Robert Leconte

Effects of flow regulation on hydrologic patterns of a large, inland delta

The Peace–Athabasca River Delta (PAD) is one of the largest freshwater deltas and most biologically productive in the world. Because regional evaporation is greater than precipitation, the thousands of lakes and wetlands dotting this area rely on periodic flooding from the Peace and Athabasca rivers to be replenished. Flood frequency significantly declined beginning in the mid-1970s, several years after the initiation of flow regulation of the Peace River. However, the drying trend was interrupted in 1996 when the PAD experienced extensive inland inundation on two separate occasions, one in the spring and one in the summer. A one-dimensional numerical hydrodynamic model was used to evaluate the role of flow regulation and hydroclimatic conditions on the water levels of major lakes found in the PAD. Three Peace River flow scenarios were analysed: the observed flows, the flow regime without the ‘precautionary drawdown’ spill which was required because of the discovery of a sinkhole at the crest of the dam, and the naturalized flow regime, which assumed no dam regulation. Modelling results indicated that the effect of the spill on the flow regime within the PAD was approximately equivalent in magnitude, although different in timing, to what would have resulted from the prevailing hydroclimatic conditions in an unregulated system. Furthermore, even in the absence of the precautionary drawdown spill, the lake levels would have risen well above the maximum daily average, suggesting that 1996 was one of the wettest years on record. Finally, the hydrodynamic regime observed at the end of the summer 1996 was very similar to that modelled under unregulated flow conditions, suggesting that flow regulation could be used to alter the hydrodynamic regime of a large delta to at least partially restore natural conditions and potentially improve ecosystem health. Copyright

Stochastic Multisite Generation of Daily Precipitation Data Using Spatial Autocorrelation

Abstract There are a number of stochastic models that simulate weather data required for various water resources applications in hydrology, agriculture, ecosystem, and climate change studies. However, many of them ignore the dependence between station locations exhibited by the observed meteorological time series. This paper proposes a multisite generation approach of daily precipitation data based on the concept of spatial autocorrelation. This theory refers to spatial dependence between observations with respect to their geographical adjacency. In hydrometeorology, spatial autocorrelation can be computed to describe daily dependence between the weather stations through the use of a spatial weight matrix, which defines the degree of significance of the weather stations surrounding each observation. The methodology is based on the use of the spatial moving average process to generate spatially autocorrelated random numbers that will be used in a stochastic weather generator. The resulting precipitation pr...

Quantitative soil moisture extraction from airborne SAR data.

Knowledge of the moisture content of soil is valuable for hydrology and climate studies, as well as for yield prediction or agricultural planning. As part of the long-term research plan at the Canada Centre for Remote Sensing (CCRS) to establish a relationship between radar backscatter and the spatial and temporal variations of soil moisture, a series of experiments were conducted in the Canadian prairies in 1988.This paper examines radar backscatter as a function of soil moisture, plant type, and phenological development. Airborne data were acquired by the CCRS C-band SAR of a test site near Outlook, Saskatchewan, in June and August 1988. The digitally recorded and processed imagery were externally calibrated via point targets of known radar cross-section. Soil dielectric measurements were collected using a portable dielectric probe in fields with similar surface roughness characteristics. These measurements were used as input to a model developed by CCRS for estimating soil volumetric water content.This...

Structural and Non-Structural Climate Change Adaptation Strategies for the Péribonka Water Resource System

This paper discusses the possibility for a privately managed hydro-power system to adapt to a projected increase in water flow in their central-Québec watersheds by adding power generation potential. Runoffs simulated by a lumped rainfall-runoff model were fed into a stochastic dynamic programming (SDP) routine to generate reservoir operating rules. These rules were optimized for maximum power generation under maximal and minimal reservoir level constraints. With these optimized rules, a power generation simulator was used to predict the amount of generated hydropower. The same steps, excluding calibration, were performed on 60 climate projections (from 23 general circulation models and 3 greenhouse gas emission scenarios) for future horizons 2036–2065 and 2071–2100. Reservoir operation rules were optimized for every climate change projection for the 3 power plants in the system. From these simulations, it was possible to determine hydropower numbers for both horizons. The same steps were performed under a modified system in which an additional turbine was added to each power plant. Results show that both the non-structural (optimizing reservoir rules) and structural (adding turbines) adaptation measures allow for increased power production, but that adapting operating rules is sufficient to reap the most of the benefits of increased water availability.

