A Library to Generate Synthetic Precipitation Data

A critical issue in biophysical modeling projects is to develop a set of reusable libraries to support the development of future applications. This is true for weather modeling as well. Rain is a software component providing a collection of stochastic approaches to generate precipitation data on daily and subdaily time steps. Synthetic data generated on a daily time step consists of precipitation occurrence and amount. Subdaily generation includes time of peak, peak intensity, storm arrival, and duration. The software design allows for extension of the models implemented without recompiling the component. The component, inclusive of a hypertext help file and of documentation generated from source code comments, has been released as compiled .NET and Java versions, allowing application development in either programming environment. Illustrative examples ofWindows-based applications using Rain are provided as source code. A sample web service and a web application were also developed as possible use of the component. LONG SEQUENCES of daily rainfall are increasingly required for driving many climate change and environmentally sensitive projects (Mearns et al., 1997; Bellocchi et al., 2004; Rivington et al., 2006). In particular, the availability of extended precipitation data is essential in the design and operation of hydrological and natural systems to quantify the uncertainty resulting from climatic variability. Most of projects focus on the daily time scale, as required by many impact models (e.g., hydrological and crop models). The shift of focus to deal with complex problems such as rainfall-related pollution effects, runoff-induced wash-off from impermeable surfaces, soil water infiltration, and rainfall-related soil erosion indicate the added uncertainties of the outcomes if high-resolution rainfall data are not available (Sivakumar et al., 2001). In such cases, methods should be available for reliably converting daily rainfall into breakpoint format (hourly or finer time resolution). Unlimited sequences of weather variables, including rainfall, can be artificially produced through a stochastic process that preserves the statistical characteristics of the actual data as they naturally occur for a site (e.g., Wilks and Wilby, 1999; Srikanthan and McMahon, 2001). Usually, precipitation sequences are generated first, and other data sequences are derived using relationships between these data and precipitation, with different relationships used for wet and dry days (Richardson, 1981). Precipitation is commonly divided into an occurrence process (i.e., whether the day is wet or dry) modeled as a Markov chain, and an amount process (the amount of precipitation on a wet day) sampled randomly from an appropriate distribution. By using different random seeds, a large number of sequences can be generated, all of which have the same statistical properties as the original data used to parameterize the generator. These methods are implemented into software tools usually called weather generators. One of the first weather generators developed for rural water quality modeling purposes was WGEN (Richardson and Wright, 1984). Numerous other weather generators have developed since then, such as USCLIMATE (Johnson et al., 1996), Climak (Danuso, 2002), and ClimGen (Stöckle et al., 2001), largely based on the generation methods used inWGEN. Cligen, the weather generator originally developed by Nicks and Gander (1994) and incorporated within the WEPP (Water Erosion Prediction Project) model (Flanagan and Nearing, 1995), adds the capability of generating rainfall intensity and duration data necessary for the Green–Ampt infiltration model (Green and Ampt, 1911), which is the basis of many hydrologic and soil loss calculation models. All such approaches illustrate that there is actually an abundance of solutions to the basic problem of generating rainfall data, coded in a variety of ways within readyto-use, user-oriented tools. Because of the statistically based approach supporting the above models (that is data dependency of the solution), it may be necessary to compare different generation methods to provide reliable rainfall variables for case-specific applications. This requires the reimplementation of model approaches in new software applications. A proper software design, targeted at achieving an intrinsic reusability of software units, can lead to sharing knowledge in an immediately reusable form. Several studies have been recently published (Acock et al., 1999; Rossiter and Riha, 1999; Donatelli et al., 2003; Fila et al., 2003; Mi et al., 2003), mostly designing reusable dynamic link librarieswithin the COM(ComponentObjectModel) technology of Windows (www.microsoft.com/com; verified 10 July 2006). Component-oriented programming, which combines object-oriented and modular features, is becoming the leading methodology in developing systems in a variety of domains, including agro-ecological modeling (Argent, 2004). A component is a discrete unit of software, with explicit dependencies, developed for composition by third parties (Szypersky et al., 2002). The component development paradigm is to make the construction of software as a matter of plugging together independent components. This requires an environment that addresses the communication issues of component interactions. The platform-independent Java language (http://java.sun.com; verified 10 July 2006) and the .NET technology of Windows (www.microsoft.com/net; veriAgriculture Research Council (Research Institute for Industrial Crops), Via di Corticella 133, 40128 Bologna, Italy. Received 15 July 2005. *Corresponding author ([email protected]). Published in Agron. J. 98:1312–1317 (2006). Software doi:10.2134/agronj2005.0210 a American Society of Agronomy 677 S. Segoe Rd., Madison, WI 53711 USA R e p ro d u c e d fr o m A g ro n o m y J o u rn a l. P u b lis h e d b y A m e ri c a n S o c ie ty o f A g ro n o m y . A ll c o p y ri g h ts re s e rv e d . 1312 Published online September 5, 2006