Water Quality Model Research Articles

Coupled Hydrodynamic and Biogeochemical Modeling in the Galician Rías Baixas (NW Iberian Peninsula) Using Delft3D: Model Validation and Performance

Estuaries are dynamic and resource-rich ecosystems renowned for their high productivity and ecological significance. The Rías Baixas, located in the northwest of the Iberian Peninsula, consist of four highly productive estuaries that support the region’s economy through key fisheries and aquaculture activities. Numerical modeling of biogeochemical processes in the rias is essential to address environmental and anthropogenic pressures, particularly in areas facing intense human development. This study presents a high-resolution water quality model developed using Delft3D 4 software, integrating the hydrodynamic (Delft3D-FLOW) and water quality (Delft3D-WAQ) modules. Calibration and validation demonstrate the robust performance and reliability of the model in simulating critical biogeochemical processes, such as nutrient cycling and phytoplankton dynamics. The model effectively captures seasonal and spatial variations in water quality parameters, including water temperature, salinity, inorganic nutrients, dissolved oxygen, and chlorophyll-a. Of the variables studied, the model performed best for dissolved oxygen, followed by nitrates, phosphates, ammonium, silicate, and chlorophyll-a. While some discrepancies were observed in the inner zones and deeper layers of the rias, the overall performance metrics aligned closely with the observed data, enhancing confidence in the model’s utility for future research and resource management. These results highlight the model’s value as a tool for research and managing water and marine resources in the Rías Baixas.

Short-term forecasting of dissolved oxygen based on spatial-temporal attention mechanism and kernel-based loss function

Dissolved Oxygen (DO) is a fundamental indicator of the health of aquatic ecosystems. This study introduces a novel spatial-temporal attention model combined with Kernel Mean Squared Error loss function (STA-KMSE) for forecasting short-term DO. An attention-based Convolutional Neural Network (A-CNN) is used to incorporate the influence of upstream stations in the spatial module, a factor often overlooked in prior studies. The temporal module captures the influence of both previous hours and days using attention-based Long Short-Term Memory (A-LSTM), offering a more comprehensive temporal context. Our approach addresses the issue of outlier sensitivity and non-linear error patterns by utilizing the KMSE loss function, which improves the model's robustness and more effectively captures intricate variations in DO levels compared to the standard Mean Squared Error (MSE) commonly used in water quality modelling. The model is trained on an extensive dataset consisting of various water quality parameters collected from twenty locations along the Ganga River, its tributary, and nallahs spanning from 1 April 2017 to 30 April 2021. In terms of RMSE, STA-MSE outperforms A-CNN by 62.669 % and A-LSTM by 7.419 %. Compared to STA-MSE, the STA-KMSE shows an average reduction in RMSE by 3.067 % for one-step, 3.197 % for two-step, and 3.428 % for three-step forecasting. Experiments indicate better performance for river locations than for nallahs. SHapley Additive exPlanation (SHAP) analysis highlights pH and temperature as crucial factors for estimating DO in rivers, while level, temperature, conductivity, total suspended solids, pH, chlorine, and chemical oxygen demand are significant for nallahs.

Insights into the effects of river network topology on sudden pollution risks

The risk of sudden water pollution is one of the biggest obstacles to ensuring the safety of river basin water environments. While external pressure factors such as illegal discharge have been extensively studied with regard to their impact on the risk of sudden pollution, the influence of the river network topology has received limited attention. To fill this gap, extensive computational experiments were conducted in this study to investigate the effects of river network topology on the potential risk of sudden pollution. Based on the real-world characteristics of dendritic river networks, we used optimal channel networks to generate various river network topologies, represented by seven indicators that encompass morphology, connectivity, and structure. The water quality model simulated the spatial and temporal responses of the samples to a wide range of sudden pollution scenarios, and quantified the associated risk of sudden pollution using three proxy indicators: the maximum standard-exceeding multiple, proportion of standard-exceeding river length, and duration of exceedance. The results showed that among the seven indicators, the average outlet path (Do) and geometric fractal dimension (Dg) had a significant impact on risk. This is because network topology alters hydrological signatures such as discharge, velocity, and travel time. Moreover, multi-group analysis of structural equations revealed that the aspect ratio influences the impact of structural and connectivity indicators on risk by modulating pollution range and concentration. This study revealed the topological factors of the risk of sudden water pollution in dendritic river networks, emphasizing the regulatory role of topology in potential sudden water pollution risk and providing valuable insights for river management and planning.

