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1.
A hydrologic model calibration methodology that is based on groundwater data is developed and implemented using the US Geological Survey's precipitation-runoff modelling system (PRMS) and the modular modelling system (MMS), which performs automatic calibration of parameters. The developed methodology was tested in the Akrotiri basin, Cyprus. The necessity for the groundwater-based model calibration, rather than a typical runoff-based one, arose from the very intermittent character of the runoff in the Akrotiri basin, a case often met in semi-arid regions. Introducing a datum and converting groundwater storage to head made the observable groundwater level the calibration indicator. The modelling of the Akrotiri basin leads us to conclude that groundwater level is a useful indicator for hydrological model calibration that can be potentially used in other similar situations in the absence of river flow measurements. However, the option of an automatic calibration of the complex hydrologic model PRMS by MMS did not ensure a good outcome. On the other hand, automatic optimisation, combined with heuristic expert intervention, enabled achievement of good calibration and constitutes a valuable means for saving effort and improving modelling performance. To this end, results must be scrutinised, melding the viewpoint of physical sense with mathematical efficiency criteria. Thus optimised, PRMS achieved a low simulation error, good reproduction of the historic trend of the aquifer water level evolution and reasonable physical behaviour (good hydrologic balance, Reasonable match of aquifer level evolution, good estimation of mean natural recharge rate).  相似文献   

2.
The curve number (CN) method is widely used for rainfall–runoff modelling in continuous hydrologic simulation models. A sound continuous soil moisture accounting procedure is necessary for models using the CN method. For shallow soils and soils with low storage, the existing methods have limitations in their ability to reproduce the observed runoff. Therefore, a simple one‐parameter model based on the Soil Conservation Society CN procedure is developed for use in continuous hydrologic simulation. The sensitivity of the model parameter to runoff predictions was also analysed. In addition, the behaviour of the procedure developed and the existing continuous soil moisture accounting procedure used in hydrologic models, in combination with Penman–Monteith and Hargreaves evapotranspiration (ET) methods was also analysed. The new CN methodology, its behaviour and the sensitivity of the depletion coefficient (model parameter) were tested in four United States Geological Survey defined eight‐digit watersheds in different water resources regions of the USA using the SWAT model. In addition to easy parameterization for calibration, the one‐parameter model developed performed adequately in predicting runoff. When tested for shallow soils, the parameter is found to be very sensitive to surface runoff and subsurface flow and less sensitive to ET. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

3.
This study compares the predictive accuracy of eight state‐of‐the‐art modelling techniques for 12 landforms types in a cold environment. The methods used are Random Forest (RF), Artificial Neural Networks (ANN), Generalized Boosting Methods (GBM), Generalized Linear Models (GLM), Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), Classification Tree Analysis (CTA) and Mixture Discriminant Analysis (MDA). The spatial distributions of 12 periglacial landforms types were recorded in sub‐Arctic landscape of northern Finland in 2032 grid squares at a resolution of 25 ha. First, three topographic variables were implemented into the eight modelling techniques (simple model), and then six other variables were added (three soil and three vegetation variables; complex model) to reflect the environmental conditions of each grid square. The predictive accuracy was measured by two methods: the area under the curve (AUC) of a receiver operating characteristic (ROC) plot, and the Kappa index (κ), based on spatially independent model evaluation data. The mean AUC values of the simple models varied between 0·709 and 0·796, whereas the AUC values of the complex model ranged from 0·725 to 0·825. For both simple and complex models GAM, GLM, ANN and GBM provided the highest predictive performances based on both AUC and κ values. The results encourage further applications of the novel modelling methods in geomorphology. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

