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Improving the accuracy of an integrated watershed-reservoir water quality modeling approach (SWAT and CE-QUAL-W2) in data scarce watersheds
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Climate change induced spatiotemporal variation in global water availability modifies the proposed design criteria of water infrastructure structures like dams and reservoirs. Although reservoir operation is treated as a potential adaptation option, obsolescence of existing operation rules in the climate change scenarios could cause devastating situation through faulty water management practices. Presently onboard simulation–optimization based reservoir operation schemes fail to capture the uncertainty involved in the climate change scenario. Hence, there is a need to identify the limiting application scenario of the existing reservoir operation rule, and subsequently, revise the operation framework to address the future supply–demand uncertainty adequately. This research develops an integrated Soil and Water Assessment Tool (SWAT) (hydrologic), HEC-ResSim (hydraulic), and genetic algorithm (GA) (optimization) based adaptive reservoir operation framework, which is competent enough in accounting the future supply–demand uncertainty. Incorporation of the newly proposed environmental flow assessment approach in the reservoir operation would assist the decision makers in guiding the reservoir release for maintaining the water quality and sustenance of the downstream aquatic species. Certainly, corresponding to the existing operation rules under both the baseline and future climate change scenarios of RCP 4.5 and 8.5, the developed SWAT-HEC-ResSim-GA based reservoir operation scheme could improve the performance of the Kangsabati reservoir with the time and volume reliability estimates of 0.631 and 0.736, respectively. Conclusively, the developed approach in this study could be the best feasible alternative for hydrologic characterization in complex reservoir catchment-command regions with the option for enhanced reservoir planning in global catchment-command regions.
Environmental factors, such as climate change and land use changes, affect water quality drastically. To consider these, various predictive models, both process-based and data-driven, have been used. However, each model has distinct limitations. In this study, a hybrid model combining the soil and water assessment tool and the reverse time attention mechanism (SWAT–RETAIN) was proposed for predicting daily streamflow and total phosphorus (TP) load of a watershed. SWAT–RETAIN was applied to Hwangryong River, South Korea. The hybrid model uses the SWAT output as input data for the RETAIN. Spatial, meteorological, and hydrological data were collected to develop the SWAT to generate high temporal resolution data. RETAIN facilitated effective simultaneous prediction. The SWAT–RETAIN exhibited high accuracy in predicting streamflow (Nash–Sutcliffe efficiency (NSE): 0.45, root mean square error (RMSE): 27.74, percent bias (PBIAS): 22.63 for test sets), and TP load (NSE: 0.50, RMSE: 423.93, PBIAS: 22.09 for test sets). This result was evident in the performance evaluation using flow duration and load duration curves. The SWAT–RETAIN provides enhanced temporal resolution and performance, enabling the simultaneous prediction of multiple variables. It can be applied to predict various water quality variables in larger watersheds.
HIGHLIGHTS
Process-based and data-driven models combined for improved prediction accuracy.;
Output of SWAT was used as input data for RETAIN.;
SWAT–RETAIN yielded superior predictive power over standalone SWAT.;
Trends in observed values were accurately captured.;
Applicable to time-series prediction of other water quality variables.;
Tropical semi-arid regions suffer with recurrent droughts and uncertain water availability, but a few research studies have been conducted to further understand those complexities and their relationships with reservoir hydrodynamics. This study assessed the hydrodynamic processes of a multiple-use reservoir located in the Brazilian semiarid region. The aim was to apply the CE-QUAL-W2 model to understand the lake’s thermal structure and its variabilities in time and space by using the Richardson’s number (Ri) as a reference. Meteorological patterns were also investigated. Results show that: (1) no significant changes were found by analysing the spatial variabilities of stratification; (2) seasonal changes were relevant as more robust stratification stability was observed in the wet period when water availability may be impacted by poor water quality; (3) from meteorological evaluations, rainfall showed a strong coefficient of determination with Ri (r² of 0.77); and (4) a threshold value of 60 mm in monthly precipitation was found as an indication of a stable stratification in the water column. Wind speed and water level partly influenced Ri’s variabilities, while low impact was noted for air temperature and inflow. These results can promote an improvement in water-resources management by linking rainfall regime and reservoir hydrodynamics.
Dissolved oxygen (DO) is one of the main prerequisites to protect amphibian biological systems and to support powerful administration choices. This research investigated the applicability of Shannon’s entropy theory and correlation in obtaining the combination of the optimum inputs, and then the abstracted input variables were used to develop three novel intelligent hybrid models, namely, NF-GWO (neuro-fuzzy with grey wolf optimizer), NF-SC (subtractive clustering), and NF-FCM (fuzzy c-mean), for estimation of DO concentration. Seven different input combinations of water quality variables, including water temperature (TE), specific conductivity (SC), turbidity (Tu), and pH, were used to develop the prediction models at two stations in California. The performance of proposed models for DO estimation was assessed using statistical metrics and visual interpretation. The results revealed the better performance of NF-GWO for all input combinations than other models where its performance was improved by 24.2–66.2% and 14.9–31.2% in terms of CC (correlation coefficient) and WI (Willmott index) compared to standalone NF for different input combinations. Additionally, the MAE (mean absolute error) and RMSE (root mean absolute error) of the NF model were reduced using the NF-GWO model by 9.9–46.0% and 8.9–47.5%, respectively. Therefore, NF-GWO with all water quality variables as input can be considered the optimal model for predicting DO concentration of the two stations. In contrast, NF-SC performed worst for most of the input combinations. The violin plot of NF-GWO-predicted DO was found most similar to the violin plot of observed data. The dissimilarity with the observed violin was found high for the NF-FCM model. Therefore, this study promotes the hybrid intelligence models to predict DO concentration accurately and resolve complex hydro-environmental problems.
Water quality modeling can be an important tool for lake management. However, if the simulation period is small and the forcing data is limited, the result uncertainty may diminish its usefulness. This study was conceived with the aim of implementing a long-term water quality simulation of a shallow eutrophic lagoon, with limited forcing data, to evaluate result uncertainty and the advantages of such an approach for the water management process. The lagoon water quality was simulated, over a period of 19 years (2000 to 2019), with the CE-QUAL-W2 model, and the watershed with the SWAT model. Two different scenarios regarding the lagoon inflow water quality characterization were considered and evaluated. The CE-QUAL-W2 model was used to simulate the lagoon water column and sediment biogeochemical fluxes using the following seventeen constituents as a calibration reference: water temperature; dissolved oxygen; orthophosphates; total phosphorus; ammonia; nitrate plus nitrite; total nitrogen; carbonaceous biochemical oxygen demand; total dissolved solids; pH; six algae biomass groups, and chlorophyll-a. The results show that despite the high level of uncertainty in the lagoon’s daily constituents’ variation, the long-term perspective on the biogeochemical fluxes, as described by the interannual constituent’s evolution, enabled the identification of ecological thresholds, the convergence and divergence of the system, and constituent trajectories. These results can represent a step forward into the establishment of cause-and-effect relationships between pressures and water body status, thus allowing an effectiveness improvement on the integrated water resource planning and management under the European Water Framework Directive.