Risk-Based River Management Case Study Of Pocong River Bangakalan Regency

Watershed (DAS) is a land area that includes rivers and their tributaries, which functions to accommodate, store, and channel rainwater to lakes or the sea naturally. In Bangkalan Regency, the Pocong River plays an important role in the local ecosystem. This river is used for various needs, such as irrigation, industry, and household consumption. The upstream part of the Pocong River, which is classified as class I, is used for drinking water and recreation, while the downstream part, in class II, is used for fish farming and crop irrigation. However, with increasing human activity, the water quality of the Pocong River has decreased due to pollution from domestic, agricultural, and industrial waste. Research using MIKE21 software will model the water quality in both parts of the river to provide an overview of the water quality conditions in the Pocong River. This study aims to model the quality of the upstream part of the Pocong River, class I and the downstream part, class II. The expected results of this study are to provide structured data and information regarding the water quality model of the Pocong River in the upstream and downstream parts.

Combined Effects of Treatment and Sewer Connections to Reduce Future Microplastic Emissions in Rivers.

Global mitigation strategies are needed to reduce the amount of microplastics reaching our oceans via rivers. However, what strategies will be most effective, and when and where to implement these strategies is unclear. We applied the global water quality model MARINA-Plastics, covering 10,226 sub-basins worldwide, to assess the effects of different emission reduction strategies on microplastic inputs to rivers worldwide over the period 2010-2100, taking time steps of 10 years. We applied four scenarios: three focused on wastewater treatment technologies, ranging from high to low technology improvement levels, and one combining high technology in wastewater treatment with source-oriented measures. The results show that the combined strategy of high wastewater treatment and source-oriented measures is expected to be the most effective for reducing future microplastics in rivers on a global scale. By 2100, this combined strategy is expected to result in a 68% microplastic reduction in global rivers compared to 2010. African rivers will be the main hotspots, receiving more than five times more microplastics in 2100 than in 2010. In 2100, wear from car tires is expected to be the dominant source of microplastics globally. Our insights support the implementation of the European Green Deal and the realization of Sustainable Development Goal 6 (clean water).

The dominant mechanisms of nutrient cycling in high-dam reservoirs: retention, transport or transformation?

Abstract High-dam reservoirs can significantly affect nutrient cycling processes across the globe. However, the research community now has two contradictive views (i.e. retention versus transformation) about the impact of high-dam reservoirs on nutrient cycling due to incomplete information obtained from limited field samplings. To resolve this issue, we develop a physically-based, three-dimensional water quality model to examine the spatiotemporal distributions of biogenic elements (nitrogen and phosphorus) in high-dam reservoirs with high spatial and temporal details. We apply the model to the Xiaowan Reservoir, a representative high-dam reservoir in the Lancang River Basin. By scrutinizing the spatiotemporal distributions of biogenic elements across space and over time, we find a unique ‘retention-transformation-transportation’ process of nitrogen and phosphorus in the high-dam reservoir, with dominant transformation occurring in the water zone before the dam during non-flood period while dominant retention occurring in the middle part of a reservoir during flood period. We further find that transformations of biogenic elements are enhanced only in low-temperature and low-oxygen environments. Our findings show solid scientific basis to resolve the contradictive views about nutrient cycling mechanisms in high-dam reservoirs, and provide important policy implications for the operation of high-dam reservoirs to achieve improved water quality while maintaining clean energy supply.

