Temporal neural networks for downscaling climate variability and extremes

This paper presents an application of temporal neural networks for downscaling global climate models (GCMs) output. Because of computational constraints, GCMs are usually run at coarse grid resolution (in the order of 100s of kilometres) and as a result they are inherently unable to present local sub-grid scale features and dynamics. Consequently, outputs from these models cannot be used directly in many climate change impact studies. This research explored the issues of 'downscaling' the outputs of GCMs using a temporal neural network (TNN) approach. The method is proposed for downscaling daily precipitation and temperature series for a region in northern Quebec, Canada. The downscaling models are developed and validated using large-scale predictor variables derived from the National Center for Environmental Prediction (NCEP) reanalysis data set. The performance of the temporal neural network downscaling model is also compared to a regression-based statistical downscaling model with emphasis on their ability in reproducing the observed climate variability and extremes. The downscaling results for the base period suggest that the TNN is an efficient method for downscaling both daily precipitation as well as daily maximum and minimum temperature series. Furthermore, the different model test results indicate that the TNN model mostly outperforms the statistical models for the downscaling of daily precipitation extremes and variability

Numerical Modeling Of Flow And Sediment Transport Within The Lower Reaches Of The Athabasca River: A Case Study

This study investigates flow and sediment transport patterns within the lower reaches of the Athabasca River (~250 km) in Alberta, Canada, which are characterized by complex bathymetry, regions of high tortuosity, and variable discharges and bed slopes. Sediment within this reach is primarily sand and gravel, but there is also a high percentage (\u3e10%) of cohesive clay with unique settling properties. A combination of 1D and 2D regional numerical modeling is used here to predict hydrodynamics of the flow and transport of suspended sediment. Bathymetry measurements were obtained from a combination of high resolution 3D Geoswath and ADCP surveys, and detailed 2D cross-section measurements. The 1D model solves the advection-diffusion equation for the cohesive sediment floc concentrations, and uses an explicit flocculation algorithm to calculate their distribution. Regional and high resolution local 2D numerical simulations are also completed using the Environmental Fluids Dynamics Code (EFDC) for the entire reach and a reach near Steepbank River (\u3c20 km) respectively. The high resolution local model helps in understanding the effects of coarse grid resolution and subsequently bathymetry resolution on the predictions. Validation of the model results is completed using field measurements including water surface elevations collected with Global Positioning System (GPS), water velocities collected using a Gurley current meter, and suspended sediment measurements obtained from the Regional Aquatics Monitoring Program

Model induction from data: towards the next generation of computational engines in hydraulics and hydrology; Proefschrift Technische Universiteit Delft.

There has been an explosive growth of methods in recent years for learning(or estimatingdependency) from data, where data refers to the known samples that are combinations of inputs and corresponding outputs of a given physical system. The main subject that is addressed in this thesisis, therefore, model induction from data for the simulation of hydrodynamic processes in the aquatic environment. First, some currently popular artificial neural network architectures are introduced, and it is then argued that these devices can be regarded as domain knowledge in capsulators by applying the method to the generation of wave equations fro m hydraulic data and showing how the equations of numerical-hydraulic models can, in their turn, be recaptured using artificial neural networks.The thesis also demonstrates how artificial neural networks can be used to generate numerical operators on non-structured grids for the simulation of hydrodynamic processes in two-dimensional flow systems and a methodology has been derived for developing generic hydrodynamic models using artificial neural network.The thesis also highlights one other model induction technique, namely that of the support vector machine, as an emerging new method with a potential to provide more robust models

Assessing climate change impact on design standards of hydraulic structures

No abstract available

Modelling the Athabasca watershed snow response to a changing climate

Study region: The Athabasca River basin (ARB) with its head-waters located within the Canadian Rockies. Study focus: Investigating the snow response of the Athabasca watershed to projected climate using the Variable Infiltration Capacity (VIC) hydrologic model and statistically downscaled future climate data from a selected set of CMIP5 GCMs forced with RCP4.5 and RCP8.5 emissions scenarios. New hydrological insights for the region: High resolution end-of-century projections of SWE over the Athabasca watershed show an overall decreasing trend in the mean monthly SWE over the watershed, with the largest decreases occurring in March and April, especially in the high-elevation sub-basin. There are also widespread decreases in annual maximum SWE (SWEmax), with the middle-basin showing slight increases under the RCP4.5 scenario. The dates of SWEmax are generally getting earlier, with RCP4.5 showing a less linear response than RCP8.5. Increases in early spring snowmelt are followed by decreases during the late spring and summer months mainly as a result of earlier start of snowmelt. An overall decrease in snow-cover duration of up to fifty days is projected with the largest decrease occurring in the high elevation sub-basin. Such projected declines in snow water storage and a shift to earlier peak SWE and snowmelt over the ARB have significant implications for the magnitude and timing of the watershed soil-moisture content and hydrologic regime of the Athabasca River

Comparative evaluation of the effects of climate and land-cover changes on hydrologic responses of the Muskeg River, Alberta, Canada

Study region: The Muskeg River Basin located in the Oil-Sands region of northern Alberta, Canada. Study focus: An integrated modelling framework, which combines a process-based distributed hydrologic model with a dynamic land-cover simulation model is used to evaluate the effects of climate and land-cover changes on the hydrological regime in the basin. Land-cover types corresponding to three hypothetical levels of future industrial expansion are synthesized based on the current lease holdings for the Oil-Sands development in the region. An ensemble of hydrologic simulations based on multiple climate-change projections is performed with future land-cover scenarios during a baseline (1980–2010) and two future (2050 s and 2080 s) periods. The effects of climate and land-cover changes are quantified through various hydrologic indicators using a range of variability approach. New hydrological insights for the region: Analysis of the relative contribution of inter-annual climate variability and land-cover change to the historical streamflow demonstrates the necessity to consider both in evaluating future water availability in the basin. Results indicate that modification to evapotranspiration rates caused from land-cover change affect spring and summer flows. Wetter and warmer conditions in the projected climate are found to increase spring and winter streamflows. Sensitivity analysis of the hydrologic indicators computed from the simulated flows shows that land-cover change may play a larger role in affecting the hydrologic regime than climate change, except that of spring runoff

Effects of Climatic Drivers and Teleconnections on Late 20th Century Trends in Spring Freshet of Four Major Arctic-Draining Rivers

Spring freshet is the dominant annual discharge event in all major Arctic draining rivers with large contributions to freshwater inflow to the Arctic Ocean. Research has shown that the total freshwater influx to the Arctic Ocean has been increasing, while at the same time, the rate of change in the Arctic climate is significantly higher than in other parts of the globe. This study assesses the large-scale atmospheric and surface climatic conditions affecting the magnitude, timing and regional variability of the spring freshets by analyzing historic daily discharges from sub-basins within the four largest Arctic-draining watersheds (Mackenzie, Ob, Lena and Yenisei). Results reveal that climatic variations closely match the observed regional trends of increasing cold-season flows and earlier freshets. Flow regulation appears to suppress the effects of climatic drivers on freshet volume but does not have a significant impact on peak freshet magnitude or timing measures. Spring freshet characteristics are also influenced by El Niño-Southern Oscillation, the Pacific Decadal Oscillation, the Arctic Oscillation and the North Atlantic Oscillation, particularly in their positive phases. The majority of significant relationships are found in unregulated stations. This study provides a key insight into the climatic drivers of observed trends in freshet characteristics, whilst clarifying the effects of regulation versus climate at the sub-basin scale