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Topographic,pedologic and climatic interactions influencing streamflow generation at multiple catchment scales 总被引：1，自引：0，他引：1
Dominant flow pathways (DFPs) in mesoscale watersheds are poorly characterized and understood. Here, we make use of a conservative tracer (Gran alkalinity) and detailed information about climatic conditions and physical properties to examine how temporally and spatially variable factors interact to determine DFPs in 12 catchments draining areas from 3.4 to 1829.5 km² (Cairngorms, Scotland). After end‐member mixing was applied to discriminate between near surface and deep groundwater flow pathways, variation partitioning, canonical redundancy analyses and regression models were used to resolve: (i) What is the temporal variability of DFPs in each catchment?; (ii) How do DFPs change across spatial scales and what factors control the differences in hydrological responses?; and (iii) Can a conceptual model be developed to explain the spatiotemporal variability of DFPs as a function of climatic, topographic and soil characteristics? Overall, catchment characteristics were only useful to explain the temporal variability of DFPs but not their spatial variation across scale. The temporal variability of DFPs was influenced most by prevailing hydroclimatic conditions and secondarily soil drainability. The predictability of active DFPs was better in catchments with soils supporting fast runoff generation on the basis of factors such as the cumulative precipitation from the seven previous days, mean daily air temperature and the fractional area covered by Rankers. The best regression model R2 was 0.54, thus suggesting that the catchments’ internal complexity was not fully captured by the factors included in the analysis. Nevertheless, this study highlights the utility of combining tracer studies with digital landscape analysis and multivariate statistical techniques to gain insights into the temporal (climatic) and spatial (topographic and pedologic) controls on DFPs. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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Spatio‐temporal tracer variability in the glacier melt end‐member — How does it affect hydrograph separation results? 
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下载免费PDF全文 Geochemical and isotopic tracers were often used in mixing models to estimate glacier melt contributions to streamflow, whereas the spatio‐temporal variability in the glacier melt tracer signature and its influence on tracer‐based hydrograph separation results received less attention. We present novel tracer data from a high‐elevation catchment (17 km2, glacierized area: 34%) in the Oetztal Alps (Austria) and investigated the spatial, as well as the subdaily to monthly tracer variability of supraglacial meltwater and the temporal tracer variability of winter baseflow to infer groundwater dynamics. The streamflow tracer variability during winter baseflow conditions was small, and the glacier melt tracer variation was higher, especially at the end of the ablation period. We applied a three‐component mixing model with electrical conductivity and oxygen‐18. Hydrograph separation (groundwater, glacier melt, and rain) was performed for 6 single glacier melt‐induced days (i.e., 6 events) during the ablation period 2016 (July to September). Median fractions (±uncertainty) of groundwater, glacier melt, and rain for the events were estimated at 49±2%, 35±11%, and 16±11%, respectively. Minimum and maximum glacier melt fractions at the subdaily scale ranged between 2±5% and 76±11%, respectively. A sensitivity analysis showed that the intraseasonal glacier melt tracer variability had a marked effect on the estimated glacier melt contribution during events with large glacier melt fractions of streamflow. Intra‐daily and spatial variation of the glacier melt tracer signature played a negligible role in applying the mixing model. The results of this study (a) show the necessity to apply a multiple sampling approach in order to characterize the glacier melt end‐member and (b) reveal the importance of groundwater and rainfall–runoff dynamics in catchments with a glacial flow regime. 相似文献
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On the physics of the Atlantic Multidecadal Oscillation 总被引：1，自引：0，他引：1
The Atlantic Multidecadal Oscillation (AMO) is a pronounced signal of climate variability in the North Atlantic sea-surface temperature field. In this paper, we propose an explanation of the physical processes responsible for the timescale and the spatial pattern of the AMO. Our approach involves the analysis of solutions of a hierarchy of models. In the lowest member of the model hierarchy, which is an ocean-only model for flow in an idealized basin, the variability shows up as a multidecadal oscillatory mode which is able to destabilize the mean thermohaline circulation. In the highest member of the model hierarchy, which is the Geophysical Fluid Dynamics Laboratory R30 climate model, multidecadal variability is found as a dominant statistical mode of variability. The connection between both results is established by tracing the spatial and temporal expression of the multidecadal mode through the model hierarchy while monitoring changes in specific quantities (mechanistic indicators) associated with its physics. The proposed explanation of the properties of the AMO is eventually based on the changes in the spatial patterns of variability through the model hierarchy.Responsible Editor: Tal Ezer 相似文献