A Review on Storage Process Models for Improving Water Quality Modeling in Rivers

Water quality is intricately linked to the global water crisis since the availability of safe, clean water is essential for sustaining life and ensuring the well-being of communities worldwide. Pollutants such as industrial chemicals, agricultural runoff, and untreated sewage frequently enter rivers via surface runoff or direct discharges. This study provides an overview of the key mechanisms governing contaminant transport in rivers, with special attention to storage and hyporheic processes. The storage process conceptualizes a ubiquitous reactive boundary between the main channel (mobile zone) and its surrounding slower-flow areas (immobile zone). Research from the last five decades demonstrates the crucial role of storage and hyporheic zones in influencing solute residence time, nutrient cycling, and pollutant degradation. A review of solute transport models highlights significant advancements, including models like the transient storage model (TSM) and multirate mass transport (MRMT) model, which effectively capture complex storage zone dynamics and residence time distributions. However, more widely used models like the classical advection–dispersion equation (ADE) cannot hyporheic exchange, limiting their application in environments with significant storage contributions. Despite these advancements, challenges remain in accurately quantifying the relative contributions of storage zones to solute transport and degradation, especially in smaller streams dominated by hyporheic exchange. Future research should integrate detailed field observations with advanced numerical models to address these gaps and improve water quality predictions across diverse river systems.

Impact of Forest Dieback on Hydrology and Nitrogen Export Using a New Dynamic Water Quality Model

AbstractForest status is crucial for catchment hydrology and water quality but is increasingly disturbed by human activities and climatic factors. Therefore, it is urgently necessary to develop water quality models that can adapt to these changes. This study used a new dynamic Hydrological Predictions for the Environment (HYPE) model to assess the effect of rapid and continuous forest changes on catchment hydrology and nitrogen export. The modified HYPE model was implemented for the 25 years period in the Große Ohe catchment in Germany, which has experienced severe forest dieback and recovery. Due to the stochastic nature of infestation events, data covering the entire process of forest change are rare. The modified HYPE model performed well at different scales for discharge and dissolved inorganic nitrogen (DIN) export. It was able to (a) capture the timing of peak flows and the seasonal DIN concentration dynamics and (b) reflect the initial increase and subsequent decrease trend in discharge and DIN export in accordance with forest dieback and regeneration. The increase in nitrogen export after forest dieback primarily resulted from reduced forest uptake and increased soil nitrogen availability from tree residues. The difference in runoff and nitrogen export increment with or without regeneration highlights the importance of forest regeneration in restoring catchment hydrology and water quality. Additionally, a decrease in DIN export after residue removal implies the impact of sound post‐disturbance management strategies. The dynamic modeling under changing catchment forests can enhance the analysis of catchment water quality and effectively support forest management.

Chlorine dosage management in drinking water systems: comparing Bayesian optimization to evolutionary algorithms

ABSTRACT To maintain a sufficient chlorine residual in water distribution systems (WDSs), chlorine dosage needs to be optimized. The majority of previous studies that aimed to optimize chlorine dosage in WDSs considered single-species water quality (WQ) models featuring chlorine decay with simple reaction kinetics. Recent efforts have proposed using multi-species water quality (MS-WQ) models to account for chlorine interactions with various chemical and microbiological species, thus providing a comprehensive and accurate evaluation of the WQ within WDSs. Nevertheless, the key challenge of implementing MS-WQ models within optimization frameworks is their high computational cost and poor scalability for larger WDSs. Furthermore, previous optimization studies generally relied on evolutionary algorithms (EAs), which require conducting a significant number of WQ simulations. Bayesian optimization (BO) has been recently suggested as an efficient alternative to EAs for the optimization of computationally expensive functions. This study aims to present a systematic comparison between BO and other widely used EAs for the optimization of MS-WQ in WDSs. A case study featuring a real-life, midsized benchmark WDS was implemented to comprehensively evaluate all three optimization techniques. The results revealed that BO is notably more computationally efficient and less sensitive to changes in the constraints than EAs.

