Generalized Likelihood Uncertainty Estimation Method Research Articles

Deciphering inter-catchment groundwater flow: A water balance perspective in the Choshui River Basin, Taiwan

Study regionFive catchments in upstream Choshui River Basin, Taiwan. Study focusInter-catchment groundwater flow (IGF)is commonly assumed negligible role in a catchment water balance due to its obscure nature. Many studies required detailed on-site geological information to analyze and confirm the significance of IGF. In contrast to these studies, this study investigated IGF using the rainfall-runoff model combined with the deterministic optimization algorithm and the stochastic generalized likelihood uncertainty estimation method, relying on simple and easily accessible water balance data. New hydrological insights for the regionThe results showed that the hydrological environment was non-stationary, and IGF inclusion in the model improved its simulation performance. Though IGF direction varied among the catchments, at least three were found with significant IGF potential, with one of the catchments accounting for 30 % (505 mm) of the yearly precipitation. Ignoring IGF may lead to larger errors, especially in smaller catchments and during the dry season. Overall, this study highlights the importance of considering IGF in a catchment water balance and provides insights into the hydrological cycle.

Uncertainty Analysis of River Water Quality Based on Stochastic Optimization of Waste Load Allocation Using the Generalized Likelihood Uncertainty Estimation Method

Uncertainty Analysis of River Water Quality Based on Stochastic Optimization of Waste Load Allocation Using the Generalized Likelihood Uncertainty Estimation Method

Optimizing Low Impact Development for Stormwater Runoff Treatment: A Case Study in Yixing, China

Low-impact development (LID) practices have been recognized as a promising strategy to control urban stormwater runoff and non-point source pollution in urban ecosystems. However, many experimental and modeling efforts are required to tailor an effective LID practice based on the hydraulic and environmental characteristics of a given region. In this study, the InfoWorks ICM was applied to simulate the runoff properties and determine the optimal LID design in a residential site at Yixing, China, based on four practical rainfall events. Additionally, the software was redeveloped using Ruby object-oriented programming to improve its efficiency in uncertainty analysis using the Generalized Likelihood Uncertainty Estimation method. The simulated runoff was in good agreement with the observed discharge (Nash–Sutcliffe model efficiency coefficients >0.86). The results of the response surface method indicated that when the sunken green belt, permeable pavement, and green roof covered 8.6%, 15%, and 10%, respectively, of the 11.3 ha study area, the designed system showed the best performance with relatively low cost. This study would provide new insights into designing urban rainfall-runoff pollution control systems.

Parameter Optimization of SWMM Model Using Integrated Morris and GLUE Methods

The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is one of the most extensively implemented numerical models for simulating urban runoff. Parameter optimization is essential for reliable SWMM model simulation results, which are heterogeneously sensitive to a variety of parameters, especially when involving complicated simulation conditions. This study proposed a Genetic Algorithm-based parameter optimization method that combines the Morris screening method with the generalized likelihood uncertainty estimation (GLUE) method. In this integrated methodology framework, the Morris screening method is used to determine the parameters for calibration, the GLUE method is employed to narrow down the range of parameter values, and the Genetic Algorithm is applied to further optimize the model parameters by considering objective constraints. The results show that the set of calibrated parameters, obtained by the integrated Morris and GLUE methods, can reduce the peak error by 9% for a simulation, and then the multi-objective constrained Genetic Algorithm reduces the model parameters’ peak error in the optimization process by up to 6%. During the validation process, the parameter set determined from the combination of both is used to obtain the optimal values of the parameters by the Genetic Algorithm. The proposed integrated method shows superior applicability for different rainfall intensities and rain-type events. These findings imply that the automated calibration of the SWMM model utilizing a Genetic Algorithm based on the combined parameter set of both has enhanced model simulation performance.

