^{1}

^{*}

^{1}

The two-layered (0 - 50 and 50 - 250 mm) surface horizon hydraulic parameters of three dryland floodplain soil-types under aquafer water management in Postmasburg, Northern Cape Province of South Africa were estimated with HYDRUS-1D model. Time dependent water infiltration measurements at 30 and 230 mm depths from simulated rainfalls on undisturbed 1 m^{2} small plots with intensities of 1.61 (high), 0.52 (medium) and 0.27 (low) mm·min^{-1}, were minimised using a two-step inversion. Firstly, separate optimisation of the van Genuchten-Mualem model parameters for the two surface-horizon layers and secondly, simultaneous optimisation for the joint two-layered horizon with first step optimal parameters entered as initial values. The model reproduced transient water-infiltration data very well with the Nash-Sutcliffe model efficiency coefficient (NSE) of 0.99 and overestimated runoff (NSE; 0.27 to 0.98). The upper surface horizon had highly optimised and variable parameters especially θs and Ks. Optimal Ks values from higher soil surface bulk-density (≥1.69 g·cm^{-3}) were lower by at least one order of magnitude to double ring infiltrometers and water infiltration properties were different (P < 0.05) for the high rainstorm due to raindrop impact and surface crusting. Optimal α and n parameter values corresponded well with texture of the Addo (Greysols), Augrabies (Ferralsols) and Brandvlei (Cambisols) soil types. However, θs and Ksshowed greater sensitivity to model output and exerted greater influence on dryland floodplain water-infiltration and runoff characteristics. Increasing rainfall simulation period to attain near-surface saturated conditions and inclusion of surface ponding data in the inverse problem could considerable improve model prediction of hydro-physical parameters controlling surface-subsurface water distribution in fluvial environments.

Soil hydraulic properties controlling infiltration and runoff play an important part in capturing and distributing water resources in dry riverbed and floodplains. These fluvial environments are strategic sites for groundwater recharge and water-resource development. Modelling surface and subsurface water-flow requires knowledge of soilhydraulic parameters. However, sedimentation and “amphibious” conditions characterising fluvial depositional environments make soil surface hydraulic properties to be highly variable [

Soil hydraulic properties, which describe water-flow in variably saturated media, include saturated hydraulic conductivity (Ks), unsaturated hydraulic conductivity (K), and the soil water retention curve (SWRC). The SWRC represents the relationship between water content (θ) and metric suction (h), and mathematically represented by various pore-size distribution models ( [

Soil types developed from colluviation and sedimentation vary from highly permeable to impermeable [

A soil crust often consists of two parts. The first is an upper skin seal, 0.1 mm thick and forms under the influence of raindrop impact, splash, slaking, swelling and sedimentation. The second is a 2-mm thick deeper region of washed-in dispersed fine particles [

Alternatively, due to the delicate nature of a crust, steady-state infiltration measurements is commonly used to indirectly estimate hydraulic properties of a well-established soil crust with a constant thickness. Touma et al. [

The aim of this study was to determine the near-surface soil hydro-physical properties of the dominant soil types found at a dryland floodplain used to monitor groundwater recharge in the Northern Cape of South Africa. Referred as a drainage or dryland floodplain, the area has several boreholes (

The study area was located at the dryland floodplains under wild life and aquafer water management of the Anglo American Kolomela Iron Ore mine, situated 30 km south of the Postmasburg town, Northern Cape Province of South Africa (

A long-term rainfall data constituting monthly minimum, maximum and averages over a 98- year period of the Postmasburg area was summarised in

A mobile field rainfall simulator was used to generate soil water infiltration and runoff data. The simulator constituted of oscillating sprinkler nozzlewith adjustable height in a closed compartment to protect operations on windy days (^{−1} (low), 0.52 mm・min^{−1} (medium) and 1.61 (high) mm・min^{−1} intensities. When calibrating the rainfall simulator, many discharge irregularities and inconsistencies confounded intensities higher than 1.62 mm・min^{−1} or 80 mm・hr^{−1} and lower than 0.27 mm・min^{−1} or 16 mm・hr^{−1}. Selected rainfall intensities intheir increasing order had simulated times of 56, 50 and 40 minutes to obtain corresponding accumulative amounts of 15, 26 and 65 mm (

