ALBERT

Description: Water resources management and planning requires accurate and reliable spring flood forecasts. In cold and snowy countries, particularly in snow-dominated watersheds, enhanced flood prediction requires adequate snowmelt estimation techniques. Whereas the majority of the studies on snow modeling have focused on comparing the performance of empirical techniques and physically based methods, very few studies have investigated empirical models and conceptual models for improving spring peak flow prediction. The objective of this study is to investigate the potential of empirical degree-day method (DDM) to effectively and accurately predict peak flows compared to sophisticated and conceptual SNOW-17 model at two watersheds in Canada: the La-Grande River Basin (LGRB) and the Upper Assiniboine river at Shellmouth Reservoir (UASR). Additional insightful contributions include the evaluation of a seasonal model calibration approach, an annual model calibration method, and two hydrological models: McMaster University Hydrologiska Byrans Vattenbalansavdelning (MAC-HBV) and Sacramento Soil Moisture Accounting model (SAC-SMA). A total of eight model scenarios were considered for each watershed. Results indicate that DDM was very competitive with SNOW-17 at both the study sites, whereas it showed significant improvement in prediction accuracy at UASR. Moreover, the seasonally calibrated model appears to be an effective alternative to an annual model calibration approach, while the SAC-SMA model outperformed the MAC-HBV model, no matter which snowmelt computation method, calibration approach, or study basin is used. Conclusively, the DDM and seasonal model calibration approach coupled with the SAC-SMA hydrologic model appears to be a robust model combination for spring peak flow estimation.

Description: Demand for radar Quantitative Precipitation Estimates (QPEs) as precipitation forcing to hydrological models in operational flood forecasting has increased in the recent past. It is practically impossible to get error-free QPEs due to the intrinsic limitations of weather radar as a precipitation measurement tool. Adjusting radar QPEs with gauge observations by combining their advantages while minimizing their weaknesses increases the accuracy and reliability of radar QPEs. This study deploys several techniques to merge two dual-polarized King City radar (WKR) C-band and two KBUF Next-Generation Radar (NEXRAD) S-band operational radar QPEs with rain gauge data for the Humber River (semi-urban) and Don River (urban) watersheds in Ontario, Canada. The relative performances are assessed against an independent gauge network by comparing hourly rainfall events. The Cumulative Distribution Function Matching (CDFM) method performed best, followed by Kriging with Radar-based Error correction (KRE). Although both WKR and NEXRAD radar QPEs improved significantly, NEXRAD Level III Digital Precipitation Array (DPA) provided the best results. All methods performed better for low- to medium-intensity precipitation but deteriorated with the increasing rainfall intensities. All methods outperformed radar only QPEs for all events, but the agreement is best in the summer.

Description: An increase in severe precipitation events of higher intensity are expected to occur in the southeastern Mediterranean due to intensification of the hydrological cycle caused by climate change. Results of the climate change model’s precipitation data for the period 1970–2100 show a decreasing trend of daily precipitation but of higher intensity. Post-flood field investigation from a severe rainfall event in a small ungauged basin located in northwest Crete produced a validated flow hydrograph, and in combination with two high-resolution digital elevation models (DEMs), were used in the 1D/2D HEC-RAS (Hydrologic Engineering Center’s River Analysis System model), in order to determine the flooded area extent. Lateral structures were designed along the stream’s overbanks, hydraulically connecting the 1D streamflow with the 2D flow areas behind levees. Manning’s roughness coefficient and the weir coefficient were the most crucial parameters in the estimation of floodplain extent. The combined 1D/2D hydraulic model provides more detailed results than the 1D model with regards to the floodplain extent at the peak outflow, maximum flood depths, and wave velocities. Furthermore, modeling with a DEM at 2 m spatial resolution showed more precise water depth output and inundated floodplains. Scenarios of increasing peak precipitation for the same event precipitation depth were used to identify the flood extent due to an increase in daily rainfall recorded by adjacent meteorological stations. These simulation results can be useful in flood risk mapping and informing civil protective measures in flood basin management, for an effective adaptation to increased flood risk caused by a changing climate.

Description: Entropy applications in hydrometric network design problems have been extensively studied in the most recent decade. Although many studies have successfully found the optimal networks, there have been assumptions which could not be logically integrated into their methodology. One of the major assumptions is the uncertainty that can arise from data processing, such as time series simulation for the potential stations, and the necessary data quantization in entropy calculations. This paper introduces a methodology called ensemble-based dual entropy and multiobjective optimization (EnDEMO), which considers uncertainty from the ensemble generation of the input data. The suggested methodology was applied to design hydrometric networks in the Nelson-Churchill River Basin in central Canada. First, the current network was evaluated by transinformation analysis. Then, the optimal networks were explored using the traditional deterministic network design method and the newly proposed ensemble-based method. Result comparison showed that the most frequently selected stations by EnDEMO were fewer and appeared more reliable for practical use. The maps of station selection frequency from both DEMO and EnDEMO allowed us to identify preferential locations for additional stations; however, EnDEMO provided a more robust outcome than the traditional approach.