Enhancing long-term water quality modeling by addressing base demand, demand patterns, and temperature uncertainty using unsupervised machine learning techniques

Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, these uncertainties can result in inaccurate simulations. This study proposes a novel framework that leverages unsupervised machine learning, specifically a Gaussian Mixture Model (GMMs), to represent and integrate these uncertainties in the simulation. By classifying historical water demand into fuzzy clusters, the framework allows for certain linguistic inputs (e.g., "high" or "low" demand) to be used in water quality simulations. The framework also incorporates representative hourly demand patterns and temperature-dependent chlorine decay constants based on historical data correlations. Validations were conducted on the Anytown network using WNTR-EPANET, comparing simulated chlorine residuals with Validation data from 181 steady-state simulations. The simulation through the framework achieved a Jensen-Shannon Divergence (JSD) of <0.008 across all demand clusters, indicating high similarity between predicted and actual probability distributions . In comparison to other simulation scenarios tested, which exhibited increased variability (JSD > 0.18), the proposed framework demonstrated improved accuracy in representing chlorine residual distributions. The methodology is adaptable to other systems, if similar historical datasets containing key variables, such as flow rates and temperature, are provided. While the framework offers a more flexible and accurate approach to handling uncertainties in WDS, its effectiveness is contingent upon the availability of robust historical demand and temperature data for decay constant calibration.

ОЦЕНКА АНТРОПОГЕННОЙ НАГРУЗКИ НА ВОДОСБОР БАССЕЙНА РЕКИ ТОБЫЛ ОТ ИСТОЧНИКОВ ЗАГРЯЗНЕНИЯ С МАТЕМАТИЧЕСКИМ МОДЕЛИРОВАНИЕМ КАЧЕСТВА ПОВЕРХНОСТНЫХ ВОД

Based on a systematic analysis of the structure and dynamics of water resource usage in the catchment area of the Tobyl River basin, the dynamics of the formation of geoecological conditions of water use in the economic sectors within the Kostanay region of the Republic of Kazakhstan were studied. The analysis revealed a significant load from return waters and pollutants, indicating a high and potentially catastrophic pressure that requires regulation of both natural and anthropogenic activities to ensure the region’s sustainable development in the future. A generalized water use scheme for the Kazakh part of the Tobyl River basin was developed, which facilitates the formation of mathematical, physical, and chemical indicators for water management and hydrochemical balance equations. This, in turn, allowed for the creation of a mathematical model of water quality and the determination of the maximum permissible discharge volume of return waters in the river basins. The model ensures compliance with sanitary-epidemiological standards and helps preserve the ecological state of water bodies while supporting the planning of water protection measures to achieve the desired level of water quality in the river.

Water quality benefits of implementing Green and Blue Infrastructure in a peri-urban catchment – Case study of a Brazilian metropolis

This paper investigates the effectiveness of Green and Blue Infrastructure (GBI) in managing non-point source pollution in the 120 km2 catchment of the Vargem das Flores reservoir, which supplies water to 600,000 inhabitants in the Belo Horizonte metropolitan area, Brazil's third-largest metropolis. A calibrated water quality model within the Storm Water Management Model (SWMM) was used to simulate total suspended solids (TSS) and nutrients loads under different future land use scenarios. The first scenario reflects maximum urban development according to current legislation. An alternative scenario was proposed considering the protection of forest remnants and the adoption of the most cost-effective GBI techniques in newly developed areas. Results indicated a substantial contribution of diffuse pollution to the reservoir's water quality degradation, mainly due to TSS loads transported from the most urbanized subcatchments. The application of bioretention cells to treat 12.6 km2 new impervious areas effectively mitigated diffuse pollution, removing 52.1% of TSS, 19.5% of total phosphorus (TP), and 17.1% of chemical oxygen demand (COD) loads flowing into the reservoir. The GBI implementation would maintain pollutant loads similar to current levels despite the increased urbanization.