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In this work, we address the problem of characterizing the heterogeneity and uncertainty of hydraulic properties for complex geological settings. Hereby, we distinguish between two scales of heterogeneity, namely the hydrofacies structure and the intrafacies variability of the hydraulic properties. We employ multiple-point geostatistics to characterize the hydrofacies architecture. The multiple-point statistics are borrowed from a training image that is designed to reflect the prior geological conceptualization. The intrafacies variability of the hydraulic properties is represented using conventional two-point correlation methods, more precisely, spatial covariance models under a multi-Gaussian spatial law. We address the different levels and sources of uncertainty in characterizing the subsurface heterogeneity, and explore their effect on groundwater flow and transport predictions. Typically, uncertainty is assessed by way of many images, termed realizations, of a fixed statistical model. However, in many cases, sampling from a fixed stochastic model does not adequately represent the space of uncertainty. It neglects the uncertainty related to the selection of the stochastic model and the estimation of its input parameters. We acknowledge the uncertainty inherent in the definition of the prior conceptual model of aquifer architecture and in the estimation of global statistics, anisotropy, and correlation scales. Spatial bootstrap is used to assess the uncertainty of the unknown statistical parameters. As an illustrative example, we employ a synthetic field that represents a fluvial setting consisting of an interconnected network of channel sands embedded within finer-grained floodplain material. For this highly non-stationary setting we quantify the groundwater flow and transport model prediction uncertainty for various levels of hydrogeological uncertainty. Results indicate the importance of accurately describing the facies geometry, especially for transport predictions. 相似文献
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This paper presents a framework to quantify the overall variability of the model estimations of Total Polychlorinated Biphenyls (Total PCBs) concentrations in the Niagara River on the basis of the uncertainty of few model parameters and the natural variability embedded in some of the model input variables. The results of the uncertainty analysis are used to understand the importance of stochastic model components and their effect on the overall reliability of the model output and to evaluate multiple sources of uncertainty that might need to be further studied. The uncertainty analysis is performed using a newly developed point estimate method, the Modified Rosenblueth method. The water quality along the Niagara River is simulated by coupling two numerical models the Environmental Fluid Dynamic Code (EFDC) – for the hydrodynamic portion of the study and the Water Quality Analysis and Simulation Program (WASP) – for the fate and transport of contaminants. For the monitoring period from May 1995 to March 1997, the inflow Total PCBs concentration from Lake Erie is the stochastic component that most influences the variability of the modeling results for the simulated concentrations at the exit of the Niagara River. Other significant stochastic components in order are as follows: the suspended sediments concentration, the point source loadings and to a minor degree the atmospheric deposition, the flow and the non-point source loadings. Model results that include estimates of uncertainty provide more comprehensive information about the variability of contaminant concentrations, such as confidence intervals, and, in general offer a better approach to compare model results with measured data. 相似文献
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Using nitrate to quantify quick flow in a karst aquifer 总被引：3，自引：0，他引：3
In karst aquifers, contaminated recharge can degrade spring water quality, but quantifying the rapid recharge (quick flow) component of spring flow is challenging because of its temporal variability. Here, we investigate the use of nitrate in a two-endmember mixing model to quantify quick flow in Barton Springs, Austin, Texas. Historical nitrate data from recharging creeks and Barton Springs were evaluated to determine a representative nitrate concentration for the aquifer water endmember (1.5 mg/L) and the quick flow endmember (0.17 mg/L for nonstormflow conditions and 0.25 mg/L for stormflow conditions). Under nonstormflow conditions for 1990 to 2005, model results indicated that quick flow contributed from 0% to 55% of spring flow. The nitrate-based two-endmember model was applied to the