Description: Flows originating from alpine dominated cold region watersheds typically experience extended winter low flows followed by spring snowmelt and summer rainfall driven high flows. In a warmer climate, there will be a temperature-induced shift in precipitation from snowfall towards rain along with changes in precipitation intensity and snowmelt timing, resulting in alterations in the frequency and magnitude of peak flow events. This study examines the potential future changes in the frequency and severity of peak flow events in the Athabasca River watershed in Alberta, Canada. The analysis is based on simulated flow data by the variable infiltration capacity (VIC) hydrologic model driven by statistically downscaled climate change scenarios from the latest coupled model inter-comparison project (CMIP5). The hydrological model projections show an overall increase in mean annual streamflow in the watershed and a corresponding shift in the freshet timing to an earlier period. The river flow is projected to experience increases during the winter and spring seasons and decreases during the summer and early fall seasons, with an overall projected increase in peak flow, especially for low frequency events. Both stationary and non-stationary methods of peak flow analysis, performed at multiple points along the Athabasca River, show that projected changes in the 100-year peak flow event for the high emissions scenario by the 2080s range between 4% and 33% depending on the driving climate models and the statistical method of analysis. A closer examination of the results also reveals that the sensitivity of projected changes in peak flows to the statistical method of frequency analysis is relatively small compared to that resulting from inter-climate model variability.

Description: Bayesian model averaging (BMA) is a popular method using the advantages of forecast ensemble to enhance the reliability and accuracy of predictions. The inherent assumptions of the classical BMA has led to different variants. However, there is not a comprehensive examination of how these solutions improve the original BMA in the context of streamflow simulation. In this study, a scenario-based analysis was conducted for assessment of various modifications and how they affect BMA results. The evaluated modifications included using various streamflow ensembles, data transformation procedures, distribution types, standard deviation forms, and optimization methods. We applied the proposed analysis in two data-poor watersheds located in northern Ontario, Canada. The results indicate that using more representative distribution types do not significantly improve BMA-derived results, while the positive effect of implementing non-constant variance on BMA probabilistic performance cannot be ignored. Also, higher reliability was obtained by applying a data transformation procedure; however, it can reduce the results’ sharpness significantly. Moreover, although considering many streamflow simulations as ensemble members does not always enhance BMA results, using different forcing precipitation scenarios besides multi-models led to better BMA-based probabilistic simulations in data-poor watersheds. Also, the reliability of the expectation-maximization algorithm in estimating BMA parameters was confirmed.

Description: The Prairie Pothole Region (PPR) of Canada contains millions of small isolated wetlands and is unique to North America. The goods and services of these isolated wetlands are highly sensitive to variations in precipitation and temperature. We evaluated the flood proofing of isolated wetlands (pothole wetlands) under various climate change scenarios in the Upper Assiniboine River Basin (UARB) at Kamsack, a headwater catchment of the Lake of the Prairies in the Canadian portion of the PPR. A modified version of the Soil Water Assessment Tool (SWAT) model was utilized to simulate projected streamflow under the potential impacts of climate change, along with changes to the distribution of pothole wetlands. Significant increases in winter streamflow (~200%) and decreases (~11%) in summer flow, driven by changes in future climates, were simulated. Simulated changes in streamflow resulting from pothole removal were between 55% for winter and 15% for summer, suggesting that climate will be the primary driver in the future hydrologic regime of the study region. This research serves as an important guide to the various stakeholder organizations involved in quantifying the aggregate impacts of pothole wetlands in the hydrology of the Canadian Prairie Region.

Description: Hydrologic models are an approximation of reality, and thus, are not able to perfectly simulate observed streamflow because of various sources of uncertainty. On the other hand, skillful operational hydrologic forecasts are vital in water resources engineering and management for preparedness against flooding and extreme events. Multi-model techniques can be used to help represent and quantify various uncertainties in forecasting. In this paper, we assess the performance of a Multi-model Seasonal Ensemble Streamflow Prediction (MSESP) scheme coupled with statistical post-processing techniques to issue operational uncertainty for the Manitoba Hydrologic Forecasting Centre (HFC). The Ensemble Streamflow Predictions (ESPs) from WATFLOOD and SWAT hydrologic models were used along with four statistical post-processing techniques: Linear Regression (LR), Quantile Mapping (QM), Quantile Model Averaging (QMA), and Bayesian Model Averaging (BMA)]. The quality of MSESP was investigated from April to July with a lead time of three months for the Upper Assiniboine River Basin (UARB) at Kamsack, Canada. While multi-model ESPs coupled with post-processing techniques improve predictability (in general), results suggest that additional avenues for improving the skill and value of seasonal streamflow prediction. Next steps towards an operational ESP system include adding more operationally used models, improving models calibration methods to reduce model bias, increasing ESP sample size, and testing ESP schemes at multiple lead times, which, once developed, will not only help HFCs in Canada but would also help Centers South of the Border.

Description: Recent years have witnessed considerable developments in multiple fields with the potential to enhance our capability of forecasting pluvial flash floods, one of the most costly environmental hazards in terms of both property damage and loss of life. This work provides a summary and description of recent advances related to insights on atmospheric conditions that precede extreme rainfall events, to the development of monitoring systems of relevant hydrometeorological parameters, and to the operational adoption of weather and hydrological models towards the prediction of flash floods. With the exponential increase of available data and computational power, most of the efforts are being directed towards the improvement of multi-source data blending and assimilation techniques, as well as assembling approaches for uncertainty estimation. For urban environments, in which the need for high-resolution simulations demands computationally expensive systems, query-based approaches have been explored for the timely retrieval of pre-simulated flood inundation forecasts. Within the concept of the Internet of Things, the extensive deployment of low-cost sensors opens opportunities from the perspective of denser monitoring capabilities. However, different environmental conditions and uneven distribution of data and resources usually leads to the adoption of site-specific solutions for flash flood forecasting in the context of early warning systems.