Impact of the sampling duration on the uncertainty of averaged velocity measurements with acoustic instruments

Average velocity in streams is a key variable for the analysis and modelling of hydrological and hydraulic processes underpinning water resources science and practice. The present study evaluates the impact of the sampling duration on the quality of average velocity measurements acquired with contemporary instruments such as Acoustic Doppler Velocimeters (ADV) an Acoustic Doppler Current Profilers (ADCP). The evaluation combines considerations on turbulent flows and principles and configurations of acoustic instruments with practical experience in conducting customized analysis for uncertainty analysis purposes. The study sheds new insights on the spatial and temporal variability of the uncertainty in the measurement of average velocities due to variable sampling durations acting in isolation from other sources of uncertainties. Sampling durations of 90 and 150 s are found sufficient for ADV and ADCP, respectively, to obtain reliable average velocities in a flow affected only by natural turbulence and instrument noise. Larger sampling durations are needed for measurements in most of the natural streams exposed to additional sources of data variability.     

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

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.     

Assessing uncertainty in best management practice effectiveness under future climate scenarios

Uncertainty of best management practice (BMP) performance in future climates is an important consideration for water resources managers. The objective of this study was to quantify the level of uncertainty in performance of seven agricultural BMPs due to climate change in reducing sediment, total nitrogen, and total phosphorus loads. The Soil and Water Assessment Tool coupled with mid‐21^st century climate data from the Community Climate System Model were used to develop climate change scenarios for the Tuttle Creek Lake Watershed of Kansas and Nebraska. Uncertainty level of each BMP was determined using Latin Hypercube Sampling, a constrained Monte Carlo sampling technique. Samples were taken from distributions of several variables (monthly precipitation, temperature, CO₂, and BMP implementation parameters). Cumulative distribution functions were constructed for each BMP, pollutant, and climate scenario combination. Results demonstrated that BMP performance uncertainty is amplified in the extreme climate scenario. Among BMPs, native grass replacement generally had higher uncertainty level but also had the greatest reductions. This study highlights the importance of incorporating uncertainty analysis into mitigation strategies aiming to reduce negative impacts of climate change on water resources. Copyright © 2013 John Wiley & Sons, Ltd.     

基于加权贝叶斯网络的海洋灾害风险评估

全球气候变化背景下,海洋灾害的群发性、难以预见性和灾害链效应日显突出,造成的损失逐年上升,开展海洋灾害的风险评估工作至关重要。针对海洋灾害评估中的不确定问题,本文首先基于风险理论剖析了海洋灾害风险的不确定性特征,构建了灾害评估指标体系;然后基于贝叶斯网络模型,提出针对不确定性灾害评估的风险贝叶斯网络,进而基于主客观定权,构建了加权贝叶斯网络评估模型;最后对我国沿海地区海洋灾害开展评估研究。实验表明,该评估模型有效实现海洋灾害的风险评估,具有实际可操作性。     

The Roles of Spatial Locations and Patterns of Initial Errors in the Uncertainties of Tropical Cyclone Forecasts

In this study,a series of sensitivity experiments were performed for two tropical cyclones (TCs),TC Longwang (2005) and TC Sinlaku (2008),to explore the roles of locations and patterns of initial errors in uncertainties of TC forecasts.Specifically,three types of initial errors were generated and three types of sensitive areas were determined using conditional nonlinear optimal perturbation (CNOP),first singular vector (FSV),and composite singular vector (CSV) methods.Additionally,random initial errors in randomly selected areas were considered.Based on these four types of initial errors and areas,we designed and performed 16 experiments to investigate the impacts of locations and patterns of initial errors on the nonlinear developments of the errors,and to determine which type of initial errors and areas has the greatest impact on TC forecasts.Overall,results from the experiments indicate the following:(1) The impact of random errors introduced into the sensitive areas was greater than that of errors themselves fixed in the randomly selected areas.From the perspective of statistical analysis,and by comparison,the impact of random errors introduced into the CNOP target area was greatest.(2) The initial errors with CNOP,CSV,or FSV patterns were likely to grow faster than random errors.(3) The initial errors with CNOP patterns in the CNOP target areas had the greatest impacts on the final verification forecasts.     

江河入海流量在线监测不确定度评估方法研究与应用

鉴于国内目前缺少流量在线监测不确定性评估方面的研究,针对江河入海流量在线监测过程中可能产生的各种不确定性,本文采用不确定度概念对不确定性进行评估,给出了不确定度的主要来源、各来源不确定度的评估方法,以及江河入海流量在线监测总不确定度评估模型,并应用该模型对辽河入海流量在线监测的不确定度进行了评估。     

对空间数据不确定性研究的思考

对GIS中空间数据不确定性研究中存在的问题进行思考后提出了5点建议.主要包括:1) 要区分GIS中确定性目标和不确定性目标;2) 要区分用离散点逼近曲线/曲面的逼近误差和离散点自身量测误差及其传播;3) 要研究GIS中的几何不确定性,更要重视研究属性和时态不确定性;4) 要研究空间数据质量,更要研究空间信息服务的质量;5) 空间数据不确定性研究要努力向实际应用转化.     

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Uncertainty of rainfall-induced landslides considering spatial variability of parameters

A cross-correlation analysis is conducted to determine the impacts of the heterogeneity of hydraulic conductivity K_s, soil cohesion c′ and soil friction angle (tan φ′) on the uncertainty of slope stability in time and space during rainfall. We find the relative importance of tan φ′ and c′ depends on the effective stress. While the sensitivity of the stability to the variability of K_s is small, the large coefficient of variation of K_s may exacerbate the variability of pore-water pressure. Therefore, characterizing the heterogeneity of hydraulic properties and pore-water distribution in the field is critical to the stability analysis.     

Bayesian methods for estimating multi-segment discharge rating curves

This study explores Bayesian methods for handling compound stage–discharge relationships, a problem which arises in many natural rivers. It is assumed: (1) the stage–discharge relationship in each rating curve segment is a power-law with a location parameter, or zero-plane displacement; (2) the segment transitions are abrupt and continuous; and (3) multiplicative measurement errors are of equal variance. The rating curve fitting procedure is then formulated as a piecewise regression problem where the number of segments and the associated changepoints are assumed unknown. Procedures are developed for describing both global and site-specific prior distributions for all rating curve parameters, including the changepoints. Estimation and uncertainty analysis is evaluated using Markov chain Monte Carlo simulation (MCMC) techniques. The first model explored accounts for parameter and model uncertainties in the interpolated area, i.e. within the range of available stage–discharge measurements. A second model is constructed in an attempt to include the uncertainty in extrapolation, which is necessary when the rating curve is used to estimate discharges beyond the highest or lowest measurement. This is done by assuming that the rate of changepoints both inside and outside the measured area follows a Poisson process. The theory is applied to actual data from Norwegian gauging stations. The MCMC solutions give results that appear sensible and useful for inferential purposes, though the latter model needs further efforts in order to obtain a more efficient simulation scheme.