Soil types and master horizons | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Addo | Augrabies | Bransvlei | ||||||||||||||

Soil characteristics | A | B1 | C | A | B1 | B2 | A | B1 | C | |||||||

Physical properties | ||||||||||||||||

Coarse sand (%) | 1.68 | 1 | 1.7 | 12.4 | 14 | 16.1 | 1.9 | 0.9 | 0.9 | |||||||

Medium sand (%) | 2.42 | 2.1 | 3 | 3.6 | 3.9 | 3.1 | 1.8 | 1.4 | 1.7 | |||||||

Fine sands (%) | 14.4 | 19.4 | 19.9 | 40 | 45.5 | 43 | 55 | 51.1 | 56.6 | |||||||

Very fine sand (%) | 19.1 | 18.9 | 18 | 16.4 | 11.1 | 16.2 | 20.7 | 22.1 | 19.5 | |||||||

Coarse silt (%) | 13.6 | 12.4 | 12.3 | 7.6 | 6.4 | 5.7 | 3 | 5 | 3 | |||||||

Fine silt (%) | 23.6 | 19.7 | 18.8 | 6 | 4.8 | 5.7 | 2.6 | 6.3 | 5.5 | |||||||

Clay (%) | 24.1 | 25 | 26 | 14.6 | 15.8 | 11.7 | 15.1 | 13.5 | 12.8 | |||||||

Bulk density (g・cm^{−3}) | 1.52 | 1.52 | 1.48 | 1.76 | 1.73 | 1.66 | 1.69 | 1.59 | 1.53 | |||||||

Exchangeable cations | ||||||||||||||||

Calcium (mg・kg^{−1}) | 7650 | 5090 | 4850 | 1788 | 1566 | 1054 | 6320 | 6240 | 2950 | |||||||

Magnesium (mg・kg^{−1}) | 1340 | 1710 | 1600 | 305 | 348 | 820 | 700 | 920 | 1450 | |||||||

Potassium (mg・kg^{−1}) | 313 | 210 | 157 | 326 | 176 | 207 | 186 | 241 | 282 | |||||||

Sodium (mg・kg^{−1}) | 60 | 41 | 46 | 18 | 18 | 21 | 25 | 29 | 101 | |||||||

Jan | Feb | Mar | Apr | May | Jun | July | Aug | Sept | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Min | 0.2 | 0.1 | 0.2 | 0.1 | 0.1 | 0.1 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 |

Max | 104.5 | 130 | 77.5 | 69.6 | 46.2 | 39.5 | 31 | 34 | 39.5 | 66.5 | 66 | 69 |

Mean | 9.6 | 12.3 | 10.7 | 9.0 | 7.0 | 5.7 | 5.3 | 5.1 | 6.2 | 8.0 | 8.7 | 9.4 |

Size | Intensity (mm・min^{−1}) | Duration (min) | Total (mm) |
---|---|---|---|

High | 1.62 | 40 | 65 |

Medium | 0.52 | 50 | 26 |

Low | 0.27 | 56 | 15 |

Time dependent soil water content and runoff measurement obtained from simulated three rainstorm treatments replicated three times from the three soil types. Installation of the mobile simulator metal frame and driven to the ground up to 10 cm depth on undisturbed land representative of soil surface conditions in situ. Centrally of each plot a 1 m long capacitance soil water measuring (DFM) probe was vertically installed such that water-measuring sensors are at 3 cm, 23 cm, 43 cm, 63 cm and 83 cm depths (

The Richard equation [

∂ θ ∂ t = ∂ ∂ z [ ∂ H ∂ z − 1 ] − S (1)

where ∂θ/∂t is the surface water flux, z is the down wide direction; H is the soil-water pressure head relative to atmospheric pressure (H = h + z), h being matric suction potential and S is the sink. HYDRUS-1D software [