A comprehensive strategy for modeling watershed restoration priority areas under epistemic uncertainty: A case study in the Atlantic Forest, Brazil

Hydrological models are useful to support ecosystem services restoration. However, when planning the expansion of Nature-based Solutions and Payments for Ecosystem Services, there are requirements including the principle of additionality, representation of multiple runoff mechanisms and calculation of farm-scale spatial outputs and epistemic uncertainty. While addressing these aspects is important to make future planning more effective (investment costs and water benefits) it is also challenging. This paper integrates, in a single modeling framework, the main requirements highlighted. This allows an improved selection of top priority areas with farm-scale spatial resolution and a deeper understanding of how epistemic modeling uncertainty affects the results, which is relevant to evaluate the risks of overestimating ecosystem services benefits. The modeling approach is demonstrated in a case study watershed in the Atlantic Forest biome (Brazil). The PLANS model is introduced, which uses the design of TOPMODEL to simulate both simulates both saturation-excess and infiltration-excess runoff at the farm-scale resolution. Also, it uses a novel saturation index based in a composition of Height Above the Nearest Drainage and Topographical Wetness Index terrain descriptors. We applied the Generalized Likelihood Uncertainty Estimation method, aided by an evolutionary algorithm, to estimate output epistemic uncertainty. Results show that uncertainty substantially affects priority definition (97% ranking change), and that simulated topographic effects can overcome local effects of land cover and soil type. With better data, uncertainty evaluation indicates that the restoration program in the study case could have the cost of its allocation policy reduced by up to 27%, showing that it can be made more cost- effective with the methods proposed.

Estimation of Parameters on Probability Density Function Using Enhanced GLUE Approach.

The most essential process in statistical image and signal processing is the parameter estimation of probability density functions (PDFs). The estimation of the probability density functions is a contentious issue in the domains of artificial intelligence and machine learning. The study examines challenges related to estimating density functions from random variables. Based on minimal predictions regarding densities, the study discusses a framework for evaluating probability density functions. During the Bayesian approach, which is to generate correct samplings that reflect the probability aspect of the variables, sampling is widely used to estimate as well as define the probabilistic model of unknown variables. Because of its effectiveness and extensive application, the generalized likelihood uncertainty estimation (GLUE) method has earned the most popularity among the various methodologies. The Bayesian technique allows parameters of the model to be estimated using prior expertise in the parameter results and experimental observations. The study uses a number of engineering issues that were lately looked into to illustrate the effectiveness of the upgraded GLUE. As the focus is on the examination of sampling effectiveness in view of engineering components, only a brief summary is provided to describe every challenge. The suggested GLUE method's outcomes are contrasted with those obtained using MCMC. Nevertheless, using the GLUE approach, the model's mean squared error of prediction is substantially higher than that of the previous algorithms. The methods' results are affected by the assumptions being made on parameter values in advance. The concepts of prediction accuracy, as well as the utility of geometric testing, are presented. Such notions are valuable in demonstrating that the GLUE approach defines an inconsistent and incoherent statistical inference process.

Assessment of multiple precipitation interpolation methods and uncertainty analysis of hydrological models in Chaohe River basin, China

Precipitation interpolation is widely used to generate continuous rainfall surfaces for hydrological simulations. However, increasing the precision of values at the unknown points generated by different spatial interpolation methods is challenging. This study used the Chaohe River Basin, which is an important source of Beijing’s drinking water, as a research area to comprehensively evaluate several precipitation interpolation methods (Thiessen polygon, inverse distance weighting, ordinary kriging and ANUSPLIN) for inputs in hydrological simulations. This research showed that the precipitation time-series surface generated using the ANUSPLIN interpolation method had higher accuracy and reliability. Using this precipitation input to drive the hydrological models, we explored the parameter uncertainties of four typical hydrological models (GR4J, IHACRES, Sacramento and MIKE SHE) based on the multi-objective generalized likelihood uncertainty estimation (GLUE) method. The GLUE method was used to study the parameter sensitivity and uncertainty of the model. Results showed that the ANUSPLIN precipitation interpolation surface combined with the Sacramento model performed best. The multi-objective GLUE method had obvious advantages in parameter uncertainty analysis in hydrological models. Simultaneously exploring the convex line and point density distributions of the behavioural parameters with multi-objective functions determined their distribution and sensitivity more effectively.

