A. Burton

RainSim: A spatial-temporal stochastic rainfall modelling system

RainSim V3 is a robust and well tested stochastic rainfall field generator used successfully in a broad range of climates and end-user applications. Rainfall fields or multi-site time series can be sampled from a spatial-temporal Neyman-Scott rectangular pulses process: storm events occur as a temporal Poisson process; each triggers raincell generation using a stationary spatial Poisson process; raincells are clustered in time lagging the storm event; each raincell contributes rainfall uniformly across its circular extent and throughout its lifetime; raincell lag, duration, radius and intensity are random variables; orographic effects are accounted for by non-uniform scaling of the rainfall field. Robust and efficient numerical optimization schemes for model calibration are identified following the evaluation of five schemes with optional log-transformation of the parameters. The log-parameter Shuffled Complex Evolution (lnSCE) algorithm with a convergence criterion is chosen for single site applications and an effort limited restarted lnSCE algorithm is selected for spatial applications. The new objective function is described and shown to improve model calibration. Linear and quadratic expressions are identified which can reduce the bias between the fitted and simulated probabilities of both dry hours and dry days as used in calibration. Exact fitting of mean rainfall statistics is also implemented and demonstrated. An application to the Dommel catchment on the Netherlands/Belgian border illustrates the ability of the improved model to match observed statistics and extremes.

Identification of key climatic factors regulating the transport of pesticides in leaching and to tile drains

BACKGROUND Key climatic factors influencing the transport of pesticides to drains and to depth were identified. Climatic characteristics such as the timing of rainfall in relation to pesticide application may be more critical than average annual temperature and rainfall. The fate of three pesticides was simulated in nine contrasting soil types for two seasons, five application dates and six synthetic weather data series using the MACRO model, and predicted cumulative pesticide loads were analysed using statistical methods. RESULTS Classification trees and Pearson correlations indicated that simulated losses in excess of 75th percentile values (0.046 mg m(-2) for leaching, 0.042 mg m(-2) for drainage) generally occurred with large rainfall events following autumn application on clay soils, for both leaching and drainage scenarios. The amount and timing of winter rainfall were important factors, whatever the application period, and these interacted strongly with soil texture and pesticide mobility and persistence. Winter rainfall primarily influenced losses of less mobile and more persistent compounds, while short-term rainfall and temperature controlled leaching of the more mobile pesticides. CONCLUSIONS Numerous climatic characteristics influenced pesticide loss, including the amount of precipitation as well as the timing of rainfall and extreme events in relation to application date. Information regarding the relative influence of the climatic characteristics evaluated here can support the development of a climatic zonation for European-scale risk assessment for pesticide fate.

Models of daily rainfall cross-correlation for the United Kingdom

Automated easy-to-use tools capable of generating spatial-temporal weather scenarios for the present day or downscaled future climate projections are highly desirable. Such tools would greatly support the analysis of hazard, risk and reliability of systems such as urban infrastructure, river catchments and water resources. However, the automatic parameterization of such models to the properties of a selected scenario requires the characterization of both point and spatial statistics. Whilst point statistics, such as the mean daily rainfall, may be described by a map, spatial properties such as cross-correlation vary according to a pair of sample points, and should ideally be available for every possible pair of locations. For such properties simple automatic representations are needed for any pair of locations. To address this need simple empirical models are developed of the lag-zero cross-correlation-distance (XCD) properties of United Kingdom daily rainfall. Following error and consistency checking, daily rainfall timeseries for the period 1961-1990 from 143 raingauges are used to calculate observed XCD properties. A three parameter double exponential expression is then fitted to appropriate data partitions assuming isotropic and piecewise-homogeneous XCD properties. Three models are developed: 1) a national aseasonal model; 2) a national model partitioned by calendar month; and 3) a regional model partitioned by nine UK climatic regions and by calendar month. These models provide estimates of lag-zero cross-correlation properties of any two locations in the UK. These cross-correlation models can facilitate the development of automated spatial rainfall modelling tools. This is demonstrated through implementation of the regional model into a spatial modelling framework and by application to two simulation domains (both ~10,000 km^2), one in north-west England and one in south-east England. The required point statistics are generally well simulated and a good match is found between simulated and observed XCD properties. The models developed here are straightforward to implement, incorporate correction of data errors, are pre-calculated for computational efficiency, provide smoothing of sample variability arising from sporadic coverage of observations and are repeatable. They may be used to parameterise spatial rainfall models in the UK and the methodology is likely to be easily adaptable to other regions of the world.