The single porosity model of van Genuchten (1980) [

θ ( h ) = θ r + θ s + θ r { 1 + | ∝ h | n } m (2)

K ( θ ) = K s S e 1 2 [ 1 − ( 1 − S e 1 m ) m ] 2 (3)

where θr and θs are residual and saturated water contents (mm・mm^{−1}), respectively; α is the air entry value also referred as bubble pressure [mm], n is the pore size distribution parameter [-], Ks is the saturated hydraulic conductivity, and l is a pore-connectivity parameter assumed to 0.5 [-]. The condition m = 1 − 1/n should be satisfied with the air entry value of −2 cm.

Initial estimate of the van Genuchten [

The HYDRUS-1D model have an inverse modelling capability and was required to optimise unknown parameters for van Genuchten [

( β , θ ) = ∑ i = 1 N W i [ θ i * ( z , t i ) − θ i ( z , t i ) , β ] 2 (4)

where ɸ(β) is the objective function of the parameter vector, β; θ_{i}* and θ_{i} are measured and predicted soil water contents, respectively; z is the depth; t is the time and N is the number of observations available; W is the weight of specific measurement by standard deviation.

The surface horizon constituted the flow domain discretised into upper (0 - 50) and lower (50 - 250 mm) layers with observation points at 30 mm and 230 mm depths, respectively. HYDRUS-1D model predicts well infiltration properties near the surface but because of neglecting the effect of entrapped air on water infiltration, it often underestimates the infiltration depth [

To improve parameter identification and uniqueness for upper and lower layers the surface horizon, a two-step inverse parameter optimisation [

| K ( h ) ( ∂ h ∂ z + 1 ) | ≤ q , (5)

h A ≤ h ≤ h S , (6)

where, q maximum potential infiltration flux under the current atmospheric conditions, h is the matric suction potential at soil surface, z is depth, K(h) is unsaturated hydraulic conductivity, and h_{A} and h_{S} are, respectively, minimum and maximum matric suction potential allowed under the prevailing soil conditions.

The objective function minimised in inverse parameter optimisation should provide sufficient information about the unknown parameters to be identified [

e i j = 100 β | ∂ a i ∂ β j | = 100 β j | a i ( β + Δ β e j ) − a i ( β ) | 1.01 β j − β j = | a i ( β + B e j ) − a i ( β ) | (7)

where e_{ij}, is the change in the auxiliary variable a_{i} corresponding to 1% change in parameter β_{j}. Thereby β is the parameter vector, while e_{j} is the j^{th} unit vector. The parameter vector included the optimised θr, θs, α, n and Ks van Genuchten-Mualem parameters. Sensitivities conducted involved soil water content, infiltration rates, accumulative infiltration, runoff rate and accumulative runoff. A high sensitivity suggested a well-defined minimum and the parameter can be optimised with greater certainty once the global minimum is verified.

The HYDRUS-1D model performance assessed with the Nash-Sutcliffe model efficiency coefficient (NSE) [

N S E = 1 − ∑ i = 1 N ( θ i − θ i * ) 2 ∑ i = 1 N ( θ i * − θ a v e ) 2 (8)

where θ_{1} is the predicted soil water content; θ*_{1} is the observed soil water content; θ_{ave} is the average soil water content of all the observed events, and N is the number of observations i.e., the number of measured events.