4.
This paper addresses the application of a data‐based mechanistic (DBM) modelling approach using transfer function models (TFMs) with non‐linear rainfall filtering to predict runoff generation from a semi‐arid catchment (795 km2) in Tanzania. With DBM modelling, time series of rainfall and streamflow were allowed to suggest an appropriate model structure compatible with the data available. The model structures were evaluated by looking at how well the model fitted the data, and how well the parameters of the model were estimated. The results indicated that a parallel model structure is appropriate with a proportion of the runoff being routed through a fast flow pathway and the remainder through a slow flow pathway. Finally, the study employed a Generalized Likelihood Uncertainty Estimation (GLUE) methodology to evaluate the parameter sensitivity and predictive uncertainty based on the feasible parameter ranges chosen from the initial analysis of recession curves and calibration of the TFM. Results showed that parameters that control the slow flow pathway are relatively more sensitive than those that control the fast flow pathway of the hydrograph. Within the GLUE framework, it was found that multiple acceptable parameter sets give a range of predictions. This was found to be an advantage, since it allows the possibility of assessing the uncertainty in predictions as conditioned on the calibration data and then using that uncertainty as part of the decision‐making process arising from any rainfall‐runoff modelling project. Copyright © 2001 John Wiley & Sons, Ltd.  相似文献   

5.
This paper suggests a multi‐criteria protocol for appropriately evaluating the predictions of hydrologic models during calibration and evaluation stages. The protocol includes different statistical, analytical and visual criteria such as analysis of peak and low flows, cumulative volumes, extreme value statistics, performance statistics, etc. Furthermore, the protocol assesses the physical consistency of model predictions by filtering the total observed hydrograph into different flow‐components (baseflow, interflow and overland flow) and using these filtered data in the calibration and evaluation processes. Based on the distributed modelling of a medium size catchment, it is shown that application of the suggested protocol, and in particular the use of the filtered flow‐components in model calibration, enhances the physical consistency of model predictions, adding considerable value to the calibration process. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

6.
Rainfall input for hydrologic modelling is assumed uniformly distributed over the entire catchment. This can lead to significant errors. Investigations of areal rainfall in mountain areas are typically limited by a lack of adequate meteorological and hydrogeological records. This study focuses on areal rainfall in mountain areas within the Kaidu River Basin, China, with the aim of analyzing the influence of areal rainfall on the simulation accuracy of runoff prediction. We conducted a simulation using MIKE 11/NAM rainfall‐runoff model over 92 days of the rain season and compared the simulation error in different methods. On the basis of properties of self‐similarity degree (SSD) in analyzing the detailed characteristics of terrain, areal rainfall was calculated to model the runoff. The results of the model simulations are generally consistent with observed data, indicating that the self‐similarity topography method is able to reflect the spatial change of rainfall. This indicates that the proposed methodology is applicable for the management of water resources in mountain area. The modelling and self‐similarity topography method study allowed quantification of the spatial rainfall and provided an insight into their implications in hydrological forecasting. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

7.
End users face a range of subjective decisions when evaluating climate change impacts on hydrology, but the importance of these decisions is rarely assessed. In this paper, we evaluate the implications of hydrologic modelling choices on projected changes in the annual water balance, monthly simulated processes, and signature measures (i.e. metrics that quantify characteristics of the hydrologic catchment response) under a future climate scenario. To this end, we compare hydrologic changes computed with four different model structures – whose parameters have been obtained using a common calibration strategy – with hydrologic changes computed with a single model structure and parameter sets from multiple options for different calibration decisions (objective function, local optima, and calibration forcing dataset). Results show that both model structure selection and the parameter estimation strategy affect the direction and magnitude of projected changes in the annual water balance, and that the relative effects of these decisions are basin dependent. The analysis of monthly changes illustrates that parameter estimation strategies can provide similar or larger uncertainties in simulations of some hydrologic processes when compared with uncertainties coming from model choice. We found that the relative effects of modelling decisions on projected changes in catchment behaviour depend on the signature measure analysed. Furthermore, parameter sets with similar performance, but located in different regions of the parameter space, provide very different projections for future catchment behaviour. More generally, the results obtained in this study prompt the need to incorporate parametric uncertainty in multi‐model frameworks to avoid an over‐confident portrayal of climate change impacts. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

