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1.
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2.
A topological representation of a rural catchment is proposed here in addition to the generally used topographic drainage network. This is an object‐oriented representation based on the identification of the inlets and outlets for surface water flow on each farmer's field (or plot) and their respective contributing areas and relationships. It represents the catchment as a set of independent plot outlet trees reaching the stream, while a given plot outlet tree represents the pattern of surface flow relationships between individual plots. In the present study, we propose to implement functions related to linear and surface elements of the landscape, such as hedges or road networks, or land use, to obtain what we call a landscape drainage network which delineates the effective contributing area to the stream, thus characterizing its topological structure. Landscape elements modify flow pathways and/or favour water infiltration, thus reducing the area contributing to the surface yield and modifying the structure of the plot outlet trees. This method is applied to a 4·4‐km2 catchment area comprising 43 955 pixels and 312 plots. While the full set of 164 plot outlet trees, with an average of 7 plots per tree, covers 100% of the total surface area of the catchment, the landscape drainage network comprises no more than 37 plot outlet trees with an average of 2 plots per tree, accounting for 52 and 7% of the catchment surface area, when taking account of linear elements and land use, respectively. This topological representation can be easily adapted to changes in land use and land infrastructure, and provides a simple and functional display for intercomparison of catchments and decision support regarding landscape and water management. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

3.
Six artificial neural network (ANN) models are developed to predict various response parameters of kinematic soil pile interaction. These responses include (1) kinematic response factors for free and fixed head piles in homogenous soil layer to derive foundation input motion (2) normalized bending moment at fixed head of pile in homogenous soil layer (3) normalized kinematic pile moment at the interface of two soil layers of sharply different soil stiffnesses. These ANN models represent simple solutions that can be implemented in a simple calculator capable of matrix operation and bypass the site response analysis and the complex wave diffraction analysis. The data required for ANN training is generated using beam on dynamic Winkler formulation (BDWF). Fifty percent of the data is used to train the ANN models while remaining 50% is used to test the ANN models. The trained ANN models show good agreement with BDWF results.  相似文献   

4.
Growing interest in the use of artificial neural networks (ANNs) in rainfall‐runoff modelling has suggested certain issues that are still not addressed properly. One such concern is the use of network type, as theoretical studies on a multi‐layer perceptron (MLP) with a sigmoid transfer function enlightens certain limitations for its use. Alternatively, there is a strong belief in the general ANN user community that a radial basis function (RBF) network performs better than an MLP, as the former bases its nonlinearities on the training data set. This argument is not yet substantiated by applications in hydrology. This paper presents a comprehensive evaluation of the performance of MLP‐ and RBF‐type neural network models developed for rainfall‐runoff modelling of two Indian river basins. The performance of both the MLP and RBF network models were comprehensively evaluated in terms of their generalization properties, predicted hydrograph characteristics, and predictive uncertainty. The results of the study indicate that the choice of the network type certainly has an impact on the model prediction accuracy. The study suggests that both the networks have merits and limitations. For instance, the MLP requires a long trial‐and‐error procedure to fix the optimal number of hidden nodes, whereas for an RBF the structure of the network can be fixed using an appropriate training algorithm. However, a judgment on which is superior is not clearly possible from this study. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

5.
太湖流域城市化对平原河网水系结构与连通性影响   总被引：1，自引：1，他引：0  
太湖流域的快速城市化使河流形态发生了改变,影响了平原河网水系结构与连通性本文以近几年城市化发展较为迅速的张家港地区为例,基于景观生态学和图论的相关理论,利用2002与2015年的水系数据,对全区以及各行政分区水面率、河网密度、河频率等指标进行研究,旨在揭示该地区河网水系结构与连通性参数变化及城市化对其影响结果表明:研究区2015年与2002年相比全区各水系结构指标都呈减小趋势,水面率、河网密度、河频率的衰减率分别为15.7%、12.6%、33.3%,河网有主干化趋势,干流面积与长度发育的同步性较好;不同行政区的水系变化有明显的空间分异,张家港南部水系较北部水面率、河频率衰减更剧烈;东北部地区河网密度与河网复杂度变化明显;张家港全区河网连通性略有提高,但地区分异明显,沿江的西北部地区河网连通能力有所加强,而东南部内河区域河网畅通程度减弱,被圩区、水闸与泵站切断的水系需要更合理的调度方案才能增强并维持水体的连通度;水面率、河网密度与人口、GDP之间相关水平较高,各地区水系结构参数与其变化率的空间分异和该地区人口、经济发展水平密切相关;河网河链数、节点数受城市化影响较大,线点率、实际连通度的变化与城市化指标相关性较弱本研究可以为后期张家港地区优化水系结构及防洪排涝建设提供基础,并帮助基于各片区的特点开展因地制宜的管理.  相似文献   