The RMSE is widely used to measure agreement between the observed data and model prediction and represented by the expression:

R M S E = ∑ I = 1 N ( θ i * − θ i ) 2 N (9)

The Duncan’s multiple range test (DMRT) was used to compare infiltration rates and accumulation infiltration of the three soil types and simulated rainstorms. The means were ranked from the highest to lowest values and ranks were compared to shortest significant ranges (R_{p}). The R_{p} was computed as follows:

R p = ( r p ) ( s d − ) 2 (10)

s d − = 2 s 2 r (11)

where rp, tabular values of the significant studentized ranges at 0.05, s d − standard error of the mean difference and s is the variance [

Time dependent soil water contents at 30 mm and 230 mm depth measured from simulated (high, medium and low) rainstorms were minimised in the objective function and results for the model fit at 30 mm depth were presented in

Initial infiltration rates declined sharply under the high rainstorm suggesting that the 1.62 mm・min^{−1} intensity was higher than soils infiltration capacity. The Addo and Augrabies approached steady infiltration rates after 20 minutes recording final infiltration rates and total infiltration of 0.71 mm・min^{−1} and 34.8 mm, and 0.91, mm・min^{−1} and 36.5 mm respectively. The lower infiltration values of the Addo was explained by the total fine silt plus clay content of 47.7% of the surface horizon compared to the 20.6% from the Augrabies. The Brandvlei had a constant initial infiltration rate for the first 5 min before declining to a final infiltration rate of 1.14 mm・min^{−1} and total infiltration of 50.51 mm. The Brandvlei higher infiltration capacity was consistent to the lower total fine silt plus clay content of 17.7%. Silt plus clay fraction were easily mobilised by high-energy raindrops into formation of surface crusts and seals that can reduce infiltration capacity by several orders of magnitudes ( [

The medium rainstorm had constant initial infiltration rates in the first 10 min from the Addo and Augrabies, and 15 min from the Brandvlei, before declining to significantly similar (P < 0.05) final infiltration rates of 0.38, 0.14 and 0.38 mm・min^{−1}, respectively. Corresponding accumulative infiltration were 22, 15.2 and 22.5 mm, respectively. The Augrabies produced lower infiltration rates under the medium rainstorm, which were also indifferent when compared to the low rainstorm. This result was attributed to the higher surface bulk density (1.71 g・cm^{−3}) that supported low porosity and permeability. Compacted or crusted surfaces of 0.1 mm thick were observed to reduce final infiltration rates by more than 10 times [^{−1} and crust thicker than 2 mm did not exceed 0.16 mm・min^{−1} [

Soil type | Ks (mm・min^{−1}) | SD (mm・mm^{−1}) | CV (%) |
---|---|---|---|

Addo | 0.05^{c} | 0.002 | 4 |

Augrabies | 1.5^{b} | 0.491 | 33 |

Brandvlei | 2.02^{a} | 0.393 | 20 |

Note: superscript letters, statistical different means depicted by different letters based on least significant Difference (LSD) mean separation test P ≤ 0.05; SD, standard deviation; CV coefficient of variation.

Soil types | Infiltration | Time to runoff | Final runoff rate | Accumulative runoff | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|

Rainstorms | Rate | Total | Observed | Predicted | Observed | Predicted | Observed | Predicted | |||

Addo | High | 0.71^{c} | 34.84 | 4 | 2.32 | 0.58 | 0.91 | 27.5 | 29.96 | ||

Medium | 0.38^{d} | 21.97 | 11 | 12 | 0.04 | 0.14 | 3.82 | 4.02 | |||

Low | 0.21^{e} | 13.39 | 22 | 23 | 0.02 | 0.06 | 1.45 | 1.73 | |||

Augrabies | High | 0.91^{b} | 41.1 | 4 | 2.36 | 0.11 | 0.71 | 25.4 | 23.71 | ||

Medium | 0.14^{de} | 15.18 | 9.3 | 10.5 | 0.06 | 0.38 | 10.43 | 10.82 | |||

Low | 0.15^{e} | 12.71 | 19 | 27 | 0.03 | 0.12 | 2 | 2.41 | |||

Brandvlei | High | 1.14^{a} | 50.51 | 4.33 | 6.11 | 0.35 | 0.47 | 13.97 | 13.88 | ||

Medium | 0.38^{d} | 22.5 | 11.67 | 18 | 0.03 | 0.144 | 2.97 | 3.51 | |||

Low | 0.18^{e} | 14 | 38 | 38 | 0.02 | 0.09 | 0.89 | 1.12 | |||

Note: superscript letters, statistical different means depicted by different letters based on the Duncan Multiple Range Test (DMRT) mean separation test at 95% confident intervals (P < 0.05). Superscripts letters also applicable for accumulative runoff (not shown).

