Infiltration Model Parameters from Rainfall Simulation for Sandy Soils

Infiltration of water into the soil is an important physical process affecting the fate of water under field conditions, especially the amount of subsurface recharge and surface runoff and hence the hazard of soil erosion. The study was conducted to evaluate the infiltration models of soils developed on coastal plain sands and to select a suitable models as a basis to improve the management of the soil. A total of 16 infiltration runs were made with the double ring infiltrometer. For the purpose of getting best fitting model, the results obtained from various infiltration models were compared with observed field data. The parameters considered for best fitting of model were correlation coefficient and coefficient of variability (CV). Model-predicted cumulative infiltration consistently deviated from field-measured data, that is, the models under-predicted cumulative infiltration by several orders of magnitude for Kostiakov, Green Ampt and Philip model but the model over predicted cumulative infiltration for Horton model. The results of the soil samples analysed revealed that the mean values of 707.50, 208.13 and 84.38 gkg-1 for sand, silt and clay with the textural class of sandy loam. The bulk density, particle density and total porosity had mean values of 1.84 gcm-3, 2.44 gcm-3 and 22.56%. However, there was a fairly good agreement between mean-measured cumulative infiltration (7.30 cm/hr, CV = 32.19%); Philips (1.93 cm/hr, CV = 42.49%); Kostiakov (0.13 cm/hr, CV = 30.77%); Horton (64.49 cm/hr, CV = 22.39%) and Green Ampt model (42.04 cm/hr, CV = 0.57%) respectively. The data however showed that the correlation coefficient for Kostiakov (1.00) was best fitting in predicting the field measured data and this was closely followed by Green Ampt (0.88); while Philip’s model and Horton model showed a negative correlation (r = -0.88 and r = -0.82) with the field measured data. Conservation measures involving mulching, cover cropping and afforestation are recommended to improve the soil structure and infiltration capacity.

Agricultural managed aquifer recharge (AgMAR) is a strategy whereby surface water is used to intentionally flood croplands to recharge underlying aquifers. However, nitrate (NO3−) contamination in agriculturally intensive regions poses a threat to groundwater resources under AgMAR. We use a reactive transport model to understand the effects of AgMAR management strategies (i.e., by varying the frequency, duration between flooding events, and amount of water) on NO3− leaching to groundwater under different stratigraphic configurations and antecedent moisture conditions. We examine the potential of denitrification and nitrogen retention in deep vadose zone sediments (∼15 m) using variable AgMAR application rates on two‐dimensional representations of differently textured soils, soils with discontinuous bands/channels, and with preferential flow paths characteristic of agricultural fields. Simulations indicate finer textured sediments, alone or embedded within/adjacent to high flow regions, are important reducing zones providing conditions needed for denitrification. Simulation results suggest that applying water all‐at‐once rather than in increments transports higher concentrations of NO3− deeper into the profile, which may exacerbate groundwater quality. This transport into deeper depths can be aggravated by wetter antecedent soil moisture conditions. However, applying water all‐at‐once also increases denitrification within the vadose zone by promoting anoxic conditions. We conclude that AgMAR can be designed to enhance denitrification in the subsurface and reduce NO3− leaching to groundwater, while specifically accounting for lithologic heterogeneity, antecedent soil moisture conditions, and depth to the water table. Our findings are potentially relevant to other systems that experience flooding inundation such as floodplains and dedicated recharge basins.

Flash floods have the potential to damage infrastructure and buildings, and pose a considerable threat to human life. The short lag times associated with flash floods (typically a few hours) present a significant challenge to existing flood warning systems. These systems currently rely on rainfall and runoff measurements, as well as hydrological models. Consequently, they are of limited applicability in ungauged catchments. This is a critical issue given that climate change is intensifying extreme rainfall and thereby increasing the potential for flash flooding.This study investigates the detection of flash floods based solely on rainfall characteristics, thus eliminating dependency on runoff measurements and hydrological infrastructure. Using high-resolution radar rainfall data and 15-minute runoff observations, 1,330 extreme rainfall-runoff events are selected across 147 German catchments with an area of up to 100 km². These events are subsequently classified as either flash or non-flash floods using a rainfall-runoff-based classification scheme as a reference, with 103 of the selected events identified as flash floods. For each event, various space-time rainfall characteristics are quantified. A random forest model for flash flood detection is then trained using only the rainfall metrics and static catchment attributes. The main aim is to assess the potential for rainfall-driven flash flood detection without relying on runoff. In addition, the most relevant rainfall characteristics associated with flash floods are identified, thereby improving our understanding of the underlying drivers.In future work, the developed detection approach will be combined with real-time rainfall nowcasting to enable earlier prediction and warning of flash floods.

