[en] In order to mitigate the detrimental effects that contaminants such as petrochemical and chemical spills may have on the environment it is critical to understand their transport. This paper presented an assessment of travel time for spills management using HEC-RAS water quality analysis on the Credit River Watershed. It is a 1000 km2 area of urban and rural landscapes drained by 90 km of the main Credit River. The study focused on the mixing characteristics of 5 stream reaches in the Credit River watershed. Dye tracing was done under three different flow conditions to obtain a longitudinal dispersion coefficient, which is a necessary parameter for predicting and modelling time concentration curves downstream of a spill. The longitudinal dispersion coefficient was input into the US Army Corp of Engineers, Hydrologic Engineering Centers River Analysis System (HEC RAS) to predict time concentration curves. The HEC RAS model produced average travel time close to those measured in the field after final calibration was completed.

[en] This paper is part of an ongoing research project designed to evaluate the effect of climate change on reservoir operation policies in the Mississippi Valley Conservation Authority. The study used the results from a first paper, including projected daily temperature and precipitation, for future streamflow calculation. This paper presented the development, calibration and validation of a rainfall-runoff NAM model for the Mississippi River watershed. The calibrated Mike11/NAM model was fed with predicted climatic data to generate long term future streamflow in the basin. Forecast flows were run in a Mike 11/HD model to estimate the corresponding lake levels. The storages and flows at Shabomeka Lake, Mazinaw Lake and Marble Lake were simulated. The results showed that climate change is likely to have implications for reservoir operations in the Mississippi River watershed, which will include changed water level regimes due to modifications in the projected future streamflow hydrograph to meet desired lake levels.

[en] Researchers have calibrated satellite signals successfully using novel artificial intelligence (AI) methods to estimate discharge at ungauged river sites accurately. However, common AI methods including neural networks have a recognized defect in time series forecasting known as input imitation error. The present study addresses this significant source of error by combining evolutionary polynomial regression (EPR) with the Nondominated Sorting Genetic Algorithm (NSGA-II) for multiobjective optimization. This new method of forecasting signal time series is called the evolutionary polynomial regression-time series predictor (EPR-T). EPR-T can simultaneously minimize the model prediction error based on traditional performance indices as well as a new index, peak similarity (PS), to prevent the model from imitating its input variables when forecasting. The prediction accuracy of the new EPR-T and traditional AI methods is compared for six case studies, namely the Connecticut, Missouri, Pee Dee, Red, White, and Willamette rivers. The results demonstrate the considerably superior accuracy of EPR-T over the regular EPR method.

[en] Hydraulic jumps generally occur subsequent to structures such as ogee spillways, control gates, and weirs. The jump roller length is considered one of the main hydraulic jump parameters. In this study, the roller length of a hydraulic jump on a rough channel bed is predicted using a novel, evolutionary, generalized structure design of a group method of data handling (GS-GMDH)-type neural network. The topology of GMDH is designed with a genetic algorithm . Initially, the three most important non-dimensional parameters affecting hydraulic jump roller length, including the Froude number upstream of a hydraulic jump $\left(Fr\right)$, the ratio of sequent depths $\left(h_2/h_1\right)$, and the relative roughness $\left(ks/h_1\right)$ were used to generate four different GS-GMDH models, and the most accurate model is identified. The best new GS-GMDH model prediction statistics, including RMSE, MARE, and correlation coefficient are 1.816, 0.081, and 0.966, respectively, while the scatter index and BIAS values are 0.084 and 1.45, respectively. A partial derivative sensitivity analysis of the input parameters for the new model is also performed. The new model predictions are then compared with predictions of a number of other models. The superior performance of the new GS-GMDH over these existing models is illustrated.

