The influence of valley aggradation and listric normal faulting on styles of river avulsion: A case study of the Brazos River, Texas, USA

The Holocene avulsion history of the lower Brazos alluvial valley of east Texas, USA, was studied using 10 drill cores, 26 radiocarbon dates, aerial photos, and a digital elevation model. This study shows that two long-term processes, aggradation and localized valley tilting (along a normal listric fault), are responsible for generating two styles of avulsion. The first process precedes avulsion-by-progradation, while the second process precedes avulsion-by-annexation. As valley aggradation migrated updip over the last 7.5 ka, three regional backstepping avulsions occurred along the lower 140 km of the valley and each generated sizable deposits. A pattern emerges of landward stepping progradational avulsions tracking the locus of valley aggradation and of valley aggradation migrating inland even after the rate of sea level rise diminishes. At the same time, several local nodal avulsions occurred between 50 and 55 km updip of the current highstand shoreline but generated no observable deposits. Geomorphic evidence indicates that, since the late Pleistocene, active movement along a previously undocumented normal listric fault has occurred at the location of the nodal avulsion. These two long-term processes do not operate mutually exclusive of each other to promote avulsions; rather, they operate concurrently. Only aggradation promotes avulsions that affect floodplain alluviation, although the total volume of these deposits comprises a small portion of the valley fill.     

Signal reconstruction of surface waves on SASW measurement using Gaussian Derivative wavelet transform

Surface wave method consists of measurement and processing of the dispersive Rayleigh waves recorded from two or more vertical transducers. The dispersive phase data are inverted and the shear wave velocity versus depth is obtained. However, in case of residual soil, the reliable phase spectrum curve is difficult to be produced. Noises from nature and other human-made sources disturb the generated surface wave data. In this paper, a continuous wavelet transform based on mother wavelet of Gaussian Derivative was used to analyze seismic waves in different frequency and time. Time-frequency wavelet spectrum was employed to localize the interested seismic response spectrum of generated surface waves. It can also distinguish the fundamental mode of the surface wave from the higher modes of reflected body waves. The results presented in this paper showed that the wavelet analysis is able to determine reliable surface wave spectrum of sandy clayey residual soil.     

Trends in the timing and magnitude of floods in Canada

This study investigates trends in the timing and magnitude of seasonal maximum flood events across Canada. A new methodology for analyzing trends in the timing of flood events is developed that takes into account the directional character and multi-modality of flood occurrences. The methodology transforms the directional series of flood occurrences into new series by defining a new location of the origin. A test of flood seasonality (multi-modality) is then applied to identify dominant flood seasons. Floods from the dominant seasons are analyzed separately by a seasonal trend analysis. The Mann–Kendall test in conjunction with the method of pre-whitening is used in the trend analysis. Over 160 streamflow records from one common observation period are analyzed in watersheds with relatively pristine and stable land-use conditions. The results show weak signals of climate variability and/or change present in the timing of floods in Canada during the last three decades. Most of the significant trends in the timing of spring snowmelt floods are negative trends (earlier flood occurrence) found in the southern part of Canada. There are no significant trends identified in the timing of fall rainfall floods. However, the significance of the fall, rainfall-dominated flood season has been increasing in several analyzed watersheds. This may indicate increasing intensity of rainfall events during the recent years. Trends in the magnitude of floods are more pronounced than the trends in the timing of floods. Almost one fifth of all the analyzed stations show significant trends in the magnitude of snowmelt floods. Most of the significant trends are negative trends, suggesting decreasing magnitudes of snowmelt floods in Canada over the last three decades. Significant negative trends are found particularly in southern Ontario, northern Saskatchewan, Alberta and British Columbia. There are no significant trends in the magnitude of rainfall floods found in the analyzed streamflow records. The results support the outcomes of previous streamflow trend studies conducted in Canada.     

Weighted estimate of extreme quantile: an application to the estimation of high flood return periods

Parametric models are commonly used in frequency analysis of extreme hydrological events. To estimate extreme quantiles associated to high return periods, these models are not always appropriate. Therefore, estimators based on extreme value theory (EVT) are proposed in the literature. The Weissman estimator is one of the popular EVT-based semi-parametric estimators of extreme quantiles. In the present paper we propose a new family of EVT-based semi-parametric estimators of extreme quantiles. To built this new family of estimators, the basic idea consists in assigning the weights to the k observations being used. Numerical experiments on simulated data are performed and a case study is presented. Results show that the proposed estimators are smooth, stable, less sensitive, and less biased than Weissman estimator.     