8.
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   

9.
Better parameterization of a hydrological model can lead to improved streamflow prediction. This is particularly important for seasonal streamflow forecasting with the use of hydrological modelling. Considering the possible effects of hydrologic non‐stationarity, this paper examined ten parameterization schemes at 12 catchments located in three different climatic zones in east Australia. These schemes are grouped into four categories according to the period when the data are used for model calibration, i.e. calibration using data: (1) from a fixed period in the historical records; (2) from different lengths of historical records prior to prediction year; (3) from different climatic analogue years in the past; and (4) data from the individual months. Parameterization schemes were evaluated according to model efficiency in both the calibration and verification period. The results show that the calibration skill changes with the different historic periods when data are used at all catchments. Comparison of model performance between the calibration schemes indicates that it is worth calibrating the model with the use of data from each individual month for the purpose of seasonal streamflow forecasting. For the catchments in the winter‐dominant rainfall region of south‐east Australia, a more significant shift in rainfall‐runoff relationships at different periods was found. For those catchments, model calibration with the use of 20 years of data prior to the prediction year leads to a more consistent performance. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

10.
In this study, the Precipitation‐Runoff Modelling System (PRMS) was used to simulate changes in surface‐water depression storage in the 1,126‐km2 Upper Pipestem Creek basin located within the Prairie Pothole Region of North Dakota, USA. The Prairie Pothole Region is characterized by millions of small water bodies (or surface‐water depressions) that provide numerous ecosystem services and are considered an important contribution to the hydrologic cycle. The Upper Pipestem PRMS model was extracted from the U.S. Geological Survey's (USGS) National Hydrologic Model (NHM), developed to support consistent hydrologic modelling across the conterminous United States. The Geospatial Fabric database, created for the USGS NHM, contains hydrologic model parameter values derived from datasets that characterize the physical features of the entire conterminous United States for 109,951 hydrologic response units. Each hydrologic response unit in the Geospatial Fabric was parameterized using aggregated surface‐water depression area derived from the National Hydrography Dataset Plus, an integrated suite of application‐ready geospatial datasets. This paper presents a calibration strategy for the Upper Pipestem PRMS model that uses normalized lake elevation measurements to calibrate the parameters influencing simulated fractional surface‐water depression storage. Results indicate that inclusion of measurements that give an indication of the change in surface‐water depression storage in the calibration procedure resulted in accurate changes in surface‐water depression storage in the water balance. Regionalized parameterization of the USGS NHM will require a proxy for change in surface‐storage to accurately parameterize surface‐water depression storage within the USGS NHM.  相似文献   

11.
The need for accurate hydrologic analysis and rainfall–runoff modelling tools has been rapidly increasing because of the growing complexity of operational hydrologic and hydraulic problems associated with population growth, rapid urbanization and expansion of agricultural activities. Given the recent advances in remote sensing of physiographic features and the availability of near real‐time precipitation products, rainfall–runoff models are expected to predict runoff more accurately. In this study, we compare the performance and implementation requirements of two rainfall–runoff models for a semi‐urbanized watershed. One is a semi‐distributed conceptual model, the Hydrologic Engineering Center‐Hydrologic Modelling System (HEC‐HMS). The other is a physically based, distributed‐parameter hydrologic model, the Gridded Surface Subsurface Hydrologic Analysis (GSSHA). Four flood events that took place on the Leon Creek watershed, a sub‐watershed of the San Antonio River basin in Texas, were used in this study. The two models were driven by the Multisensor Precipitation Estimator radar products. One event (in 2007) was used for HEC‐HMS and GSSHA calibrations. Two events (in 2004 and 2007) were used for further calibration of HEC‐HMS. Three events (in 2002, 2004 and 2010) were used for model validation. In general, the physically based, distributed‐parameter model performed better than the conceptual model and required less calibration. The two models were prepared with the same minimum required input data, and the effort required to build the two models did not differ substantially. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