response of Barton Springs to a storm and results compared to those produced using the same model with δ18 O and specific conductance (SC) as tracers. Additionally, the mixing model was modified to allow endmember quick flow values to vary over time. Of the three tracers, nitrate appears to be the most advantageous because it is conservative and because the difference between the concentrations in the two endmembers is large relative to their variance. The δ18 O-based model was very sensitive to variability within the quick flow endmember, and SC was not conservative over the timescale of the storm response. We conclude that a nitrate-based two-endmember mixing model might provide a useful approach for quantifying the temporally variable quick flow component of spring flow in some karst systems. 相似文献
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Quantification of river water infiltration in shallow aquifers using acesulfame and anthropogenic gadolinium 下载免费PDF全文
下载免费PDF全文 This study has investigated the use of the artificial sweetener acesulfame and the magnetic resonance imaging contrast agent gadolinium as quantitative tracers for river water infiltration into shallow groundwater. The influence of a river on alluvial groundwater in a subalpine catchment in western Europe has been assessed using the ‘classical’ hydrochemical tracer chloride and the trace contaminants acesulfame and anthropogenic gadolinium. Mixing ratios for riverine bank filtrate with ambient groundwater and the uncertainties associated with the temporal and spatial tracer variability were calculated using acesulfame and gadolinium and compared with those obtained using chloride. The temporal variability of tracer concentrations in river water of gadolinium (standard deviation SD: 63%) and acesulfame (SD: 71%) both exceeded that of chloride (SD: 27%), and this was identified as the main source of uncertainty in the mixing analysis. Similar spatial distributions were detected in the groundwater for chloride and gadolinium, but not for acesulfame. Mixing analyses using acesulfame resulted in calculated mixing ratios that differed from those obtained using gadolinium and chloride by up to 83% and 92%, respectively. At the investigated site, which had oxic conditions and moderate temperatures, acesulfame was found to be a less reliable tracer than either gadolinium or chloride, probably because of natural attenuation and input from other sources. There was no statistically significant difference between the mixing ratios obtained using chloride or gadolinium, the mixing ratios obtained using gadolinium were 40–50% lower than those obtained using chloride. This is mainly due to a bias of the mean gadolinium concentration in river water towards higher values. In view of the uncertainties of the two tracers, neither could be preferred over the other for the quantification of bank filtrate in groundwater. At this specific site gadolinium was able to reliably identify river water infiltration and was a more precise tracer than chloride at low mixing ratios (<20%), because of the exclusive occurrence of gadolinium in river water and its high dynamic range. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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Identifying climate internal variability (CIV) generated by non-linear interactions and feedbacks among many components of the climate system is essential and challenging because of its irreducible and unpredictable characteristics. A range of studies have addressed this issue; however, these studies focused on the first order moments of few representative climate variables at relatively larger spatial and temporal scales. To investigate the magnitude and the spatial pattern of CIV relevant at finer spatial (point) and temporal (hourly) scales, CIV is assessed over a 30-year period in South Korea by analyzing 100-member ensemble generated using an hourly weather generator and a bootstrapping approach. Statistics addressing the first and second order moments, occurrences, and extremes are successfully verified at various temporal scales. The CIV is then estimated by the ‘detrended’ and ‘differenced’ methods for the four metrics proposed at different scales that signify rainfall volume, maxima, and occurrence. Consequently, the implications of this study are the following: (1) the estimation of CIV using bootstrapped ensembles often fails to represent the proper uncertainty range, resulting in high chances of underestimating extreme statistics, such as the maximum rainfall depth; (2) regardless of which of the two methods is used, no significant difference in the CIV estimation is observed; and (3) a temporal scale-dependency is observed for the proposed metrics used to identify the magnitude and the seasonal pattern of the CIV—the utility of an hourly time series and its associated extreme properties deserves significant attention. Ultimately, the spatial mapping and grouping of CIV will provide valuable information to identify which regions have high variability compared to climatological norms and thus are more vulnerable to extremes, and will serve as a guide for planning adaptation and mitigation measures against future extreme events. 相似文献