Assessment of Uncertainty in Grid-Based Rainfall-Runoff Model Based on Formal and Informal Likelihood Measures

Damage prevention from the local storms and typhoons in Korea, the development of a rainfall-runoff model reflecting local geological, meteorological and physical characteristics is necessary. The accuracy of the rainfall-runoff model is influenced by the various uncertainty factors that can occur in the modeling processes, including input data, model parameters, modeling simplification, and so on. Thus, the objectives of this study are (1) to estimate runoff for two rainfall events using Grid Rainfall-Runoff Model (GRM); (2) to quantify the uncertainty of the GRM model using the Generalized Likelihood Uncertainty Estimation (GLUE) method, and (3) to assess the uncertainty ranges of the GRM based on different likelihood functions. For this, two rainfall events were implemented to the GRM in the Cheongmicheon watershed, and informal likelihood functions (LNSE, LPBIAS, LRSR, and LLOG) based on the fitness indices (NSE, PBIAS, RSR, and Log-normal) were used for uncertainty analysis and quantification using GLUE method. As a result, the GRM parameters varied according to the different rainfall patterns even in the same watershed. In addition, among the GRM parameters, the CRC (Channel Roughness Coefficient) and CSHC (Correction factor for Soil Hydraulic Conductivity) characteristics are the most sensitive. Moreover, this study showed that the uncertainty range of the GRM model can be changed with the subjective selection of likelihood functions and thresholds. The GRM model is open source and has good accessibility. Especially, this open-source model allows various approaches to disaster prevention plans such as flood forecasting and flood insurance policies. In addition, if the parameter range of GRM is quantified and standardized at domestic watersheds, it is expected that the reliability of the rainfall-runoff simulation can be increased by the reduction of the uncertainty factors.

Integrating partitioned evapotranspiration data into hydrologic models: Vegetation parameterization and uncertainty quantification of simulated plant water use

AbstractAccurate simulation of plant water use across agricultural ecosystems is essential for various applications, including precision agriculture, quantifying groundwater recharge, and optimizing irrigation rates. Previous approaches to integrating plant water use data into hydrologic models have relied on evapotranspiration (ET) observations. Recently, the flux variance similarity approach has been developed to partition ET to transpiration (T) and evaporation, providing an opportunity to use T data to parameterize models. To explore the value of T/ET data in improving hydrologic model performance, we examined multiple approaches to incorporate these observations for vegetation parameterization. We used ET observations from five eddy covariance towers located in the irrigated San Joaquin Valley, California, to parameterize orchard crops in an integrated land surface—groundwater model. By using ET, or both ET and T data, we examined the impact of multiple model parameterization approaches ranging from simple performance metrics to the generalized likelihood uncertainty estimation method. We find that a simple approach of selecting the parameter sets based on ET and T performance metrics works best at these study sites. Selecting parameters based on performance relative to observed ET creates an uncertainty of 27% relative to the observed value. When parameters are selected using both T and ET data, this uncertainty drops to 24%. Similarly, the uncertainty in potential groundwater recharge drops from 63% to 58% when parameters are selected with ET or T and ET data, respectively. While these improvements are minor in an irrigated setting, the value of partitioning ET data may be more useful in non‐irrigated settings. Additionally, using crop type parameters results in similar levels of simulated ET as using site‐specific parameters. Different irrigation schemes create high amounts of uncertainty and highlight the need for accurate estimates of irrigation when performing water budget studies.

Overcoming equifinality: time-varying analysis of sensitivity and identifiability of SWAT runoff and sediment parameters in an arid and semiarid watershed.

