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In the Himalayan regions, precipitation-runoff relationships are amongst the most complex hydrological phenomena, due to
varying topography and basin characteristics. In this study, different artificial neural networks (ANNs) algorithms were used to
simulate daily runoff at three discharge measuring sites in the Himalayan Kosi River Basin, India, using various combinations
of precipitation-runoff data as input variables. The data used for this study was collected for the monsoon period (June to October)
during the years of 2005 to 2009. ANNs were trained using different training algorithms, learning rates, length of data
and number of hidden neurons. A comprehensive multi-criteria validation test for precipitation-runoff modeling has been undertaken
to evaluate model performance and test its validity for generating scenarios. Global statistics have demonstrated that
the multilayer perceptron with three hidden layers (MLP-3) is the best ANN for basin comparisons with other MLP networks
and Radial Basis Functions (RBF). Furthermore, non-parametric tests also illustrate that the MLP-3 network is the best network
to reproduce the mean and variance of observed runoff. The performance of ANNs was demonstrated for flows during
the monsoon season, having different soil moisture conditions during period from June to October. 相似文献
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径流量的变化与区域经济社会发展、生态平衡以及水资源管理和水环境保护密切相关,月径流量的研究对水量配置、调度等均具有重要意义。针对小尺度流域普遍存在的资料有限问题,研究BP人工神经网络在华南湿润区小流域月径流模拟的适用性。以滨江流域长序列逐日降水径流资料为基础,采用BP人工神经网络进行月径流量模拟,并将其与径流系数法、新安江模型和HSPF模型所得进行对比分析。研究表明:BP人工神经网络使用简便,变化趋势预测准确,在降水径流模拟方面优势明显,全年确定性系数为0.91,高于径流系数法所得0.85,与新安江模型的0.92、HSPF的0.96相当,具有良好的应用前景,其汛期的模拟效果优于非汛期,但模拟结果相对偏大,存在一定的改进和提高空间。 相似文献
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Maintenance of steady streamflow is a critical attribute of the continental river systems for safeguarding downstream ecosystems and agricultural production.Global climate change imposes a potential risk to water supply from the headwater by changing the magnitude and frequency of precipitation and evapotranspiration in the region.To determine if and to what extent the recent climate changes affected streamflow in major river systems,we examined the pattern of temporal variations in precipitation,temperature,evapotranspiration and changes in runoff discharge during 1958–2017 in the headwater region of the Yellow River in northeastern Tibetan Plateau.We identified 1989 as the turning point for a statistically significant 14% reduction in streamflow discharge(P 0.05) for the period 1989–2017 compared with 1958–1988,approximately coinciding with changes in the monthly distribution but not the interannual variations of precipitation,and detected a mismatch between precipitation and runoff after 2000.Both annual precipitation and runoff discharge displayed fourand eight-year cyclic patterns of changes for the period 1958–1988,and a six-year cyclic pattern of changes for the period 1989–2017,with two intensified two-year cyclic patterns in the changes of precipitation and a three-year cyclic pattern in the change of runoff further detected for the later period.Our results indicate that the temporal changes in runoff are not strictly consistent with the temporal variations of precipitation in the headwater region of Yellow River during the period 1958–2017.In particular,a full recovery in annual precipitation was not reflected in a full recovery in runoff toward the end of the study period.While a review of literature yielded no apparent evidence of raised evapotranspiration in the region due to recent warming,we draw attention to increased local retention of rainwater as a possible explanation of differential changes in precipitation and runoff. 相似文献
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Graham A. Sexstone Jessica M. Driscoll Lauren E. Hay John C. Hammond Theodore B. Barnhart 《水文研究》2020,34(11):2365-2380
The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of sub-grid or sub-watershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the precipitation-runoff modelling system (NHM-PRMS) represents the sub-grid variability of SWE with snow depletion curves (SDCs), which relate snow-covered area to watershed-mean SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM-PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation values and a lognormal probability distribution function. The NHM-PRMS was simulated at a daily time step for each SDC over a 14-year period. Results highlight that increasing the sub-grid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as decreases in simulated snowmelt rate by 1 mm day−1 resulted in decreases in runoff ratio by 1.8% on average in snow-dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water-limited dryness index was associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modelling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modelling applications. 相似文献
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Does the complexity in temporal precipitation disaggregation matter for a lumped hydrological model?
ABSTRACTFlood peaks and volumes are essential design variables and can be simulated by precipitation–runoff (P–R) modelling. The high-resolution precipitation time series that are often required for this purpose can be generated by various temporal disaggregation methods. Here, we compare a simple method (M1, one parameter), focusing on the effective precipitation duration for flood simulations, with a multiplicative cascade model (M2, 32/36 parameters). While M2 aims at generating realistic characteristics of precipitation time series, M1 aims only at accurately reproducing flood variables by P–R modelling. Both disaggregation methods were tested on precipitation time series of nine Swiss mesoscale catchments. The generated high-resolution time series served as input for P–R modelling using a lumped HBV model. The results indicate that differences identified in precipitation characteristics of disaggregated time series vanish when introduced into the lumped hydrological model. Moreover, flood peaks were more sensitive than flood volumes to the choice of disaggregation method. 相似文献
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