Watershed Water Quality Models Research Articles

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.

A hybrid approach to improvement of watershed water quality modeling by coupling process-based and deep learning models.

Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process-based models (PBMs) are typically used to simulate water quality modeling. In watershed modeling utilizing PBMs, it is crucial to effectively reflect the actual watershed conditions by appropriately setting the model parameters. However, parameter calibration and validation are time-consuming processes with inherent uncertainties. Addressing these challenges, this research aims to address various challenges encountered in the calibration and validation processes of PBMs. To achieve this, the development of a hybrid model, combining uncalibrated PBMs with data-driven models (DDMs) such as deep learning algorithms is proposed. This hybrid model is intended to enhance watershed modeling by integrating the strengths of both PBMs and DDMs. The hybrid model is constructed by coupling an uncalibrated Soil and Water Assessment Tool (SWAT) with a Long Short-Term Memory (LSTM). SWAT, a representative PBM, is constructed using geographical information and 5-year observed data from the Yeongsan River Watershed. The output variables of the uncalibrated SWAT, such as streamflow, suspended solids (SS), total nitrogen (TN), and total phosphorus (TP), as well as observed precipitation for the day and previous day, are used as training data for the deep learning model to predict the TP load. For the comparison, the conventional SWAT model is calibrated and validated to predict the TP load. The results revealed that TP load simulated by the hybrid model predicted the observed TP better than that predicted by the calibrated SWAT model. Also, the hybrid model reflects seasonal variations in the TP load, including peak events. Remarkably, when applied to other sub-basins without specific training, the hybrid model consistently outperformed the calibrated SWAT model. In conclusion, application of the SWAT-LSTM hybrid model could be a useful tool for decreasing uncertainties in model calibration and improving the overall predictive performance in watershed modeling. PRACTITIONER POINTS: We aimed to enhance process-based models for watershed water-quality modeling. The Soil and Water Assessment Tool-Long Short-Term Memory hybrid model's predicted and total phosphorus (TP) matched the observed TP. It exhibited superior predictive performance when applied to other sub-basins. The hybrid model will overcome the constraints of conventional modeling. It will also enable more effective and efficient modeling.

Is comprehensive event sampling necessary for constraining process models of water quality? A comparison of high and low frequency phosphorus sampling programs for constraining the HYPE water quality model

Model parameter calibration is an important step in process-based watershed water quality modelling. Calibration is commonly performed using readily available water quality data from long-term monitoring programs that collect samples periodically at a relatively low frequency (i.e. monthly). Higher frequency synoptic and targeted event data is becoming available from more intensive monitoring programs, yet there remains little consensus on whether relatively lower frequency data are sufficient to constrain process-based watershed water quality models. To investigate the effects of using water quality data from differing sampling regimes for calibration, we use an implementation of HYPE (HYdrological Predictions for the Environment), a process-based watershed water quality model, in Southern Ontario, Canada. HYPE was calibrated with two stream water quality datasets, one associated with the long-term Ontario Provincial (Stream) Water Quality Monitoring Network and characterized by routine approximately monthly sampling frequency, and one associated with the Multi-Watershed Nutrient Study and characterized by semi-regular synoptic sampling during baseflow and at increased frequency (∼4 to 8 h) during event flow. Performances varied widely between sites, with validation ranges of daily predictions having Nash-Sutcliffe Efficiencies (NSE) from > −1 to 0.48 for flow and > −1 to 0.98 for total Phosphorus. Medians for daily data during validation ranged from 0.15 to 0.24 for flow and from 0.19 to 0.36 for total phosphorus. We found that model performance and simulated total phosphorus loads were similar for the two calibration datasets, potentially suggesting that the dataset with approximately monthly water quality sampling was adequate to constrain the HYPE model. We attribute this to a combination of similar levels of statistical variability in the calibration datasets and prior knowledge in the HYPE model structure. Furthermore, while model performance was similar when HYPE was calibrated with datasets of differing sampling strategies, there was a large range in model performance within each modelling domain. Results suggest that this range in performance could be due in part to poor representation of cold-weather processes in the HYPE model, as sites with higher mean annual temperature and fewer freezing days had better model performance.

