摘要 :
When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a
hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water ...
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When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a
hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water content). The
simulated result can be quite good. However, when a key soil hydraulic property, such as the saturated hydraulic conductivity, is
overestimated or underestimated, the traditional soil water assimilation process will produce a persistent bias in its predictions.
In this paper, we present and demonstrate a new multi-scale assimilation method by combining the direct insertion assimilation
method, particle swarm optimisation (PSO) algorithm and Richards equation. We study the possibility of estimating root zone
soil water with a multi-scale assimilation method by using observed in situ data from the Wudaogou experiment station,
Huaihe River Basin, China. The results indicate there is a persistent bias between simulated and observed values when the
direct insertion assimilation surface soil water content is used to estimate root zone soil water contents. Using a multi-scale
assimilation method (PSO algorithm and direct insertion assimilation) and an assumed bottom boundary condition, the results
show some obvious improvement, but the root mean square error is still relatively large. When the bottom boundary condition
is similar to the actual situation, the multi-scale assimilation method can well represent the root zone soil water content. The
results indicate that the method is useful in estimating root zone soil water when available soil water data are limited to the
surface layer and the initial soil water content even when the soil hydraulic conductivities are uncertain. Copyright 2011
John Wiley & Sons, Ltd.
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摘要 :
This study characterizes layer- and local-scale heterogeneities in hydraulic parameters (i.e., matrix permeability and porosity) and investigates the relative effect of layer- and local-scale heterogeneities on the uncertainty ass...
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This study characterizes layer- and local-scale heterogeneities in hydraulic parameters (i.e., matrix permeability and porosity) and investigates the relative effect of layer- and local-scale heterogeneities on the uncertainty assessment of unsaturated flow and tracer transport in the unsaturated zone of Yucca Mountain, USA. The layer-scale heterogeneity is specific to hydrogeologic layers with layerwise properties, while the local-scale heterogeneity refers to the spatial variation of hydraulic properties within a layer. A Monte Carlo method is used to estimate mean, variance, and 5th, and 95th percentiles for the quantities of interest (e.g., matrix saturation and normalized cumulative mass arrival). Model simulations of unsaturated flow are evaluated by comparing the simulated and observed matrix saturations. Local-scale heterogeneity is examined by comparing the results of this study with those of the previous study that only considers layer-scale heterogeneity. We find that local-scale heterogeneity significantly increases predictive uncertainty in the percolation fluxes and tracer plumes, whereas the mean predictions are only slightly affected by the local-scale heterogeneity. The mean travel time of the conservative and reactive tracers to the water table in the early stage increases significantly due to the local-scale heterogeneity, while the influence of local-scale heterogeneity on travel time gradually decreases over time. Layer-scale heterogeneity is more important than local-scale heterogeneity for simulating overall tracer travel time, suggesting that it would be more cost-effective to reduce the layer-scale parameter uncertainty in order to reduce predictive uncertainty in tracer transport.
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摘要 :
Climatic variability and human activities are the two primary factors that affect basin hydrology, and thus quantification of their effects is of great importance for water resources management and sustainable development at a cat...
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Climatic variability and human activities are the two primary factors that affect basin hydrology, and thus quantification of their effects is of great importance for water resources management and sustainable development at a catchment scale. In this study, the writers investigated the long-term trends and abrupt changes in hydroclimatic variables, including precipitation, potential evapotranspiration (PET), and runoff, from 1957-2000 in the Hutuo River Basin by the nonparametric Mann-Kendall test and the precipitation-runoff double cumulative curve method. A two-parameter hydrological model and linear regression method were employed to separate and quantify the effects of climatic variability and human activities on runoff. The results are the following: (1) significant downward trends for annual precipitation and annual runoff were detected by the Mann-Kendall test at a 99% confidence level, (2) a change in the gradient of precipitation-runoff double cumulative curves and an abrupt change in runoff series can both be found in 1979, indicating that the relationship between precipitation and runoff has changed; as a result, the annual runoff from 1957-2000 can be divided into two periods termed the baseline (1957-1979) and human-induced (1980-2000) periods, and (3) the climate variability was the primary cause for the decrease in annual runoff from the baseline to the human-induced period, despite certain effects of human activities on the change with respect to annual runoff.
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