<p>The Green-Ampt model was developed more than 100 years ago and is still one of the most commonly used approaches to consider infiltration in rainfall-runoff models, which can be either conceptual catchment models as well as 2D hydrodynamic models. When coupling, for example, the Green-Ampt model for infiltration with a 2D shallow water model for the flow, the calculated ponding water depths are transferred from the flow model to the Green-Ampt model to calculate the infiltration rates, and the resulting infiltration rates represent then sinks in the mass balance equation of the shallow water model. The so-called Green-Ampt parameters in terms of saturated water content, hydraulic conductivity, and suction head at the wetting front, are needed as model input in addition to the initial water content. Often, the Green-Ampt parameters are not directly measured in the field for the area that should be modeled but are only assumed based on average values from the literature depending on the dominant soil texture class. If reliable data of certain rainfall-runoff events are available for the study area, the values of the Green-Ampt parameters can be determined besides other calibration parameters within reasonable ranges. However, in some cases, a calibration of the Green-Ampt parameters is not possible due to a lack of measurements, for example during suddenly occurring flash floods or in completely ungauged basins. This study aims to investigate with a coupled shallow water flow and infiltration model if the Green-Ampt parameters could be appropriately assumed based on average values from literature depending on the given soil texture classes. Furthermore, the effects that could lead to an inappropriate representation of infiltration with tabulated Green-Ampt parameters are studied, such as surface clogging, sub-grid rill-flow, and coarse DEM resolution. To investigate the general suitability of using average Green-Ampt parameters from literature dependent on soil texture classes, different small-scale test cases with available data for calibration are shown, where two of them are laboratory experiments and one is a rainfall-runoff experiment on a small plot in Senegal. Finally, a case study on flash floods in a desert region in Egypt is represented. The results show that in the laboratory experiments, the infiltration rates with average Green-Ampt parameters are underestimated, while for the field experiment in Senegal infiltration rates are overestimated. For the case study in Egypt, infiltration with Green-Ampt parameters from literature as well as with measured infiltration rates from double ring infiltrometer tests is strongly overestimated in the model. It is planned to conduct plot-scale rainfall-runoff experiments with a rainfall simulator for the study area in Egypt to better represent the natural conditions during heavy rainfalls and compare the measured infiltration rates with the ones from literature and double ring infiltrometer test.</p>

Abstract. Prediction of streamflow at ungauged catchments requires transfer of hydrologic information (e.g., model parameters, hydrologic indices, streamflow values) from gauged (donor) to ungauged (receiver) catchments. A common metric used for the selection of ideal donor catchments is the spatial proximity between donor and receiver catchments. However, it is not clear whether information transfer among nearby catchments is suitable across a wide range of climatic and geographic regions. We examine this issue using the data from 756 catchments within the continental United States. Each catchment is considered ungauged in turn and daily streamflow is simulated through distance-based interpolation of streamflows from neighboring catchments. Results show that distinct geographic regions exist in US where transfer of streamflow values from nearby catchments is useful for retrospective prediction of daily streamflow at ungauged catchments. Specifically, the high predictability catchments (Nash-Sutcliffe efficiency NS > 0.7) are confined to the Appalachian Mountains in eastern US, the Rocky Mountains, and the Cascade Mountains in the Pacific Northwest. Low predictability catchments (NS < 0.3) are located mostly in the drier regions west of Mississippi river, which demonstrates the limited utility of gauged catchments in those regions for predicting at ungauged basins. The results suggest that high streamflow similarity among nearby catchments (and therefore, good predictability at ungauged catchments) is more likely in humid runoff-dominated regions than in dry evapotranspiration-dominated regions. We further find that higher density and/or closer distance of gauged catchments near an ungauged catchment does not necessarily guarantee good predictability at an ungauged catchment.