Assessing ammonium pollution and mitigation measures through a modified watershed non-point source model

Watershed water quality modeling is a valuable tool for managing ammonium (NH4+) pollution. However, simulating NH4+ pollution presents unique challenges due to the inherent instability of NH4+ in natural environment. This study modified the widely-used Soil and Water Assessment Tool (SWAT) model to simulate non-point source (NPS) NH4+ processes, specifically incorporating the simulation of land-to-water NH4+ delivery. The Jiulong River Watershed (JRW) is the study area, a coastal watershed in Southeast China with substantial sewage discharge, livestock farming, and fertilizer application. The results demonstrate that the modified model can effectively simulate the NPS NH4+ processes. It is recommended to use multiple sets of observations to calibrate NH4+ simulation to enhance model reliability. Despite constituting a minor proportion (5.6 %), point source inputs significantly contribute to NH4+ load at watershed outlet (32.4∼51.9 %), while NPS inputs contribute 15.3∼17.3 % of NH4+ loads. NH4+ primarily enters water through surface runoff and lateral flow, with negligible leaching. Average NH4+ land-to-water delivery rate is about 2.35 to 2.90 kg N/ha/a. High delivery rates mainly occur at agricultural areas. Notably, proposed NH4+ mitigation measures, including urban sewage treatment enhancement, livestock manure management improvement, and fertilizer application reduction, demonstrate potential to collectively reduce the NH4+ load at watershed outlet by 1/4 to 1/3 and significantly enhance water quality standard compliance frequency. Insights gained from modeling experience in the JRW offer valuable implications for NH4+ modeling and management in regions with similar climates and significant anthropogenic nitrogen inputs.

Assimilating Low‐Cost High‐Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach

AbstractUncertainty reduction in watershed water quality (WWQ) modeling remains a major challenge. One important reason is the lack of sufficient available water quality observations because traditional laboratory analysis of water samples has high labor, financial and time costs. Low‐cost high‐frequency water quality data from in‐situ sensors provide an opportunity to solve this problem. However, long‐term sensing in complex natural environments usually suffers more significant errors. This study aimed to develop a novel method to utilize in‐situ sensor data in WWQ modeling, namely, the Bayesian calibration using multisource observations (BCMSO), which can simultaneously assimilate laboratory‐based observations and in‐situ sensor data. Both synthetic and real‐world cases of nitrate modeling were used to demonstrate the methodology, and the Soil and Water Assessment Tool was employed as the WWQ model. The results indicated that direct assimilation of sensor data using traditional Bayesian calibration generated obvious deviations in parameter inference and model simulation, which could consequently bias future predictions and affect management decision correctness. However, after proper treatment of errors in sensor data, the BCMSO method could extract meaningful information from sensor data and prevent negative impacts of errors. The modeling uncertainty was also greatly reduced. In the real‐world case, with 1 yr of subhourly electrical conductivity sensor data incorporated, the modeling uncertainty of nitrate concentration and management cost of controlling nitrate pollution were reduced by 70%. The BCMSO method provides a flexible framework to accommodate nonconventional observations in environmental modeling and can be easily extended to other modeling fields.

Using Arc GIS to explore the impact of environmental and ecological factors on water body

As water pollution is more and more serious, ArcGIS is proposed to explore the impact of environmental and ecological factors on water. Taking the river water quality as the research object, this paper simulates and analyzes the endogenous and non-point source pollution and water quality through indoor physical model experiment, hydrological and water quality numerical model and water quality numerical model, and analyzes the impact of different environmental changes on river water quality and pollution sources from micro and macro perspectives. The main contents include: experimental study on the influence mechanism of overlying water velocity, disturbance and water temperature on sediment endogenous release, construction and simulation of watershed non-point source pollution model, construction and simulation of watershed river water quality model, as well as the impact of environmental change on river water quality and quantitative analysis of river pollution sources.