12.
H. S. Kim  S. Lee 《水文研究》2014,28(13):4023-4041
This study aimed to evaluate the effectiveness of the regionalization method on the basis of a combination of a parsimonious model structure and a multi‐objective calibration technique. For this study, 12 gauged catchments in the Republic of Korea were used. The parsimonious model structure, requiring minimal input data, was used to avoid adverse effects arising from model complexity, over‐parameterization and data requirements. The IHACRES rainfall‐runoff model was applied to represent the dynamic response characteristics of catchments in Korea. A multi‐objective approach was adopted to reduce the predictive uncertainty arising from the calibration of a rainfall‐runoff model, by increasing the amount of information retrieved from the available data. The regional relationships (or models) between the model parameters and the catchment attributes were established via a multiple regression approach, incorporating correlation analysis and stepwise regression on linear and logarithmic scales. The impacts of the parameters, calibrated by the multi‐objective approach, on the adequacy of regional relationships were assessed by comparison with impacts obtained by the single‐objective approach. The regional relationships were well defined, despite limited available data. The drainage area, the effective soil depth, the mean catchment slope and the catchment gradient appeared to be the main factors for describing the hydrologic response characteristics in the areas studied. The overall model performance of the regional models based on the multi‐objective approach was good, producing reasonable results for high and low flows and for the overall water balance, simultaneously. The regional models based on the single‐objective approach yielded accurate predictions in high flows but showed limited predictive capability for low flows and the overall water balance. This was due to the optimal model parameter estimates when using a single‐objective measure. The parameters calibrated by the single‐objective approach decreased the predictability of the regional models. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

13.
The use of detailed groundwater models to simulate complex environmental processes can be hampered by (1) long run‐times and (2) a penchant for solution convergence problems. Collectively, these can undermine the ability of a modeler to reduce and quantify predictive uncertainty, and therefore limit the use of such detailed models in the decision‐making context. We explain and demonstrate a novel approach to calibration and the exploration of posterior predictive uncertainty, of a complex model, that can overcome these problems in many modelling contexts. The methodology relies on conjunctive use of a simplified surrogate version of the complex model in combination with the complex model itself. The methodology employs gradient‐based subspace analysis and is thus readily adapted for use in highly parameterized contexts. In its most basic form, one or more surrogate models are used for calculation of the partial derivatives that collectively comprise the Jacobian matrix. Meanwhile, testing of parameter upgrades and the making of predictions is done by the original complex model. The methodology is demonstrated using a density‐dependent seawater intrusion model in which the model domain is characterized by a heterogeneous distribution of hydraulic conductivity.  相似文献   

14.
Output generated by hydrologic simulation models is traditionally calibrated and validated using split‐samples of observed time series of total water flow, measured at the drainage outlet of the river basin. Although this approach might yield an optimal set of model parameters, capable of reproducing the total flow, it has been observed that the flow components making up the total flow are often poorly reproduced. Previous research suggests that notwithstanding the underlying physical processes are often poorly mimicked through calibration of a set of parameters hydrologic models most of the time acceptably estimates the total flow. The objective of this study was to calibrate and validate a computer‐based hydrologic model with respect to the total and slow flow. The quick flow component used in this study was taken as the difference between the total and slow flow. Model calibrations were pursued on the basis of comparing the simulated output with the observed total and slow flow using qualitative (graphical) assessments and quantitative (statistical) indicators. The study was conducted using the Soil and Water Assessment Tool (SWAT) model and a 10‐year historical record (1986–1995) of the daily flow components of the Grote Nete River basin (Belgium). The data of the period 1986–1989 were used for model calibration and data of the period 1990–1995 for model validation. The predicted daily average total flow matched the observed values with a Nash–Sutcliff coefficient of 0·67 during calibration and 0·66 during validation. The Nash–Sutcliff coefficient for slow flow was 0·72 during calibration and 0·61 during validation. Analysis of high and low flows indicated that the model is unbiased. A sensitivity analysis revealed that for the modelling of the daily total flow, accurate estimation of all 10 calibration parameters in the SWAT model is justified, while for the slow flow processes only 4 out of the set of 10 parameters were identified as most sensitive. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

