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Recently, a single-site stochastic precipitation model called ''the mixture of generalized chain-dependent processes conditioned on a climate variable'' was developed. The model can effectively eliminate overdispersion--that is, underestimation in variance of seasonal precipitation total. In this paper, the single-site model is further developed into a multisite stochastic precipitation model by driving a collection of individual single-site models, but with spatial dependence following a method proposed by D. S. Wilks. Specifically, a computationally effective algorithm for estimating the spatial dependence of precipitation occurrence is developed to replace the construction of the empirical curves in the Wilks method. An effective and straightforward approach for correcting the bias of the spatial correlation of precipitation intensity is also proposed. This model is tested on a small network of sites from a significant hydroelectric power generation region of South Island, New Zealand. [ABSTRACT FROM AUTHOR]

This paper investigates the predictability of heavy precipitation in the economically important Waikato River basin of New Zealand. A 2-yr archive of Global Forecast System (GFS) model data to ++180 h for the period August 2005--August 2007 forms the basis of the study. GFS model predictions of precipitation are compared to surface measurements from 22 stations in and around the river basin. Categorical hit rate and bias, threat score, false-alarm ratio, probability of detection, RMSE, skill score, and mean error are all plotted as a function of model lead time for the 2-yr period. The general synoptic structures of heavy precipitation events are often identified at long lead times. An example is shown for January 2006 when heavy rainfall is identified by GFS at ++150 h, accurate location and timing is given at ++48 h. Precipitation amounts are systematically underpredicted by GFS and the spatial distribution of rainfall is limited by model resolution. The value of GFS lies in its ability to provide early warning of potential heavy precipitation. Three heavy rainfall events were selected for higher-resolution Weather Research and Forecasting (WRF) model simulation. The WRF model produced more realistic geographical distributions of precipitation but cannot compensate for errors in the global model. WRF underpredicts precipitation for all three cases; the best simulation generates 92%% of observed precipitation. Unobserved spurious convective rainfall can be generated by the WRF model following the passage of a frontal weather system. A possible mechanism is suggested that links this to underpredicted frontal rainfall. [ABSTRACT FROM AUTHOR]

Abstract: Investigation of hydrological impacts of climate change at the regional scale requires the use of a downscaling technique. Significant progress has already been made in the development of new statistical downscaling techniques. Statistical downscaling techniques involve the development of relationships between the large scale climatic parameters and local variables. When the local parameter is precipitation, these relationships are often very complex and may not be handled efficiently using linear regression. For this reason, a number of non-linear regression techniques and the use of Artificial Neural Networks (ANNs) was introduced. But due to the complexity and issues related to finding a global solution using ANN-based techniques, the Genetic Programming (GP) based techniques have surfaced as a potential better alternative. Compared to ANNs, GP based techniques can provide simpler and more efficient solutions but they have been rarely used for precipitation downscaling. This paper presents the results of statistical downscaling of precipitation data from the Clutha Watershed in New Zealand using a non-linear regression model developed by the authors using Gene Expression Programming (GEP), a variant of GP. The results show that GEP-based downscaling models can offer very simple and efficient solutions in the case of precipitation downscaling. [Copyright &y& Elsevier]

1. Seed size and seed number are key functional traits that can affect invasion success and evolve in the introduced range. With finite resources, seed size and number are expected to trade off. If trade-offs are relaxed in the introduced range, this could promote invasion. Few studies have compared life-history trade-offs between ranges, so these dynamics are still poorly understood. 2. Using three Rumex spp. we asked: (a) Is there evidence for differences in seed size, number or trade-offs in response to climate in the native and introduced ranges? (b) Do trade-offs differ between provenances when grown in common gardens? and (c) What is the relative contribution of environment and maternal line to seed size variation? 3. We quantified seed size and fecundity of ~160 individual plants across 16 populations per species in each range and grew around 100 maternal lines per species across common gardens in New Zealand. We modelled seed size, seed number and their trade-off, accounting for climate, range and plant height. We also examined seed size in common gardens as a function of maternal seed size to assess the relative contribution of environment and maternal line. 4. Two widespread invasive alien species, R. crispus and R. obtusifolius, had greater seed production in the introduced range. Although R. conglomeratus and R. crispus showed seed size-number trade-offs in the native range, none of the three species showed trade-offs in the introduced range. Maternal seed size explained ≥35% of the variation in seed size in common gardens for R. conglomeratus and R. crispus. In R. obtusifolius climate was more important, where seed size increased in response to precipitation in the introduced range. 5. Synthesis. For the first time, we document changes in seed size-number trade-offs between the native and introduced ranges for two alien plant species. Greater seed production in the introduced range for plants from both ranges suggests that abundant resources, rather than evolution, allowed these species to avoid tradeoffs in the introduced range. Heritable variation in seed size and different climate responses between ranges suggest that seed size may evolve as plants adapt to their introduced range. [ABSTRACT FROM AUTHOR]