6.
Despite decades of research on the ecological consequences of stream network expansion, contraction and fragmentation, surprisingly little is known about the hydrological mechanisms that shape these processes. Here, we present field surveys of the active drainage networks of four California headwater streams (4–27 km2) spanning diverse topographic, geologic and climatic settings. We show that these stream networks dynamically expand, contract, disconnect and reconnect across all the sites we studied. Stream networks at all four sites contract and disconnect during seasonal flow recessions, with their total active network length, and thus their active drainage densities, decreasing by factors of two to three across the range of flows captured in our field surveys. The total flowing lengths of the active stream networks are approximate power‐law functions of unit discharge, with scaling exponents averaging 0.27 ± 0.04 (range: 0.18–0.40). The number of points where surface flow originates obey similar power‐law relationships, as do the lengths and origination points of flowing networks that are continuously connected to the outlet, with scaling exponents averaging 0.36–0.48. Even stream order shifts seasonally by up to two Strahler orders in our study catchments. Broadly, similar stream length scaling has been observed in catchments spanning widely varying geologic, topographic and climatic settings and spanning more than two orders of magnitude in size, suggesting that network extension/contraction is a general phenomenon that may have a general explanation. Points of emergence or disappearance of surface flow represent the balance between subsurface transmissivity in the hyporheic zone and the delivery of water from upstream. Thus the dynamics of stream network expansion and contraction, and connection and disconnection, may offer important clues to the spatial structure of the hyporheic zone, and to patterns and processes of runoff generation. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

7.
Little is known about how active stream network expansion during rainstorms influences the ability of riparian buffers to improve water quality. We used aerial photographs to quantify stream network expansion during the wet winter season in five agricultural catchments in western Oregon, USA. Winter stream drainage densities were nearly two orders of magnitude greater than summer stream densities, and agricultural land use was much more abundant along transient portions (e.g. swales, road ditches) of stream networks. Water moving from agricultural fields into expanded stream networks during large hydrologic events has the opportunity to bypass downstream riparian buffers along perennial streams and contribute nonpoint‐source pollutants directly into perennial stream channels. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

8.
Headwater streams expand, contract, and disconnect in response to seasonal moisture conditions or those related to individual precipitation events. The fluctuation of the surface flow extent, or active drainage network, reflects catchment storage characteristics and has important impacts on stream ecology; however, the hydrological mechanisms that drive this phenomenon are still uncertain. Here, we present field surveys of the active drainage networks of four headwater streams in Central Idaho's Frank Church‐River of No Return Wilderness (7–21 km2) spanning the spring and summer months of 2014. We report the total length of the active drainage networks, which varied as a power law function with stream discharge with an average exponent of 0.11 ± 0.03 (range of 0.05–0.20). Generally, these active drainage networks were less responsive to changes in discharge than many streams in past studies. We observed that the locations where surface flow originates, or flowheads, were often stable, and an average of 64% of the change in active drainage network length was explained by downstream discontinuities. Analysis of geologic and geomorphic characteristics of individual watersheds and flowheads suggests that most flowheads below approximately 2200 m are supported by stable flowpaths controlled by bedrock structure. At higher elevations, small accumulation areas and saturation of shallow and conductive soil and colluvium after snowmelt result in more mobile flowhead locations. The dynamics of active drainage networks can help illuminate the spatiotemporal structure of flowpaths supporting surface flow. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

9.
Digital flow networks derived from digital elevation models (DEMs) sensitively react to errors due to measurement, data processing and data representation. Since high‐resolution DEMs are increasingly used in geomorphological and hydrological research, automated and semi‐automated procedures to reduce the impact of such errors on flow networks are required. One such technique is stream‐carving, a hydrological conditioning technique to ensure drainage connectivity in DEMs towards the DEM edges. Here we test and modify a state‐of‐the‐art carving algorithm for flow network derivation in a low‐relief, agricultural landscape characterized by a large number of spurious, topographic depressions. Our results show that the investigated algorithm reconstructs a benchmark network insufficiently in terms of carving energy, distance and a topological network measure. The modification to the algorithm that performed best, combines the least‐cost auxiliary topography (LCAT) carving with a constrained breaching algorithm that explicitly takes automatically identified channel locations into account. We applied our methods to a low relief landscape, but the results can be transferred to flow network derivation of DEMs in moderate to mountainous relief in situations where the valley bottom is broad and flat and precise derivations of the flow networks are needed. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

10.
Short‐term prediction of environmental variables such as stream flow rate is useful to members of the general public and environmental scientists alike, providing the ability to predict environmental disasters or scientifically interesting events. Here, a neural‐network based method is presented, which is capable of providing advance flood warnings or the prediction of high stream flow events for research purposes in a small upland headwater in NE Scotland. This method relies on training from past time series data acquired in the field, and provides the ability to predict a range of hydrological and meteorological variables up to 24 h ahead using feedback of predicted values at time t as new inputs for the next time step t + 1. The system is rapid and effective, relies on standard neural network training methods, and has the potential to be implemented in a web‐based monitoring and prediction package. The model design could be implemented at any study site where time series data has been gathered, and is sufficiently flexible to accept whatever data is available. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

