT+, dubbed IPEAT+ (Integrated Parameter Estimation and Uncertainty Analysis Tool Plus). There are four major advantages: (i) Open-source code to general users; (ii) compiled and integrated directly with SWAT+ source code as a single executable; (iii) supported by the SWAT developer group; and, (iv) built with efficient optimization technique. The coupling work between IPEAT+ and SWAT+ is fairly simple, which can be conducted by users with minor efforts. IPEAT+ will be regularly updated with the latest SWAT+ revision. If users would like to integrate IPEAT+ with various versions of SWAT+, only few lines in the SWAT+ source code are required to be updated. IPEAT+ is the first automatic calibration tool integrated with SWAT+ source code. Users can take advantage of the tool to pursue more cutting-edge and forward-thinking scientific questions.

A Normal Cloud Model-Based Method for Water Quality Assessment of Springs and Its Application in Jinan

Springs are a source of drinking water and a famous tourist attraction in Jinan, China. In this paper, a multi-index evaluation method was proposed based on a normal cloud model. This model is new graphic model, which could synthetically picture the randomness and fuzziness of concepts. Ten parameters were selected, and water quality was classified into five levels. Three numerical characteristics were calculated, and the weights were assigned by an integrated weighting algorithm. The uncertainty of each spring was calculated by a cloud generator and the integrated certainty grades of water quality were determined. To ensure the accuracy of the normal cloud model, the proposed method was used to assess the water quality of springs in Jinan, China. The results obtained by the proposed method were compared with that of the other four methods. The results obtained by different methods are highly consistent. The proposed cloud model-based method can reflect the water quality level and provides a practical guide for water quality evaluation, as demonstrated in Jinan springs.

A new method for water quality assessment: by harmony degree equation

Water quality assessment is an important basic work in the development, utilization, management, and protection of water resources, and also a prerequisite for water safety. In this paper, the harmony degree equation (HDE) was introduced into the research of water quality assessment, and a new method for water quality assessment was proposed according to the HDE: by harmony degree equation (WQA-HDE). First of all, the calculation steps and ideas of this method were described in detail, and then, this method with some other important methods of water quality assessment (single factor assessment method, mean-type comprehensive index assessment method, and multi-level gray correlation assessment method) were used to assess the water quality of the Shaying River (the largest tributary of the Huaihe in China). For this purpose, 2 years (2013-2014) dataset of nine water quality variables covering seven monitoring sites, and approximately 189 observations were used to compare and analyze the characteristics and advantages of the new method. The results showed that the calculation steps of WQA-HDE are similar to the comprehensive assessment method, and WQA-HDE is more operational comparing with the results of other water quality assessment methods. In addition, this new method shows good flexibility by setting the judgment criteria value HD₀ of water quality; when HD₀ = 0.8, the results are closer to reality, and more realistic and reliable. Particularly, when HD₀ = 1, the results of WQA-HDE are consistent with the single factor assessment method, both methods are subject to the most stringent "one vote veto" judgment condition. So, WQA-HDE is a composite method that combines the single factor assessment and comprehensive assessment. This research not only broadens the research field of theoretical method system of harmony theory but also promotes the unity of water quality assessment method and can be used for reference in other comprehensive assessment.

A cloud model-based approach for water quality assessment

Water quality assessment entails essentially a multi-criteria decision-making process accounting for qualitative and quantitative uncertainties and their transformation. Considering uncertainties of randomness and fuzziness in water quality evaluation, a cloud model-based assessment approach is proposed. The cognitive cloud model, derived from information science, can realize the transformation between qualitative concept and quantitative data, based on probability and statistics and fuzzy set theory. When applying the cloud model to practical assessment, three technical issues are considered before the development of a complete cloud model-based approach: (1) bilateral boundary formula with nonlinear boundary regression for parameter estimation, (2) hybrid entropy-analytic hierarchy process technique for calculation of weights, and (3) mean of repeated simulations for determining the degree of final certainty. The cloud model-based approach is tested by evaluating the eutrophication status of 12 typical lakes and reservoirs in China and comparing with other four methods, which are Scoring Index method, Variable Fuzzy Sets method, Hybrid Fuzzy and Optimal model, and Neural Networks method. The proposed approach yields information concerning membership for each water quality status which leads to the final status. The approach is found to be representative of other alternative methods and accurate.