Investigating the Spatial and Temporal Variability of Precipitation using Entropy Theory

Abstract This study uses entropy theory to develop a novel application of the apportionment entropy disorder index (AEDI) to capture both spatial and temporal variability in monthly precipitation for various types of hydrologic modeling. In total, 41 Environment Canada stations across Ontario with long term (1955 to 2005) records and a very low percentage of missing data were selected. It was found that the fall and summer seasons are the major contributors to annual precipitation variability. Spatial variability of annual precipitation was observed to be increasing from southern to northern Ontario. The AEDI index map of Ontario, developed in this study, has been successfully integrated into several hydrologic models

Improving the accuracy of a remotely-sensed flood warning system using a multi-objective pre-processing method for signal defects detection and elimination

One of the primary goals of watershed management is to proactively monitor and forecast flood water levels to provide early warning for timely evacuation plans and save lives. One of the most economical ways to accomplish this objective is to use remotely-sensed satellite signals. Previous studies have indicated that an Advanced Microwave Scanning Radiometer (AMSR) sensor can be used for river water level monitoring combined with a few in-situ hydrometric gauges for the ground-truth data collection. However, space-based signals are influnced by many error-inducing natural factors, such as dust and cloud cover. Hence, a hybrid method is proposed, which comprises of a multi-objective particle swarm optimization model, a decision tree classification algorithm, the Hotelling’s $T^{2}$ outlier detection, and a regression model to identify and replace inaccurate space-based signals. This complex hybrid method will be referred to, in this study, with the acronym (OCOR). In the first phase of this hybrid method, the outlier signals are detected and eliminated from the dataset, and in the second phase, the eliminated signals along with signals lost due to satellite technical problems are estimated by ground-truth data calibration using in situ hydrometric stations. The two case studies of the White and Willamette Rivers demonstrate the performance of OCOR in practical situations

Improving the accuracy of a remotely-sensed flood warning system using a multi-objective pre-processing method for signal defects detection and elimination

One of the primary goals of watershed management is to proactively monitor and forecast flood water levels to provide early warning for timely evacuation plans and save lives. One of the most economical ways to accomplish this objective is to use remotely-sensed satellite signals. Previous studies have indicated that an Advanced Microwave Scanning Radiometer (AMSR) sensor can be used for river water level monitoring combined with a few in-situ hydrometric gauges for the ground-truth data collection. However, space-based signals are influnced by many error-inducing natural factors, such as dust and cloud cover. Hence, a hybrid method is proposed, which comprises of a multi-objective particle swarm optimization model, a decision tree classification algorithm, the Hotelling’s $T^{2}$ outlier detection, and a regression model to identify and replace inaccurate space-based signals. This complex hybrid method will be referred to, in this study, with the acronym (OCOR). In the first phase of this hybrid method, the outlier signals are detected and eliminated from the dataset, and in the second phase, the eliminated signals along with signals lost due to satellite technical problems are estimated by ground-truth data calibration using in situ hydrometric stations. The two case studies of the White and Willamette Rivers demonstrate the performance of OCOR in practical situations

Better management of construction sites to protect inland waters

Several areas within the Lake Simcoe watershed, Canada, are experiencing rapid urban development. The construction of new homes and businesses is frequently associated with elevated rates of soil erosion stemming from land clearing and grading activities. During development, rates of soil erosion can climb to levels that are typically 200 times above background conditions, with the eroded sediments entering waterways and causing harm to the biota living therein. This is a serious challenge for the communities around Lake Simcoe because the transport of sediment has previously been identified as a contributor to the eutrophication of the lake’s waters. To mitigate the negative impacts associated with development, many jurisdictions across North America and elsewhere have developed a suite of construction-phase stormwater management (CPSWM) guidelines, which entail the use of onsite best management practices that capture, detain, and treat sediment-laden surface runoff. Here, we review CPSWM guidelines for effluent discharge and receiving water quality and discuss the relative strengths and weaknesses of each approach. Finally, proposed revisions to the current Ontario guidelines are suggested based on a combination of field observations at predevelopment and active construction sites, as well as the reviewed literature. If adopted, the proposed revisions would help to reduce sediment transport from construction sites in rapidly urbanizing areas such as Lake Simcoe