15.
The record length and quality of instantaneous peak flows (IPFs) have a great influence on flood design, but these high resolution flow data are not always available. The primary aim of this study is to compare different strategies to derive frequency distributions of IPFs using the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrologic model. The model is operated on a daily and an hourly time step for 18 catchments in the Aller‐Leine basin, Germany. Subsequently, general extreme value (GEV) distributions are fitted to the simulated annual series of daily and hourly extreme flows. The resulting maximum mean daily flow (MDF) quantiles from daily simulations are transferred into IPF quantiles using a multiple regression model, which enables a direct comparison with the simulated hourly quantiles. As long climate records with a high temporal resolution are not available, the hourly simulations require a disaggregation of the daily rainfall. Additionally, two calibrations strategies are applied: (1) a calibration on flow statistics; (2) a calibration on hydrographs. The results show that: (1) the multiple regression model is capable of predicting IPFs with the simulated MDFs; (2) both daily simulations with post‐correction of flows and hourly simulations with pre‐processing of precipitation enable a reasonable estimation of IPFs; (3) the best results are achieved using disaggregated rainfall for hourly modelling with calibration on flow statistics; and (4) if the IPF observations are not sufficient for model calibration on flow statistics, the transfer of MDFs via multiple regressions is a good alternative for estimating IPFs. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

16.
The resolution of a digital elevation model (DEM) is a crucial factor in watershed hydrologic and environmental modelling. DEM resolution can cause significant variability in the representation of surface topography, which further affects quantification of hydrologic connectivity and simulation of hydrologic processes. The objective of this study is to examine the effects of DEM resolution on (1) surface microtopographic characteristics, (2) hydrologic connectivity, and (3) the spatial and temporal variations of hydrologic processes. A puddle‐to‐puddle modelling system was utilized for surface delineation and modelling of the puddle‐to‐puddle overland flow dynamics, surface runoff, infiltration, and unsaturated flow for nine DEM resolution scenarios of a field plot surface. Comparisons of the nine modelling scenarios demonstrated that coarser DEM resolutions tended to eliminate topographic features, reduce surface depression storage, and strengthen hydrologic connectivity and surface runoff. We found that reduction in maximum depression storage and maximum ponding area was as high as 97.56% and 76.36%, respectively, as the DEM grid size increased from 2 to 80 cm. The paired t‐test and fractal analysis demonstrated the existence of a threshold DEM resolution (10 cm for the field plot), within which the DEM‐based hydrologic modelling was effective and acceptable. The effects of DEM resolution were further evaluated for a larger surface in the Prairie Pothole Region subjected to observed rainfall events. It was found that simulations based on coarser resolution DEMs (>10 m) tended to overestimate ponded areas and underestimate runoff discharge peaks. The simulated peak discharge from the Prairie Pothole Region surface reduced by approximately 50% as the DEM resolution changed from 2 to 90 m. Fractal analysis results elucidated scale dependency of hydrologic and topographic processes. In particular, scale analysis highlighted a unique constant–threshold–power relationship between DEM scale and topographic and hydrologic parameters/variables. Not only does this finding allow one to identify threshold DEM but also further develop functional relationships for scaling to achieve valid topographic characterization as well as effective and efficient hydrologic modelling. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

17.
This work examines future flood risk within the context of integrated climate and hydrologic modelling uncertainty. The research questions investigated are (1) whether hydrologic uncertainties are a significant source of uncertainty relative to other sources such as climate variability and change and (2) whether a statistical characterization of uncertainty from a lumped, conceptual hydrologic model is sufficient to account for hydrologic uncertainties in the modelling process. To investigate these questions, an ensemble of climate simulations are propagated through hydrologic models and then through a reservoir simulation model to delimit the range of flood protection under a wide array of climate conditions. Uncertainty in mean climate changes and internal climate variability are framed using a risk‐based methodology and are explored using a stochastic weather generator. To account for hydrologic uncertainty, two hydrologic models are considered, a conceptual, lumped parameter model and a distributed, physically based model. In the conceptual model, parameter and residual error uncertainties are quantified and propagated through the analysis using a Bayesian modelling framework. The approach is demonstrated in a case study for the Coralville Dam on the Iowa River, where recent, intense flooding has raised questions about potential impacts of climate change on flood protection adequacy. Results indicate that the uncertainty surrounding future flood risk from hydrologic modelling and internal climate variability can be of the same order of magnitude as climate change. Furthermore, statistical uncertainty in the conceptual hydrological model can capture the primary structural differences that emerge in flood damage estimates between the two hydrologic models. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

