Use of the Connectivity of Runoff Model (CRUM) to investigate the influence of storm characteristics on runoff generation and connectivity in semi‐arid areas

Abstract

Abstract:Much attention has been given to the surface controls on the generation and transmission of runoff in semi-arid areas. However, the surface controls form only one part of the system; hence, it is important to consider the effect that the characteristics of the storm event have on the generation of runoff and the transmission of flow across the slope. The impact of storm characteristics has been investigated using the Connectivity of Runoff Model (CRUM). This is a distributed, dynamic hydrology model that considers the hydrological processes relevant to semi-arid environments at the temporal scale of a single storm event. The key storm characteristics that have been investigated are the storm duration, rainfall intensity, rainfall variability and temporal structure. This has been achieved through the use of a series of defined storm hydrographs and stochastic rainfall. Results show that the temporal fragmentation of high-intensity rainfall is important for determining the travel distances of overland flow and, hence, the amount of runoff that leaves the slope as discharge. If the high-intensity rainfall is fragmented, then the runoff infiltrates a short distance downslope. Longer periods of high-intensity rainfall allow the runoff to travel further and, hence, become discharge. Therefore, storms with similar amounts of high-intensity rainfall can produce very different amounts of discharge depending on the storm characteristics. The response of the hydrological system to changes in the rainfall characteristics can be explained using a four-stage model of the runoff generation process. These stages are: (1) all water infiltrating, (2) the surface depression store filling or emptying without runoff occurring, (3) the generation and transmission of runoff and (4) the transmission of runoff without new runoff being generated. The storm event will move the system between the four stages and the nature of the rainfall required to move between the stages is determined by the surface characteristics. This research shows the importance of the variable-intensity rainfall when modelling semi-arid runoff generation. The amount of discharge may be greater or less than the amount that would have been produced if constant rainfall intensity is used in the model.

Rural land management impacts on catchment scale flood risk

“…Handbook (FEH, 1999), to semi-distributed continuous simulation models, like CLASSIC (Climate and Land Use Scenario simulation in catchments) (Crooks and Davies, 2001) and ARNO (Todini, 1996), to physically-based distributed models, like CRUM3 (Reaney et al, 2007) and SHE (Abbott, 1986a;1986b;Bathurst, 1986). These model classifications were outlined in Chapter 2, and this section explains how these specific models have been applied to flood risk modelling.…”

“…This adaptive timestep maintains model stability in more computationally intensive parts of the simulation. CRUM-3 has been used in several catchments for both academic (Reaney et al, 2007;Reaney, 2008;Lane et al, 2009) and commercial (Conlan et al, 2005) purposes.…”

Simulating typhoon-induced storm hydrographs in subtropical mountainous watershed: an integrated 3-layer TOPMODEL

“…It implies that even suffering the similar rainfall, the observed discharges may not be identical, but must be much similar in modeling. Reaney et al (2007) concluded that storms with similar amounts of total rainfall but with varying rainfall allocation can generate very different discharge patterns, depending on the storm characteristics. Pebesma et al (2007) applied QPBRRM, a quasi-physically based rainfall-runoff model, on R-5 catchment with 72 storm events and concluded that by considering event variables (e.g.…”

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

“…Foremost are the models SCS (Michaud and Sorooshian, 1994;Symeonakis and Drake, 2004), VIC (Abdulla and Lettenmaier, 1997;Stephen et al, 2010), KINEROS (Michaud and Sorooshian, 1994;Hernandez et al, 2000;Al-Qurashi et al, 2008), TOPMODEL (Candela et al, 2005), MIKE SHE (McMichael et al, 2006), CRUM (Reaney et al, 2007), HBV (Masih et al, 2010;Jia and Sun, 2012), IHACRES (Jakeman et al, 1990;Jakeman and Hornberger, 1993;Evans and Jakeman, 1998;Dye and Croke, 2003;Croke et al, 2006;McIntyre and Al-Qurashi, 2009), HEC-HMS (Halwatura and Najim, 2013), and SWAT (Hernandez et al, 2000;Yu et al, 2011;Perrin et al, 2012). Some of these, however, share certain problems that reduce their effectiveness in semiarid regions.…”