The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to the unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R=0.9450, RMSE=1.1253 ppm, and KGE=0.9146) outperformed the ANFIS-SMA (R=0.9406, RMSE=1.1534 ppm, and KGE=0.8793), ANFIS-ACOR (R=0.9402, RMSE=1.1388 ppm, and KGE=0.8653), Lasso-Reg (R=0.9358), and ANFIS (R=0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl- (mg/l), Mg2+ (mg/l), Na+ (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered a reliable tool to assess water salinity in coastal aquifers.
Nutrient runoff from agricultural production is one of the main causes of water quality deterioration in river systems and coastal waters. Water quality modeling can be used for gaining insight into water quality issues in order to implement effective mitigation efforts. Process-based nutrient models are very complex, requiring a lot of input parameters and computationally expensive calibration. Recently, ML approaches have shown to achieve an accuracy comparable to the process-based models and even outperform them when describing nonlinear relationships. We used observations from 242 Estonian catchments, amounting to 469 yearly TN and 470 TP measurements covering the period 2016–2020 to train random forest (RF) models for predicting annual N and P concentrations. We used a total of 82 predictor variables, including land cover, soil, climate and topography parameters and applied a feature selection strategy to reduce the number of dependent features in the models. The SHAP method was used for deriving the most relevant predictors. The performance of our models is comparable to previous process-based models used in the Baltic region with the TN and TP model having an R² score of 0.83 and 0.52, respectively. However, as input data used in our models is easier to obtain, the models offer superior applicability in areas, where data availability is insufficient for process-based approaches. Therefore, the models enable to give a robust estimation for nutrient losses at national level and allows to capture the spatial variability of the nutrient runoff which in turn enables to provide decision-making support for regional water management plans.
Due to eutrophication, many lakes require periodic management and restoration, which becomes unpredictable due to internal nutrient loading. To provide better lake management and restoration strategies, a deterministic, one-dimensional water quality model MINLAKE2020 was modified from daily MINLAKE2012 by incorporating chlorophyll-a, nutrients, and biochemical oxygen demand models into the regional year-around temperature and dissolved oxygen (DO) model. MINLAKE2020 was applied to six lakes (varying depth and trophic status) in Minnesota focusing on studying the internal nutrient dynamics. The average root-mean-square errors (RMSEs) of simulated water temperature and DO in six lakes are 1.51 °C and 2.33 mg/L, respectively, when compared with profile data over 2–4 years. The average RMSE of DO simulation decreased by 24.2% when compared to the MINLAKE2012 model. The internal nutrient dynamics was studied by analyzing time series of phosphorus, chlorophyll-a, and DO over several years and by performing a sensitivity analysis of model parameters. A long-term simulation (20 years) of Lake Elmo shows that the simulated phosphorus release from sediment under the anoxic condition results in surface phosphorus increase, which matches with the observed trends. An average internal phosphorus loading increase of 92.3 kg/year increased the average daily phosphorus concentration by 0.0087 mg/L.
The soil and water assessment tool (SWAT) is a well-known hydrological modeling tool that has been applied in various hydrologic and environmental simulations. A total of 206 studies over a 15-year period (2005–2019) were identified from various peer-reviewed scientific journals listed on the SWAT website database, which is supported by the Centre for Agricultural and Rural Development (CARD). These studies were categorized into five areas, namely applications considering: water resources and streamflow, erosion and sedimentation, land-use management and agricultural-related contexts, climate-change contexts, and model parameterization and dataset inputs. Water resources studies were applied to understand hydrological processes and responses in various river basins. Land-use and agriculture-related context studies mainly analyzed impacts and mitigation measures on the environment and provided insights into better environmental management. Erosion and sedimentation studies using the SWAT model were done to quantify sediment yield and evaluate soil conservation measures. Climate-change context studies mainly demonstrated streamflow sensitivity to weather changes. The model parameterization studies highlighted parameter selection in streamflow analysis, model improvements, and basin scale calibrations. Dataset inputs mainly compared simulations with rain-gauge and global rainfall data sources. The challenges and advantages of the SWAT model’s applications, which range from data availability and prediction uncertainties to the model’s capability in various applications, are highlighted. Discussions on considerations for future simulations such as data sharing, and potential for better future analysis are also highlighted. Increased efforts in local data availability and a multidimensional approach in future simulations are recommended.
Prediction of dissolved oxygen which is an important water quality (WQ) parameter is crucial for aquatic managers who have responsibility for the ecosystem health's maintenance and for the management of reservoirs related to WQ. This study proposes a new ensemble method, Bayesian model averaging (BMA), for estimating hourly dissolved oxygen. The potential of the BMA was investigated and compared with five data-driven methods, extreme leaning machine (ELM), artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), classification and regression tree (CART), and multilinear regression (MLR), by considering hourly temperature, pH, and specific conductivity data as inputs. The methods were compared with respect to three statistics, root mean square errors (RMSE), Nash-Sutcliffe efficiency, and determination coefficient. Results based on two stations' data indicated that the proposed method performed superior to the ELM, ANN, ANFIS, CART, and MLR in estimation of hourly dissolved oxygen; corresponding improvements obtained by BMA are about 5-8%, 13-12%, 7-9%, and 18-27% with respect to RMSE. The ELM also outperformed the other four methods (ANN, ANFIS, CART, and MLR), and the CART and MLR indicated the lowest estimation accuracy in both stations. Examination of various input combinations revealed that the most effective variable is water temperature while the specific conductivity has negligible effect on hourly dissolved oxygen.
Reservoirs are an important nitrogen sink as a result of their retention effect, but their retention performance may vary with hydrologic conditions with time-varying characteristics, which also change them from being a sink to source over time. This study uses a coupled modelling system (Soil and Water Assessment Tool (SWAT) and a two-dimensional hydrodynamic and water quality model (CE-QUAL-W2) to analyze the nitrogen retention effect and influential factors at annual, monthly, and daily scales in Shanmei Reservoir in southeast China. The results showed that there was a positive retention effect of total nitrogen (TN), nitrate-nitrogen (NO3-N) and ammonia nitrogen (NH4-N) in most years, with average retention rates up to 12.7%, 7.83% and 26.17%, respectively. The reservoir serves mainly as a nitrogen sink at an annual scale. The monthly retention performances of TN and NO3-N were observed during the wet season (April–October) with higher water temperature and lower velocity, while a release effect occurred during the dry season (November–March). For NH4-N, which is prone to nitrification, the retention effect lasted longer, from May to December. The daily nitrogen retention process changed more dramatically, with the retention rate varying from −292.49 to 58.17%. During the period of dispatch, the regulated discharge was the primary factor of daily retention performance, while the hydraulic residence time, velocity and water level were all significantly correlated with nitrogen retention during the period without dispatch.
Kim D, Kim Y, Kim B. Simulation of eutrophication in a reservoir by CE-QUAL-W2 for the evaluation of the importance of point sources and summer monsoon. Lake Reserv Manage. 35:64–76.