A versatile weather generator for daily precipitation and temperature

Stochastic daily weather generators are often used to generate long time series of weather variables to drive hydrological and agricultural models. More recently, they have also been used as a downscaling tool for studying the impacts of climate change. This article describes a versatile stochastic weather generator (WeaGETS) for producing daily precipitation, and maximum and minimum temperatures (Tmax and Tmin). WeaGETS regroups several options of other weather generators into one package, such as three Markov models to produce precipitation occurrence, four distributions to generate wet day precipitation amount, and two methods to simulate Tmax and Tmin. More importantly, a spectral correction approach is included in WeaGETS for correcting the underestimation of interannual variability, which is a problem common to all weather generators. The performance of WeaGETS is demonstrated through a comparison against two well-known weather generators (WGEN and CLIGEN) with respect to the generation of precipitation, Tmax, and Tmin for two Canadian meteorological stations. The results show that the widely used first-order Markov model is adequate for producing precipitation occurrence, but it underestimates the longest dry spell for the low-precipitation station. The higher-order models have positive effects. The mixed exponential and skewed normal Pearson III distributions are consistently better than the exponential and gamma distributions at generating precipitation amounts. The two-component mixed exponential distribution is better at representing extreme precipitation events than the other three distributions. WeaGETS is consistently better than WGEN and CLIGEN at producing Tmax and Tmin. Both WGEN and CLIGEN underestimate the monthly and interannual variances of precipitation and temperatures. However, WeaGETS successfully preserves the observed low-frequency variability and autocorrelation functions of precipitation and temperatures. Overall, WeaGETS is consistently better than the other two weather generators (WGEN and CLIGEN) for producing precipitation, Tmax, and Tmin. The Matlab freeware allows for easy modification of all routines, making it easy to add additional weather variables to simulate.

Restoring ice-jam floodwater to a drying Delta ecosystem

Abstract Overbank flooding is essential to the ecological health of riparian landscapes, particularly river deltas. One of the worlds largest freshwater deltas, the Peace-Athabasca Delta (PAD) in northern Canada, has experienced a series of wetting and drying cycles because of inter-annual variations in flooding. Recent research has found that most of the major floods affecting this system are produced by spring ice jams. For approximately two decades, however, the combination of climatic and flow-regulation effects precluded significant ice-jam flooding of the PAD. Resultant drying caused major changes to the ecology of the delta and led to the evaluation of a number of methods to restore water flows. Since most of delta is contained within a national park (Wood Buffalo National Park), a major goal was to employ non-structural measures. Hence, in an effort to manage the water problems of this delta, the final report of a multi-agency “Northern River Basins Study” made the recommendation that the spring flow-release strategy of the upstream hydro electric reservoir be modified to increase the probability of ice-jam flooding near the PAD. This was to be conducted in years when downstream hydrometeorological conditions (snowpack magnitude and ice-cover strength) appeared conducive to ice-jam formation. Such favourable conditions developed in the spring of 1996, a natural ice jam began to develop, and regulated flows were increased to assist in potential flooding. As a result, the PAD experienced its first major flood in over 20 years. This paper reviews the hydrometeorological conditions that led to the ice-jam formation, compares the conditions to historical events, analyzes the spatial extent of the flood, and evaluates the effectiveness of the flow release.

