Runoff Curve Numbers for Small Grain Under German Cropping Conditions

Auerswald, K.; Haider, Josef

doi:10.1006/jema.1996.0048

Cited by 28 publications

(24 citation statements)

References 2 publications

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“…Using the CN approach according to Mockus (1972) increased the RMSE of runoff volume by about 50 % (RMSE = 7.7 mm). The same was true when using the CN approach by Auerswald and Haider (1996) (RMSE = 7.9 mm). …”

Section: Model Validationsupporting

confidence: 61%

“…It is remarkable that the CN approach by Auerswald and Haider (1996) did not perform better than the original version by Mockus (1972) although Auerswald and Haider (1996) had used a subset of our data to develop their equation, which predicts CN from soil cover. Within their subset of data, soil cover mainly changed due to early plant growth and hence it had statistically a similar power to t sT .…”

Section: Hydrograph Shapementioning

confidence: 85%

“…For fallow, row crop and small grain a low runoff disposition was always assumed. Furthermore, CNs were estimated with an alternative approach following Auerswald and Haider (1996) using soil cover of row crops and small grains, respectively. The second CN approach was developed using a subset of the data set used in this study (Auerswald and Haider, 1996).…”

Section: Model and Validationmentioning

confidence: 99%

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Statistical analysis and modelling of surface runoff from arable fields in central Europe

Fiener

Auerswald²,

Winter³

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. Surface runoff generation on arable fields is an important driver of flooding, on-site and off-site damages by erosion, and of nutrient and agrochemical transport. In general, three different processes generate surface runoff (Hortonian runoff, saturation excess runoff, and return of subsurface flow). Despite the developments in our understanding of these processes it remains difficult to predict which processes govern runoff generation during the course of an event or throughout the year, when soil and vegetation on arable land are passing many states. We analysed the results from 317 rainfall simulations on 209 soils from different landscapes with a resolution of 14 286 runoff measurements to determine temporal and spatial differences in variables governing surface runoff, and to derive and test a statistical model of surface runoff generation independent from an a priori selection of modelled process types. Measured runoff was related to 20 time-invariant soil properties, three variable soil properties, four rain properties, three land use properties and many derived variables describing interactions and curvilinear behaviour. In an iterative multiple regression procedure, six of these properties/variables best described initial abstraction and the hydrograph. To estimate initial abstraction, the percentages of stone cover above 10 % and of sand content in the bulk soil were needed, while the hydrograph could be predicted best from rain depth exceeding initial abstraction, rainfall intensity, soil organic carbon content, and time since last tillage. Combining the multiple regressions to estimate initial abstraction and surface runoff allowed modelling of event-specific hydrographs without an a priori assumption of the underlying process. The statistical model described the measured data well and performed equally well during validation. In both cases, the model explained 71 and 58 % of variability in accumulated runoff volume and instantaneous runoff rate (RSME: 5.2 mm and 0.23 mm min −1 , respectively), while RMSE of runoff volume predicted by the curve number model was 50 % higher (7.7 mm). Stone cover, if it exceeded 10 %, was most important for the initial abstraction, while time since tillage was most important for the hydrograph. Time since tillage is not taken into account either in typical lumped hydrological models (e.g. SCS curve number approach) or in more mechanistic models using Horton, Green and Ampt, or Philip type approaches to address infiltration although tillage affects many physical and biological soil properties that subsequently and gradually change again. This finding should foster a discussion regarding our ability to predict surface runoff from arable land, which seemed to be dominated by agricultural operations that introduce man-made seasonality in soil hydraulic properties.