Water quality was modeled by a 2-dimensional model (CE-QUAL-W2) in a reservoir (Lake Uiam, Korea) receiving nutrients from both nonpoint sources and point sources. Due to the summer monsoon climate and the aggregated seasonal precipitation pattern, the phosphorus export from nonpoint sources is severely concentrated in the rainy season. For several decades, a sewage treatment plant (STP) on the shore of the lake has been releasing effluent, which was suspected as the major cause of eutrophication. However, the total annual phosphorus loading from the STP was smaller than the loadings from nonpoint sources, which aroused skepticism about the effectiveness of an additional chemical P-removal process in the STP as a eutrophication control strategy. The result of scenario simulations in this study showed that P reduction in the STP effluent from 0.9 mg/L to 0.1 mg/L would effectively reduce the chlorophyll a concentration in the lake by 62%. According to the results of the simulation, the addition of a chemical P-removal process was suggested to the municipal government and was installed in 2012. After the process, the chlorophyll a concentration in the lake decreased as predicted in the simulation. The effect of phosphorus loading can have quite different effects on phytoplankton growth depending on the runoff pattern and hydrological characteristics of the receiving water bodies. Flow rate and nutrient loadings are very dynamic, far from a steady state, in the summer monsoon regions, which may be a unique limnological feature of East Asian countries.
Automated calibration of complex deterministic water quality models with a large number of biogeochemical parameters can reduce time-consuming iterative simulations involving empirical judgements of model fit. We undertook autocalibration of the one-dimensional hydrodynamic-ecological lake model DYRESM-CAEDYM, using a Monte Carlo sampling (MCS) method, in order to test the applicability of this procedure for shallow, polymictic Lake Rotorua (New Zealand). The calibration procedure involved independently minimizing the root-mean-square error (RMSE), maximizing the Pearson correlation coefficient (r) and Nash–Sutcliffe efficient coefficient (Nr) for comparisons of model state variables against measured data. An assigned number of parameter permutations was used for 10 000 simulation iterations. The “optimal” temperature calibration produced a RMSE of 0.54 ∘C, Nr value of 0.99, and r value of 0.98 through the whole water column based on comparisons with 540 observed water temperatures collected between 13 July 2007 and 13 January 2009. The modeled bottom dissolved oxygen concentration (20.5 m below surface) was compared with 467 available observations. The calculated RMSE of the simulations compared with the measurements was 1.78 mg L-1, the Nr value was 0.75, and the r value was 0.87. The autocalibrated model was further tested for an independent data set by simulating bottom-water hypoxia events from 15 January 2009 to 8 June 2011 (875 days). This verification produced an accurate simulation of five hypoxic events corresponding to DO < 2 mg L-1 during summer of 2009–2011. The RMSE was 2.07 mg L-1, Nr value 0.62, and r value of 0.81, based on the available data set of 738 days. The autocalibration software of DYRESM-CAEDYM developed here is substantially less time-consuming and more efficient in parameter optimization than traditional manual calibration which has been the standard tool practiced for similar complex water quality models.
The Enxoé Reservoir was built in 1998. Since 2000, it has exhibited frequent high chlorophyll-a concentrations, reaching a geometric mean three times higher than the national limit for eutrophication, presenting the reservoir with the highest eutrophic state in Portugal. Toxic algal blooms have also often been observed, which pose serious challenges to water managers, as the reservoir is used for potable water production (25,000 inhabitants). The objective of this study was to implement a reservoir model (CE-QUAL-W2), with inputs from a watershed model (SWAT), in order to represent the actual reservoir state and to test management measures to reduce its trophic level and algal bloom concentration peaks. The integrated model was used to depict the origin of its trophic status. Simulations were also compared to measured data at the reservoir surface (water level, nitrate, orthophosphate, suspended solids, and oxygen) and in water profiles (temperature, oxygen). The model was able to represent stratification and thermocline depths, as well as the actual chlorophyll-a and dissolved oxygen concentrations. The model results showed that internal phosphorus load from deposited sediments was an important factor in fuelling the algal blooms. This process occurs predominantly in summer, when stratification takes place and aeration is reduced, promoting anoxic conditions in the bottom waters. Since the reservoir is relatively shallow (average 5 m), released phosphorus is then easily able to reach the photic zone in most parts of the reservoir, where it is consumed. Different management scenarios were tested, suggesting that a mesotrophic level could barely be reached and maintained simply by reducing the nutrient loads (both external and internal). It is suggested that only an increase in the reservoir dam height could produce a mesotrophic level, averting anoxia by blocking the release of phosphorus from sediments to the photic zone. Future work should focus on a cost–benefit analysis to test the feasibility of each of the proposed scenarios, taking advantage of the integration strategy to assess where in the watershed load reductions would be most effective.
This study provides an innovative process-based modelling approach using the SWAT model and shows its application to support the implementation of the European environmental policies in large river basins. The approach involves several pioneering modelling aspects: the inclusion of current management practices; an innovative calibration and validation methodology of streamflow and water quality; a sequential calibration starting from crop yields, followed by streamflow and nutrients; and the use of concentrations instead of loads in the calibration. The approach was applied in the Danube River Basin (800,000 km2), the second largest river basin in Europe, that is under great nutrients pressure. The model was successfully calibrated and validated at multiple gauged stations for the period 1995–2009. About 70% and 61% of monthly streamflow stations reached satisfactory performances in the calibration and validation datasets respectively. N-NO3 monthly concentrations were in good agreement with the observations, albeit SWAT could not represent accurately the spatial variability of the denitrification process. TN and TP concentrations were also well captured. Yet, local discrepancies were detected across the Basin. Baseflow and surface runoff were the main pathways of water pollution. The main sinks of TN and TP diffuse emissions were plant uptake which captured 58% of TN and 92% of TP sources, then soil retention (35% of TN and 2% of TP), riparian filter strips (2% both for TN and TP) and river retention (2% of TN and 4% of TP). Nitrates in the aquifer were estimated to be around 3% of TN sources. New reliable “state-of-the-art” knowledge of water and nutrients fluxes in the Danube Basin were thus provided to be used for assessing the impact of best management practices and for providing support to the implementation of the European Environmental Directives.
Eutrophication is a serious water pollution problem in many lakes and reservoirs. One method of understanding the causes of eutrophication and devising strategies to address this phenomenon is watershed modeling. An integrated model is developed in this paper for simulating and evaluating the water quality protection strategies of reservoirs based on controlling the upstream point and non-point sources of pollution and also reservoir operation. To achieve this, Soil and Water Assessment Tool (SWAT) model is used for modeling of the surface runoff and transportation of pollutant load over the watershed area and then, the numerical model CE-Qual-W2 is used for simulating the reservoir water quality. These models are linked to simulate the transmission and distribution of water quality variables in the Seimare watershed-reservoir system, west part of Iran. After calibration of the models, different strategies for reduction of pollution over the watershed are simulated and ranked based on their efficiency in reducing the pollution load entering into the reservoir. Results show that completion and modernization of the sewage network and wastewater treatment plant of Kermanshah city as long as adopting some measures against the direct release of municipal wastewater into the Gharehsou River can reduce the pollution load by 40–50% as the best short term strategy. It is also found that in the long-term period, watershed management and decreasing local animal husbandry activities are the most effective measure for reducing the nutrient load entering Seimare reservoir, and thus need to be considered in the future watershed management policies and programs.