The phenomenon of "equifinality for different parameters" limits the link between parameters and catchment characteristics; however, solving the equifinality problem is a major challenge in the development, generalization, and application of a model. This study focused on the Yanhe River Watershed to investigate the time-varying characteristics of sensitivity and identifiability of SWAT (Soil and Water Assessment Tool) runoff and sediment parameters based on the Sobol' and generalized likelihood uncertainty estimation methods. The results indicate that (i) the nondominated sorting genetic algorithm-II has good adaptability and reliability in parameter calibration of the SWAT model in the Yanhe River Watershed. The evaluation indicators (Nash-Sutcliffe efficiency, R2, and percent bias) of monthly runoff and sediment in the Ganguyi hydrological station were all satisfactory per the SWAT model during the calibration and validation periods. (ii) The interaction between runoff and sediment parameters is a crucial reason for parameter sensitivity, which has obvious time-varying characteristics and is largely dependent on precipitation in the Yanhe River Watershed. Temporal and spatial variability of precipitation should be considered in the detailed analysis of parameter identifiability, and watershed managers should not ignore changes in the runoff process when regulating sediment. (iii) Only a relatively small number of parameters can be identified in the runoff and sediment simulation process of the Yanhe River Watershed, such as CN2 (initial soil conservation service runoff curve number for moisture condition II), CH_K2 (effective hydraulic conductivity in main channel alluvium), ALPHA_BF (baseflow alpha factor), USLE_C (cover and management factor), USLE_P (support practice factor), and USLE_K (soil erodibility factor), due to high surface runoff, reduced lag time, reduced low flows, increased peak flows, and channel erosion, respectively. More importantly, there is a strong positive correlation between parameter identifiability and parameter sensitivity. Both are effective methods of parameter diagnosis, but the identifiability of parameters is not equivalent to its sensitivity. Our results strongly suggest that a detailed parameter sensitivity and identifiability analysis is a critical step in improving hydrological model performance to reduce the risk of "equifinality for different parameters" while articulating all relevant hydrological processes.

Advanced sensitivity analysis of the impact of the temporal distribution and intensity of rainfall on hydrograph parameters in urban catchments

Abstract. Knowledge of the variability of the hydrograph of outflow from urban catchments is highly important for measurements and evaluation of the operation of sewer networks. Currently, hydrodynamic models are most frequently used for hydrograph modeling. Since a large number of their parameters have to be identified, there may be problems at the calibration stage. Hence, sensitivity analysis is used to limit the number of parameters. However, the current sensitivity analytical methods ignore the effect of the temporal distribution and intensity of precipitation in a rainfall event on the catchment outflow hydrograph. This article presents a methodology of constructing a simulator of catchment outflow hydrograph parameters (volume and maximum flow). For this purpose, uncertainty analytical results obtained with the use of the GLUE (generalized likelihood uncertainty estimation) method were used. A novel analysis of the sensitivity of the hydrodynamic catchment models was also developed, which can be used in the analysis of the operation of stormwater networks and underground infrastructure facilities. Using the logistic regression method, an innovative sensitivity coefficient was proposed to study the impact of the variability of the parameters of the hydrodynamic model depending on the distribution of rainfall, the origin of rainfall (on the Chomicz scale), and the uncertainty of the estimated simulator coefficients on the parameters of the outflow hydrograph. The developed model enables the analysis of the impact of the identified SWMM (Storm Water Management Model) parameters on the runoff hydrograph, taking into account local rainfall conditions, which have not been analyzed thus far. Compared with the currently developed methods, the analyses included the impact of the uncertainty of the identified coefficients in the logistic regression model on the results of the sensitivity coefficient calculation. This aspect has not been taken into account in the sensitivity analytical methods thus far, although this approach evaluates the reliability of the simulation results. The results indicated a considerable influence of rainfall distribution and intensity on the sensitivity factors. The greater the intensity and rainfall were, the lower the impact of the identified hydrodynamic model parameters on the hydrograph parameters. Additionally, the calculations confirmed the significant impact of the uncertainty of the estimated coefficient in the simulator on the sensitivity coefficients. In the context of the sensitivity analysis, the obtained results have a significant effect on the interpretation of the relationships obtained. The approach presented in this study can be widely applied at the model calibration stage and for appropriate selection of hydrographs for identification and validation of model parameters. The results of the calculations obtained in this study indicate the suitability of including the origin of rainfall in the sensitivity analysis and calibration of hydrodynamic models, which results from the different sensitivities of models for normal, heavy, and torrential rain types. In this context, it is necessary to first divide the rainfall data by origin, for which analyses will be performed, including sensitivity analysis and calibration. Considering the obtained results of the calculations, at the stage of identifying the parameters of hydrodynamic models and their validation, precipitation conditions should be included because, for the precipitation caused by heavy rainfall, the values of the sensitivity coefficients were much lower than for torrential ones. Taking into account the values of the sensitivity coefficients obtained, the calibration of the models should not only cover episodes with high rainfall intensity, since this may lead to calculation errors at the stage of applying the model in practice (assessment of the stormwater system operating conditions, design of reservoirs and flow control devices, green infrastructure, etc.).