Satellite remote sensing methods related to terrestrial hydrology hold promise for the future, but their current utility for aquifer characterisation is highly questionable. One objective of the GNS SMART aquifer characterisation (SAC) project called for assessing the potential application of satellite remote sensing methods to characterise New Zealand aquifers. Four methods were specified in the project proposal: (1) using GRACE satellite gravimetry to indicate seasonal change in groundwater volume over large areas; (2) using interferometric synthetic aperture RADAR (InSAR) to evaluate seasonal changes in groundwater volume caused by groundwater abstraction at a finer spatial scale than GRACE; (3) using satellite estimates of precipitation (P) and evapotranspiration (ET) to estimate rainfall recharge by their difference (i.e., P - ET); and (4) satellite thermal infrared (TIR) imagery to identify zones of groundwater inflow to rivers. Two additional methods came up during the assessment: (1) estimation of soil moisture from satellite remote sensing data, both with regard to its role in groundwater recharge and as a surrogate for identifying variation in depth of the shallow groundwater table; and (2) modelling of an equilibrium water table to estimate water table depth using satellite remote sensing input data. The first step of the project for this objective was a review of the world-wide scientific literature in order to decide which methods had potential for improved characterisation of New Zealand aquifers and should proceed to testing in New Zealand catchments. This review indicated that five of these six methods are not currently useful for this purpose in New Zealand. However, it was concluded that the use of InSAR technology for both estimating seasonal changes in groundwater volume and for quantification of confined aquifer specific storage and confining layer vertical hydraulic conductivity has potential utility for New Zealand and merits further assessment. It was recommended that this method be applied to test catchments in New Zealand during the next phase of the project. [ABSTRACT FROM AUTHOR]

Estimates of the predictability of New Zealand monthly and seasonal temperature and rainfall anomalies are calculated using a cross-validated linear regression procedure. Predictors are indices of the largescale circulation, sea-surface temperatures, the Southern Oscillation Index and persistence. Statistical significance is estimated through a series of Monte Carlo trials. No significant forecast relationships are found for rainfall anomalies at either the monthly or seasonaltime scale. Temperature forecasts are however considered to exhibit significant skill, with variance reductions of the order of 10-20% inindependent trials. Temperature anomalies are most skillfully predicted over the North Island, and skill is greatest in Spring and Summerin most areas. At the monthly time scale, predictors local to the New Zealand region account for most of the forecast skill, while at theseasonal time scale, skill depends strongly upon 'remote' predictorsdefined over regions of the southern hemisphere distant from New Zealand. Indices of meridional flow over the Tasman Sea/New Zealand region are found to be useful predictors, especially for monthly forecasts, perhaps as a proxy for atmospherically-forced sea surface temperature anomalies. Sea surface temperature anomalies to the west of New Zealand and in the tropical Indian Ocean are also useful, especially for seasonal predictions. Forecast skill is more reliably estimated atthe monthly time scale than at the seasonal time scale, as a result of the larger sample size of monthly mean data. While long-term mean levels of skill may be estimated reliably over the whole data set, statistically significant decadal-scale variations are found in the predictability of temperature anomalies. Therefore, even if long-term forecast skill levels are reliably estimated, it may be impossible to predict the short-term skill of operational seasonal climate forecasts. Implications for operational climate predictions in mid-latitudes are disc [ABSTRACT FROM AUTHOR]