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This revised, updated textbook presents a systems approach to the planning, management, and operation of water resources infrastructure in the environment. Previously published in 2005 by UNESCO and Deltares (Delft Hydraulics at the time), this new edition, written again with contributions from Jery R. Stedinger, Jozef P. M. Dijkman, and Monique T. Villars, is aimed equally at students and professionals. It introduces readers to the concept of viewing issues involving water resources as a system of multiple interacting components and scales. It offers guidelines for initiating and carrying out water resource system planning and management projects. It introduces alternative optimization, simulation, and statistical methods useful for project identification, design, siting, operation and evaluation and for studying post-planning issues. The authors cover both basin-wide and urban water issues and present ways of identifying and evaluating alternatives for addressing multiple-purpose and multi-objective water quantity and quality management challenges. Reinforced with cases studies, exercises, and media supplements throughout, the text is ideal for upper-level undergraduate and graduate courses in water resource planning and management as well as for practicing planners and engineers in the field.
The most fundamental human needs for water are for drinking, cooking, and personal hygiene. The quality of the water used to meet these needs must pose no risk to human health.
Performance measures (PMs) and corresponding performance evaluation criteria (PEC) are important as-pects of calibrating and validating hydrologic and water quality models and should be updated with advances in modeling science. We synthesized PMs and PEC from a previous special collection, performed a meta-analysis of performance data reported in recent peer-reviewed literature for three widely published watershed-scale models (SWAT, HSPF, WARMF), and one field-scale model (ADAPT), and provided guidelines for model performance evaluation. Based on the synthesis, meta-analysis, and personal modeling experiences, we recommend coefficient of determination (R2; in conjunction with gradient and intercept of the corresponding regression line), Nash Sutcliffe efficiency (NSE), index of agreement (d), root mean square error (RMSE; alongside the ratio of RMSE and standard deviation of measured data, RSR), percent bias (PBIAS), and several graphical PMs to evaluate model performance. We recommend that model performance can be judged “satisfactory” for flow simulations if monthly R2 > 0.70 and d > 0.75 for field-scale models, and daily, monthly, or annual R2 > 0.60, NSE > 0.50, and PBIAS ≤±15% for watershed-scale models. Model performance at the watershed scale can be evaluated as “satisfactory” if monthly R2 > 0.40 and NSE > 0.45 and daily, monthly, or annual PBIAS ≤±20% for sediment; monthly R2 > 0.40 and NSE > 0.35 and daily, monthly, or annual PBIAS ≤±30% for phosphorus (P); and monthly R2 > 0.30 and NSE > 0.35 and daily, monthly, or annual PBIAS ≤±30% for nitrogen (N). For RSR, we rec-ommend that previously published PEC be used as detailed in this article. We also recommend that these PEC be used primarily for the four models for which there were adequate data, and used only with caution for other models. These PEC can be adjusted within acceptable bounds based on additional considerations, such as quality and quantity of avail-able measured data, spatial and temporal scales, and project scope and magnitude, and updated based on the framework presented herein. This initial meta-analysis sets the stage for more comprehensive meta-analysis to revise PEC as new PMs and more data become available.
The geographical extent of Brazil exceeds 8.5 million km 2 and encompasses a complex mix of biomes and other environmental conditions. Multipe decision support tools are needed to help support management of these diverse Brazilian natural resources including ecohydrological models. The use of the Soil and Water Assessment Tool (SWAT) ecohydrological watershed-scale model in Brazil has increased greatly during the past decade. Well over 100 SWAT studies were identified in this review which have been published during 1999 to 2015 in Brazilian and international journals, conference proceedings, and as theses or dissertations, many of which are written in Portuguese. The majority of these studies (102 total) are reviewed here as part of an extensive survey covering the 1999 to 2013 time period. Temporal and spatial distributions, a summary of hydrologic calibration and validation results and a synopsis of the types of applications that were performed are reported for the surveyed studies. A smaller subset of recent Brazilian studies published in English between 2012 and 2015 in scientific journals are also reviewed, with emphasis on hydrologic and sediment transport testing results as well as scenario applications that were performed. The majority of the surveyed SWAT studies were performed for watersheds located in the South and Southeast regions of Brazil (67%) and were conducted in the context of academic research. Nearly 50% of the surveyed studies reported only hydrologic results. Similar trends were found for the subset of more recent English publications. Limited studies have been reported that describe applications of SWAT in Brazil by private firms or government agencies; this review indicates that the potential exists for increased numbers of such studies in the future. However, there is evidence that a lack of accessibility to adequate quality input data is a possible hindrance to the more general use of SWAT for watershed applications in Brazil.
Despite the importance of calculating the flux of solutes and particulates through the global fluvial network the number of studies that have considered the bias and precision of any method is limited. Furthermore, no study has, on the basis of the bias of the method, proposed new methods with a lower bias nor considered the implications of the bias estimation for existing published studies. Using 3 years of high frequency data (hourly) for dissolved organic carbon (DOC) this study systematically degraded the data and recalculated the flux for varying sample frequencies and considered a range of interpolation, ratio and extrapolation methods. The results show that: (i) Interpolation and ratio methods showed a consistent, small bias for sampling frequencies up to every 14 days, but bias rapidly increased for lower sample frequencies with the flux estimates being between 40% and 45% of the "true" flux at 31 day (monthly) sampling. (ii) The best ratio method was based upon correction against an unrealistic assumption that river flow was normally distributed. (iii) Extrapolation methods based on fixed sampling period monitoring proved to be erratic but no better than interpolation methods. Based upon the nature of the sources of variation within the flow and solute datasets we propose the following method for calculating the fluvial flux (F) of a solute: F = KE(C-i)Q(total) where: Q(total) = the total flow in a year (m(3)/Yr); E(C-i)= the expected value of the sampled concentrations (mg/l); and K = a conversion factor. This new method preserved all the available flow information and had a bias of as low as 8% for monthly sampling. When the method was applied to DOC flux from Great Britain bias correction meant a 97% increase in the national flux over previous estimates.
A combination of driving forces are increasing pressure on local, national, and regional water supplies needed for irrigation, energy production, industrial uses, domestic purposes, and the environment. In many parts of Europe groundwater quantity, and in particular quality, have come under sever degradation and water levels have decreased resulting in negative environmental impacts. Rapid improvements in the economy of the eastern European block of countries and uncertainties with regard to freshwater availability create challenges for water managers. At the same time, climate change adds a new level of uncertainty with regard to freshwater supplies. In this research we build and calibrate an integrated hydrological model of Europe using the Soil and Water Assessment Tool (SWAT) program. Different components of water resources are simulated and crop yield and water quality are considered at the Hydrological Response Unit (HRU) level. The water resources are quantified at subbasin level with monthly time intervals. Leaching of nitrate into groundwater is also simulated at a finer spatial level (HRU). The use of large-scale, high-resolution water resources models enables consistent and comprehensive examination of integrated system behavior through physically-based, data-driven simulation. In this article we discuss issues with data availability, calibration of large-scale distributed models, and outline procedures for model calibration and uncertainty analysis. The calibrated model and results provide information support to the European Water Framework Directive and lay the basis for further assessment of the impact of climate change on water availability and quality. The approach and methods developed are general and can be applied to any large region around the world.