Under the low rainstorm, initial infiltration rates were constant in the first 20, 25 and 35 min from the Addo, Augrabies and Brandvlei, respectively. Correspondingly, final or steady infiltration rates and total infiltration were 0.21, 0.15 and 0.18 mm・min^{−1} and 13.4, 12.7 and 14 mm, respectively. The longer constant initial infiltration rates showed that the low rainstorm intensity of 0.27 mm・min^{−1} was close to soil-types infiltration capacity, hence steady infiltration rates were significantly similar (P < 0.05). However, this result was not surprising because as rainstorm decreases the wetting rate and infiltration curve decreases slowly resulting to lower final infiltration rates ( [^{−1}) and magnesium (1340 mg・kg^{−1}) recognised as good flocculators versus exchangeable sodium (60 mg・kg^{−1}) with high dispersivity. Such a distribution favoured a stable than dispersive surface aggregates, which supported infiltration especially under less disruptive rainstorm intensity. Stern et al. [^{−1} were associated with stable aggregates while less than 0.08 mm・min^{−1} characterised dispersive soils. In this study, final infiltration rate was greater than 0.13 mm・min^{−1} from all soil types suggesting that distribution of exchangeable cation did not favour dispersive infiltration reduction. However, in less dispersive soils, surface crusting and rainfall characteristics controlled final infiltration rates [

^{−1}) and highest (2.02 mm・min^{−1}) from the Addo and Brandvlei, respectively. Unsaturated hydraulic conductivity of soil types showed greater variation among rainstorms at near saturation. Corresponding K(θ) values at near saturation of the Addo, Augrabies and Brandvleifor high, medium and low rainstorms are 0.66, 0.23 and 0.11 mm・min^{−1}, 0.09, 0.03 and 0.02 mm・min^{−1}, and 0.53, 0.16 and 0.07 mm・min^{−1}, respectively. The result showed that K(θ) function at near saturation increased with rainstorm amount and intensity suggesting volume of hydraulic active pores increases with rainstorm size until rainstorm intensity excess steady infiltration rate ( [^{−1} when compared to corresponding Ks values. Such a reduction in near saturation conductivity is explained by the breaking down and slaking of soil surface aggregates by raindrop energy impact, which can reduce Ks exponentially [

In addition to soil-types infiltration,

Due to shorter simulation periods (≤56 min), ponding was assumed to be negligible at small plots scale. Presence of dry and dormant vegetation on plots that can influence surface water storage and delay time to runoff [

The Brandvlei had longest time to runoff (4.3 to 38 min) for all rainstorms while the Addo and Augrabies had shortest times for the high and lower rainstorms, respectively. Total runoff increased with opportunity time making the Addo and Augrabies to have highest runoff for the respective high (27.5 mm) and lower (2.41 mm) rainstorms. This result collaborated with the higher total fine silt plus clay (47.7%) fraction of the Addo and bulk density (1.76 g・cm^{−3}) of the Augrabies; observed earlier in this study as factors that reduced infiltration. Soils with high quartz fraction like in the Augrabies have poor bonding properties to protect aggregates against raindrop impact and hence, are highly dispersive and susceptible to surface crusting [

^{−1}) and most (Ks = 0.23 mm・min^{−1}) permeable, respectively. However, optimised Ks values described the Addo as the most permeable (Ks; 0.11 to 0.78 mm・min^{−1}) and the Augrabies as least permeable (Ks; 0.2 to 0.52 mm・min^{−1}). This result showed that parameters derived from theoretical pedo-transfer functions were ill posed for describing soil-water dynamic processes in-situ. Soil heterogeneity and occurrence of superficial crusted surface layers and variability in atmospheric-surface boundary conditions are among the common reasons ( [