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The temporal‐spatial resolution of input data‐induced uncertainty in a watershed‐based water quality model, Hydrologic Simulation Program‐FORTRAN (HSPF), is investigated in this study. The temporal resolution‐induced uncertainty is described using the coefficient of variation (CV). The CV is found to decrease with decreasing temporal resolution and follow a log‐normal relation with time interval for temperature data while it exhibits a power‐law relation for rainfall data. The temporal‐scale uncertainties in the temperature and rainfall data follow a general extreme value distribution and a Weibull distribution, respectively. The Nash‐Sutcliffe coefficient (NSC) is employed to represent the spatial resolution induced uncertainty. The spatial resolution uncertainty in the dissolved oxygen and nitrate‐nitrogen concentrations simulated using HSPF is observed to follow a general extreme value distribution and a log‐normal distribution, respectively. The probability density functions (PDF) provide new insights into the effect of temporal‐scale and spatial resolution of input data on uncertainties involved in watershed modelling and total maximum daily load calculations. This study exhibits non‐symmetric distributions of uncertainty in water quality modelling, which simplify weather and water quality monitoring and reducing the cost involved in flow and water quality monitoring. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive network-based fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009. These models were used for 1-, 3- and 5-day streamflow forecasting and the better model was used for uncertainty evaluation using bootstrap resampling method. Meanwhile, a simple conceptual hydrological model GR4J was used to evaluate parametric uncertainty based on generalized likelihood uncertainty estimation method. Results indicated that: (1) GA and PSO did not help improve the forecast performance of the model. However, the hybrid model with WD significantly improved the forecast performance; (2) the hybrid model with WD as a data preprocessing procedure can clarify hydrological effects of water reservoirs and can capture peak high/low flow changes; (3) Forecast accuracy of data-driven models is significantly influenced by the availability of streamflow data. More human interferences from the upper to the lower East River basin can help to introduce greater uncertainty in streamflow forecasts; (4) The structure of GR4J may introduce larger parametric uncertainty at the Longchuan station than at the Boluo station in the East river basin. This study provides a theoretical background for data-driven model-based streamflow forecasting and a comprehensive view about data and parametric uncertainty in data-scarce river basins. 相似文献
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The estimation of site intensity occurrence probabilities in low seismic activity regions has been studied from different points of view. However, no method has been definitively established because several problems arise when macroseismic historical data are incomplete and the active zones are not well determined. The purpose of this paper is to present a method that estimates site occurrence probabilities and at the same time measures the uncertainties inherent in these probabilities in low activity regions. The region to be studied is divided into very broad seismic zones. An exponential intensity probability law is adjusted for each zone and the degree of uncertainty in the assumed incompleteness of the catalogue is evaluated for each intensity. These probabilities are used to establish what may be termed ‘prior site occurrence models’. A Bayesian method is used to improve ‘prior models’ and to obtain the ‘posterior site occurrence models’. Epicentre locations are used to recover spatial information lost in the prior broad zoning. This Bayesian correction permits the use of specific attenuation for different events and may take into account, by means of conservative criteria, epicentre location errors. Following Bayesian methods, probabilities are assumed to be random variables and their distribution may be used to estimate the degree of uncertainty arising from (a) the statistical variance of estimators, (b) catalogue incompleteness and (c) mismatch of data to prior assumptions such as Poisson distribution for events and exponential distribution for intensities. The results are maps of probability and uncertainty for each intensity. These maps exhibit better spatial definition than those obtained by means of simple, broad zones. Some results for Catalonia (NE of Iberian Peninsula) are shown. 相似文献
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Two‐dimensional continuous simulation of spatiotemporally varied hydrological processes using the time‐area method 下载免费PDF全文