Evaluation of regional climate model simulations versus gridded observed and regional reanalysis products using a combined weighting scheme

This study presents a combined weighting scheme which contains five attributes that reflect accuracy of climate data, i.e. short-term (daily), mid-term (annual), and long-term (decadal) timescales, as well as spatial pattern, and extreme values, as simulated from Regional Climate Models (RCMs) with respect to observed and regional reanalysis products. Southern areas of Quebec and Ontario provinces in Canada are used for the study area. Three series of simulation from two different versions of the Canadian RCM (CRCM4.1.1, and CRCM4.2.3) are employed over 23?years from 1979 to 2001, driven by both NCEP and ERA40 global reanalysis products. One series of regional reanalysis dataset (i.e. NARR) over North America is also used as reference for comparison and validation purpose, as well as gridded historical observed daily data of precipitation and temperatures, both series have been beforehand interpolated on the CRCM 45-km grid resolution. Monthly weighting factors are calculated and then combined into four seasons to reflect seasonal variability of climate data accuracy. In addition, this study generates weight averaged references (WARs) with different weighting factors and ensemble size as new reference climate data set. The simulation results indicate that the NARR is in general superior to the CRCM simulated precipitation values, but the CRCM4.1.1 provides the highest weighting factors during the winter season. For minimum and maximum temperature, both the CRCM4.1.1 and the NARR products provide the highest weighting factors, respectively. The NARR provides more accurate short- and mid-term climate data, but the two versions of the CRCM provide more precise long-term data, spatial pattern and extreme events. Or study confirms also that the global reanalysis data (i.e. NCEP vs. ERA40) used as boundary conditions in the CRCM runs has non-negligible effects on the accuracy of CRCM simulated precipitation and temperature values. In addition, this study demonstrates that the proposed weighting factors reflect well all five attributes and the performances of weighted averaged references are better than that of the best single model. This study also found that the improvement of WARs’ performance is due to the reliability (accuracy) of RCMs rather than the ensemble size.     

Availability of barley straw application on oil spill clean up

Oil spills are a global concern due to the environmental and economical impact. Various commercial systems have been developed to control these spills, including the use of fibers as sorbents. However, most sorbents end up in landfills or in cineration after single use. These options either produce another source of pollution or increase the oil recovery cost. Plant biomass is renewable resource that can be converted into useful materials and energy. Barley straw, an agricultural waste, was used as sorbent material. The present study examines the absorption capacity of raw barley straw for different petroleum products and water pick up. The investigate revealed that the capacity of fibers to remove crude oil from sea water was related to the surface properties of the fibers, concentration of the oil, size of the fiber, amount of the fibers, as well as the temperature of the crude oil. Cyclic sorption / desorption studies indicated that a simple squeezing operation was enough to remove most of the oil sorbed and that recycling was feasible.     

Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator

This study provides a multi-site hybrid statistical downscaling procedure combining regression-based and stochastic weather generation approaches for multisite simulation of daily precipitation. In the hybrid model, the multivariate multiple linear regression (MMLR) is employed for simultaneous downscaling of deterministic series of daily precipitation occurrence and amount using large-scale reanalysis predictors over nine different observed stations in southern Québec (Canada). The multivariate normal distribution, the first-order Markov chain model, and the probability distribution mapping technique are employed for reproducing temporal variability and spatial dependency on the multisite observations of precipitation series. The regression-based MMLR model explained 16?%?~?22?% of total variance in daily precipitation occurrence series and 13?%?~?25?% of total variance in daily precipitation amount series of the nine observation sites. Moreover, it constantly over-represented the spatial dependency of daily precipitation occurrence and amount. In generating daily precipitation, the hybrid model showed good temporal reproduction ability for number of wet days, cross-site correlation, and probabilities of consecutive wet days, and maximum 3-days precipitation total amount for all observation sites. However, the reproducing ability of the hybrid model for spatio-temporal variations can be improved, i.e. to further increase the explained variance of the observed precipitation series, as for example by using regional-scale predictors in the MMLR model. However, in all downscaling precipitation results, the hybrid model benefits from the stochastic weather generator procedure with respect to the single use of deterministic component in the MMLR model.     

Stochastic differential dynamic programming for multi-reservoir system control

: As with all dynamic programming formulations, differential dynamic programming (DDP) successfully exploits the sequential decision structure of multi-reservoir optimization problems, overcomes difficulties with the nonconvexity of energy production functions for hydropower systems, and provides optimal feedback release policies. DDP is particularly well suited to optimizing large-scale multi-reservoir systems due to its relative insensitivity to state-space dimensionality. This advantage of DDP encourages expansion of the state vector to include additional multi-lag hydrologic information and/or future inflow forecasts in developing optimal reservoir release policies. Unfortunately, attempts at extending DDP to the stochastic case have not been entirely successful. A modified stochastic DDP algorithm is presented which overcomes difficulties in previous formulations. Application of the algorithm to a four-reservoir hydropower system demonstrates its capabilities as an efficient approach to solving stochastic multi-reservoir optimization problems. The algorithm is also applied to a single reservoir problem with inclusion of multi-lag hydrologic information in the state vector. Results provide evidence of significant benefits in direct inclusion of expanded hydrologic state information in optimal feedback release policies.     

Nonstationary frequency analysis of extreme daily precipitation amounts in Southeastern Canada using a peaks-over-threshold approach

Theoretical and Applied Climatology - In this paper, a statistical inference of Southeastern Canada extreme daily precipitation amounts is proposed using a classical nonstationary...