Soil parameters | High rainstorm | Medium rainstorm | Low rainstorm | |||||||
---|---|---|---|---|---|---|---|---|---|---|

Addo | Rosetta | 3 cm | 23 cm | RMSE | 3 cm | 23cm | RMSE | 3 cm | 23cm | RMSE |

Qr | 0.07 | 0.07 | 0.07 | 0.001 | 0.07 | 0.07 | 0.002 | 0.07 | 0.07 | 0.002 |

Qs | 0.39 | 0.43 | 0.41 | 0.001 | 0.42 | 0.41 | 0.002 | 0.43 | 0.46 | 0.002 |

alpha | 0.002 | 0.007 | 0.007 | 0.001 | 0.008 | 0.007 | 0.002 | 0.007 | 0.007 | 0.002 |

n | 1.46 | 1.674 | 1.856 | 0.001 | 1.808 | 1.890 | 0.002 | 1.765 | 1.890 | 0.002 |

Ks | 0.05 | 0.66 | 0.78 | 0.001 | 0.23 | 0.74 | 0.002 | 0.11 | 0.72 | 0.002 |

L | 0.50 | 5.81 | 0.24 | 0.001 | 0.0003 | 0.25 | 0.002 | 0.00002 | 1.96 | 0.002 |

Augrabies | ||||||||||

Qr | 0.05 | 0.07 | 0.07 | 0.005 | 0.07 | 0.06 | 0.001 | 0.07 | 0.07 | 0.003 |

Qs | 0.32 | 0.43 | 0.45 | 0.005 | 0.42 | 0.45 | 0.001 | 0.42 | 0.45 | 0.003 |

alpha | 0.004 | 0.002 | 0.002 | 0.005 | 0.002 | 0.002 | 0.001 | 0.002 | 0.002 | 0.003 |

n | 1.33 | 2.680 | 1.410 | 0.005 | 2.680 | 1.410 | 0.001 | 2.919 | 2.680 | 0.003 |

Ks | 0.10 | 0.33 | 0.51 | 0.005 | 0.03 | 0.06 | 0.001 | 0.02 | 0.03 | 0.003 |

L | 0.50 | 0.50 | 6.03 | 0.005 | 0.17 | 0.73 | 0.001 | 0.00002 | 0.80 | 0.003 |

Brandvlei | ||||||||||

Qr | 0.05 | 0.04 | 0.05 | 0.011 | 0.04 | 0.04 | 0.002 | 0.04 | 0.04 | 0.003 |

Qs | 0.343 | 0.41 | 0.43 | 0.011 | 0.44 | 0.41 | 0.002 | 0.41 | 0.41 | 0.003 |

alpha | 0.0031 | 0.011 | 0.014 | 0.011 | 0.011 | 0.011 | 0.002 | 0.007 | 0.011 | 0.003 |

n | 1.4869 | 2.801 | 2.680 | 0.011 | 2.801 | 2.801 | 0.002 | 3.857 | 2.801 | 0.003 |

Ks | 0.23 | 0.60 | 1.915 | 0.011 | 0.17 | 0.45 | 0.002 | 0.07 | 0.111 | 0.003 |

L | 0.5 | 0.50 | 0.00003 | 0.011 | 0.000001 | 0.000001 | 0.002 | 2.19 | 0.109 | 0.003 |

Optimised parameters varied considerably for upper and lower surface horizons. The Ks fitted for upper layer was always lower when compared to the lower surface horizon. This result showed that the soil surface was susceptible to compaction and raindrop impact especially under higher rainstorm intensities [^{−3} and disruptive power of high intensity raindrop were the reasons. Despite the initially higher α parameters, which represented the coarseness of the Augrabies (0.004) and Brandvlei (0.003), the optimised α values for the former was remarkably lower (0.002). The lowest α parameter depicted the Augrabies as a fine textured soil because of higher bulk density of 1.76 g・cm^{−3}, which reduced permeability and porosity for the upper surface horizon in particular. Larger n parameter represented greater pore-size distribution uniformity. The Addo had lowest n parameter (1.674 to 1.890) and were always lower in the upper compared to lower surface horizon. This result corresponded to the lower bulk density (1.52 g・cm^{−3}) and higher total fine silt and clay (47.7%). Bonding and cementing properties of clays improved aggregate stability and inter-aggregate porosity [