Settling and rising velocities of microplastics: Laboratory experiments and lattice Boltzmann modeling

Microplastics (MPs) have become pervasive in marine ecosystems, potentially causing environmental degradation, impacting ecological function, and posing a serious public health risk. Despite the widespread distribution of MPs, their vertical transport within a water column has limited understanding, representing a key knowledge gap in the development of water quality models to minimize these risks. In this study, 6152 individual particles of six common types of MPs were observed through water column experiments to examine a range of drivers of the vertical velocity of MPs, including particle density and size, biofilm growth, water temperature, and salinity. The experimental results revealed that the vertical velocity of MPs obeyed Stokes’ law under laminar conditions; increasing salinity decreased the settling tendency of the particles. Moreover, biofilm attachment induced notable alterations in particle characteristics within 60 days, resulting in slower settling velocities (up to a 21.9% change for non-buoyant MPs) and even a reversed vertical direction (up to several times for buoyant particles). Furthermore, a lattice Boltzmann model could predict the vertical velocity of MPs with reasonable accuracy, especially for small particles. This work facilitates the development of sophisticated models/formulas that integrate particle morphology, hydrodynamics, and biological factors to enhance the understanding of MP transport through the river-to-coastal continuum.

Assessment and Modeling of Water Quality in the Niger River: A Comprehensive Study Using Water Quality Indices and Intelligent Techniques

The Niger River is essential to the livelihoods of those in Bamako and its neighboring areas, providing vital resources for drinking water, agriculture, livestock, industry, and fishing. Given its significance across these sectors, there is a pressing need to establish an effective water resource management strategy that incorporates a thorough qualitative assessment of the river's water quality. This research seeks to characterize the water quality of the Niger River by employing Water Quality Indices (WQI) and intelligent modeling techniques. To fulfil this objective, various physicochemical parameters, including pH, electrical conductivity (EC), nitrate (NO3-), nitrite (NO2-), and iron (Fe), were collected from 40 sampling points along the river during three distinct periods: December 2017, March 2018, and July 2018. The study utilized a weighted arithmetic approach to compute the WQIs, while the predictive models were developed using two of the most famous and effective modeling techniques namely Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In order to evaluate the predictive performance of the models, the dataset was partitioned into three distinct segments, allocating 60% for training purposes, 20% for validation, and the remaining 20% for testing, with the segments organized in a sequence from upstream to downstream. The performance of both models was evaluated using metrics such as the correlation coefficient (r²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The computed Water Quality Indices (WQIs) vary from 0.44 to 1887.40, indicating a diverse range of water quality across the samples analyzed. The classification of these samples reveals that 62.5% are considered excellent, while 15% are categorized as good, another 15% as poor, 2.5% as very poor, and 5% as unsuitable for consumption. Furthermore, the results derived from ANN with five inputs, one hidden layer (13 neurons) and one output (WQI) demonstrates superior efficiency in assessing water quality.

How uncertainty in calibration data affects the modeling of non-point source pollutant loads in baseflow