Current CE-QUAL-W2 mainly simulates hydrodynamics and eutrophication processes in the water column. The benthic sediment processes and sediment–water interactions have been neglected or very much simplified using zero-order and first-order rates. In this study a benthic sediment diagenesis module was developed and integrated into CE-QUAL-W2. Enhanced CE-QUAL-W2 was capable of simulating the dynamic releases of ammonium, nitrate, phosphorus, dissolved silica and dissolved methane from the sediment to the overlying water, as well as benthic sediment oxygen demand. The oxidation of sulfides is included for salt water sediments. The ability of CE-QUAL-W2 model to correctly predict sediment–water nutrient fluxes and sediment oxygen demand was evaluated against SedFlux and CE-QUAL-ICM models through a series of case studies. These case studies were chosen for representing various sedimentation and environmental conditions. The simulated sediment–water nutrient fluxes and sediment oxygen demand over time were generally in good agreement with these two model results for all data sets. The effect of benthic sediment diffusive thickness, particle mixing coefficients on nutrient releases from sediments and sediment oxygen demand were examined. Enhanced CE-QUAL-W2 model was also applied to the Lower Minnesota River for further evaluating its performance. This paper presents the sediment diagenesis module development, validation tests and application of the enhanced CE-QUAL-W2 model.
In Portugal, the precipitation regimes present one of the highest volumes of extreme precipitation occurrence in Europe, and one of the largest mean precipitation spatial gradient (annual observed values above 2,500 mm in the NW and under 400 mm in the SE). Moreover, southern Europe is one of the most vulnerable regions in the world to climate change. In the ENSEMBLES framework many climate change assessment studies were performed, but none focused on Portuguese precipitation. An extensive evaluation and ranking of the RCMs results addressing the representation of mean precipitation and frequency distributions was performed through the computation of statistical errors and frequency distribution scores. With these results, an ensemble was constructed; giving the same weight to mean precipitation and distribution model skills. This ensemble reveals a good ability to describe the precipitation regime in Portugal, and enables the evaluation of the eventual impact of climate change on Portuguese precipitation according to the A1B scenario. The mean seasonal precipitation is expected to decrease substantially in all seasons, excluding winter. This reduction is statistically significant; it spans from less than 20 % in the north to 40 % in the south in the intermediate seasons, and is above 50 % in the largest portion of mainland in summer. At a basin level the precipitation diminishes in all months for all the basins with exception of December. Total precipitation PDFs reveal an important decrease of the contribution from low to moderate/high precipitation bins, and a striking rise for days with extreme rainfall, up to 30 %.
The Soil and Water Assessment Tool (SWAT) is an integrated river basin
model that is widely applied within the Nile basin. Up to date, more
than 20 peer-reviewed papers describe the use of SWAT for a variety of
problems in the upper Nile basin countries, such as erosion modelling,
land use and climate change impact modelling and water resources
management. The majority of the studies are focused on locations in the
tropical highlands in Ethiopia and around Lake Victoria. The popularity
of SWAT is attributed to the fact that the tool is freely available and
that it is readily applicable through the development of geographic
information system (GIS) based interfaces and its easy linkage to
sensitivity, calibration and uncertainty analysis tools. The online and
free availability of basic GIS data that are required for SWAT made its
applicability more straightforward even in data-scarce areas. However,
the easy use of SWAT may not always lead to appropriate models which is
also a consequence of the quality of the available free databases in
these regions. In this paper, we aim at critically reviewing the use of
SWAT in the context of the modelling purpose and problem descriptions in
the tropical highlands of the Nile basin countries. To evaluate the
models that are described in journal papers, a number of criteria are
used to evaluate the model set-up, model performances, physical
representation of the model parameters, and the correctness of the
hydrological model balance. On the basis of performance indicators, the
majority of the SWAT models were classified as giving satisfactory to
very good results. Nevertheless, the hydrological mass balances as
reported in several papers contained losses that might not be justified.
Several papers also reported the use of unrealistic parameter values.
More worrying is that many papers lack this information. For this
reason, most of the reported SWAT models have to be evaluated
critically. An important gap is the lack of attention that is given to
the vegetation and crop processes. None of the papers reported any
adaptation to the crop parameters, or any crop-related output such as
leaf area index, biomass or crop yields. A proper simulation of the land
cover is important for obtaining correct runoff generation,
evapotranspiration and erosion computations. It is also found that a
comparison of SWAT applications on the same or similar case study but by
different research teams and/or model versions resulted in very
different results. It is therefore recommended to find better methods to
evaluate the representativeness of the distributed processes and
parameters (especially when land use studies are envisaged) or
predictions of the future through environmental changes. The main
recommendation is that more details on the model set-up, the parameters
and outputs should be provided in the journal papers or supplementary
materials in order to allow for a more stringent evaluation of these
models.
Previous reservoir sedimentation models have ignored two key factors for large spatial and temporal modeling of multiple reservoirs: trapping by upstream dams and decreasing sediment trapping as reservoirs fill. We developed a spreadsheet-based model that incorporates both factors. Using California as a case study, we used measured sedimentation rates to estimate sediment yields for distinct geomorphic regions and applied those rates to unmeasured reservoirs by region. Statewide reservoirs have likely filled with 2.1 billion m3 of sediment to date, decreasing total reservoir capacity by 4.5%. About 200 reservoirs have likely lost more than half their initial capacity to sedimentation.
In this study precipitation from a high resolution WRF climate simulation is presented and evaluated against daily gridded observations in the Iberian Peninsula. The simulation corresponds to a dynamical downscaling of ERA-Interim, in the period 1989–2009, performed with two nested grids, at 27 and 9 km horizontal resolution. The higher resolution simulation indicates a significantly improved representation of Iberian precipitation fields, at all timescales, with emphasis on the representation of variability and of extreme weather statistics. Results compare well with recent studies with other models and/or for other regions, further supporting the use of WRF as a regional climate model. Copyright 2012 Royal Meteorological Society
Nutrients are conveyed from terrestrial and upstream sources through drainage networks. Streams and rivers contribute to regulate the material exported downstream by means of transformation, storage, and removal of nutrients. It has been recently suggested that the efficiency of process rates relative to available nutrient concentration in streams eventually declines, following an efficiency loss (EL) dynamics. However, most of these predictions are based at the reach scale in pristine streams, failing to describe the role of entire river networks. Models provide the means to study nutrient cycling from the stream network perspective via upscaling to the watershed the key mechanisms occurring at the reach scale. We applied a hybrid process-based and statistical model (SPARROW, Spatially Referenced Regression on Watershed Attributes) as a heuristic approach to describe in-stream nutrient processes in a highly impaired, high stream order watershed (the Llobregat River Basin, NE Spain). The in-stream decay specifications of the model were modified to include a partial saturation effect in uptake efficiency (expressed as a power law) and better capture biological nutrient retention in river systems under high anthropogenic stress. The stream decay coefficients were statistically significant in both nitrate and phosphate models, indicating the potential role of in-stream processing in limiting nutrient export. However, the EL concept did not reliably describe the patterns of nutrient uptake efficiency for the concentration gradient and streamflow values found in the Llobregat River basin, posing in doubt its complete applicability to explain nutrient retention processes in stream networks comprising highly impaired rivers.