Larger n parameter (2.801 to 3.857) characterised the Brandvlei followed by the Augrabies (2.680 to 2.919). Greater uniformity depicted by larger n values for the Brandvlei collaborated with the 76% total sand fraction excluding course and medium sand fraction. In addition to larger n parameter, larger α parameter (0.007 to 0.14) described the Brandvlei surface horizon suggesting it was a uniformly course textured soil. This description supported the higher infiltration and lower runoff of the Brandvlei from all rainstorm treatment.

Sensitivities of soil water content (θ), infiltration rate (I), accumulative infiltration (Z), runoff rate (Ro_{r}) and accumulative runoff (Acc. Ro) to 1% change in optimised parameters for the medium rainstorm were presented in Figures 10-12 for the Addo, Augrabies and Brandvlei soil forms, respectively. Similar results were observed under the high and low rainstorms (data not shown).Sensitivities of model output to 1% change in parameters were noticeable at two infiltration stages; firstly, passing of wetting front at 30 mm depth resulting to a sharp rise in θ and secondly, when θ approached near saturation values.

Parameters | Addo | Augrabies | Brandvlei |
---|---|---|---|

RMSE | RMSE | RMSE | |

Optimised value | 0.002 | 0.001 | 0.0015 |

Qr | 0.011 | 0.001 | 0.006 |

Qs | 0.004 | 0.002 | 0.003 |

Alpha | 0.003 | 0.001 | 0.007 |

N | 0.003 | 0.001 | 0.012 |

Ks | 0.003 | 0.005 | 0.006 |

Sensitivities of soil water content to 1% change in parameters were highly variable among soil types. In the Addo, soil water content sensitivity to all parameters was limited to the first 15 minutes with well-defined peaks at 10 minutes of 0.02 and 0.01 for respective θr and θs after which became constant. Soil water content from the Augrabies showed greater sensitivity to θs and Ks after 10 minutes reaching respective peaks of 0.005 and 0.007 at 15 and 30 minutes, consecutively. Brandvlei soil water contents after 15 minutes showed sensitivity to all with the least being n parameter. Only θs had a well-defined peak at 18 min of 0.006 while α, θr and Ks parameters increased with time reaching respective peaks of 0.009, 0.008 and 0.007, before assuming nearly constant trends. Sensitivity of soil water content to Ks appeared to increase with infiltration time especially for the Augrabies and Brandvlei, soils forms, and both clay content of less than 20% in all their horizons.

Sensitivities of infiltration rates to 1% change of parameters was characterised by defined peaks after detection of the wetting front with some parameters increasing sensitivity with time. In the Addo, except n parameter, parameters had distinct peaks within 12 to 20 minutes after which all parameter sensitivity to infiltration rate were diffused to nearly constant rates of less than 0.004. Soil water content displayed a different parameter sensitivity for the Augrabies and Brandvlei. Infiltration rate showed greater sensitivity to θs and Ks that increased after the 25 min time mark to reach sensitivities of 0.18 and 0.23, respectively. From the Brandvlei, all parameters had definite peaks at 18 minutes with the α, θr and Ks assuming sensitivities that increased with time after the 20 min time mark suggesting that increasing infiltration time measurement would have improved these parameters information. However, research has showed that sensitivity of infiltration rate to hydraulic parameters was limited to the period when of detecting wetting front to the time when gravity began to influence infiltration [

Sensitivities of accumulative infiltration from soil types increased with time following rapid increase in soil water content. For the Addo sensitivities increased after 12 min from all parameters reaching 0.189 for α parameter by the end of the infiltration. Despite Ks being the least sensitive from the Addo, it was the most sensitive along with θs in the Augrabies increasing after the 30 min time mark to reach 0.447 the highest realised from this study. This trend would have continued further with time suggesting that increasing duration of the infiltration experiment would have improved parameter identification especially θs and Ks parameters. Simunek and van Genuchten [