Uncertainty analysis of the HORTSYST model applied to fertigated tomatoes cultivated in a hydroponic greenhouse system

Aim of study: The objective was to perform an uncertainty analysis (UA) of the dynamic HORTSYST model applied to greenhouse grown hydroponic tomato crop. A frequentist method based on Monte Carlo simulation and the Generalized Likelihood Uncertainty Estimation (GLUE) procedure were used.Area of study: Two tomato cultivation experiments were carried out, during autumn-winter and spring-summer crop seasons, in a research greenhouse located at University of Chapingo, Chapingo, Mexico.Material and methods: The uncertainties of the HORTSYST model predictions PTI, LAI, DMP, ETc, Nup, Pup, Kup, Caup, and Mgup uptake, were calculated, by specifying the uncertainty of model parameters 10% and 20% around their nominal values. Uniform PDFs were specified for all model parameters and LHS sampling was applied. The Monte Carlo and the GLUE methods used 10,000 and 2,000 simulations, respectively. The frequentist method included the statistical measures: minimum, maximum, average values, CV, skewness, and kurtosis whilst GLUE used CI, RMSE, and scatter plots.Main results: As parameters were changed 10%, the CV, for all outputs, were lower than 15%. The smallest values were for LAI (10.75%) and DMP (11.14%) and the largest was for ETc (14.47%). For Caup (12.15%) and Pup (12.27%), the CV was lower than the one for Nup and Kup. Kurtosis and skewness values were close as expected for a normal distribution. According to GLUE, crop density was found to be the most relevant parameter given that it yielded the lowest RMSE value between the simulated and measured values.Research highlights: Acceptable fitting of HORTSYST was achieved since its predictions were inside 95% CI with the GLUE procedure.

Uncertainty Analysis of Reservoir Operation Based on Stochastic Optimization Approach Using the Generalized Likelihood Uncertainty Estimation Method

Uncertainty Analysis of Reservoir Operation Based on Stochastic Optimization Approach Using the Generalized Likelihood Uncertainty Estimation Method

Probabilistic Analysis of a Linearized Servo-Hydraulic Model for Real-Time Structural Testing

ABSTRACT Servo-hydraulic actuators are often used in real-time hybrid simulation to impose desired displacements to specimens and to maintain the boundary compatibility between substructures. Previous studies often consider the servo-hydraulic system as deterministic while experimental results indicate otherwise due to different sources of uncertainties. This study utilizes the generalized likelihood uncertainty estimation method to explore the uncertainties in servo-hydraulic dynamics. Probabilistic distribution and statistical moments are derived for parameters of a linearized servo-hydraulic model based on experimental results. The analysis results are further applied to demonstrate the effects of servo-hydraulic uncertainties on actuator tracking and compared with the corresponding deterministic model.

Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change

An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning.