In this study, calibrated watershed and reservoir models are used to explore a range of possible watershed conditions and potential management options to reduce available nutrients and algal growth in the Lake Waco reservoir. The management options are divided between watershed and reservoir options. The watershed management options include wetland construction, manure haul-off, agriculture conversion to pasture, absolute nutrient retention in the watershed and control of urban nutrient run-off. For the reservoir, management options of phosphorus inactivation and increased algal consumption by grazers were evaluated. For all individual management scenarios, only complete conversion of agricultural lands into rangeland decreased nutrient levels and algae growth significantly and achieved target levels for chlorophyll-a and total phosphorus. Combined management scenarios including wetland construction, manure haul-off from dairy operations and increased in-reservoir herbivory could further reduce chlorophyll-a and nutrient values, but with less efficiency than agricultural conversion alone. The management option study showed that decreasing nutrient inputs and water clarity were important factors for controlling algal growth in Lake Waco, and that substantial reduction in total phosphorus is needed to achieve target conditions.
We describe a method for using spatially referenced regressions of contaminant transport on watershed attributes (SPARROW) in regional water-quality assessment. The method is designed to reduce the problems of data interpretation caused by sparse sampling, network bias, and basin heterogeneity. The regression equation relates measured transport rates in streams to spatially referenced descriptors of pollution sources and land-surface and stream-channel characteristics. Regression models of total phosphorus (TP) and total nitrogen (TN) transport are constructed for a region defined as the nontidal conterminous United States. Observed TN and TP transport rates are derived from water-quality records for 414 stations in the National Stream Quality Accounting Network. Nutrient sources identified in the equations include point sources, applied fertilizer, livestock waste, nonagricultural land, and atmospheric deposition (TN only). Surface characteristics found to be significant predictors of land-water delivery include soil permeability, stream density, and temperature (TN only). Estimated instream decay coefficients for the two contaminants decrease monotonically with increasing stream size. TP transport is found to be significantly reduced by reservoir retention. Spatial referencing of basin attributes in relation to the stream channel network greatly increases their statistical significance and model accuracy. The method is used to estimate the proportion of watersheds in the conterminous United States (i.e., hydrologic cataloging units) with outflow TP concentrations less than the criterion of 0.1 mg/L, and to classify cataloging units according to local TN yield (kg/km 2 /yr).
A new approach to the analysis of long-term surface water-quality data is proposed and implemented. The goal of this approach is to increase the amount of information that is extracted from the types of rich water-quality datasets that now exist. The method is formulated to allow for maximum flexibility in representations of the long-term trend, seasonal components, and discharge-related components of the behavior of the water-quality variable of interest. It is designed to provide internally consistent estimates of the actual history of concentrations and fluxes as well as histories that eliminate the influence of year-to-year variations in streamflow. The method employs the use of weighted regressions of concentrations on time, discharge, and season. Finally, the method is designed to be useful as a diagnostic tool regarding the kinds of changes that are taking place in the watershed related to point sources, groundwater sources, and surface-water nonpoint sources. The method is applied to datasets for the nine large tributaries of Chesapeake Bay from 1978 to 2008. The results show a wide range of patterns of change in total phosphorus and in dissolved nitrate plus nitrite. These results should prove useful in further examination of the causes of changes, or lack of changes, and may help inform decisions about future actions to reduce nutrient enrichment in the Chesapeake Bay and its watershed.
Hirsch, Robert M., Douglas L. Moyer, and Stacey A. Archfield, 2010. Weighted Regressions on Time, Discharge, and Season (WRTDS), With an Application to Chesapeake Bay River Inputs. Journal of the American Water Resources Association (JAWRA) 46(5):857-880. DOI: 10.1111/j.1752-1688.2010.00482.x
Degradation of water quality is the major health concern for lakes and reservoirs in the central regions of the United States as a result of heavily devoted agricultural production. A vital key to the development of a reservoir management strategy is to identify nutrient loading that describes associated water quality conditions in reservoirs. This study integrated AnnAGNPS watershed and BATHTUB lake models to simulate actual lake water quality conditions of Cheney Reservoir, KS, and demonstrated the use of the coupled model for simulating lake response to changes in different watershed land use and management scenarios. The calibrated current-conditions model simulated in-lake reductions as much as 52% for TN, 48% for TP, and 70% for chlorophyll a due to conversion to native grass, and increases as much as 4% for TN, 9% for TP and 6% for chlorophyll a due to conversion of land from the Conservation Reserve Program (CRP) to cropland (15.5% of watershed). This model also demonstrated an increase in chlorophyll a (19%) as the lake sediment capacity was reached over the next century.
Water resources planning and management are dependable on an adequate integration of physical, chemical, biological, and socio-economic realities of multiple water users. The dynamic of water quantity and quality in rivers are affected by several conditions, such as land use, soil characteristics, and meteorological and hydrological processes. Among these, the presence of hydraulic structures, such as dams and reservoirs, are often responsible for hydrodynamic and geomorphological alterations, leading to impacts on water quality and ecological behavior. In this context, this study presents the combination of a solution of the one-dimensional flow and transport and fate of contaminants in rivers (SihQual model) with a continuously stirred tank reactor (CSTR) to represent the reservoir. The first model solves the hydrodynamic and water quality equations under unsteady state, while the second approach considers the reservoir as a complete mixed system with inputs that vary over time. The main goal is to provide an integrated analysis for planning and management in a watershed where water has multiple purposes of use. The case study is the Iguaçu watershed, where the main river is affected by several dams for hydroelectric power generation. The simulation covers 542 km of the Iguaçu River and the Foz do Areia reservoir (flooded area of 139.5 km²), using data from 12 monitoring stations. The region also has issues of water quality impairment and water scarcity events due to deforestation, and urban and agricultural activities, exemplifying challenges throughout the world. Results show that the integrated models can reproduce the expected variability in different systems, although calibration challenges arise in multiscale modeling. The data indicate that, overall, the lentic environment is able to deplete organic matter and phosphorus, in comparison to levels in the fluvial flow. Nonetheless, experiments show that the river-reservoir system may be highly sensible to external and internal changes, such as water availability throughout time and pollution from the main tributary, as well as outlet discharges and transformation processes in the water column, leading to a possibility of critical events. Therefore, the study highlights how planning and managing actions in the watershed can benefit from an integrated river-reservoir analysis.
