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Abstract The South African Weather Service (SAWS) issues routine experimental, near real-time rainfall maps from daily raingauge networks, radar networks and satellite images, as well as merged rainfall fields. These products are potentially useful for near real-time forecasting, especially in areas of fast hydrological response, and also to simulate the “now state” of various hydrological state variables such as soil moisture content, streamflow, and reservoir inflows. The purpose of this paper is to evaluate their skill as inputs to hydrological simulations and, in particular, the skill of the merged field in terms of better hydrological results relative to the individual products. Rainfall fields derived from raingauge, radar, satellite, conditioned satellite and the merged (gauge/radar/satellite) were evaluated for two selected days with relatively high amounts of rainfall, as well as for a continuous period of 90 days in the Mgeni catchment, South Africa. Streamflows simulated with the ACRU model indicate that the use of raingauge as well as merged fields of satellite/raingauge and satellite/radars/raingauge provides relatively realistic rainfall results, without much difference in their hydrological outputs, whereas the radar and raw satellite information by themselves cannot be used in operational hydrological application in their current status. Citation Ghile, Y., Schulze, R. & Brown, C. (2010) Evaluating the performance of ground-based and remotely sensed near real-time rainfall fields from a hydrological perspective. Hydrol. Sci. J. 55(4), 497–511. 相似文献
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Hydrogeochemical processes that accompany seawater intrusion in coastal aquifers can alter the resulting water quality and are important ingredients in coastal aquifer management. The presence of dissolution–precipitation reactions and ion exchange in the mixing zone of the Biscayne aquifer (FL, USA) are suggested based on changes in major ion concentrations and mineral saturation indices (SI). Major ion concentrations from 11 groundwater samples are compared with theoretical mixing between freshwater and seawater. PHREEQC code was used to calculate saturation indices of the samples with respect to common phases in the Biscayne aquifer. High Ca2+ and HCO3 ? content of the samples is typical of waters in contact with carbonate aquifers. Water quality of the samples is mainly attributed to mixing and precipitation–dissolution reactions with calcite and dolomite. The samples were saturated with calcite (SI ~ 0) and undersaturated for dolomite (SI < 0), while a few samples showed dolomite saturation. Because gypsum and halite SI could be predicted by theoretical mixing, reactions with those minerals, if present, are thought to be insignificant. In the active intrusion areas, cation exchange also appears to modify water quality leading to excess Ca2+, but depleted Na+, Mg2+ and K+ concentrations. On the other hand, samples from previous intrusion areas plotted very close to the theoretical mixing line and approached equilibrium with the seawater. 相似文献
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Acta Geotechnica - The precise mechanics simulation of shallow shield tunneling through soft soil is an engineering challenge. Elastic–plastic deformation causes progressive failure for the... 相似文献
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Parameter Estimation for Groundwater Models under Uncertain Irrigation Data 总被引：1，自引：0，他引：1 
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下载免费PDF全文 Yonas Demissie Albert Valocchi Ximing Cai Nicholas Brozovic Gabriel Senay Mekonnen Gebremichael 《Ground water》2015,53(4):614-625
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression‐based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least‐squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least‐squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least‐squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes. 相似文献
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This paper presents the results of an investigation into the problems associated with using downscaled meteorological data for hydrological simulations of climate scenarios. The influence of both the hydrological models and the meteorological inputs driving these models on climate scenario simulation studies are investigated. A regression‐based statistical tool (SDSM) is used to downscale the daily precipitation and temperature data based on climate predictors derived from the Canadian global climate model (CGCM1), and two types of hydrological model, namely the physically based watershed model WatFlood and the lumped‐conceptual modelling system HBV‐96, are used to simulate the flow regimes in the major rivers of the Saguenay watershed in Quebec. The models are validated with meteorological inputs from both the historical records and the statistically downscaled outputs. Although the two hydrological models demonstrated satisfactory performances in simulating stream flows in most of the rivers when provided with historic precipitation and temperature records, both performed less well and responded differently when provided with downscaled precipitation and temperature data. By demonstrating the problems in accurately simulating river flows based on downscaled data for the current climate, we discuss the difficulties associated with downscaling and hydrological models used in estimating the possible hydrological impact of climate change scenarios. