Algae reduce and methylate arsenate, producing arsenite (As(III)) when the growth rates are high and dimethylarsinic acid (DMA) when the growth rates are low. In lakes, this leads to high As(III) concentrations in the early stages of spring and fall blooms and high DMA concentrations in the summer. We hypothesize that under phosphorus (P)‐limited conditions, which usually exist in the summer, algae take up phosphate (PO4) and, because of similar chemical characteristics, As(V) as well. Inside the cell, As(V) is reduced to As(III), methylated to monomethylarsonic acid (MMA) and DMA, and then excreted. However, under non—P‐limited conditions, which exist in the early stages of blooms, algae up‐regulate their PO4 transport system to take up excess P, a phenomenon known as luxury uptake. Since As(V) is taken up by the PO4 transport system, As(V) uptake also increases at this time. Within the cell, the reduction of As(V) to As(III) is fast, but methylation is slower, causing As(III) to build up in the cell and be excreted, which, in turn, causes an increase in extracellular As(III). This mechanism permits the synergistic (luxury uptake) and antagonistic (competition) effects of PO4 on As(V) uptake and can therefore explain the seemingly contradictory results found in the literature. A mathematical model is constructed on the basis of existing established algal—nutrient interaction models and is used to simulate As transformation in two laboratory batch experiments. In addition to algal and P responses, the model can reasonably well reproduce the observed As(III) peak during the log growth phase and the more gradual appearance of DMA during the stationary phase.

Nonparametric function estimation refers to methods that strive to approximate a target function locally, i.e., using data from a “small” neighborhood of the point of estimate. “Weak” assumptions, such as continuity of the target function and its differentiability to some order in the neighborhood, rather than an a priori assumption of the global form (e.g., linear or quadratic) of the entire target function are used. Traditionally, parametric assumptions (e.g., hydraulic conductivity is log normally distributed, floods follow a log Pearson III (LP3) distribution, annual stream flow is either log normal or gamma distributed, daily rainfall amounts are exponentially distributed, and the variograms of spatial hydrologic data follow a power law) have dominated statistical hydrologic estimation. Applications of nonparametric methods to some classical problems (frequency analysis, classification, spatial surface fitting, trend analysis, time series forecasting and simulation) of stochastic hydrology are reviewed.

Abstract. We present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the response variable in a space of dimension (D) with a delay time (τ ). To obtain a forecast from a given time point t, three steps are involved: (i) the current state of the system is mapped on to the state space, known as the feature vector, (ii) a small number (K = α * n, α=fraction (0,1] of the data, n=data length) of neighbors (and their future evolution) to the feature vector are identified in the state space, and (iii) a polynomial of order p is fitted to the identified neighbors, which is then used for prediction.

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 val ues 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 LalIet al. [i995]. 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.

A statistically and physically based framework is put forward to investigate the relationship between Tropical Moisture Exports (TMEs), extreme precipitation and floods in the Northeastern United States (NE-US). We found that the NE-US floods in the four seasons are closely related to TMEs and four major moisture sources of TMEs in the tropics account for approximately 85% of all the TMEs that enter the NE-US. The seasonality and interannual variation of the birth processes in the four source regions determine their contribution to the NE-US. Moisture born in Gulf of Mexico (GP) and Gulf stream (GS) are the year-around sources, with some winter contribution from Pineapple Express (PE) region, and West Pacific (WP) region contributes the least. The overall order of their contribution to NE-US is GP>GS>PE>WP. Seasonal association between TMEs birth and ENSO are also found. The seasonal and interannual variations in atmospheric circulation patterns also play an important role in determining the TMEs’ entrance to NE-US. Strong influence of active TMEs periods on the occurrence of extreme rainfall is also identified. We show that the extreme daily precipitation events are dominated by extreme TMEs’ entering the NE-US in every season.