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B. Rajagopalan U. Lall D. G. Tarboton D. S. Bowles 《Stochastic Environmental Research and Risk Assessment (SERRA)》1997,11(1):65-93
A nonparametric resampling technique for generating daily weather variables at a site is presented. The method samples the
original data with replacement while smoothing the empirical conditional distribution function. The technique can be thought
of as a smoothed conditional Bootstrap and is equivalent to simulation from a kernel density estimate of the multivariate
conditional probability density function. This improves on the classical Bootstrap technique by generating values that have
not occurred exactly in the original sample and by alleviating the reproduction of fine spurious details in the data. Precipitation
is generated from the nonparametric wet/dry spell model as described in Lall et al. [1995]. A vector of other variables (solar
radiation, maximum temperature, minimum temperature, average dew point temperature, and average wind speed) is then simulated
by conditioning on the vector of these variables on the preceding day and the precipitation amount on the day of interest.
An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, USA, is provided. 相似文献
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N. Satyavani Kalachand Sain Malcolm Lall B. J. P. Kumar 《Marine Geophysical Researches》2008,29(3):167-175
Seismic data from the Andaman offshore region has been examined to investigate for the presence of gas hydrates. The seismic
data displays reflection characteristics such as blanking, enhanced reflection patterns, shadows in instantaneous frequency,
and increase in amplitude with the offset, which are indicative of gas hydrates and underlying free gas. A prominent bottom-simulating
reflection, BSR, coupled with reverse polarity is observed around 650–700 ms. Seismic attributes such as the reflection strength
and instantaneous frequency are computed along this reflector in order to probe for the presence of gas hydrates or free gas
in this region. The reflection plot shows a strong reflector paralleling the seafloor. In addition, attenuation of the high
frequency signal is noticed, indicating the presence of free gas below the BSR. 相似文献
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Ferdi?L.?HellwegerEmail author Alan?F.?Blumberg Peter?Schlosser David?T.?Ho Theodore?Caplow Upmanu?Lall Honghai?Li 《Estuaries and Coasts》2004,27(3):527-538
The effects of estuarine circulation and tidal trapping on transport in the Hudson estuary were investigated by a large-scale,
high-resolution numerical model simulation of a tracer release. The modeled and measured longitudinal profiles of surface
tracer concentrations (plumes) differ from the ideal Gaussian shape in two ways: on a large scale the plume is asymmetric
with the downstream end stretching out farther, and small-scale (1–2 km) peaks are present at the upstream and downstream
ends of the plume. A number of diagnostic model simulations (e.g., remove freshwater flow) were performed to understand the
processes responsible for these features. These simulations show that the large-scale asymmetry is related to salinity. The
salt causes an estuarine circulation that decreases vertical mixing (vertical density gradient), increases longitudinal dispersion
(increased vertical and lateral gradients in longitudinal velocities), and increases net downstream velocities in the surface
layer. Since salinity intrusion is confined to the downstream end of the tracer plume, only that part of the plume is effected
by those processes, which leads to the largescale asymmetry. The small-scale peaks are due to tidal trapping. Small embayments
along the estuary trap water and tracer as the plume passes by in the main channel. When the plume in the main channel has
passed, the tracer is released back to the main channel, causing a secondary peak in the longitudinal profile. 相似文献
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A. Sharma U. Lall D. G. Tarboton 《Stochastic Environmental Research and Risk Assessment (SERRA)》1998,12(1):33-52
A new approach for streamflow simulation using nonparametric methods was described in a recent publication (Sharma et al.
1997). Use of nonparametric methods has the advantage that they avoid the issue of selecting a probability distribution and
can represent nonlinear features, such as asymmetry and bimodality that hitherto were difficult to represent, in the probability
structure of hydrologic variables such as streamflow and precipitation. The nonparametric method used was kernel density estimation,
which requires the selection of bandwidth (smoothing) parameters. This study documents some of the tests that were conduced
to evaluate the performance of bandwidth estimation methods for kernel density estimation. Issues related to selection of
optimal smoothing parameters for kernel density estimation with small samples (200 or fewer data points) are examined. Both
reference to a Gaussian density and data based specifications are applied to estimate bandwidths for samples from bivariate
normal mixture densities. The three data based methods studied are Maximum Likelihood Cross Validation (MLCV), Least Square
Cross Validation (LSCV) and Biased Cross Validation (BCV2). Modifications for estimating optimal local bandwidths using MLCV
and LSCV are also examined. We found that the use of local bandwidths does not necessarily improve the density estimate with
small samples. Of the global bandwidth estimators compared, we found that MLCV and LSCV are better because they show lower
variability and higher accuracy while Biased Cross Validation suffers from multiple optimal bandwidths for samples from strongly
bimodal densities. These results, of particular interest in stochastic hydrology where small samples are common, may have
importance in other applications of nonparametric density estimation methods with similar sample sizes and distribution shapes.
