Evaluation and improvement of the CLIGEN model for storm and rainfall erosivity generation in Central Chile

Lobo, Gabriel P.; Frankenberger, J. R.; Flanagan, D. C.; Bonilla, Carlos A.

doi:10.1016/j.catena.2015.01.002

Cited by 24 publications

(16 citation statements)

References 14 publications

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“…The application of WGEN in Chile, Taulis, and Milke [31] reported an underestimation of the annual precipitation of 42% in arid region, and identified gaps to be addressed in order to assemble a new and better stochastic model. Also, Lobo et al [10] used CLIGEN (CLImate GENerator) in central Chile (32°-39° S), and found that prior to calibration, the storm durations and maximum intensities were consistently overestimated and underestimated, respectively, especially in the wet season.…”

Section: Introductionmentioning

confidence: 99%

“…This model was implemented as WGEN (Weather GENerator) by Richardson and Wright (1984) [1], which used a simple Markov Chain for precipitation occurrence, a gamma distribution for simulation of rainfall amounts, and an autoregressive model for the remaining variables. A number of subsequent WGs, such as WXGEN [8], CLIGEN [9,10], LARS-WG [11][12][13], ClimGen [14], WeaGETS [15,16], Met and Roll [17], MOFRBC [18,19], WeatherMan [20], MarkSim [21], AAFC-WG [22,23], WM2 [24], KnnCAD [25][26][27], and the WG used by the UK Met Office (UKCP09) [28,29], all share the basic principles of stochastic simulation presented in WGEN. These WGs are station-scale generators, with time scales that range from daily (or even hourly in the case of rainfall) to annual, daily resolution being the most common.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

Urdiales

Meza

Gironás

et al. 2018

Water

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Stochastic weather simulation, or weather generators (WGs), have gained a wide acceptance and been used for a variety of purposes, including climate change studies and the evaluation of climate variability and uncertainty effects. The two major challenges in WGs are improving the estimation of interannual variability and reducing overdispersion in the synthetic series of simulated weather. The objective of this work is to develop a WG model of daily rainfall, incorporating a covariable that accounts for interannual variability, and apply it in three climate regions (arid, Mediterranean, and temperate) of Chile. Precipitation occurrence was modeled using a two-stage, first-order Markov chain, whose parameters are fitted with a generalized lineal model (GLM) using a logistic function. This function considers monthly values of the observed Sea Surface Temperature Anomalies of the Region 3.4 of El Niño-Southern Oscillation (ENSO index) as a covariable. Precipitation intensity was simulated with a mixed exponential distribution, fitted using a maximum likelihood approach. The stochastic simulation shows that the application of the approach to Mediterranean and arid climates largely eliminates the overdispersion problem, resulting in a much improved interannual variability in the simulated values.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

Urdiales

Meza

Gironás

et al. 2018

Water

View full text Add to dashboard Cite

show abstract

“…(11) with Δt = 0.5 yields the estimated I 30 using rainfall data with a temporal resolution of 0.5 h. However, in the absence of 0.5-h rainfall records in the study sites, the accuracy of Eqs. (10) and (11) was tested by using the 0.5-h rainfall erosivity obtained using Wenzel's (1982) IDF curve as a reference and the methods described in Lobo et al (2015). This IDF curve was chosen as a reference because it best represents the frontal nature of storms in Central Chile, as other methods, such as Bell's (1969) conversion factors, tend to overestimate I 30 in the study sites (Lobo et al, 2015).…”

Section: Comparison Methods and Correction Factorsmentioning

confidence: 99%

“…(10) and (11) was tested by using the 0.5-h rainfall erosivity obtained using Wenzel's (1982) IDF curve as a reference and the methods described in Lobo et al (2015). This IDF curve was chosen as a reference because it best represents the frontal nature of storms in Central Chile, as other methods, such as Bell's (1969) conversion factors, tend to overestimate I 30 in the study sites (Lobo et al, 2015). Moreover, the KolmogorovSmirnov test with an α = 0.5 was used to test if the I 30 estimates obtained using the regression lines and Wenzel's IDF were consistent.…”

Section: Comparison Methods and Correction Factorsmentioning

confidence: 99%

“…The climate in this portion of Chile is mainly semi-arid and the rainfall is usually of a frontal nature (Escobar and Aceituno, 1998), with intensities that rarely exceed 5.5 mm h −1 (Table 1). Also, the rainfall is highly erosive in some areas, and the precipitation amount increases with latitude (Bonilla and Vidal, 2011;Lobo et al, 2015).…”

Section: Study Sites and Rainfall Datamentioning

confidence: 98%

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Effect of temporal resolution on rainfall erosivity estimates in zones of precipitation caused by frontal systems

Lobo

Bonilla

2015

CATENA

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Spatial discretization of large watersheds and its influence on the estimation of hillslope sediment yield

et al. 2015

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The combined use of water erosion models and geographic information systems has facilitated soil loss estimation at the watershed scale. Tools such as the Geo‐spatial interface for the Water Erosion Prediction Project (GeoWEPP) model provide a convenient spatially distributed soil loss estimate but require discretization to identify hillslopes and channels. In GeoWEPP, the TOpographic PArameteriZation (TOPAZ) model is used as an automated procedure to extract a watershed boundary, hillslopes and channels from a digital elevation model (DEM). Previous studies in small watersheds have shown that the size of the hillslopes and the channel distribution affect the model estimates, but in large watersheds, the effects on the soil loss estimates have yet to be tested. Therefore, the objective of this study was to evaluate the effect of discretization on the hillslope sediment yield estimates using GeoWEPP in two large watersheds (>10 km2). The watersheds were selected and discretized varying the TOPAZ parameters [critical source area (CSA) and minimum source channel length (MSCL)] in a 30‐m resolution digital elevation model. The drainage networks built with TOPAZ were compared with each other using the drainage density index. The results showed that the discretization affected hillslope sediment yield estimates and their spatial distribution more than the total runoff. The drainage density index and the hillslope sediment yield were proportional but inversely related; thus, soil loss estimates were highly affected by the spatial discretization. As a result of this analysis, a method to choose the CSA and MSCL values that generates the greatest fraction of hillslopes having profile lengths less than 200 m was developed. This slope length condition is particularly crucial when using the WEPP and GeoWEPP models, in order for them to produce realistic estimates of sheet and rill erosion. Finally, and as a result of this analysis, a more reliable method was developed for selecting the TOPAZ channel network parameters (CSA and MSCL). Copyright © 2015 John Wiley & Sons, Ltd.

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Evaluation and improvement of the CLIGEN model for storm and rainfall erosivity generation in Central Chile

Cited by 24 publications

References 14 publications

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

Effect of temporal resolution on rainfall erosivity estimates in zones of precipitation caused by frontal systems

Spatial discretization of large watersheds and its influence on the estimation of hillslope sediment yield

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