The establishment of water quality prediction models is vital for aquatic ecosystems analysis. The traditional methods of water quality index (WQI) analysis are time-consuming and associated with a high degree of errors. These days, the application of artificial intelligence (AI) based models are trending for capturing nonlinear and complex processes. Therefore, the present study was conducted to predict the WQI in the Kinta River, Malaysia by employing the hybrid AI model i.e., GA-EANN (genetic algorithm-emotional artificial neural network). The extreme gradient boosting (XGB) and neuro-sensitivity analysis (NSA) approaches were utilized for feature extraction, and six different model combinations were derived to examine the relationship among the WQI with water quality (WQ) variables. The efficacy of the proposed hybrid GA-EANN model was evaluated against the backpropagation neural network (BPNN) and multilinear regression (MLR) models during calibration, and validation periods based on Nash–Sutcliffe efficiency (NSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) indicators. According to results of appraisal the hybrid GA-EANN model produced better outcomes (NSE = 0.9233/ 0.9018, MSE = 10.5195/ 9.7889 mg/L, RMSE = 3.2434/ 3.1287 mg/L, MAPE = 3.8032/ 3.0348 mg/L, CC = 0.9609/ 0.9496) in calibration/ validation phases than BPNN and MLR models. In addition, the results indicate the better performance and suitability of the hybrid GA-EANN model with five input parameters in predicting the WQI for the study site.
Small and shallow lakes represent the majority of inland freshwater bodies. However, the effects of climate change on such ecosystems have rarely been quantitatively addressed. We propose a methodology to evaluate the thermal response of small and shallow lakes to long-term changes in the meteorological conditions, through model simulations. To do so, a 3D thermal-hydrodynamic model is forced with meteorological data and used to hindcast the evolution of an urban lake in the Paris region between 1960 and 2017. Its thermal
response is assessed through a series of indices describing its thermal regime in terms of water temperature, thermal stratification and potential cyanobacteria production. These indices and the meteorological forcing are first analysed over time to test the presence of long-term monotonic trends. 3D simulations are then exploited to highlight the presence of spatial heterogeneity. The analyses show that climate change has strongly impacted the thermal regime of the study site. Its response is highly correlated with three meteorological variables: air temperature, solar radiation and wind speed. Mean annual water temperature shows a considerable warming trend of 0.6°C.dec−1, accompanied by longer stratification and by an increase of thermal energy favourable to cyanobacteria proliferation. The strengthening of thermal conditions favourable for cyanobacteria is particularly strong during spring and summer, while stratification increases especially during spring and autumn. The 3D
analysis allows to detect a sharp separation between deeper and shallower portions of the basin in terms of stratification dynamics and potential cyanobacteria production. This induces highly dynamic patterns in space and time within the study site that are particularly favourable to cyanobacteria growth and bloom initiation.
Efficient and accurate prediction of river water quality is challenging due to the complex hydrological and environmental processes affecting their nature. The challenge is even bigger in unmonitored watersheds. Both process- and data-based approaches are utilized for this purpose, with each having its own strengths and weaknesses. The development of a hybrid model can potentially give robust solutions in this regard. To improve the water quality predictions in unmonitored watersheds, we developed a hybrid model by combining a process-based watershed model and artificial neural network (ANN). Combining these two models helped to optimize the calibration and validation process while accounting for the complex hydrological and water quality processes. The developed model was applied to watersheds in the Atlanta metropolitan area, USA, to predict monthly nitrate, ammonium, and phosphate loads. We treated the watersheds as unmonitored and tested the skill of the hybrid model accordingly. The hybrid model had good skills in predicting all three constituents. The model worked especially well for nitrate. As a matter of fact, it even outperformed SWAT models calibrated at each site. This work emphasizes the potential benefits of the proposed hybrid modeling framework for the prediction of water quality parameters in unmonitored watersheds.
Lake Spokane, a reservoir in eastern Washington State, USA, was previously hypereutrophic due to phosphorus discharges from the City of Spokane wastewater treatment plant (WWTP). This system subsequently recovered to a meso-oligotrophic state after implementation of advanced phosphorus removal. The present study tests whether the mechanistic Lake Spokane water quality (WQ) model realistically represents the sensitivity of this reservoir's hypolimnetic oxygen concentrations to phosphorus inputs. We compared the observed relationship between the mean summer input total phosphorus concentration (TPin) and the minimum volume weighted hypolimnetic dissolved oxygen concentration (DOmin) to model values for conditions ranging from hypereutrophic to oligotrophic. Prior to advanced phosphorus removal, TPin and DOmin averaged 86 ± 37 (± SD) μg/L and 1.4 ± 1.3 mg/L, respectively. Currently (2010-2014), these values average 14 ± 3 μg/L and 6.5 ± 0.8 mg/L, respectively. In contrast, the model's DOmin response for similar TPin concentrations was much less pronounced, with hypereutrophic and contemporary DOmin averaging 3.8 ± 0.4 and 4.7 ± 0.04 mg/L, respectively. The model also has a structural DO deficit (saturated DO - DOmin) of 5.3 mg/L that was evident when all TP inputs to the reservoir were set to zero. Similarly, when all WWTP effluent sources were set to TPeff = 0 μg/L the reservoir epilimnetic TP concentrations were ≈ 8 μg/L higher than the Spokane River inputs. The water quality model indicates that even if effluent phosphorus concentrations are reduced to zero it is not possible to meet the dissolved oxygen goals for Lake Spokane.
Hydrologic modelling is pre-requisite to water resources management. Unfortunately, hydrologic modelling in data scare basin has always been difficult. The current study, explored the use of “data limited” model Soil Water Assessment Tool (SWAT) in modelling lower Aswa basin located in northern Uganda. The study adopted different techniques in generating and estimating various missing model parameters and input especially solar radiation, saturated soil hydraulic conductivity, available soil water content, Universal Soil Lost Equation erodibility factor and moist soil albedo. Soil Water Assessment Tool model was then manually calibrated using monthly historical streamflow records. The calibration was successful with coefficient of determination (R2) value of 0.618 and the Nash and Sutcliffe efficiency value of 0.47. Validation of the calibrated model using independent dataset shows even better model performance with Nash and Sutcliffe efficiency value of 0.64 and coefficient of determination (R2) value of 0.56. Successful calibration of hydrologic model Soil Water Assessment Tool under the data scarcity still proves the potential of the application of the model even in data limited basin, but more especially by water resources managers who needs understanding of existing condition and modelling possible future.
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ∗∗∗, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
Quantification and reduction of uncertainty associated to decision making is one of the primary functions of modeling and monitoring targeted to assist decision making in reservoir, river, and lake water quality management. In many practical activities such as environmental impact assessment, the inference is bound to be based primarily on subjective, expert judgments, supported by empirical data and models. A bulk of analytical approaches is presently available for modeling purposes. The paper discusses selected decision analytic approaches to the handling of uncertainty and subjectivity associated to information available, as a decision criterion, and as a component influencing the model structure. Computational solutions based on experience on seven case studies are reviewed.