Received: November 12, 1997 相似文献
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Balaji Rajagopalan Upmanu Lall David G. Tarboton 《Stochastic Hydrology and Hydraulics》1997,11(6):523-547
Kernel density estimators are useful building blocks for empirical statistical modeling of precipitation and other hydroclimatic
variables. Data driven estimates of the marginal probability density function of these variables (which may have discrete
or continuous arguments) provide a useful basis for Monte Carlo resampling and are also useful for posing and testing hypotheses
(e.g bimodality) as to the frequency distributions of the variable. In this paper, some issues related to the selection and
design of univariate kernel density estimators are reviewed. Some strategies for bandwidth and kernel selection are discussed
in an applied context and recommendations for parameter selection are offered. This paper complements the nonparametric wet/dry
spell resampling methodology presented in Lall et al. (1996). 相似文献
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Kenneth Broad Alexander Pfaff Renzo Taddei A. Sankarasubramanian Upmanu Lall Franciso de Assis de Souza Filho 《Climatic change》2007,84(2):217-239
We assess the potential benefits from innovative forecasts of the stream flows that replenish reservoirs in the semi-arid
state of Ceará, Brazil. Such forecasts have many potential applications. In Ceará, they matter for both water-allocation and
participatory-governance issues that echo global debates. Our qualitative analysis, based upon extensive fieldwork with farmers,
agencies, politicians and other key actors in the water sector, stresses that forecast value changes as a society shifts.
In the case of Ceará, current constraints on the use of these forecasts are likely to be reduced by shifts in water demand,
water allocation in the agricultural Jaguaribe Valley, participatory processes for water allocation between this valley and
the capital city of Fortaleza, and risk perception. Such changes in the water sector can also have major distributional impacts.
Broad, Pfaff and Taddei equally share lead authorship. 相似文献
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Large scale climate and rainfall seasonality in a Mediterranean Area: Insights from a non‐homogeneous Markov model applied to the Agro‐Pontino plain 
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下载免费PDF全文 Francesco Cioffi Federico Conticello Upmanu Lall Lucia Marotta Vito Telesca 《水文研究》2017,31(3):668-686
In the context of climate change and variability, there is considerable interest in how large scale climate indicators influence regional precipitation occurrence and its seasonality. Seasonal and longer climate projections from coupled ocean–atmosphere models need to be downscaled to regional levels for hydrologic applications, and the identification of appropriate state variables from such models that can best inform this process is also of direct interest. Here, a Non‐Homogeneous Hidden Markov Model (NHMM) for downscaling daily rainfall is developed for the Agro‐Pontino Plain, a coastal reclamation region very vulnerable to changes of hydrological cycle. The NHMM, through a set of atmospheric predictors, provides the link between large scale meteorological features and local rainfall patterns. Atmospheric data from the NCEP/NCAR archive and 56‐years record (1951–2004) of daily rainfall measurements from 7 stations in Agro‐Pontino Plain are analyzed. A number of validation tests are carried out, in order to: 1) identify the best set of atmospheric predictors to model local rainfall; 2) evaluate the model performance to capture realistically relevant rainfall attributes as the inter‐annual and seasonal variability, as well as average and extreme rainfall patterns. Validation tests show that the best set of atmospheric predictors are the following: mean sea level pressure, temperature at 1000 hPa, meridional and zonal wind at 850 hPa and precipitable water, from 20°N to 80°N of latitude and from 80°W to 60°E of longitude. Furthermore, the validation tests show that the rainfall attributes are simulated realistically and accurately. The capability of the NHMM to be used as a forecasting tool to quantify changes of rainfall patterns forced by alteration of atmospheric circulation under climate change and variability scenarios is discussed. 相似文献