Parallel Hydrological Model Parameter Uncertainty Analysis Based on Message-Passing Interface

Parameter uncertainty analysis is one of the hot issues in hydrology studies, and the Generalized Likelihood Uncertainty Estimation (GLUE) is one of the most widely used methods. However, the scale of the existing research is relatively small, which results from computational complexity and limited computing resources. In this study, a parallel GLUE method based on a Message-Passing Interface (MPI) was proposed and implemented on a supercomputer system. The research focused on the computational efficiency of the parallel algorithm and the parameter uncertainty of the Xinanjiang model affected by different threshold likelihood function values and sampling sizes. The results demonstrated that the parallel GLUE method showed high computational efficiency and scalability. Through the large-scale parameter uncertainty analysis, it was found that within an interval of less than 0.1%, the proportion of behavioral parameter sets and the threshold value had an exponential relationship. A large sampling scale is more likely than a small sampling scale to obtain behavioral parameter sets at high threshold values. High threshold values may derive more concentrated posterior distributions of the sensitivity parameters than low threshold values.

A hybrid runoff generation modelling framework based on spatial combination of three runoff generation schemes for semi-humid and semi-arid watersheds

It is challenging to accurately differentiate and simulate different runoff components yielded under the saturation-excess and infiltration-excess mechanisms in semi-arid and semi-humid watersheds. Fixed model structures and runoff generation modes in most of the existing conventional hydrological models limit their ability to account for respective contributions of saturation-excess and infiltration-excess flows to flood processes. To conquer this limitation, this study developed a new flexible hybrid runoff generation modeling framework, which is named as spatial combination computing models (SCCMs) for runoff generation. SCCMs can determine the dominant runoff generation mode, either saturation-excess or infiltration-excess, on the sub-watershed level and then adopt one of the Xinanjiang (XAJ), Xinanjiang-Green Ampt (XAJG) and Green-Ampt (GA) runoff generation schemes to compute runoff generation at each sub-watershed. The logical combination of the above three runoff generation schemes led to six flavors of SCCMs. Curve number and topographic index are used for dividing a watershed into infiltration-excess dominated and saturation-excess dominated sub-watersheds. The proposed models were tested in two typical semi-humid (Dongwan) and semi-arid (Zhidan) watersheds. The multi-objective generalized likelihood uncertainty estimation method (GLUE) was used to analyze the parameter uncertainty of SCCMs. The results show that all two-scheme SCCMs outperform any of the single-scheme SCCMs. SCCM-2c (a combination model of XAJ and XAJG) achieves the best performance in the semi-humid Dongwan Watershed, while SCCM-5c (a combination model of XAJG and GA) performs best in the semi-arid Zhidan Watershed. The multi-objective GLUE-based uncertainty analysis shows that parameter uncertainties of the two-scheme SCCMs (SCCM-2c and SCCM-5c) are slightly lower than those of the single-scheme SCCMs (SCCM-1n, i.e., the XAJ model, and SCCM-6n, i.e., the GA model), indicating that increased model complexity in SCCMs does not cause increased model parameter uncertainty. Our results indicate that SCCMs with more realistic representation of spatial heterogeneity can achieve higher simulation accuracies and even lower uncertainties than the conventional single-scheme models.

Staged optimization of urban drainage systems considering climate change and hydrological model uncertainty

Flood risk increases with climate change and rapid urbanization, which urgently needs to improve the capacity of urban drainage systems. In the study, a staged optimization model considering climate change and hydrological model uncertainty (SOCU) was proposed for urban drainage system design. The SOCU model refers to the construction of drainage system by stages instead of traditional implement-once plans, which was established based on an integration of staged optimization policy, urban hydrological model, generalized likelihood uncertainty estimation (GLUE) method and chance-constrained programming (CCP) model. The staged optimization policy was employed to deal with the uncertainty of climate change. The GLUE method was adopted to analyze the uncertainty of urban hydrological model established by PCSWMM. Considering the uncertainty from urban hydrological simulation, the CCP model (one of the main methods of stochastic mathematical programming) was used to deal with the uncertainty of the optimization model. Subsequently, a case study of the Haidian Island of Hainan Province in China was used to demonstrate the proposed model. The result shows that the optimal investment is 183 million Yuan with a pumping capacity of 28.3 m3/s for the first stage and 38.4 m3/s for the next stage. The SOCU model is not only more flexible to adapt to climate change, but also is economically efficient (10% lower than implement-once plans). The urban drainage system design obtained from the SOCU model is more reliable and robust than traditional implement-once plans since the climate change and hydrological model uncertainty are simultaneously taken into account. Furthermore, the investment increases from 166 million Yuan to 195 million Yuan when the flood constraint satisfaction probability increases from 0.75 to 0.95. Therefore, the model could provide richer decision-making information than traditional implement-once plans and help decision makers seek a trade-off between system investment and acceptable flood damage. The study outcomes provide a reliable optimization model for urban drainage design and may have profound implications and contributions for urban flood management.

