Evaluation method dependency of measured saturated hydraulic conductivity

Fodor, Nándor; Sándor, Renata; Orfánus, Tomáš; Lichner, Ľubomír; Rajkai, Kálmán

doi:10.1016/j.geoderma.2011.07.004

Cited by 89 publications

(63 citation statements)

References 66 publications

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“…According to these results, simultaneous use of fractal parameters of particle and micro-aggregate size distributions, in addition to the SCBD, is the best combination of input variables in the estimation of K s . It seems that the influences of soil texture [23] and soil structure [28] on the K s are the main reasons of this result. Moreover, fractal parameters can be good predictors for K s .…”

Section: Development Of Ptfs Using Annsmentioning

confidence: 84%

Estimation of soil saturated hydraulic conductivity by artificial neural networks ensemble in smectitic soils

2016

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The saturated hydraulic conductivity (K s ) of the soil is one of the main soil physical properties. Indirect estimation of this parameter using pedo-transfer functions (PTFs) has received considerable attention. The Purpose of this study was to improve the estimation of K s using fractal parameters of particle and micro-aggregate size distributions in smectitic soils. In this study 260 disturbed and undisturbed soil samples were collected from Guilan province, the north of Iran. The fractal model of Bird and Perrier was used to compute the fractal parameters of particle and micro-aggregate size distributions. The PTFs were developed by artificial neural networks (ANNs) ensemble to estimate K s by using available soil data and fractal parameters. There were found significant correlations between K s and fractal parameters of particles and microaggregates. Estimation of K s was improved significantly by using fractal parameters of soil micro-aggregates as predictors. But using geometric mean and geometric standard deviation of particles diameter did not improve K s estimations significantly. Using fractal parameters of particles and micro-aggregates simultaneously, had the most effect in the estimation of K s . Generally, fractal parameters can be successfully used as input parameters to improve the estimation of K s in the PTFs in smectitic soils. As a result, ANNs ensemble successfully correlated the fractal parameters of particles and micro-aggregates to K s .

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Section: Development Of Ptfs Using Annsmentioning

confidence: 84%

Estimation of soil saturated hydraulic conductivity by artificial neural networks ensemble in smectitic soils

2016

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“…29.1% of the rainfall. Such water volumes cannot be effectively stored in the natural catchment environment -soil, vegetation cover (Fodor et al, 2011). Either such storage (retention) volumes need to be artificially created, or each part of the catchment river network elements adapted to such discharge carrying capacity, as to be able to control the storm rainfall inflow.…”

Section: Discussionmentioning

confidence: 99%

Estimating Flash Flood Peak Discharge in Gidra and Parná Basin: Case Study for the 7-8 June 2011 Flood

Pekárová¹,

Svoboda²,

Miklánek³

et al. 2012

Journal of Hydrology and Hydromechanics

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We analyzed the runoff and its temporal distribution during the catastrophic flood events on river Gidra (32.9 km 2 ) and Parná (37.86 km 2 ) of the 7th June 2011. The catchments are located in the Small Carpathian Mountains, western Slovakia. Direct measurements and evaluation of the peak discharge values after such extreme events are emphasized in the paper including exceedance probabilities of peak flows and of their causal flash rainfall events. In the second part of the paper, plausible modeling mode is presented, using the NLC (Non Linear Cascade) rainfall-runoff model. Several hypothetical extreme flood events were simulated by the NLC model for both rivers. Also the flood runoff volumes are evaluated as basic information on the natural or artificial catchment storage. Predložený príspevok analyzuje tvorbu a priebeh odtoku počas katastrofickej povodňovej situácie na Gidre (32,9 km 2 ) a na Parnej (37,86 km 2 ) dňa 7. 6. 2011. Povodia týchto tokov sa nachádzajú v Malých Karpatoch na západnom Slovensku. V príspevku sa kladie dôraz na priame zameranie a vyhodnotenie kulminačných prietokov po výskyte takýchto povodní. Diskutujú sa problémy vyjadrenia pravdepodobnosti prekročenia kulminačných prietokov a dažďov, ktoré ich spôsobili. V druhej časti príspevku je prezentovaný možný spôsob modelovania povodne jednoduchým zrážkovo-odtokovým modelom NLC. Daným modelom NLC sú následne simulované prietoky Gidry v stanici Píla a Parná v stanici Horné Orešany za extrémnej hypotetickej zrážkovej udalosti. Hodnotené sú objemy odtoku počas povodní, ako základný údaj pre reálny odhad ich prirodzeného alebo umelého zadržania.KĽÚČOVÉ SLOVÁ: bleskové povodne, rieky Gidra a Parná, analýza povodní, modelovanie odtoku.

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“…A sample size of N = 5 for a given treatment was chosen since a small area in the field is better characterized by averaging a few closely spaced replicated measurements rather than by a single measurement due to spatial variability of soil hydrodynamic parameters (Fodor, Sándor, Orfanus, Lichner, & Rajkai, ; Nano, Nicolardot, & Ubertosi, ).…”

Section: Methodsmentioning

confidence: 99%

A test of water pouring height and run intermittence effects on single‐ring infiltration rates

Alagna

Bagarello

Cecere

et al. 2018

Hydrological Processes

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Assessing how the infiltration process depends on the water impact energy improves interpretation of hydrological processes. Impact energies vary with the height of water pouring; that is, the distance between the water delivery point and the soil surface. The effects of the height of water pouring on infiltration in an initially near saturated soil can be tested in the field by two repeated Beerkan infiltration runs separated by a short pause (30 min) and using both low (non‐perturbing) and high (perturbing) heights of water application. The double two‐stage Beerkan run methodology was applied in two soils. The infiltration rate at the end of the perturbing stage of the experiment was 0.2–0.3 (sandy‐loam soil) and 0.15 (loam soil) times that obtained with the initial, nonperturbing stage whereas, without any perturbing effect, infiltration rates at the end of the second run were 0.5 times those at the end of the first run. Therefore, the methodology distinguished between a decrease in infiltration rate due to water redistribution during the pause and that attributable to soil surface perturbation. Maintaining a small depth of water (10 mm) on the infiltration surface was not an alternative to the classical Beerkan run for the non‐perturbing stage of the experiment since two times higher infiltration rates were measured with a greater hydrostatic pressure. In conclusion, the relationship between infiltration into an initially near saturated soil and the energy of the applied water can be determined directly in the field at any time with a simple and parsimonious experiment. This circumstance could improve soil hydraulic characterization for interpreting and simulating hydrological processes.

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Evaluation method dependency of measured saturated hydraulic conductivity

Cited by 89 publications

References 66 publications

Estimation of soil saturated hydraulic conductivity by artificial neural networks ensemble in smectitic soils

Estimation of soil saturated hydraulic conductivity by artificial neural networks ensemble in smectitic soils

Estimating Flash Flood Peak Discharge in Gidra and Parná Basin: Case Study for the 7-8 June 2011 Flood

A test of water pouring height and run intermittence effects on single‐ring infiltration rates

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