[en] Soil temperature (ST) is an important dynamic parameter, whose prediction is a major research topic in various fields including agriculture because ST has a critical role in hydrological processes at the soil surface. In this study, a new linear methodology is proposed based on stochastic methods for modeling daily soil temperature (DST). With this approach, the ST series components are determined to carry out modeling and spectral analysis. The results of this process are compared with two linear methods based on seasonal standardization and seasonal differencing in terms of four DST series. The series used in this study were measured at two stations, Champaign and Springfield, at depths of 10 and 20 cm. The results indicate that in all ST series reviewed, the periodic term is the most robust among all components. According to a comparison of the three methods applied to analyze the various series components, it appears that spectral analysis combined with stochastic methods outperformed the seasonal standardization and seasonal differencing methods. In addition to comparing the proposed methodology with linear methods, the ST modeling results were compared with the two nonlinear methods in two forms: considering hydrological variables (HV) as input variables and DST modeling as a time series. In a previous study at the mentioned sites, Kim and Singh Theor Appl Climatol 118:465-479, (2014) applied the popular Multilayer Perceptron (MLP) neural network and Adaptive Neuro-Fuzzy Inference System (ANFIS) nonlinear methods and considered HV as input variables. The comparison results signify that the relative error projected in estimating DST by the proposed methodology was about 6%, while this value with MLP and ANFIS was over 15%. Moreover, MLP and ANFIS models were employed for DST time series modeling. Due to these models’ relatively inferior performance to the proposed methodology, two hybrid models were implemented: the weights and membership function of MLP and ANFIS (respectively) were optimized with the particle swarm optimization (PSO) algorithm in conjunction with the wavelet transform and nonlinear methods (Wavelet-MLP & Wavelet-ANFIS). A comparison of the proposed methodology with individual and hybrid nonlinear models in predicting DST time series indicates the lowest Akaike Information Criterion (AIC) index value, which considers model simplicity and accuracy simultaneously at different depths and stations. The methodology presented in this study can thus serve as an excellent alternative to complex nonlinear methods that are normally employed to examine DST.

[en] Highlights: • Presented a novel design for roadside drainage system to protect sensitive areas • Presented a comprehensive scientific design framework for regulatory compliance • Constructed, instrumented and monitored field-scale test site for three years • The new design can be used to protect sensitive aquatic life and groundwater. Stormwater runoff from roadways that encroach upon environmentally sensitive areas (ESAs) is one of the leading causes of degradation in urbanizing watersheds around the world. This is due to toxicity of the pollutant cocktail commonly found in roadway runoff, including heavy metals and sediments, as well as road salts from winter maintenance operations. This paper presents a novel design of an enhanced roadside drainage system (ERDS); an improved roadside drainage system that is intended to protect groundwater recharge zones and sensitive aquatic species in ESAs. The methods highlighted in this paper can be used to select soil amendments and size filter media for ERDS based on a combination of anticipated roadway pollutants and loads, treatment media efficacy and capacity, and consideration of applicable regulatory guidelines. The design of the ERDS must ensure compliance with the regulatory guidelines related to the protection of groundwater recharge zones as well as the receiving streams to protect priority species living therein. The performance monitoring results from a pilot-scale ERDS are presented to provide guidance for the key novel aspects of the design.

[en] Highlights: • A novel strategy for spatial and temporal evaluation of the runoff was introduced. • A machine learning model based remote sensing data in large-scale was developed • Nine stations at Quebec catchment in Canada were investigated. • The precipitation values are modelled with the CanEsm2 climate change model. • The zoning map of runoff for two periods of 2020–2039 and 2040–2059 was established. Accurate runoff forecasting plays a considerable role in the appropriate water resource planning and management. The spatial and temporal evaluation of the flood susceptibility was explored in the Quebec basin, Canada. This study provides a new strategy for runoff modelling as one of the complicated variables by developing new machine learning techniques along with remote sensing. A novel scheme of the Group Method of Data Handling (GMDH) known as the generalized structure of GMDH (GSGGMDH) is developed to overcome this classical approach's limitation. A simple time series based scenario with exogenous variables including precipitation and Normalized Difference Vegetation Index (NDVI) was introduced for runoff forecasting. MODIS data included MOD13Q1 product was employed and a JavaScript code was developed to preprocess collected data in the Google Earth Engine (GEE) environment. Using different seasonal and non-seasonal lags of all input variables, the developed GSGMDH found the most optimum input combination for each station in terms of simplicity and accuracy, simultaneously (average values; SI = 0.554, RMSRE = 1.55, MAE = 5.076). The precipitation values are modelled with the CanEsm2 climate change model. To apply NDVI for runoff forecasting, a simple spatial-temporal GSGMDH based model was developed (average values; SI = 0.27; RMSRE = 8.27, MAE = 0.08). The forecasting results indicated that the months in which the maximum runoff occurred have changed, and these months have increased compared to the historic period. In the historical period, the frequency of maximum runoff was in April and March. Still, for the two forecasting periods (i.e. 2020–2039 and 2040–2059), the months in which the maximum runoff has occurred have changed, and their amount has been reduced and added to other months, especially February and August.