下载免费PDF全文 Distributed, continuous hydrologic models promote better understanding of hydrology and enable integrated hydrologic analyses by providing a more detailed picture of water transport processes across the varying landscape. However, such models are not widely used in routine modelling practices, due in part to the extensive data input requirements, computational demands, and complexity of routing algorithms. We developed a two‐dimensional continuous hydrologic model, HYSTAR, using a time‐area method within a grid‐based spatial data model with the goal of providing an alternative way to simulate spatiotemporally varied watershed‐scale hydrologic processes. The model calculates the direct runoff hydrograph by coupling a time‐area routing scheme with a dynamic rainfall excess sub‐model implemented here using a modified curve number method with an hourly time step, explicitly considering downstream ‘reinfiltration’ of routed surface runoff. Soil moisture content is determined at each time interval based on a water balance equation, and overland and channel runoff is routed on time‐area maps, representing spatial variation in hydraulic characteristics for each time interval in a storm event. Simulating runoff hydrographs does not depend on unit hydrograph theory or on solution of the Saint Venant equation, yet retains the simplicity of a unit hydrograph approach and the capability of explicitly simulating two‐dimensional flow routing. The model provided acceptable performance in predicting daily and monthly runoff for a 6‐year period for a watershed in Virginia (USA) using readily available geographic information about the watershed landscape. Spatial and temporal variability in simulated effective runoff depth and time area maps dynamically show the areas of the watershed contributing to the direct runoff hydrograph at the outlet over time, consistent with the variable source area overland flow generation mechanism. The model offers a way to simulate watershed processes and runoff hydrographs using the time‐area method, providing a simple, efficient, and sound framework that explicitly represents mechanisms of spatially and temporally varied hydrologic processes. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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In watershed modelling, the traditional practice of arbitrarily filling topographic depressions in digital elevation models has raised concerns. Advanced high‐resolution remote sensing techniques, including airborne scanning laser altimetry, can identify naturally occurring depressions that impact overland flow. In this study, we used an ensemble physical and statistical modelling approach, including a 2D hydraulic model and two‐point connectivity statistics, to quantify the effects of depressions on high‐resolution overland flow patterns across spatial scales and their temporal variations in single storm events. Computations for both models were implemented using graphic processing unit‐accelerated computing. The changes in connectivity statistics for overland flow patterns between airborne scanning laser altimetry‐derived digital elevation models with (original) and without (filled) depressions were used to represent the shifts of overland flow response to depressions. The results show that depressions can either decrease or increase (to a lesser degree and shorter duration) the probability that any two points (grid locations) are hydraulically connected by overland flow pathways. We used macro‐connectivity states (Φ) as a watershed‐specific indicator to describe the spatiotemporal thresholds of connectivity variability caused by depressions. Four states of Φ are identified in a studied watershed, and each state represents different magnitudes of connectivity and connectivity changes (caused by depressions). The magnitude of connectivity variability corresponds to the states of Φ, which depend on the topological relationship between depressions, the rising/recession limb, and the total rainfall amount in a storm event. In addition, spatial distributions of connectivity variability correlate with the density of depression locations and their physical structures, which cause changes in streamflow discharge magnitude. Therefore, this study suggests that depressions are “nontrivial” in watershed modelling, and their impacts on overland flow should not be neglected. Connectivity statistics at different spatial scales and time points within a watershed provide new insights for characterizing the distributed and accumulated effects of depressions on overland flow. 相似文献
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A methodology to derive solute transport models at any flow rate is presented. The novelty of the proposed approach lies in the assessment of uncertainty of predictions that incorporate parameterisation based on flow rate. A simple treatment of uncertainty takes into account heteroscedastic modelling errors related to tracer experiments performed over a range of flow rates, as well as the uncertainty of the observed flow rates themselves. The proposed approach is illustrated using two models for the transport of a conservative solute: a physically based, deterministic, advection-dispersion model (ADE), and a stochastic, transfer function based, active mixing volume model (AMV). For both models the uncertainty of any parameter increases with increasing flow rate (reflecting the heteroscedastic treatment of modelling errors at different observed flow rates), but in contrast the uncertainty of travel time, computed from the predicted model parameters, was found to decrease with increasing flow rate. 相似文献