Baseflow is a major transport pathway for non-point source (NPS) pollutants. Watershed water quality (WWQ) models calibrated by low-quality data may produce misleading predictions of baseflow NPS pollutant loads, resulting in poor management decisions. We evaluated how models of the baseflow nitrate loads in the Huron River basin, southwest of Lake Erie, were affected by uncertainty in the calibration data. Based on a five-year time series of daily streamflow, nitrate concentration, and specific conductance, two sets of “observed” baseflow nitrate load data that include uncertainty were estimated using various tracer-based and non-tracer-based hydrograph separation methods, in conjunction with assumptions regarding baseflow nitrate concentrations. We calibrated the Soil and Water Assessment Tool plus (SWAT+) model with the two “observed” data sets and used the Generalized Likelihood Uncertainty Estimation (GLUE) approach to quantify parameter and predictive uncertainties. The results showed that baseflow accounted for 26 %–34 % of the mean annual total streamflow (11.8 m3/s) and 8 %–37 % of the mean annual total nitrate load (14.3 kg·ha−1·year−1) in the Huron River basin. The baseflow and nitrate load estimates from the non-tracer-based methods resembled those from the tracer-based method but had greater uncertainty. The posterior parameter distributions, as well as the weighted means and 90 % prediction intervals of the simulated baseflow nitrate loads, exhibited minimal variation when different calibration data sets for SWAT+ and different threshold likelihood values for GLUE were used. Our analysis emphasizes the necessity of calibrating WWQ models with baseflow pollutant loads/concentrations when addressing water quality issues related to baseflow. It also demonstrates the feasibility of utilizing multiple non-tracer-based hydrograph separation methods to estimate baseflow NPS pollutant loads. These non-tracer-based methods offer a simplicity and broader applicability compared to tracer-based methods. This study has provided insights into how calibration data uncertainty impacts the modeling of NPS pollution in baseflow and highlights the practical value of non-tracer-based hydrograph separation methods.

Application of a reaction-based water quality model to the total dissolved solids concentration of the Pasig River.

With the goal to support effective water resource management, water quality models have gained popularity as tools for evaluating the distributions of pollutants and sediments. This work focuses on the application of the numerical solution of an advection-dispersion-reaction (ADR) water quality model for rivers and streams to a major Philippine waterway, the Pasig River. The water quality constituent is described by a system of reaction and advection-dispersion-reaction equations. The model and method are based on a previously used strategy where Guass-Jordan decomposition is applied to the matrix system and the resulting conservative form of the model is solved numerically using the fully implicit scheme and finite element method. The methodology is demonstrated by a case study in Pasig River involving the concentrations of total dissolved solids (TDS) obtained from the Department of Environment and Natural Resources (DENR) through the Pasig River Unified Monitoring Stations (PRUMS) report. Sensitivity analysis and parameter estimation are also applied to the model to assess which parameters influence the model output the most.

The importance of source data in river network connectivity modeling: A review

AbstractRiver network connectivity (RC) describes the hydrologic exchange of water, nutrients, sediments, and pollutants between the river channel and other “sites” via heterogenous flowpaths along the river corridor. As water moves downstream it carries these constituents, creating a stream‐to‐ocean continuum of connectivity that regulates global water, carbon, and nutrient cycling. River network connectivity models have developed over many decades, culminating in recent years with network‐scale RC models that explicitly simulate the transport and exchange of water and elements from headwaters to coasts, sometimes requiring models to contain tens of millions of river reaches. These advances provide transformative insights into the aggregate effects of RC on water and material transport across scales from local to global. Yet, recent reviews have pointed to several challenges that need to be overcome to continue advancing network‐scale RC modeling. In service of these goals, I summarize recent network‐scale RC maps and models to identify similarities and differences across the large‐scale RC modeling landscape. Although our computational and upscaling abilities have significantly improved and have revealed new insights, current models are still limited by the quantity, quality, resolution, and lack of standardization of the available in situ databases and source data maps necessary for the modeling. This suggests that we can extend recent advances if we keep improving these source datasets, while continuously revisiting our physics and theory to explain those new data. In doing so, we will continue to expand the role of network‐scale RC models in informing water quality modeling and management into the future.

Water Quality Modeling of Wastewater Discharges for Prediction of Sediment Transport and Deposition in Surface Water

Sediment deposition in surface water bodies can have a range of significant impacts, affecting everything from the aquatic ecosystem to water quality and infrastructure. It can affect the availability of food sources for aquatic organisms; and change the physical structure of habitats, such as riverbeds and lake bottoms. It can equally smother aquatic plants and animals, leading to a decline in biodiversity. A model of the governing equations of particle motion and fluid flow was developed with COMSOL Multiphysics 5.3a. With a coefficient of determination of 0.99, the model result demonstrated good agreement after result validation using experimental data. According to study findings, sediments in the study area, Ele River move more slowly than sediments in the midstream which causes a high deposition of sediments at the river banks. This sediment deposition affects the irrigation system for crop production considering the ability of the sediments to block sprinkler nozzles which limits it from supplying sufficient water for the production of crops.

