Plot-Scale Experimental Studies

Abstract

1.1 Plot-scale experimental studies: structure, equipment, hydrologic monitoring Plot-scale experimental studies are generally part of broader research projects aimed at improving the understanding of interrelations between processes involving hydrological, climatic and biological factors (Wainwright et al., 2000). Recently, these studies have become multidisciplinary, integrating fields such as hydrology, ecology and geomorphology. In a global environmental change and degradation context, plot-scale studies may provide information about runoff mechanisms, soil erosion and vegetation dynamics processes that result from these changes (Abrahams et al., 1995; Parsons et al., 1996). Furthermore, plotscale studies may focus on water fluxes and sediment transport processes at controlled conditions using rainfall simulation (Wainwright et al., 2000; Rickson, 2001). It is important to note that process control generally involves simplifying a complex system that is highly variable in time and space (Wainwright et al., 2000; Abrahams et al., 1998; Parsons et al., 1998). However, plot-scale studies have the advantage of allowing for detailed process monitoring at small scale, providing a basic description of the most relevant aspects (Michaelides et al., 2009). Plot-scale studies are also useful in providing experimental data involving rainfall, surface runoff and soil erosion. These data are used as reference in modeling conception, calibration and validation. However, there can be considerable variability in soil erosion processes, as well as limitations of models atempting to simulate these complexities (Nearing, 2004). For example, in a study using 40 cultivated plots in the United States the experimental data coefficient of variation ranged between 18-91%. In addition, this variation was found to decrease with increasing rainfall erosive power (Wendt et al., 1986). Ruttimann et al. (1995) found that soil loss varied up to 173% between replicates under the same treatment. In general, the capacity of the model in representing local physical system can be tested by comparing observed and simulated model data, using regression analysis. Regression coefficient values from several studies demonstrate that model efficiency increases as erosion variability decreases, such as when mean annual soil loss data are used (Nearing, 1998; Risse et al., 1993; Zhang et al., 1996). The USLE-Universal Soil Loss Erosion (Wischmeyer & Smith, 1978) soil erosion model was originally conceived by using statistical