Surface horizon hydraulic parameters controlling water infiltration and runoff of the van Genuchten-Mualem analytic model were estimated from three floodplain soil types using HYDRUS-1D model. A two-step inversion approach was used to estimate optimal parameter values for a two-layered surface horizon discretised at 0 - 50 mm and 50 - 250 mm depths. Inversion of time dependent soil water contentinfiltration measurements from the high, medium and low rainfall simulated experiments were satisfactory from all soil types with the Nash-Sutcliffe model efficiency coefficient (NSE) of not less than 0.99. Overestimation of runoff rates (NSE; 0.27) and accumulative runoff (0.53 ≤ NSE ≤ 0.98) was suggestive that inclusion of transient water infiltration data was insufficient for the water infiltration-runoff inverse problem. Inclusion of additional data to define the inverse problem such as ponding and surface water-storage parameters would require more computational time but considerably improve inverse solution and agreement between measured and predicted runoff data.

The upper surface horizon had highly optimised and variable parameters especially θs and Ks. Higher soil-surface bulk density (≥1.69 g・cm^{−3}) had optimal Ks values lower by at least one order of magnitude to the double ring infiltrometers. This finding showed soil surface compaction and sedimentary crust to be important factors of influence for saturated hydraulic parameters, which also determined floodplain soil-type infiltration and runoff characteristics. Considering longer rainfall simulation, periods to attain saturation and steady state conditions would therefore improve model prediction of floodplain soil-types water infiltration and runoff. Significant differences (P < 0.05) in soil-types infiltration rates and accumulative infiltration under the simulated high rainstorm confirmed that surface crusting and sealing was an important factor in dryland floodplain water infiltration-runoff characteristics. Optimal parameter values were typical of fine textured, compacted sands and uniformly course textured for the respective Addo (Greysols), Augrabies (Ferralsols), Brandvlei (Cambisols) soil types.

The authors wish to acknowledge the management and staff of Kolomela Iron Ore mine (Anglo-American) for the technical, logistic and financial support of this research project.

The following symbols are used in this paper:

a_{i} = auxiliary variable;

α = air entry value also referred as bubble pressure;

β = parameter vector;

β_{j} = specific parameter;

P < 0.05 = probability at 5% level of significance

≥ = greater than or equal to;

≤ = less than or equal to;

∂ = partial differential;

ɸ = objective function;

cosα = angle between the flow direction and the vertical axis;

e_{j} = j^{th} unit vector;

h = metric suction potential;

h_{A} = minimum pressure head;

h_{S} = maximum pressure head;

H = is the soil-water pressure head relative to atmospheric pressure;

K(h) = matric suction based unsaturated hydraulic conductivity;

K(θ) = water content based unsaturated hydraulic conductivity;

Ks = saturated or steady state hydraulic conductivity;

l = pore-connectivity parameter assumed;

m = empirical parameter

N = number of observations;

n = pore size distribution parameter;

θ = volumetricwater content;

θ_{ave} = average soil water content;

θr = residual soil water content;

θs = saturated soil water content;

θ_{i}* = observed soil water contents;

θ_{i} = predicted soil water contents;

q = water flux through a specified flow domain also depicted;

R_{p} = shortest significant ranges;

rp = tabular values of significant studentized ranges;

S = sink;

s = variance;

s d − = standard error of the mean difference;

t = time;

W = weight by standard deviation;

z = depth; and,

Z = vertical down wide direction or gravitational potential.

The authors declare no conflicts of interest regarding the publication of this paper.

Mavimbela, S.S.W. and van Rensburg, L.D. (2019) Estimating Soil Hydraulic Parameters Characterizing Rainwater Infiltration and Runoff Properties of Dryland Floodplains. Computational Water, Energy, and Environmental Engineering, 8, 11-40. https://doi.org/10.4236/cweee.2019.81002