11.
Predicting runoff and erosion from watersheds burned by wildfires requires an understanding of the three-dimensional structure of both hillslope and channel drainage networks. We investigate the small- and large-scale structures of drainage networks using field studies and computer analysis of 30- m digital elevation model. Topologic variables were derived from a composite 30-m DEM, which included 14 order 6 watersheds within the Pikes Peak batholith. Both topologic and hydraulic variables were measured in the field in two smaller burned watersheds （3.7 and 7.0 hectares） located within one of the order 6 watersheds burned by the 1996 Buffalo Creek Fire in Central Colorado. Horton ratios of topologic variables （stream number, drainage area, stream length, and stream slope） for small-scale and large-scale watersheds are shown to scale geometrically with stream order （i.e., to be scale invariant）. However, the ratios derived for the large-scale drainage networks could not be used to predict the rill and gully drainage network structure. Hydraulic variables （width, depth, cross- sectional area, and bed roughness） for small-scale drainage networks were found to be scale invariant across 3 to 4 stream orders. The relation between hydraulic radius and cross-sectional area is similar for fills and gullies, suggesting that their geometry can be treated similarly in hydraulic modeling. Additionally, the rills and gullies have relatively small width-to-depth ratios, implying sidewall friction may be important to the erosion and evolutionary process relative to main stem channels.  相似文献   

12.
Rill network development not only potentially affects hillslope and drainage network evolution, but also causes severe soil degradation. However, the studies on rill network development remain inconclusive. This study aimed to investigate the temporal and spatial development of hillslope rill networks and their characteristics based on rainfall simulations and field observations. A soil pan (10.0 m long × 3.0 m wide × 0.5 m deep) on a 20° slope was applied three successive simulated rains at two intensities of 50 and 100 mm h–1. The field observations were performed on two bare hillslope runoff plots (10.0 m long × 3.0 m wide) at 20°. Three typical erosive natural rainfall events were observed in the field, and rills were measured in detail, similar to the laboratory rainfall simulation. The results indicated that with increases in rainfall events, the rill network morphology varied from incipient formation to the maximum drainage network density. Four rill network development indicators (rill distribution density, distance between rills, rill bifurcation number, and confluence point number) exhibited different changes over time and space. Among the four indicators, the rill bifurcation number was the best indicator for describing rill network development. Rill flow energy increased and decreased cyclically on a slope ranging between ~3 and 4 m. Moreover, rill networks on loessial hillslopes generally evolved into dendritic rather than parallel forms. The development characteristics of the rill network were relatively similar between the laboratory simulation and natural field conditions. Over time, rill erosion control measures become increasingly difficult to implement as the rill network develops. The morphology of eroding rills evolved over time and space, which led to corresponding rill network development. Further study should quantify the impacts of rill network development on soil degradation and land development. © 2020 John Wiley & Sons, Ltd.  相似文献   

13.
Stream networks used in studies of basin morphometry, network topology, flood hydrology, and sediment production should be defined as precisely as possible. Previous work has drawn attention to the way in which stream network definition varies on maps of different scales, on maps employing different conventions devised in relation to the dynamic network, and according to whether maps, remote sensing or field survey sources are used. Networks also vary in extent according to the date of survey and after considering the instructions to surveyors it is shown that such changes, over periods of 100 years reflect changes in network extent. For three areas of Britain, network change can be identified by comparison of maps of different dates, by comparison of these changes with the results of field survey, and by reference to dateable features such as inclosure boundaries. Changes of drainage networks since the nineteenth century are shown to be significant in extent and they have often occurred as a result of the replacement of flushes by clearly defined stream channels. This transformation has often occurred as a result of new or modified systems of stormwater drainage from roads, tracks or farms, and the planning of the future disposal of road drainage should be considered carefully in relation to such stream network changes. The changes of drainage networks identified from maps of different dates and editions can provide a useful data base for studies of network topology and may also be significant in relation to palaeohydrological investigations.  相似文献   