Integrating field observations and process-based modeling to predict watershed water quality under environmental perturbations

Watersheds play a critical role in supplying water resources needed for human use and ecosystem health. Understanding and predicting how, when, and where changes in the quantity and quality of water resources occur under different environmental stresses including extreme events is crucial for sustainable management of water resources under a changing environment. However, few studies have attempted to quantify or identify the factors and process interactions controlling the impact of extreme events across watershed systems. Only few large-scale studies include coordinated monitoring and modeling efforts, which limits our ability to assess the large-scale impact of extreme events on water supply and quality. Methods are lacking to propagate uncertainty in process understanding through an integrated hydro-biogeochemical model framework and evaluate its importance, thus failing to take full advantage of the information potentially available through transformative advances in characterization technologies from high-resolution mass spectrometry to airborne and satellite-based remote sensing. There are consequent risks to our nation’s water security and to human and ecosystem health that may become exacerbated with the increasing frequency of extreme events that is projected for the coming decades. This paper reviews the current status of watershed science for both water quantity and quality and identifies critical gaps in our current knowledge and modeling capability in addressing the emergent needs in predicting watershed hydrologic and biogeochemical responses (i.e., water quantity and quality) under natural and anthropogenic perturbations. We highlight the need to (1) understand how environmental perturbations including extreme events like floods and droughts and anthropogenic changes such as deforestation and urbanization propagate through watershed systems and assess their short- and long-term impacts on watershed biogeochemistry, water quality and their recovery pathways; (2) develop and improve a watershed water quality model that reflects the state of scientific understanding gained from observations; and (3) construct a data-model fusion system for watershed characterization, process identification, and mechanistic model parameterization. A large base of modeling, monitoring and data capabilities have been built by various federal government agencies given the relevance of water to their critical missions. An emerging need is to build an integrated national capability for watershed water availability and quality that can address water-related missions across multiple federal agencies.

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

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

Integrated Water Quality Model and Its Application in Baihe River Basin

The integrated watershed water quality model is an important tool for studying the water environment of the basin. This study explores several comprehensive watershed water quality models, analyzes the development history, structural principle, characteristics and application of the model. This paper preliminary applied SWAT model in the Baihe River Basin, Beijing, China to analyze the water quality changes. Finally, this study briefly analyzes the development trend of the basin comprehensive water quality model.

Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

Abstract Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.

Drought and weir construction impact stationarity assumption in watershed water quality modeling in South Korea

Existing watershed water quality models assume that the physio-biochemical processes affecting water quality variables are stationary. Using Hydrologic Simulation Program-Fortran (HSPF) parameterized before drought and weir construction, this study investigates if this assumption is valid in the model used for simulating water quality variables after drought and weir construction. The study area is located in South Korea, where tremendous ecological stress occurred following a drought and the completion of weir construction in 2012. To assess model performance and explore temporal changes in water quality variables, various goodness-of-fit measures and wavelet analyses are used. This study indicates that drought is the dominant stressor on water quality, and the co-occurring impact of weir construction is not discernible. Drought and weir construction cause nonstationary temporal changes in individual water quality variables and in their interrelationships, for example, dependence of dissolved oxygen on water temperature. This study demonstrated that the stationarity assumption implicit in the model parameterized under a non-drought condition is not valid during a drought condition. As a result, the model performance degrades when simulating drought-influenced physio-biochemical processes of water quality variables. The HSPF model lacks functionality for translating temporal changes in water temperature into kinetic parameters that govern biochemical processes of various water quality variables. Wavelet analysis is a good tool for elucidating unique aspects of nonstationary temporal change at various temporal scales, especially for recognizing algal succession pattern changes and identifying nonstationary relationships between any two variables.