<p>Rural Iowa towns often lack flood mitigation because of a lack of resources and inability to compete for federal assistance funds. The Federal Emergency Management Agency relies on a benefit-cost analysis which produces benefit-cost ratios (BCRs) for proposed projects to determine which communities receive funding, with an emphasis on the economic BCR, which compares potential future benefits with estimated capital costs. The FEMA requirement for an economic BCR is at least 0.75. The economic BCRs for mitigation projects in rural towns are often lower compared to those in urban centers due lower potential future benefits from lower building count and potential exposure. Here we use a multijurisdictional approach which analyzes flood mitigation at a watershed scale to join upstream agricultural potential future benefits with downstream potential avoided benefits in rural towns. We predicted BCRs of simulated flood retention wetlands using HAZUS-MH to find the potential future benefits a range of estimated capital costs via a percent reduction approach and a targeted peak flow approach to calculating wetland effects on peak flow.</p> <p>The percent reduction approach generated BCRs of over 0.75 in the Mud Creek watershed for estimated capital costs per wetland up to $177,400. However, the simulated flood retention wetlands did not generate BCRs high enough to meet the minimum requirement in the Hinkle Creek watershed by itself. However, a multijurisdictional approach is not limited to each watershed individually. When the simulated flood retention wetland projects in each watershed were combined, the BCRs were high enough to meet the FEMA requirement. The combined BCRs were over 0.75 for estimated capital costs up to $143,300.</p> <p>The targeted peak flow approach included BCRs which account for dry and wet antecedent soil moisture conditions and minimum, maximum and average peak flow change scenarios. The scenarios with dry antecedent soil moisture conditions created BCRs higher than wet antecedent soil moisture conditions. Further, the maximum peak change scenarios generated BCRs higher than average peak change scenarios, which in turn generated higher BCRs than the minimum peak change scenarios. In the Mud Creek watershed, the only scenario to generate BCRs above 0.75 for any part of the range of estimated capital costs was the maximum peak change scenario under dry antecedent soil moisture conditions. However, the maximum and average peak change scenarios under dry antecedent soil moisture conditions and the maximum peak change scenario under wet soil moisture conditions generated BCRs over 0.75 in the Hinkle Creek watershed. When the simulated flood retention wetland projects for both watersheds were combined, only the maximum peak change scenario under dry antecedent soil moisture conditions generated BCRs above 0.75. We found that a multijurisdictional approach is a viable method for rural watersheds to analyze potential flood mitigation projects to help increase their BCRs.</p>

The quickflow responses of six subcatchment areas in a small hill country catchment in the Craigieburn Range, South Island, New Zealand, were compared for a range of storm sizes, rainfall intensities and antecedent wetness conditions. Topography and soil characteristics suggested that all subcatchments would receive subsurface stormflow input, but that some would receive larger saturation overland flow inputs than others. Quickflow yields and response ratios were positively correlated with storm size and antecedent wetness conditions in the subcatchment most suited to producing saturation overland flow. In subcatchments more likely to be dominated by subsurface flow, quickflow yields and response ratios were positively correlated with storm size, but were either not correlated, or negatively correlated, with antecedent wetness. Quickflow responses were either not significantly or negatively correlated with rainfall intensity variables. Quickflow from the subcatchment most suited to produce saturation overland flow providing an increasing proportion of total catchment quickflow in larger storms and as antecedent conditions became wetter. Subcatchment responses varied greatly in space and time and there was less pattern to the variation than had been expected. Where topographic and pedologic conditions permit substantial responses to storm rainfall by both saturation overland flow and subsurface stormflow, simple topographic and soil indicators may not be useful guides to the relative importance of runoff mechanisms, or to the identification of runoff‐source areas.