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One-dimensional vertical and three-dimensional fine-resolution numerical models of sediment transport have been developed and applied to the Torres Strait region of northern Australia. The one-dimensional model, driven by measured waves and currents, was calibrated against measured suspended sediment concentrations using a sequential data assimilation algorithm. The algorithm produced a good match between model and data, but this was achieved only by allowing some temporal variability in parameter values, suggesting that there were underlying uncertainties in the model structure and forcing data. Implications of the assimilation results to the accuracy of the numerical modelling are discussed and the need for observational programmes having an extensive spatial and temporal coverage is highlighted. The three-dimensional sediment model, driven by modelled waves and currents, simulates sediment transport over the shelf during the monsoon and trade-wind seasons covering 1997–2000. The model predicts strong seasonal variability of the sediment transport on the shelf attributed to seasonally varying hydrodynamics, and illustrates significant inter-annual variability of the sediment fluxes driven by extreme events. The developed model provides a platform for testing scientific hypothesis. With additional calibration, including uncertainty analysis, it can also be used in a management context. 相似文献
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Mark A. Nearing 《地球表面变化过程与地形》2000,25(9):1035-1043
One of the important methods used to evaluate the effectiveness of soil erosion models is to compare the predictions given by the model to measured data from soil loss collected on plots taken under natural rainfall conditions. While it is recognized that plot data contain natural variability, this factor is not quantitatively considered during such evaluations because our knowledge of natural variability between plots which have the same treatments is very limited. The goal of this study was to analyse sufficient replicated plot data and present methodology to allow the model evaluator to take natural, within‐treatment variability of erosion plots into account when models are tested. A large amount of data from pairs of replicated erosion plots was evaluated and quantified. The basis for the evaluation method presented is that if the difference between the model prediction and a measured plot data value lies within the population of differences between pairs of measured values, then the prediction is considered ‘acceptable’. A model ‘effectiveness’ coefficient was defined for studies undertaken on large numbers of prediction versus measured data comparisons. This method provides a quantitative criterion for taking into account natural variability and uncertainty in measured erosion plot data when those data are used to evaluate erosion models. Published in 2000 by John Wiley & Sons, Ltd. 相似文献
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Technological advances, by facilitating extensive data collection, better data sharing, formulation of sophisticated methods, and development of complex models, have brought hydrologic research to a whole new level. Despite these obvious advances, there are also concerns about their general use in practice. On the one hand, it is natural to develop more complex models than perhaps needed (i.e. representations having too many parameters and requiring too much data); on the other hand, it is often difficult to ‘translate’ results from one specific situation to another. Recent studies have addressed these concerns, albeit in different forms, such as dominant processes, thresholds, model integration, and model simplification. A common aspect in some of these studies is that they recognize the need for a globally agreed upon ‘classification system’ in hydrology. The present study explores this classification issue further from a simple phase‐space data reconstruction perspective. The reconstruction involves representation of the given multidimensional hydrologic system using only an available single‐variable series through a delay coordinate procedure. The ‘extent of complexity’ of the system (defined especially in the context of variability of relevant data) is identified by the ‘region of attraction of trajectories’ in the phase space, which is then used to classify the system as potentially low‐, medium‐ or high‐dimensional. A host of river‐related data, representing different geographic and climatic regions, temporal scales, and processes, are studied. Yielding ‘attractors’ that range from ‘very clear’ ones to ‘very blurred’ ones, depending on data, the results indicate the usefulness of this simple reconstruction concept for studying hydrologic system complexity and classification. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献