Multivariate nonparametric resampling scheme for generation of daily weather variables

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.     

Kernel bandwidth selection for a first order nonparametric streamflow simulation model

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     

大西洋庸鲽(Hippoglossus hipoglossus L.)精液脂肪酸分析及激素GnRHa诱导对其组成的影响

采用气相色谱检测的方法,进行大西洋庸鲽精液脂肪酸组成分析及激素GnRHa诱导对其组成影响的研究。结果表明,大西洋庸鲽精液中含量最高的脂肪酸种类为22:6n-3(DHA,二十二碳六烯酸),占总脂肪酸比例25.67%±0.94%;其次为16:0(PA,棕榈酸)、20:5n-3(EPA,二十碳五烯酸);重要必需不饱和脂肪酸20:4n-6(AA,花生四烯酸)含量较低,为1.76%±0.01%。精液中高不饱和脂肪酸(PUFA)含量较高,为44.25%±0.30%;饱和脂肪酸(SAT)含量为27.72%±0.22%。重要脂肪酸比例DHA/EPA为2.33±0.26;EPA/AA为6.30±0.51;n-3/n-6为9.22±0.60。激素诱导未对精液中脂肪酸组成产生显著影响。重要必需脂肪酸DHA、EPA、AA,以及n-3、n-6等重要种类的脂肪酸总量在激素诱导组与非诱导对照组样品间无显著差异;在激素诱导后的三个取样时间的样品间也无显著差异(P0.05)。     

Seismic attribute study for gas hydrates in the Andaman Offshore India

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.     

Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida

Concerns about the potential effects of anthropogenic climate change have led to a closer examination of how climate varies in the long run, and how such variations may impact rainfall variations at daily to seasonal time scales. For South Florida in particular, the influences of the low-frequency climate phenomena, such as the El Nino Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO), have been identified with aggregate annual or seasonal rainfall variations. Since the combined effect of these variations is manifest as persistent multi-year variations in rainfall, the question of modeling these variations at the time and space scales relevant for use with the daily time step-driven hydrologic models in use by the South Florida Water Management District (SFWMD) has arisen. To address this problem, a general methodology for the hierarchical modeling of low- and high-frequency phenomenon at multiple rain gauge locations is developed and illustrated. The essential strategy is to use long-term proxies for regional climate to first develop stochastic scenarios for regional climate that include the low-frequency variations driving the regional rainfall process, and then to use these indicators to condition the concurrent simulation of daily rainfall at all rain gauges under consideration. A newly developed methodology, called Wavelet Autoregressive Modeling (WARM), is used in the first step after suitable climate proxies for regional rainfall are identified. These proxies typically have data available for a century to four centuries so that long-term quasi-periodic climate modes of interest can be identified more reliably. Correlation analyses with seasonal rainfall in the region are used to identify the specific proxies considered as candidates for subsequent conditioning of daily rainfall attributes using a Non-homogeneous hidden Markov model (NHMM). The combined strategy is illustrated for the May–June–July (MJJ) season. The details of the modeling methods and results for the MJJ season are presented in this study.     

Climate,stream flow prediction and water management in northeast Brazil: societal trends and forecast value

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.     

Transport in the Hudson estuary: A modeling study of estuarine circulation and tidal trapping

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.