The Green–Ampt infiltration equations are based on physical parameters of the soil that can be either measured or calculated reasonably easily. However, neither the infiltration rate equation nor the cumulative depth of infiltration equation is in a form that can be used easily in hydrologic modeling, as both equations are in an implicit form when time is the independent variable. Therefore iterative procedures must be used to find either the infiltration rate or cumulative depth at a specific time.Explicit infiltration equations have been developed, based on the Green–Ampt infiltration model. These equations describe the infiltration rate and the cumulative depth of infiltration with a maximum deviation from the Green–Ampt equations of 2.5% for any time. The derived equations have been put in dimensionless form by choosing appropriate length and time scales. Approximate values for the length and time scales for 11 different soil textures, ranging from sand to clay, are given. Key words: infiltration, Green–Ampt explicit equations, hydrology, hydrologic models, surface irrigation, surface irrigation models.

The Green–Ampt infiltration equation is an incomplete governing equation for rainfall infiltration due to the absence of an inertia term. The estimation of the capillary pressure head at the wetting front is difficult to determine. Thus, a major limitation of the Green–Ampt model is the constant, non‐zero surface ponding depth. This paper proposes an integrated rainfall infiltration model based on the Green–Ampt model and the SCS‐CN model. It achieves a complete governing equation for rainfall infiltration by momentum balance and the water budget based on the Green–Ampt assumption, and uses the curve number from the SCS‐CN method to calculate the initial abstraction, which is used as a basic parameter for the governing equation of the intensity of rainfall loss during the runoff period. The integrated rainfall infiltration model resolves the dilemma for capillary pressure head estimation, overcomes the limitation of constant, non‐zero surface ponding depth, and facilitates the calculation of runoff for individual flood simulations. Copyright © 2014 John Wiley & Sons, Ltd.

Infiltration is an important component of the hydrological cycle. It provides soil moisture in the vadose zone to support plant growth. This study was conducted to compare the validity of four infiltration models with measured values from the double ring infiltrometer. The parameters of the four models compared were estimated using the linear regression analysis. The C.C was used to show the performance of the predictability of the models. The RMSE, MAE and MBE were employed to check the anomalies between the predicted and the observed values. The results showed that, average values of the C.C ranged from 0.9294 - 0.9852. The average values of the RMSE were 4.0033, −17.489, 11.2400 and 49.8448; MAE were 3.1341, 15.9802, 10.6525, and 61.4736; and MBE were 0.0786, 9.5755, 0.0007 and 47.0204 for Philip, Horton, Green Ampt and Kostiakov respectively for the wetland soils. Statistical results also from the Fisher's multiple comparison test show that the mean infiltration rate estimated from the Green Ampt's, Philip's and Horton's model was not significantly different (p > 0.05) from the observed. The results indicated that the Kostiakov's model had the highest deviations as it overestimated the measured data in all the plots. Comparison of the statistical parameters C.C, RMSE, MAE, and MBE for the four models indicates that the Philip's model agreed well with the measured data and therefore, performed better than the Green Ampt's, Horton's and Kostiakov's models respectively in that order for Besease wetland soils. Estimation of infiltration rate by the Philip's model is important in the design of irrigation schemes and scheduling. Therefore, in the absence of measured infiltration data, the Philip's model could be used to produce infiltration information for inland valley bottom soils that exhibit similar characteristic as Besease wetland soils.

Arid and semi-arid regions present significant challenges in efficient irrigation management and mitigation of soil salinity. To understand the dynamics of water and solute movement, such as salt transport in soil, software tools like HYDRUS are widely utilized. Hydrus-1D uses linear finite elements to numerically solve the Richards equation for saturated-unsaturated water flow, and has been widely applied in irrigation management to focus on solute and water movement. This research focuses on estimating the hydraulic properties at field scale level using Kenyan soil data analyzed with soil spectroscopy and infiltration experiments. Saturated hydraulic conductivity (Ks) has been obtained by fitting data to infiltration obtained by the Green-Ampt (GA) model and Hydrus1D in three scenarios: with bounds on Ks and the product of front suction and effective porosity, assigning a uniform value to effective porosity and considering flow preferential paths. The results are compared with others pedotransfer functions (PTFs) and Hydrus-1D. The Hydrus-1D software was used to study the water retention curve due to different Ks estimations. The findings show significant variations in the Ks estimations, highlighting the impact of salinity and preferential flows in heterogeneous soils. The comparison of the results provides valuable insights into the dynamics of water and salinity, essential for irrigation management in these regions. This research emphasizes how crucial it is to choose and modify hydrological models for particular salinity situations and how important it is to take into account spatial variability and flow preferential paths when predicting and applying Ks through models. The results have significant implications for improving irrigation management and controlling soil salinity in semi-arid regions.   Keywords: Saturated hydraulic conductivity, Green-Ampt, HYDRUS-1D, irrigation management, soil salinity control.   "ACKNOWLEDGMENT This article belongs to PCI2020-120694-2 Project funded by MCIN/AEI/10.13039/ 501100011033 and the European Union “NextGenerationEU”/PRTR. We would like to thanks to One Planet Fellowship from African Women in Agricultural Research and Development (AWARD) and Agropolis Fondation for funding the analysis. “

