DEM resolution and stream delineation threshold effects on the results of geomorphologic-based rainfall runoff models

Azizian, Asghar; Shokoohi, Alireza

doi:10.3906/muh-1401-13

Cited by 18 publications

(8 citation statements)

References 23 publications

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“…In the last two decades, the automatic identification of drainage networks and drainage basins from DEMs has become increasingly popular through the use of various GIS software packages (García and Camarasa, 1999;Turcotte et al, 2000;Robl et al, 2008;Bahrami, 2013;Ariza-Villaverde et al, 2015;Gürbüz et al, 2015). Several studies highlighted the effect of DEM resolution on the accuracy of hydrologic modelling and associated drainage networks (Garbrecht and Martz, 1994;Zhang and Montgomery, 1994;Cotter et al, 2003;Chaubey et al, 2005;Hosseinzadeh, 2011;Sharma et al, 2011;Azizian and Shokoohi, 2014). DEMs of different resolutions have the potential to significantly affect watershed and stream network delineation.…”

Section: Delineation Of Drainage Networkmentioning

confidence: 99%

Detection and analysis of morphotectonic features utilizing satellite remote sensing and GIS: An example in SW Jordan

et al. 2016

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This study investigates the dominant orientations of morphological features and the relationship between these trends and the spatial orientation of tectonic structures in SW Jordan. Landsat 8 and hill-shaded images, constructed from 30 m-resolution ASTER-GDEM data, were used for automatically extracting and mapping geological lineaments. The ASTER-GDEM was further utilized to automatically identify and extract drainage network. Morphological features were analyzed by means of azimuth frequency and length density distributions. Tectonic controls on the land surface were evaluated using longitudinal profiles of many westerly flowing streams. The profiles were taken directly across the northerly trending faults within a strong topographic transition between the low-gradient uplands and the deeply incised mountain front on the east side of the Dead Sea Fault Zone. Streams of the area are widely divergent, and show numerous anomalies along their profiles when they transect faults and lineaments. Five types of drainage patterns were identified: dendritic, parallel, rectangular, trellis, and modified dendritic/trellis. Interpretation and analysis of the lineaments indicate the presence of four main lineament populations that trend ~E-W, ~N-S, NE-SW, and NW-SE. Azimuthal distribution analysis of both the measured structures and drainage channels shows similar trends, except for very few differences in the prevailing directions. The similarity in orientation of lineaments, drainage system, and subsurface structural trends highlights the degree of control exerted by underlying structure on the surface geomorphological features. Faults and lineaments serve as a preferential conduit for surface running waters. The extracted lineaments were divided into five populations based on the main age of host rocks outcropping in the study area to obtain information about the temporal evolution of the lineament trends through geologic time. A general consistency in lineament trends over the different lithological units was observed, most probably because repeated reactivation of tectonism along preexisting deep structural discontinuities which are apparently crustal weakness zones. The reactivation along such inherited discontinuities under the present-day stress field is the most probable explanation of the complicated pattern and style of present-day landscape features in SW Jordan.

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Section: Delineation Of Drainage Networkmentioning

confidence: 99%

Detection and analysis of morphotectonic features utilizing satellite remote sensing and GIS: An example in SW Jordan

et al. 2016

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“…To evaluate the model response for the different soil representations, the model should have a sufficient number of sub-basins. We did not set equal threshold areas for both of the watersheds (Figure 4a), because the required threshold area for the small watershed creates too many sub-basins for the large watershed that would need smart computer architecture [48]. The digitized stream networks were used to improve hydrographic segmentation and sub-watershed boundary delineation.…”

Section: Model Setup and Calibrationmentioning

confidence: 99%

Assessing Digital Soil Inventories for Predicting Streamflow in the Headwaters of the Blue Nile

Adem

Dile

Worqlul³

et al. 2020

Hydrology

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Comprehensive spatially referenced soil data are a crucial input in predicting biophysical and hydrological landscape processes. In most developing countries, these detailed soil data are not yet available. The objective of this study was, therefore, to evaluate the detail needed in soil resource inventories to predict the hydrologic response of watersheds. Using three distinctively different digital soil inventories, the widely used and tested soil and water assessment tool (SWAT) was selected to predict the discharge in two watersheds in the headwaters of the Blue Nile: the 1316 km2 Rib watershed and the nested 3.59 km2 Gomit watershed. The soil digital soil inventories employed were in increasing specificity: the global Food and Agricultural Organization (FAO), the Africa Soil Information Service (AfSIS) and the Amhara Design and Supervision Works Enterprise (ADSWE). Hydrologic simulations before model calibration were poor for all three soil inventories used as input. After model calibration, the streamflow predictions improved with monthly Nash–Sutcliffe efficiencies greater than 0.68. Predictions were statistically similar for the three soil databases justifying the use of the global FAO soil map in data-scarce regions for watershed discharge predictions.

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“…Besides, he showed that the accuracy of computed T c is largely controlled by the accuracy of roughness coefficient. Azizian and Shokoohi (2014;2015a) investigated the scale effects and streams delineation threshold on geomorphological parameters on the performance of KW-GIUH model and found that the coefficients of variation of river length, river mean slope and time of concentration is about 18.8, 19.3 and 21%. There are several statistical and mathematical methods that can be used to propagate input uncertainties through the model into output uncertainties, of which the First-Order-Analysis (FOA) proposed by Tung and Mays (1981) is one of the most common methods.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of Time of Concentration Equations based on First-Order-Analysis (FOA) Method

Azizian¹

2018

American Journal of Engineering and Applied Sciences

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The time of concentration (Tc) is one the most important time parameters to predict the response of a catchment to a given rainfall and plays a key role in the hydrologic design and rainfall-runoff modeling. There are a huge number of empirical/semi-empirical equations for estimation of T c and depending on several parameters such as rainfall attributes, topographic and land cover map scale, DEM resolution and streams delineation threshold causes significant uncertainties in the T c value. How to quantitatively evaluate the uncertainties in model parameters and the resulting uncertainty impacts on model outputs has always been a question which has attracted much attention. In this study, the method based on the First-Order-Analysis (FOA) is used to analyze the uncertainty and the contribution of each parameter on the output of 47 T c formulas in Kasilian and Amameh watersheds. The results show that among the 47 T c equations, equations which are based on watershed's characteristics, rainfall attributes and land cover-related coefficients such as Overton-Meadows, ASCE, Akan, Kinematic-Wave, McCuen et al. and Izzard have relatively high uncertainty and the average CV of these equations is about 45%. In addition, equations that are based on only geomorphological parameters have relatively low uncertainty (the average CV is about 16%). Further analysis of the effects of parameter uncertainties on the T c equations reveals that the uncertainty associated with rainfall attributes and land cover-related coefficients have great impacts on results of the T c equations and the uncertainty caused by these factors in humid regions relative to dry/ semi dry regions is different. Moreover, in the geomorphological-based equations, the uncertainty caused by streams delineation threshold is approximately 3-6 times of scale effects' uncertainty.

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DEM resolution and stream delineation threshold effects on the results of geomorphologic-based rainfall runoff models

Cited by 18 publications

References 23 publications

Detection and analysis of morphotectonic features utilizing satellite remote sensing and GIS: An example in SW Jordan

Detection and analysis of morphotectonic features utilizing satellite remote sensing and GIS: An example in SW Jordan

Assessing Digital Soil Inventories for Predicting Streamflow in the Headwaters of the Blue Nile

Uncertainty Analysis of Time of Concentration Equations based on First-Order-Analysis (FOA) Method

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