14.
Stream networks expand and contract through time, impacting chemical export, aquatic habitat, and water quality. Although recent advances improve prediction of the extent of the wetted channel network (L ) based on discharge at the catchment outlet (Q ), controls on the temporal variability of L remain poorly understood and unquantified. Here we develop a quantitative, conceptual framework to explore how flow regime and stream network hydraulic scaling factors co-determine the relative temporal variability in L (denoted here as the total wetted channel drainage density). Network hydraulic scaling determines how much L changes for a change in Q , while the flow regime describes how Q changes in time. We compiled datasets of co-located dynamic stream extent mapping and discharge to analyze all globally available empirical data using the presented framework. We found that although variability in L is universally damped relative to variability in Q (i.e., streamflow is relatively more variable in time than network extent), the relationship is elastic, meaning that for a given increase in the variability in Q , headwater catchments will experience greater-than-proportional increases in the variability of L . Thus, under anticipated climatic shifts towards more volatile precipitation, relative variability in headwater stream network extents can be expected to increase even more than the relative variability of discharge itself. Comparison between network extents inferred from the L -Q relationship and blue lines on USGS topographic maps shows widespread underestimation of the wetted channel network by the blue line network.  相似文献   

15.
The rainfall–runoff relationship is not only nonlinear and complex but also difficult to model. Artificial neural network (ANN), as a data-driven technique, has gained significant attention in recent years and has been shown to be an efficient alternative to traditional methods for hydrological modeling. However, for different input combinations, ANN models can yield different results. Therefore, input variables and ANN types need to be carefully considered, when using an ANN model for stream flow forecasting. This study proposes the copula-entropy (CE) theory to identify the inputs of an ANN model. The CE theory permits to calculate mutual information (MI) and partial MI directly which avoids calculating the marginal and joint probability distributions. Three different ANN models, namely multi-layer feed (MLF) forward networks, radial basis function networks and general regression neural network, were applied to predict stream flow of Jinsha River, China. Results showed that the inputs selected by the CE method were better than those by the traditional linear correlation analysis, and the MLF ANN model with the inputs selected by CE method obtained the best predicted results for the Jinsha River at Pingshan gauging station.  相似文献   

16.
The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

17.
Application of artificial neural network (ANN) models has been reported to solve variety of water resources and environmental related problems including prediction, forecasting and classification, over the last two decades. Though numerous research studies have witnessed the improved estimate of ANN models, the practical applications are sometimes limited. The black box nature of ANN models and their parameters hardly convey the physical meaning of catchment characteristics, which result in lack of transparency. In addition, it is perceived that the point prediction provided by ANN models does not explain any information about the prediction uncertainty, which reduce the reliability. Thus, there is an increasing consensus among researchers for developing methods to quantify the uncertainty of ANN models, and a comprehensive evaluation of uncertainty methods applied in ANN models is an emerging field that calls for further improvements. In this paper, methods used for quantifying the prediction uncertainty of ANN based hydrologic models are reviewed based on the research articles published from the year 2002 to 2015, which focused on modeling streamflow forecast/prediction. While the flood forecasting along with uncertainty quantification has been frequently reported in applications other than ANN in the literature, the uncertainty quantification in ANN model is a recent progress in the field, emerged from the year 2002. Based on the review, it is found that methods for best way of incorporating various aspects of uncertainty in ANN modeling require further investigation. Though model inputs, parameters and structure uncertainty are mainly considered as the source of uncertainty, information of their mutual interaction is still lacking while estimating the total prediction uncertainty. The network topology including number of layers, nodes, activation function and training algorithm has often been optimized for the model accuracy, however not in terms of model uncertainty. Finally, the effective use of various uncertainty evaluation indices should be encouraged for the meaningful quantification of uncertainty. This review article also discusses the effectiveness and drawbacks of each method and suggests recommendations for further improvement.  相似文献   

18.
Modelling evaporation using an artificial neural network algorithm   总被引：1，自引：0，他引：1  
This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

19.
湖北牛山湖小型鱼类的群落结构及多样性   总被引：2，自引：1，他引：1  
流域数据模型是流域特征的语义、行为和规则的表达,是进行集成流域模拟和管理的空间数据组织的重要内容．本文以长江三角洲太湖流域上游的西苕溪流域为例,将汇流单元分为自然流域、“大包围”、圩区三种形式,分别建立了各自内部的河湖网络关系,即山区由自然流域单元,内包含水库(或湖泊)、河流、水工点的树状河湖网络关系;平原区由人工汇流单元,内包含大包围、圩区、湖泊、湿地、河道、水工点的网状河湖网络关系,为建立适合我国的流域数据模型做了有益的探索．  相似文献   

20.
In a basin developed on a stream table, effluent subsurface flow supported a channel network that evolved by a combination of headward growth, lateral widening and divide decay. The area occupied by the developing network increased with time. Circularity was used to characterize network evolution which occurred in three phases (initiation, extension and abstraction). Basin sediment discharge declined exponentially with time. Pronounced quasi-cyclic variability was superimposed upon this general trend. Some of the variability was directly linked to changes in the amount of sediment supplied to the channel. The variation of mean network sediment yield (mean sediment discharge scaled by network area) with time adequately described the general decline in sediment discharge as the network evolved.  相似文献   

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