Co-transport of heavy metals in layered saturated soil: Characteristics and simulation

Interest in soil pollution by multiple heavy metals has been growing over the last decades. However, few experiments combining numerical analyses with solute transport in layered soil can be found in the literature. Here, the retention and fate of three coexisting metal ions, Cu, Cd, and Zn, in layered soils were investigated to evaluate soil co-contamination through batch and column experiments. Results showed high amounts of Cu adsorption and retention by soils, followed by Cd and Zn. The partial concentration of Zn in effluent was greater than the input from competition adsorption and the ‘snow plow effect’. These findings indicate the high potential risk of Zn and Cd groundwater pollution when Cu, Cd, and Zn co-exist in the soil. Adsorption isotherms obtained from batch experiments were well described by Freundlich equation. Breakthrough curves (BTCs) obtained from column experiments were well described by standard convection-dispersion equation (CDE) for Br, and Tow-site (TSM) and One-site models (OSM) for metals except for Zn, using the Levenberg-Marquardt nonlinear optimization algorithm. However, the parameters were poorly constrained by the available observational data due to high correlation between parameters, rather than insensitivity to model outputs. The Generalized Likelihood Uncertainty Estimation (GLUE) method did not only qualify the uncertainty of parameters for solute transport in layered medium, but estimate prediction uncertainty. Prediction bounds basically captured the observed Br, Zn and Cd BTCs, while systematically overestimated the effluent Cu concentration.Comparing with the optimization, GLUE method can improve prediction reliability of heavy metal transport in layered soils.

Optimizing Genetic Parameters of CSM-CERES Wheat and CSM-CERES Maize for Durum Wheat, Common Wheat, and Maize in Italy

The expected increase in population and the pressure posed by climate change on agricultural production require the assessment of future yield levels and the evaluation of the most suitable management options to minimize climate risk and promote sustainable agricultural production. Crop simulation models are widely applied tools to predict crop development and production under different management practices and environmental conditions. The aim of this study was to parameterize CSM-CERES-Wheat and CSM-CERES-Maize models, implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) software, to predict phenology and grain yield of durum wheat, common wheat, and maize in different Italian environments. A 10-year (2001–2010) dataset was used to optimize the genetic parameters for selected varieties of each species and to evaluate the models considering several statistical indexes. The generalized likelihood uncertainty estimation method, and trial and error approach were used to optimize the cultivar-specific parameters of these models. Results show good model performances in reproducing crop phenology and yield for the analyzed crops, especially with the parameters optimized with the trial and error procedure. Highly significant (p ≤ 0.001) correlations between observed and simulated data were found for both anthesis and yield in model calibration and evaluation (p ≤ 0.01 for grain yield of maize in model evaluation). Root mean square error (RMSE) values range from six to nine days for anthesis and from 1.1 to 1.7 t ha-1 for crop yield and index of agreement (d-index) from 0.96 to 0.98 for anthesis and from 0.8 to 0.87 for crop yield. The set of genetic parameters obtained for durum wheat, common wheat, and maize may be applied in further analyses at field, regional, and national scales to guide operational (farmers), strategic, and tactical (policy makers) decisions.