Numerical modeling of flow and morphology induced by a solitary wave on a sloping beach     

《Applied Ocean Research》2019

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Comparison of abstraction scenarios simulated by SWAT and SWAT-MODFLOW     

Eugenio Molina-Navarro  Ryan T. Bailey  Hans Estrup Andersen  Hans Thodsen  Anders Nielsen  Seonggyu Park 《水文科学杂志》2019,64(4):434-454

In hydrological modelling of catchments, wherein streams are groundwater-fed, an accurate representation of groundwater processes and their interaction with surface water is crucial. With this purpose, a coupled model was recently developed linking SWAT (Soil and Water Assessment Tool) with the fully-distributed groundwater model MODFLOW (Modular Groundwater Flow). In this study, SWAT and SWAT-MODFLOW were applied to a Danish groundwater-dominant catchment, simulating groundwater abstraction scenarios and assessing the benefits and drawbacks of SWAT-MODFLOW. Both models demonstrated good performance. However, SWAT-MODFLOW provided more realistic outputs when simulating abstraction: the decrease in streamflow was similar to the volume of water abstracted, while in SWAT the impact was negligible. SWAT also showed impacts on streamflow only when abstractions were taken from the shallow aquifer, not from the deep aquifer. Overall, SWAT-MODFLOW demonstrated wider possibilities for groundwater analysis, providing more insights than SWAT in supporting decision making in relation to environmental assessment.  相似文献   

A continuous modelling approach for design flood estimation on sub-daily time scale     

B. Winter  K. Schneeberger  N.V. Dung  M. Huttenlau  S. Achleitner  J. Stötter 《水文科学杂志》2019,64(5):539-554

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Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network   总被引：1，自引：1，他引：0  

Arman Ahmadi  Dimitri P. Solomatine 《水文科学杂志》2019,64(9):1080-1094

Due to the complicated nature of environmental processes, consideration of uncertainty is an important part of environmental modelling. In this paper, a new variant of the machine learning-based method for residual estimation and parametric model uncertainty is presented. This method is based on the UNEEC-P (UNcertainty Estimation based on local Errors and Clustering – Parameter) method, but instead of multilayer perceptron uses a “fuzzified” version of the general regression neural network (GRNN). Two hydrological models are chosen and the proposed method is used to evaluate their parametric uncertainty. The approach can be classified as a hybrid uncertainty estimation method, and is compared to the group method of data handling (GMDH) and ordinary kriging with linear external drift (OKLED) methods. It is shown that, in terms of inherent complexity, measured by Akaike information criterion (AIC), the proposed fuzzy GRNN method has advantages over other techniques, while its accuracy is comparable. Statistical metrics on verification datasets demonstrate the capability and appropriate efficiency of the proposed method to estimate the uncertainty of environmental models.  相似文献   

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models     

Ignacio Fraga  Luis Cea  Jernimo Puertas 《水文研究》2019,33(1):160-173

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.  相似文献   

Interannual variability in glacier contribution to runoff from a high‐elevation Andean catchment: understanding the role of debris cover in glacier hydrology     

Flavia Burger  Alvaro Ayala  David Farias  Thomas E. Shaw  Shelley MacDonell  Ben Brock  James McPhee  Francesca Pellicciotti 《水文研究》2019,33(2):214-229

We present a field‐data rich modelling analysis to reconstruct the climatic forcing, glacier response, and runoff generation from a high‐elevation catchment in central Chile over the period 2000–2015 to provide insights into the differing contributions of debris‐covered and debris‐free glaciers under current and future changing climatic conditions. Model simulations with the physically based glacio‐hydrological model TOPKAPI‐ETH reveal a period of neutral or slightly positive mass balance between 2000 and 2010, followed by a transition to increasingly large annual mass losses, associated with a recent mega drought. Mass losses commence earlier, and are more severe, for a heavily debris‐covered glacier, most likely due to its strong dependence on snow avalanche accumulation, which has declined in recent years. Catchment runoff shows a marked decreasing trend over the study period, but with high interannual variability directly linked to winter snow accumulation, and high contribution from ice melt in dry periods and drought conditions. The study demonstrates the importance of incorporating local‐scale processes such as snow avalanche accumulation and spatially variable debris thickness, in understanding the responses of different glacier types to climate change. We highlight the increased dependency of runoff from high Andean catchments on the diminishing resource of glacier ice during dry years.  相似文献   

Streamwater ages in nested,seasonally cold Canadian watersheds     

Samuel Bansah  Genevieve Ali 《水文研究》2019,33(4):495-511

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Measured and modelled above‐ and below‐canopy turbulent fluxes for a snow‐dominated mountain forest using GEOtop     

Andrew T. Fullhart  Thijs J. Kelleners  Heather N. Speckman  Daniel Beverly  Brent E. Ewers  John M. Frank  William J. Massman 《水文研究》2019,33(18):2464-2480

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Travel time computations using a compact eikonal equation for vertical transverse isotropic media          下载免费PDF全文

Peyman Moghaddam  Reza Khajavi  Henk Keers 《Geophysical Prospecting》2018,66(8):1475-1484

Eikonal solvers often have stability problems if the velocity model is mildly heterogeneous. We derive a stable and compact form of the eikonal equation for P‐wave propagation in vertical transverse isotropic media. The obtained formulation is more compact than other formulations and therefore computationally attractive. We implemented ray shooting for this new equation through a Hamiltonian formalism. Ray tracing based on this new equation is tested on both simple as well as more realistic mildly heterogeneous velocity models. We show through examples that the new equation gives travel times that coincide with the travel time picks from wave equation modelling for anisotropic wave propagation.  相似文献   

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?     

《地学前缘(英文版)》2020,11(3):871-883

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.  相似文献