Multiple-response Bayesian calibration of watershed water quality models with significant input and model structure errors

While watershed water quality (WWQ) models have been widely used to support water quality management, their profound modeling uncertainty remains an unaddressed issue. Data assimilation via Bayesian calibration is a promising solution to the uncertainty, but has been rarely practiced for WWQ modeling. This study applied multiple-response Bayesian calibration (MRBC) to SWAT, a classic WWQ model, using the nitrate pollution in the Newport Bay Watershed (southern California, USA) as the study case. How typical input and model structure errors would impact modeling uncertainty, parameter identification and management decision-making was systematically investigated through both synthetic and real-situation modeling cases. The main study findings include: (1) with an efficient sampling scheme, MRBC is applicable to WWQ modeling in characterizing its parametric and predictive uncertainties; (2) incorporating hydrology responses, which are less susceptible to input and model structure errors than water quality responses, can improve the Bayesian calibration results and benefit potential modeling-based management decisions; and (3) the value of MRBC to modeling-based decision-making essentially depends on pollution severity, management objective and decision maker's risk tolerance.

Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management

Watershed-scale water quality (WWQ) models are now widely used to support management decision-making. However, significant uncertainty in the model outputs remains a largely unaddressed issue. In recent years, Markov Chain Monte Carlo (MCMC), a category of formal Bayesian approaches for uncertainty analysis (UA), has become popular in the field of hydrological modeling, but its applications to WWQ modeling have been rare. This study systematically evaluated the applicability of MCMC in assessing the uncertainty of WWQ modeling, using Differential Evolution Adaptive Metropolis (DREAM(ZS)) and SWAT as the representative MCMC algorithm and WWQ model, respectively. The nitrate pollution in Newport Bay watershed was the case study for numerical experiments. It has been concluded that the efficiency and effectiveness of a MCMC algorithm would depend on some critical designs of the UA, including: (i) how many and which model parameters to be considered as random in the MCMC analysis; (ii) where to fix the non-random model parameters; and (iii) which criteria to stop the Markov Chain. The study results also indicate that the MCMC UA has to be management-oriented, that is, management objectives should be factored into the designs of the UA, rather than be considered after the UA.

Isotope‐based Fluvial Organic Carbon (ISOFLOC) Model: Model formulation, sensitivity, and evaluation

AbstractWatershed‐scale carbon budgets remain poorly understood, in part due to inadequate simulation tools to assess in‐stream carbon fate and transport. A new numerical model termed ISOtope‐based FLuvial Organic Carbon (ISOFLOC) is formulated to simulate the fluvial organic carbon budget in watersheds where hydrologic, sediment transport, and biogeochemical processes are coupled to control benthic and transported carbon composition and flux. One ISOFLOC innovation is the formulation of new stable carbon isotope model subroutines that include isotope fractionation processes in order to estimate carbon isotope source, fate, and transport. A second innovation is the coupling of transfers between carbon pools, including algal particulate organic carbon, fine particulate and dissolved organic carbon, and particulate and dissolved inorganic carbon, to simulate the carbon cycle in a comprehensive manner beyond that of existing watershed water quality models. ISOFLOC was tested and verified in a low‐gradient, agriculturally impacted stream. Results of a global sensitivity analysis suggested the isotope response variable had unique sensitivity to the coupled interaction between fluvial shear resistance of algal biomass and the concentration of dissolved inorganic carbon. Model calibration and validation suggested good agreement at event, seasonal, and annual timescales. Multiobjective uncertainty analysis suggested inclusion of the carbon stable isotope routine reduced uncertainty by 80% for algal particulate organic carbon flux estimates.