Agronomic and environmental effects of forage-cutting schedule and nitrogen fertilization for bermudagrass (Cynodon dactylon, L.)

Bermudagrass (Cynodon dactylon, L.) is widely used as a forage in ruminant diets owing to its nutritional value and its capacity to grow under various agroecological conditions. For the southeastern United States climate conditions, previous research on bermudagrass recommended the cultivar Tifton 85 for forage production and a monthly frequency for forage-cutting. Even though bermudagrass cultivars are known to positively respond to nitrogen (N) fertilization, the interplay of the forage-cutting and N fertilization rates is not well understood. As a result, there is not sufficient guidance for adequate harvesting schedules and fertilization management to optimize bermudagrass forage production. Hence, this study aims to clarify the interplay of biomass-cutting events and N fertilization rates on forage quality and quantity, and N footprint. The study used experimental data of bermudagrass Tifton 85 forage production under three N fertilization rates (i.e., high = 504 kg N ha−1, medium = 336 kg N ha−1, and low = 168 kg N ha−1), to calibrate and validate Root Zone Water Quality Model 2 (RZWQM2) for biomass weight and biomass N content. For each N fertilization rate, multiyear simulations of four biomass-cutting scenarios were used to investigate the joint effects of harvesting schedules and N fertilization rates on biomass weight, biomass N content, N use efficiency (NUE), and N leaching. Results show statistically significant effects of the biomass-cutting scenarios on biomass weight and biomass N content for both high and medium N fertilization rates. The interplay of biomass-cutting and N fertilization reflected differently on forage quality and quantity, and N footprint. The low N fertilization did not show any statistically significant effect except for the biomass weight. NUE values were higher with both medium and low N fertilization rates compared to the high N fertilization which showed a relatively high N leaching. The outcomes of this study can be used to inform bermudagrass cutting and fertilization options to achieve forage yield goals with an understanding of the potential environmental consequence of N leaching and low NUE.

Modelling pesticide concentrations in Japanese paddy fields using the RICEWQ model

Increasing concerns about plant protection products in surface waters have highlighted the importance of pesticide monitoring and modelling, both nowadays integral components of the pesticide registration process. The Rice Water Quality (RICEWQ) model predicts the fate and transport of pesticides under various paddy environmental conditions. The model has been used widely for regulatory purpose in the U.S., while its use in Europe has been limited to regulatory submissions, despite multiple recommendations to adopt RICEWQ as a high tier assessment model for regulatory purposes. The applicability of the RICEWQ model under Japanese agricultural conditions remains unexplored. This study leverages field experimental data to evaluate the RICEWQ model's capability to simulate daily concentrations of two herbicides, mefenacet and pretilachlor, in paddy water and paddy soil in Japan.The RICEWQ model provided a reasonable level of performance for predicting the fate and transport of the two herbicides by ensuring that the simulated daily water balance corresponded with field observations and with minor pesticide-specific calibration. To further improve the performance of the simulations, we implemented a straightforward calibration framework. Calibrating the RICEWQ model improved the accuracy of all simulations; for both herbicides and in both paddy water and paddy soil compartments, the lowest Nash-Sutcliffe efficiency achieved was 0.725, demonstrating excellent performance. However, the RICEWQ model consistently underestimated the peak concentrations of herbicides in paddy water within the first three days of simulation. Simulation results suggested that approximately 50 % of the total applied herbicide was lost from the paddy system. Increasing the duration of the water holding period after pesticide application or increasing the storage capacity of the rice field via excess water storage depth management has the potential to reduce greatly herbicide loss to below 10 % of the total applied herbicide.