Considerable progress has been made in developing physically based, distributed parameter, hydrologic/water quality (HIWQ) models for planning and control of nonpoint-source pollution. The widespread use of these models is often constrained by the excessive and time-consuming input data demands and the lack of computing efficiencies necessary for iterative simulation of alternative management strategies. Recent developments in geographic information systems (GIS) provide techniques for handling large amounts of spatial data for modeling nonpoint-source pollution problems. Because a GIS can be used to combine information from several sources to form an array of model input data and to examine any combinations of spatial input/output data, it represents a highly effective tool for HiWQ modeling. This paper describes the integration of a distributed-parameter model (AGNPS) with a GIS (ARC/INFO) to examine nonpoint sources of pollution in an agricultural watershed. The ARC/INFO GIS provided the tools to generate and spatially organize the disparate data to support modeling, while the AGNPS model was used to predict several water quality variables including soil erosion and sedimentation within a watershed. The integrated system was used to evaluate the effectiveness of several alternative management strategies in reducing sediment pollution in a 417-ha watershed located in southern Iowa. The implementation of vegetative filter strips and contour buffer (grass) strips resulted in a 41 and 47% reduction in sediment yield at the watershed outlet, respectively. In addition, when the integrated system was used, the combination of the above management strategies resulted in a 71% reduction in sediment yield. In general, the study demonstrated the utility of integrating a simulation model with GIS for nonpoini-source pollution control and planning. Such techniques can help characterize the diffuse sources of pollution at the landscape level. 52 refs., 6 figs., 1 tab.
The application of the two-dimensional hydrothermal and water quality model, CE-QUAL-W2, to the Conowingo Reservoir is presented. The performance of the CE-QUAL-W2 model was enhanced with the addition of multiple particle size settling rates, and an algorithm to account for the scour process within a reservoir. The benefit of the Conowingo Reservoir to perform sediment and nutrient trapping was determined from the calculation of removal efficiencies which showed important characteristics for this reservoir which is near the end of its useful lifespan in regards to trapping capability.
Kim, Y. and B. Kim. 2006. Application of a 2-dimensional water quality model (CE-QUAL-W2) to the turbidity interflow in a deep reservoir (Lake Soyang, Korea). Lake and Reserv. Manage. 22(3):213-222. The temporal and spatial distribution of water temperature was surveyed and simulated in a deep warm monomictic reservoir (Lake Soyang, Korea). The great depth (maximum depth 118 m) and wind-sheltered dendritic shape caused stable thermal stratification in summer. Turbid storm runoff during the summer monsoon formed a 20-40 m intermediate layer distinct from the clearer epilimnion and hypolimnion. The temperature distribution and movements of the density current were simulated by using the 2-dimensional hydrologic model, CE-QUAL-W2. The model was calibrated with data from 1996 and verified with data from 1995-2002 by apply-ing the same set of parameters and constants as used in calibration. The model could simulate temperature profiles with excellent agreement. Movement of the intermediate density current also was well simulated. The CE-QUAL-W2 model was useful in the prediction of temperature distribution and movement of density current in reservoirs, which implies merit for further employment of this model in water quality simulations.
A conceptual, continuous time model called SWAT (Soil and Water Assessment Tool) was developed to assist water resource managers in assessing the impact of management on water supplies and nonpoint source pollution in watersheds and large river basins. The model is currently being utilized in several large area projects by EPA, NOAA, NRCS and others to estimate the off-site impacts of climate and management on water use, non-point source loadings, and pesticide contamination. Model development, operation, limitations, and assumptions are discussed and components of the model are described. In Part II, a GIS input/output interface is presented along with model validation on three basins within the Upper Trinity basin in Texas.
Cultural eutrophication of lakes caused by excess phosphorus (P) loading from agricultural areas is a persistent and serious
environmental problem. We quantified P flows in a watershed-lake ecosystem using a simple mathematical model that coupled
in-lake and upland processes to assess and compare the long-term impacts of various management strategies. Our model compares
abatement by in-lake strategies (such as increasing the flux of P from algae to consumers and alum application) with riparian
management to decrease P flow and with balancing P budgets at the watershed scale. All of these strategies are effective to
some extent. However, only reducing the amount of fertilizer P imported to the watershed will decrease the total P in the
system at steady state. Soil P—a large reservoir with slow turnover rate—governs long-term flux to the lake and must be decreased
in size to maintain long-term control of eutrophication.
The Orinoco River, which is hydrologically unregulated and has a minimally disturbed watershed, was sampled quantitatively over a four-year interval. In conjunction with the sampling, a method was developed for quantifying statistical uncertainty in the estimates of annual transport. The discharge-weighted mean concentration of total suspended solids in the Orinoco River is 80 mg/l, which corresponds to total annual transport of 90 × 106 t/y, or, expressed per unit of watershed area, 960 kg/ha/y, of which 96% is inorganic. The mean for dissolved solids is 34 mg/l, of which 25 mg/l is inorganic. The total transport of inorganic material, with a small allowance for bedload, is 128 × 106 t/y, which corresponds to an erosion rate of 4 cm/1000 y. Concentrations of dissolved and suspended constituents derived from rock weathering are very low because of dilution from high runoff (1190 mm/y), coverage of the southern part of the drainage by shield rock, and minimal watershed disturbance. Seasonal patterns in dissolved and suspended constituents are repeated with a high degree of consistency from one year to the next.
For most variables, relationships between transport and discharge are described adequately by a power function. There are three categories of response to changing discharge: purging (exponent > 1: soluble organic fractions and all particulate fractions), dilution (exponent 0–1: major ionic solids and silicon), and conservation (exponent < 0: nitrate, interannual). Variability across seasons and across years is highest for the particulate constituents, but within this group variability is lower for the organic than for the inorganic components. Major ions that originate primarily from the atmosphere have a higher seasonal variability than major ions that originate primarily from weathering. Potassium and soluble silicon have the lowest variabilities. Variability is much lower across years than across seasons for most constituents.
Because of high runoff per unit area, the Orinoco drainage has a high specific transport of organic carbon (72 kg/ha/y, 6.8 × 106 t/y, 1.6% of global river transport), even though the concentrations of organic carbon in the river are not exceptionally high (mean, 4.4 mg/l dissolved, 1.4 mg/l particulate). Concentrations of ammonium (35 μg/l as N) and of nitrate (80 μg/l as N) are high given the undisturbed nature of the watershed and the high amount of runoff. The high transport rate for total nitrogen (5.7 kg/ha/y, 0.54 × 106 t/y, l.5% of global river transport) can be sustained only by high rates of nitrogen fixation within the watershed. Concentrations of soluble phosphorus are within the range expected for undisturbed river systems (20 μg/l), but concentrations of particulate phosphorus are low because the amounts of particulate matter are small and the phosphorus per unit weight of suspended matter is low. Phosphorus transport (0.75 kg/ha/y) can be accounted for easily by weathering of the parent material, even within the Guayana Shield, where weathering rates are lowest. Biological modification of nutrient and carbon fractions during transit along the main stem are minimal.