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Section: Model Validationsupporting

confidence: 61%

Section: Hydrograph Shapementioning

confidence: 85%

Section: Model and Validationmentioning

confidence: 99%

See 1 more Smart Citation

Statistical analysis and modelling of surface runoff from arable fields in central Europe

Fiener

Auerswald²,

Winter³

et al. 2013

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The rainfall simulators eliminate the need for natural storm events and rainfall intensity, and can be adjusted during an experimental run to mimic natural rain (Auerswald and Haider, 1996). They are calibrated against natural rainfall considering drop size distribution, spatial uniformity and fall velocity (Frasson et al, 2011).…”

Section: Equipmentmentioning

confidence: 99%

“…Previous studies with rainfall simulators have shown that rainfall events of 3 h duration are sufficient to guarantee steady state condition for field measurements of surface runoff (Auerswald and Haider, 1996;Jain et al, 2006). The maximum 3 h rainfall depths of different return periods for Iowa (Table 9.2) were obtained from the United States rainfall distribution maps and the SCS 24 h Type II rainfall distribution (Figure 9.3a).…”

Section: Calibrationmentioning

confidence: 99%

Runoff Curve Number and Saturated Hydraulic Conductivity Estimation via Direct Rainfall Simulator Measurements

Elhakeem

Papanicolaou

2012

JWMM

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Evaluation of a dynamic multi‐class sediment transport model in a catchment under soil‐conservation agriculture

Fiener

Govers

Oost

2008

Earth Surf Processes Landf

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Soil erosion models are essential tools for the successful implementation of effective and adapted soil conservation measures on agricultural land. Therefore, models are needed that predict sediment delivery and quality, give a good spatial representation of erosion and deposition and allow us to account for various soil conservation measures.Here, we evaluate how well a modified version of the spatially distributed multi-class sediment transport model (MCST) simulates the effectiveness of control measures for different event sizes. We use 8 year runoff and sediment delivery data from two small agricultural watersheds (0·7 and 3·7 ha) under optimized soil conservation. The modified MCST model successfully simulates surface runoff and sediment delivery from both watersheds; one of which was dominated by sheet and the other was partly affected by rill erosion. Moreover, first results of modelling enrichment of clay in sediment delivery are promising, showing the potential of MCST to model sediment enrichment and nutrient transport.In general, our results and those of an earlier modelling exercise in the Belgian Loess Belt indicate the potential of the MCST model to evaluate soil erosion and deposition under different agricultural land uses. As the model explicitly takes into account the dominant effects of soil-conservation agriculture, it should be successfully applicable for soil-conservation planning/evaluation in other environments.In order to improve the reliability, generality and accuracy of erosion prediction, more physical process-based erosion models have been developed within the last decades. The more recent ones are: the Water Erosion Prediction Project (WEPP, Flanagan and Nearing, 1995), the European Soil Erosion Model (EUROSEM, Morgan et al., 1998), the Kinematic Runoff and Erosion Model (KINEROS2, Smith et al., 1995) and the Limburg Soil Erosion Model (LISEM, De Roo et al., 1996a, 1996b. Due to the complexity and the spatial and temporal variation of erosion processes a large number of parameters have been integrated in these models. Hence, much attention was paid to the acquisition of input data (Jetten et al., 1996). Nevertheless, these models do not necessarily perform better than the lumped, empirical based models, mainly because input errors increase with model complexity (Jetten et al., 2003) and because of uncertainties in model structure and process representation (Parsons et al., 2004).In this context, reduced complexity modelling has received increasing attention during the past few years. This tendency can be found generally in the environmental sciences and is basically due to the fact that it is now realized that better predictions might be obtained using simpler model structures with a reduced parameter space rather than very complex model systems for which the necessary parameter values and input data are impossible to obtain (see, e.g., Brazier et al., 2000Brazier et al., , 2001. Examples of such models are the Sealing Transfer Runoff Erosion Agricultural Modification model (STREAM,...

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Runoff Curve Numbers for Small Grain Under German Cropping Conditions

Cited by 28 publications

References 2 publications

Statistical analysis and modelling of surface runoff from arable fields in central Europe

Statistical analysis and modelling of surface runoff from arable fields in central Europe

Runoff Curve Number and Saturated Hydraulic Conductivity Estimation via Direct Rainfall Simulator Measurements

Evaluation of a dynamic multi‐class sediment transport model in a catchment under soil‐conservation agriculture

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