Time-Series-Based Air Temperature Forecasting Based on the Outlier Robust Extreme Learning Machine

In this study, an improved version of the outlier robust extreme learning machine (IORELM) is introduced as a new method for multi-step-ahead hourly air temperature forecasting. The proposed method was calibrated and used to estimate the hourly air temperature for one to ten hours in advance after finding its most optimum values (i.e., orthogonality effect, activation function, regularization parameter, and the number of hidden neurons). The results showed that the proposed IORELM has an acceptable degree of accuracy in predicting hourly temperatures ten hours in advance (R = 0.95; NSE = 0.89; RMSE = 3.74; MAE = 1.92)

Hydrologic Impacts of Climate Change in Relation to Ontario’s Source Water Protection Planning Program

Water management activities are currently predicated on the assumption of a stationary climate, despite the reality of climate change. Hydrologic impacts of climate change for three sub-watersheds north of Toronto for 2041-70 were investigated using the Precipitation-Runoff Modeling System to model six GCM projections from each of RCP 2.6, RCP 4.5, and RCP 8.5. Annual groundwater recharge, evapotranspiration, and the 7Q20 low streamflow statistic were projected to change from 1976-2005 conditions by -2.2% to +20.5%, +0.9% to +14.4%, and -25.5% to +9.8%, respectively. Seasonal shifts included an earlier date of peak streamflow for the majority of simulations and a +14.0% to +103.9% increase in winter recharge. A steady-state MODFLOW model was employed as a preliminary assessment into the effects of climate change on Source Water Protection outputs. The results of this research further the understanding of climate change impacts on human and ecological systems in southern Ontario.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

A modified FAO evapotranspiration model for refined water budget analysis for Green Roof systems

Many metropolitan cities around the world are promoting the implementation of Green Roofs (GR) as effective stormwater management systems to mitigate adverse effects of urbanization. Hourly water balance calculations are necessary for accurate prediction of the antecedent soil moisture conditions of a GR, a key parameter for rainfall-runoff modeling. However, existing evapotranspiration (ET) models have substantial errors for hourly ET predictions over a range of moisture conditions to accurately assess the hydrological performance of the GRs during storm events. Therefore, a modified Penman-Monteith equation was developed to provide improved prediction of hourly ET specifically for GR applications. ET rates predicted by the modified and established Penmen-Monteith equations were compared with ET data collected from a GR lysimeter over the summer and fall of 2016. The proposed modified Penman-Montieth equation eliminates the water availability factor () included in the equation’s advection term and separates the effect of plant coefficients on radiation/energy and advection terms. The modified equation was found to improve ET estimates by 8–9% (RMSE: 0.535–0.493, MAD: 0.375–0.343), under non-water limited conditions. For water-limited conditions, errors were significantly reduced. RMSE improved by 37% (0.703–0.440) and MAD was improved by 31% (0.429–0.294).The authors acknowledge the financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC) through a Strategic Project Grant (STPGP 447409 - 13). In kind contributions for this research were provided by Bioroof Systems, DH Water Management Services, Sky Solar (Canada), IRC Building Science Group. Equipment used in this research was purchased through funding provided by the Canadian Foundation for Innovation through the John R. Evans Leaders Fund (Grant # 32232)

Short-Term Precipitation Forecasting Based on the Improved Extreme Learning Machine Technique

In this study, an improved version of the Extreme Learning Machine, namely the Improved Weighted Regularization ELM (IWRELM), is proposed for hourly precipitation forecasting that is multi-steps ahead. After finding the optimal values of the proposed method, including the number of hidden neurons, the activation function, the weight function, the regularization parameter, and the effect of orthogonality, the IWRELM model was calibrated and validated. Thereafter, the calibrated IWRELM model was used to estimate precipitation up to ten hours ahead. The results indicated that the proposed IWRELM (R = 0.9996; NSE = 0.9993; RMSE = 0.015; MAE = 0.0005) has acceptable accuracy in short-term hourly precipitation forecasting up to ten hours ahead