Improving water quality forecasting via data assimilation – Application of maximum likelihood ensemble filter to HSPF

Summary An ensemble data assimilation (DA) procedure is developed and evaluated for the Hydrologic Simulation Program – Fortran (HSPF), a widely used watershed water quality model. The procedure aims at improving the accuracy of short-range water quality prediction by updating the model initial conditions (IC) based on real-time observations of hydrologic and water quality variables. The observations assimilated include streamflow, biochemical oxygen demand (BOD), dissolved oxygen (DO), chlorophyll a (CHL-a), nitrate (NO 3 ), phosphate (PO 4 ) and water temperature (TW). The DA procedure uses the maximum likelihood ensemble filter (MLEF), which is capable of handling both nonlinear model dynamics and nonlinear observation equations, in a fixed-lag smoother formulation. For evaluation, the DA procedure was applied to the Kumho Catchment of the Nakdong River Basin in the Republic of Korea. A set of performance measures was used to evaluate analysis and prediction of streamflow and water quality variables. To remove systematic errors in the model simulation originating from structural and parametric errors, a parsimonious bias correction procedure is incorporated into the observation equation. The results show that the DA procedure substantially improves predictive skill for most variables; reduction in root mean square error ranges from 11% to 60% for Day-1 through 3 predictions for all observed variables except DO. It is seen that MLEF handles highly nonlinear hydrologic and biochemical observation equations very well, and that it is an effective DA technique for water quality forecasting.

Nonlinear bacterial load‐streamflow response on a marine beach

AbstractBacterial concentration (Escherichia coli) is used as the key indicator for marine beach water quality in Hong Kong. For beaches receiving streamflow from unsewered catchments, water quality is mainly affected by local nonpoint source pollution and is highly dependent on the bacterial load contributed from the catchment. As most of these catchments are ungauged, the bacterial load is generally unknown. In this study, streamflow and the associated bacterial load contributed from an unsewered catchment to a marine beach, Big Wave Bay, are simulated using a modelling approach. The physically based distributed hydrological model, MIKE‐SHE, and the empirical watershed water quality model (Hydrological Simulation Program – Fortran) are used to simulate streamflow and daily‐averaged E. coli concentration/load, respectively. The total daily derived loads predicted by the model during calibration (June–July 2007) and validation (July–October 2008) periods agree well with empirical validation data, with a percentage difference of 3 and 2%, respectively. The simulation results show a nonlinear relationship between E. coli load and rainfall/streamflow and reveal a source limiting nature of nonpoint source pollution. The derived load is further used as an independent variable in a multiple linear regression (MLR) model to predict daily beach water quality. When compared with the MLR models based solely on hydrometeorological input variables (e.g. rainfall and salinity), the new model based on bacterial load predicts much more realistic E. coli concentrations during rainstorms. Copyright © 2014 John Wiley & Sons, Ltd.

Estimation and Propagation of Parameter Uncertainty in Lumped Hydrological Models: A Case Study of HSPF Model Applied to Luxapallila Creek Watershed in Southeast USA

Estimation and Propagation of Parameter Uncertainty in Lumped Hydrological Models: A Case Study of HSPF Model Applied to Luxapallila Creek Watershed in Southeast USA Explicit quantification of the uncertainty associated to the predictions of a hydrologic model is a necessary activity to objectively evaluate and report the limitations of the model caused by different sources of error. The current state of the practice of hydrologic modeling indicates that parametric uncertainty is considered as one of the most important sources of uncertainty. Some of the most relevant problems remaining in the practice include the identification of the principal parameters affecting model predictions and quantification of parameter ranges. This study evaluated stochastically one of the most popular deterministic watershed water quality models for decision making in USA.

Investigation and Modeling of winter streamflow timing and magnitude under changing climate conditions for the Catskill Mountain region, New York, USA