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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Paleo-reconstructed hydrologic records offer the potential to evaluate water resources system performance under conditions that may be more extreme than seen in the historical record. This study uses a stochastic simulation framework consisting of a non-homogeneous Markov chain model (NHMM) to simulate the climate state using Palmer Drought Severity Index (PDSI)-reconstructed data, and K-nearest neighbor (K-NN) to resample observational net basin supply magnitudes for the Great Lakes of North America. The method was applied to generate 500 plausible simulations, each with 100 years of monthly net basin supply for the Upper Great Lakes, to place the observed data into a longer temporal context. The range of net basin supply sequences represents what may have occurred in the past 1,000 years and which can occur in future. The approach was used in evaluation of operational plans for regulation of Lake Superior outflows with implications for lake levels of Superior, Michigan, Huron and Erie, and their interconnecting rivers. The simulations generally preserved the statistics of the observed record while providing new variability statistics. The framework produced a variety of high and low net basin supply sequences that provide a broader estimate of the likelihood of extreme lake levels and their persistence than with the historical record. The method does not rely on parametrically generated net basin supply values unlike parametric stochastic simulation techniques, yet still generates new variability through the incorporation of the paleo-record. The process described here generated new scenarios that are plausible based on the paleo and historic record. The evaluation of Upper Great Lakes regulation plans, subject to these scenarios, was used to evaluate robustness of the regulation plans. While the uncertain future climate cannot be predicted, one can evaluate system performance on a wide range of plausible climate scenarios. 相似文献
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Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada 总被引：3，自引：1，他引：2
Y. B. Dibike P. Gachon A. St-Hilaire T. B. M. J. Ouarda Van T.-V. Nguyen 《Theoretical and Applied Climatology》2008,91(1-4):149-170
Summary Uncertainty analysis is used to make a quantitative evaluation of the reliability of statistically downscaled climate data
representing local climate conditions in the northern coastlines of Canada. In this region, most global climate models (GCMs)
have inherent weaknesses to adequately simulate the climate regime due to difficulty in resolving strong land/sea discontinuities
or heterogeneous land cover. The performance of the multiple regression-based statistical downscaling model in reproducing
the observed daily minimum/maximum temperature, and precipitation for a reference period (1961–1990) is evaluated using climate
predictors derived from NCEP reanalysis data and those simulated by two coupled GCMs (the Canadian CGCM2 and the British HadCM3).
The Wilcoxon Signed Rank test and bootstrap confidence-interval estimation techniques are used to perform uncertainty analysis
on the downscaled meteorological variables. The results show that the NCEP-driven downscaling results mostly reproduced the
mean and variability of the observed climate very well. Temperatures are satisfactorily downscaled from HadCM3 predictors
while some of the temperatures downscaled from CGCM2 predictors are statistically significantly different from the observed.
The uncertainty in precipitation downscaled with CGCM2 predictors is comparable to the ones downscaled from HadCM3. In general,
all downscaling results reveal that the regression-based statistical downscaling method driven by accurate GCM predictors
is able to reproduce the climate regime over these highly heterogeneous coastline areas of northern Canada. The study also
shows the applicability of uncertainty analysis techniques in evaluating the reliability of the downscaled data for climate
scenarios development.
Authors’ addresses: Dr. Yonas B. Dibike, NSERC Research Fellow, OURANOS Consortium, 550 Sherbrooke Street West, 19th Floor, Montreal (QC) H3A 1B9, Canada; Philippe Gachon, Adaptation and Impact Research Division (AIRD), Atmospheric Science
and Technology Directorate, Environment Canada at Ouranos, Montreal (QC), Canada; André St-Hilaire and Taha B. M. J. Ouarda,
Institut National de la Recherche Scientifique Centre Eau, Terre & Environnement (INRS-ETE), University of Québec, 490 Rue
de La Couronne, Québec (QC) G1K 9A9, Canada; Van T.-V. Nguyen, Department of Civil Engineering and Applied Mechanics, McGill
University, 817 Sherbrooke Street West, Montreal (QC) H3A 2K6, Canada. 相似文献
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Uncertainty analysis of statistical downscaling methods using Canadian Global Climate Model predictors 总被引：1，自引：0，他引：1
Three downscaling models, namely the Statistical Down‐Scaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS‐WG) model and an artificial neural network (ANN) model, have been compared in terms of various uncertainty attributes exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperature. The uncertainty attributes are described by the model errors and the 95% confidence intervals in the estimates of means and variances of downscaled data. The significance of those errors has been examined by suitable statistical tests at the 95% confidence level. The 95% confidence intervals in the estimates of means and variances of downscaled data have been estimated using the bootstrapping method and compared with the observed data. The study has been carried out using 40 years of observed and downscaled daily precipitation data and daily maximum and minimum temperature data, starting from 1961 to 2000. In all the downscaling experiments, the simulated predictors of the Canadian Global Climate Model (CGCM1) have been used. The uncertainty assessment results indicate that, in daily precipitation downscaling, the LARS‐WG model errors are significant at the 95% confidence level only in a very few months, the SDSM errors are significant in some months, and the ANN model errors are significant in almost all months of the year. In downscaling daily maximum and minimum temperature, the performance of all three models is similar in terms of model errors evaluation at the 95% confidence level. But, according to the evaluation of variability and uncertainty in the estimates of means and variances of downscaled precipitation and temperature, the performances of the LARS‐WG model and the SDSM are almost similar, whereas the ANN model performance is found to be poor in that consideration. Further assessment of those models, in terms of skewness and average dry‐spell length comparison between observed and downscaled daily precipitation, indicates that the downscaled daily precipitation skewness and average dry‐spell lengths of the LARS‐WG model and the SDSM are closer to the observed data, whereas the ANN model downscaled precipitation underestimated those statistics in all months. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献