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Land surface hydrology controls runoff production and the associated transport of sediments, and a wide variety of anthropogenic organic chemicals, and nutrients from upland landscape areas and hillslopes to streams and other water bodies. Based on interactions between landscape characteristics and precipitation inputs, watersheds respond differently to different climatic inputs (e.g. precipitation and solar radiation). This study compares the hydrologic responses of the MidAtlantic watersheds, and identifies the landscape and climatic descriptors that control those responses. Our approach was to select representative watersheds from the Mid-Atlantic region, group the watersheds by physiographic province and ecoregion, and then collect landscape, climate, and hydrologic response descriptor data for each selected watershed. For example, we extracted extensive landscape descriptor data from soil, land use and land cover, and digital elevation model geographic information system (GIS) databases. After sufficient data was collected, we conducted a variety of studies to determine how different landscape and climatic descriptors influence the hydrologic response of Mid-Atlantic watersheds. This report is comprised of four main parts. Part I describes the selection of the representative study watersheds and the determination of representative physical landscape descriptors for each watershed using geographic information system analysis tools. Part II characterizes the climate and associated hydrologic responses of the study watersheds. To select climate descriptors that are good predictors of hydrologic response, we examined a large number of candidate descriptors. Based on our examination, we selected dryness index and mean monthly rainfall as the best hydrologic response predictors. In Part II, we also present the results of our study hydrologic response comparisons of the study watersheds using a water balance approach. The water balance approach was based on comparisons of precipitation, streamflow, and evapotranspiration at annual, monthly, and daily time scales. These comparisons revealed that elevation and latitudinal position strongly influence hydrologic response. The results also showed that mountainous watersheds of the Appalachian Plateau, Ridge and Valley, and Blue Ridge Physiographic Provinces have more streamflow and less evapotranspiration than watersheds located in the Piedmont Province, and that snowmelt contributes a large portion of streamflow. Part III presents relationships we derived between landscape-climatic descriptors and the hydrologic response descriptors. Flow duration indices (Q1...Q95) were used to represent the hydrologic responses of the study watersheds. In Part III, we also present comparisons of the hydrologic responses of the study watersheds at high flow condition, represented by the Q1 index, medium flow condition represented by the Q50 index, and low flow condition represented by the Q95 index. These comparisons revealed that: the Appalachian Plateau, ridge-dominated Ridge and Valley, and Blue Ridge watersheds have the highest Q1 and Q50 indices; the valley-dominated Ridge and Valley watersheds have the lowest Q50 index, and the Piedmont watersheds have the lowest Q1 index and a relatively high Q95 index. Finally, Part IV discusses some of the implications of the study results for watershed management. We also present applications of the research for hydrologic modeling and watershed assessment.
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A physiographical space‐based kriging method is proposed for regional flood frequency estimation. The methodology relies on the construction of a continuous physiographical space using physiographical and meteorological characteristics of gauging stations and the use of multivariate analysis techniques. Two multivariate analysis methods were tested: canonical correlation analysis (CCA) and principal components analysis. Ordinary kriging, a geostatistical technique, was then used to interpolate flow quantiles through the physiographical space. Data from 151 gauging stations across the southern part of the province of Quebec, Canada, were used to illustrate this approach. In order to evaluate the performance of the proposed method, two validation techniques, cross validation and split‐sample validation, were applied to estimate flood quantiles corresponding to the 10, 50, and 100 year return periods. Results of the proposed method were compared to those produced by a traditional regional estimation method using the canonical correlation analysis. The proposed method yielded satisfactory results. It allowed, for instance, for estimating the 10 year return period specific flow with a coefficient of determination of up to 0.78. However, this performance decreases with the increase in the quantile return period. Results also showed that the proposed method works better when the physiographical space is defined using canonical correlation analysis. It is shown that kriging in the CCA physiographical space yields results as precise as the traditional estimation method, with a fraction of the effort and the computation time.
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Semantic Scholar extracted view of "CLIMATE VARIABILITY AND CHANGE IN CANADA" by E. Barrow et al.