AbstractSnowfall is an important part of the yearly water balance for the Catskill Mountains in New York State, the location of water supply reservoirs for New York City. Recent studies have shown that the effects of climate change on the hydrology of the Catskills will most likely create (1) a decrease in the proportion of precipitation falling as snow, (2) a shift in the timing of snowmelt that will cause snowmelt‐supplemented streamflow events to occur earlier in the fall and winter, and (3) a decrease in the magnitude of traditionally high April streamflow. The shift in timing of snowmelt‐influenced streamflow events is measured by the winter‐early spring centre of volume (WSCV), defined as the Julian Day on which half the total streamflow volume from January to May occurs. Studies of streamflow, precipitation, and temperature trends in the last 50 years have shown that the WSCV is already earlier by about 5–10 days. This study investigates the use of watershed‐scale snowpack and snowmelt algorithms that are incorporated in two existing watershed water quality models, Generalized Watershed Loading Functions‐Variable Source Area (GWLF‐VSA) and Soil and Water Assessment Tool (SWAT), to capture the potential effects of climate change on the timing and magnitude of streamflow during the late fall, winter, and early spring for the Catskill Mountain region. The GWLF‐VSA model reasonably simulated the recent shifts in the winter streamflow timing, with simulations over the previous 50‐year period yielding shifts in WSCV of 2–15 days. The SWAT model yielded similar results as the GWLF‐VSA simulations. Scenarios of potential climate change 100 years in the future showed a similar shift in direction of timing winter streamflow, but at a larger magnitude than observed to date with WSCV occurring 15–20 days earlier. Copyright © 2011 John Wiley & Sons, Ltd.

Uncertainty assessment for watershed water quality modeling: A Probabilistic Collocation Method based approach

Watershed water quality models are increasingly used in management. However, simulations by such complex models often involve significant uncertainty, especially those for non-conventional pollutants which are often poorly monitored. This study first proposed an integrated framework for watershed water quality modeling. Within this framework, Probabilistic Collocation Method (PCM) was then applied to a WARMF model of diazinon pollution to assess the modeling uncertainty. Based on PCM, a global sensitivity analysis method named PCM-VD (VD stands for variance decomposition) was also developed, which quantifies variance contribution of all uncertain parameters. The study results validated the applicability of PCM and PCM-VD to the WARMF model. The PCM-based approach is much more efficient, regarding computational time, than conventional Monte Carlo methods. It has also been demonstrated that analysis using the PCM-based approach could provide insights into data collection, model structure improvement and management practices. It was concluded that the PCM-based approach could play an important role in watershed water quality modeling, as an alternative to conventional Monte Carlo methods to account for parametric uncertainty and uncertainty propagation.

A simple concept for calibrating runoff thresholds in quasi‐distributed variable source area watershed models

AbstractMost semi‐distributed watershed water quality models divide the watershed into hydrologic response units (HRU) with no flow among them. This is problematic when watersheds are delineated to include variable source areas (VSAs) because it is the lateral flows from upslope areas to downslope areas that generate VSAs. Although hydrologic modellers have often successfully calibrated these types of models, there can still be considerable uncertainty in model results. In this paper, a topographic‐index‐based method is described and tested to distribute effective soil water holding capacity among HRUs, which can be subsequently adjusted using the watershed baseflow coefficient. The method is tested using a version of the Soil and Water Assessment Tool (SWAT) model that simulates VSA runoff and is applied to two watersheds: a New York State (NYS) watershed, and one in the head waters of the Blue Nile Basin (BNB) in Ethiopia. Daily streamflow predicted using effective soil water storage capacities based only on the topographic index were reassuringly accurate in both the NYS watershed (daily Nash Sutcliffe (E) = 0·73) and in the BNB (E = 0·70). Using the baseflow coefficient to adjust the effective soil water storage capacity only slightly improved streamflow predictions in NYS (E = 0·75) but substantially improved the BNB predictions (E = 0·80). By comparison, the standard SWAT model, which uses the traditional look‐up tables to determine a runoff curve number, performed considerably less accurately in un‐calibrated form (E = 0·51 for NYS and E = 0·45 for BNB), but improved substantially when explicitly calibrated to streamflow measurements (E = 0·76 for NYS and E = 0·67 for the BNB). The calibration method presented here provides a parsimonious, systematic approach to using established models in VSA watersheds that reduces the ambiguity inherent in model calibration. Copyright © 2011 John Wiley & Sons, Ltd.