18.
Hydrologic modelling has been applied to assess the impacts of projected climate change within three study areas in the Peace, Campbell and Columbia River watersheds of British Columbia, Canada. These study areas include interior nival (two sites) and coastal hybrid nival–pluvial (one site) hydro‐climatic regimes. Projections were based on a suite of eight global climate models driven by three emission scenarios to project potential climate responses for the 2050s period (2041–2070). Climate projections were statistically downscaled and used to drive a macro‐scale hydrology model at high spatial resolution. This methodology covers a large range of potential future climates for British Columbia and explicitly addresses both emissions and global climate model uncertainty in the final hydrologic projections. Snow water equivalent is projected to decline throughout the Peace and Campbell and at low elevations within the Columbia. At high elevations within the Columbia, snow water equivalent is projected to increase with increased winter precipitation. Streamflow projections indicate timing shifts in all three watersheds, predominantly because of changes in the dynamics of snow accumulation and melt. The coastal hybrid site shows the largest sensitivity, shifting to more rainfall‐dominated system by mid‐century. The two interior sites are projected to retain the characteristics of a nival regime by mid‐century, although streamflow‐timing shifts result from increased mid‐winter rainfall and snowmelt, and earlier freshet onset. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

19.
With the availability of spatially distributed data, distributed hydrologic models are increasingly used for simulation of spatially varied hydrologic processes to understand and manage natural and human activities that affect watershed systems. Multi‐objective optimization methods have been applied to calibrate distributed hydrologic models using observed data from multiple sites. As the time consumed by running these complex models is increasing substantially, selecting efficient and effective multi‐objective optimization algorithms is becoming a nontrivial issue. In this study, we evaluated a multi‐algorithm, genetically adaptive multi‐objective method (AMALGAM) for multi‐site calibration of a distributed hydrologic model—Soil and Water Assessment Tool (SWAT), and compared its performance with two widely used evolutionary multi‐objective optimization (EMO) algorithms (i.e. Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Non‐dominated Sorted Genetic Algorithm II (NSGA‐II)). In order to provide insights into each method's overall performance, these three methods were tested in four watersheds with various characteristics. The test results indicate that the AMALGAM can consistently provide competitive or superior results compared with the other two methods. The multi‐method search framework of AMALGAM, which can flexibly and adaptively utilize multiple optimization algorithms, makes it a promising tool for multi‐site calibration of the distributed SWAT. For practical use of AMALGAM, it is suggested to implement this method in multiple trials with relatively small number of model runs rather than run it once with long iterations. In addition, incorporating different multi‐objective optimization algorithms and multi‐mode search operators into AMALGAM deserves further research. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

20.
Fens, which are among the most biodiverse of wetland types in the USA, typically occur in glacial landscapes characterized by geo‐morphologic variability at multiple spatial scales. As a result, the hydrologic systems that sustain fens are complex and not well understood. Traditional approaches for characterizing such systems use simplifying assumptions that cannot adequately capture the impact of variability in geology and topography. In this study, a hierarchical, multi‐scale groundwater modelling approach coupled with a geologic model is used to understand the hydrology of a fen in Michigan. This approach uses high‐resolution data to simulate the multi‐scale topographic and hydrologic framework and lithologic data from more than 8500 boreholes in a statewide water well database to capture the complex geology. A hierarchy of dynamically linked models is developed that simulates groundwater flow at all scales of interest and to delineate the areas that contribute groundwater to the fen. The results show the fen receiving groundwater from multiple sources: an adjacent wetland, local recharge, a nearby lake and a regional groundwater mound. Water from the regional mound flows to an intermediate source before reaching the fen, forming a ‘cascading’ connection, while other sources provide water through ‘direct’ connections. The regional mound is also the source of water to other fens, streams and lakes in this area, thus creating a large, interconnected hydrologic system that sustains the entire ecosystem. In order to sustainably manage such systems, conservation efforts must include both site‐based protection and management, as well as regional protection and management of groundwater source areas. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

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