A review of Canadian remote sensing applications in hydrology, 1995-1999

The potential of remote sensing for providing information to hydrologists and water resources practitioners has been recognized since the 1970s. The variety of satellite and airborne platforms and the greater ease of access to imagery now make it possible to evaluate and quantify an increasingly large number of watershed physical characteristics and state variables. Canadian scientists have been very active over the last 5 years creating algorithms to extract hydrological information from remotely sensed data and to develop new, or adapt existing, hydrological methods capable of making efficient use of this new information. Over the years, research and applications of remote sensing in Canadian hydrology have embraced a variety of topics and recent research has placed significant emphasis on radar remote sensing as the Canadian RADARSAT satellite was launched successfully on 4 November 1995. This paper reviews recent (1995-99) remote sensing contributions in hydrology by Canadians, specifically focusing on the usefulness and applicability of current remote sensing technology for water management purposes. A very brief description of the theory underlying each application as well as relevant sensors is presented.

An Improved Stochastic Weather Generator for Hydrological Impact Studies

A stochastic weather generator based on the WGEN model has been tested on 13 meteorological stations in Quebec, Canada. The generator, called WeaGETS, accounts for longer persistence of wet and dry spells by including second and third order Markov chain models. It also includes regional correction factors to adjust the precipitation percentile values as simulated by the WGEN model with respect to observed precipitation. This is a first step toward the development of a model to construct basin scale projections of future changes in climate intended for hydrological impact studies. A direct validation of the generator using selected extreme indices of precipitation has shown that the modified generator generally performed better than WGEN at simulating daily precipitation distribution, quantity and occurrence. Some discrepancies still remained or were amplified which appear to be season-related, suggesting recourse to seasonal correction factors. However, because the generator is aimed at developing climate change projections, no additional parameters were introduced in the model to keep it as parsimonious as possible. WeaGETS was indirectly validated by conducting a series of hydrological modelling experiments on the Châteauguay River Basin located in southern Quebec. Results of the simulations show that WeaGETS was able to adequately represent the duration of summer low flow events as well as the annual direct runoff. However an overestimation of the peak flows was observed for the more extreme flood events with return periods exceeding 50 years. Whether or not such an overestimation is solely caused by the generator overestimating extreme precipitation events and/or consistent combinations of precipitation and temperature needs to be further addressed through additional modelling experiments on various watersheds and with more observed climatic data before drawing definitive conclusions.

A Backscatter Modeling for River Ice: Analysis and Numerical Results

A microwave backscatter model was developed to help provide an understanding of the interaction of a radar signal with the different ice types formed on natural freshwater bodies. This model was based on the radiative transfer theory, which is solved by the doubling matrix method. This numerical method provides an explanation for scattering effects due to volume, boundaries, boundary-volume interactions and interactions between layers. Three ice types were analyzed: columnar ice, frazil ice, and snow ice. Simulations with the model proved that the radar response from river ice cover depends on both ice-cover boundaries. The shape and distribution of air inclusions within the different ice types seem to have a significant impact on their contributions to the total response. The presence of tubular air inclusions within columnar ice causes an increase in the total response as a result of a double-bounce scattering. Small spherical and closed air inclusions within snow ice and frazil ice generate significant backscattering at high frequencies due to volume and surface-volume scattering. A further increase in the ice-cover thickness with air inclusions also causes increased scattering. Superposing two or more of these ice types causes considerable multiple scattering between layers. Finally, radar ice measurements collected over the Athabasca River were employed to further validate the model, and satisfactory results were obtained.

Quick Profiler (QuiP): a friendly tool to extract roughness statistical parameters using a needle profiler

QuiP is a graphical user interface (GUI) developed in MATLAB to extract and analyze soil surface profile measurements obtained from a needle profiler in a few minutes. Surface roughness parameters can be extracted from any needle profiler with simple modifications to the tool. QuiP calculates many statistics useful in radar remote sensing while generating the profiles. Furthermore, a QuiP function can be used to join several profiles. Long profiles can therefore be created from a short profiler (e.g., 1 m) that is easier to carry and handle in the field. Extraction of profiles by QuiP is repeatable and therefore reduces user’s errors from data processing.