Catchment response to precipitation is a complex, highly nonlinear, threshold-driven process influenced by various factors, including rainfall characteristics, topography, soil type, and antecedent soil moisture conditions. Identifying specific rainfall and soil moisture values beyond which runoff is generated—potentially leading to hazardous river discharges—is crucial in hydrology and fundamental for flash flood forecasting. However, this is not always straightforward, especially in data-scarce regions, due to ground data’s limited availability and representativeness. Satellite-based soil moisture retrievals provide a viable alternative for defining these thresholds, particularly in data-scarce catchments, though their application still needs to be explored. This study examines the ability of Soil Moisture Active Passive (SMAP) level 4 data to define surface runoff (SR) generation thresholds in six small catchments (i.e., smaller than 250 km2) with varied lithologies and land uses in the hilly and mountainous regions of central Italy—catchments representative of many in the Mediterranean. We analyze responses to 435 rainfall–runoff events, focusing on the antecedent soil moisture within the top 30 cm, estimated by SMAP level 4 (L4), up to 3 h before rainfall onset. Our findings show that the threshold-driven behavior of runoff generation can be effectively captured only by considering SMAP L4 antecedent soil moisture and rainfall depth together. In contrast, the use of rainfall or soil moisture alone is insufficient. This study demonstrates that SMAP L4 is a viable alternative to in situ observations for SR threshold identification, with practical applications for issuing flash flood warnings and alerts.

The quality of hydrological discharge simulations depends to a great extent on the uncertainties in the meteorological input and the model parameterization. To quantify these uncertainties, we adopt ensemble techniques in a 4‐year nowcast experiment for two nested flash‐flood–prone basins in the southern Swiss Alps.The spatiotemporal uncertainties in the weather radar quantitative precipitation estimates (QPE) were accounted for by applying an ensemble of 25 radar fields. To account for uncertainties in model parameterization, a Monte Carlo experiment was run to find 26 equifinal model realizations. The resulting parameter ensemble, consisting of 26 members, was run with precipitation input obtained from interpolated pluviometer data and with the deterministic operational weather radar QPE.To produce the discharge nowcast, the PREcipitation‐Runoff‐EVApotranspiration HRU Model (PREVAH) was used. PREVAH was calibrated for the main catchment Verzasca. The results for the sub‐catchment Pincascia are an independent internal verification of the nowcasting system.The three ensemble nowcasts and the two deterministic nowcasts are evaluated for a 4‐year time series and for two events included in that period. The event analysis shows no clear superiority for either pluviometer‐based or radar‐based nowcasts. The performance for single events depends heavily on the storm characteristics. However, the evaluation of the 4‐year nowcast shows that pluviometer‐based nowcasts outperform radar‐based nowcasts in the gauged and calibrated catchment and that there is added value in the application of parameter ensembles.For the small, ungauged catchment, the results achieved by the radar‐based nowcasts are superior to the pluviometer‐based nowcasts. Especially the radar ensemble proves to be of significant advantage for flash flood nowcasts in such catchments. Copyright © 2012 John Wiley & Sons, Ltd.

Determination of ponding condition and infiltration into layered soils under unsteady rainfall

Although a number of studies of the variation of soil transport with increase in slope angle have appeared, few include an information on the interaction of sheetwash and rainsplash on high slope angles, which is necessary to test Horton's proposed polynomial relationship. Virtually no studies are available which compare the influence of changes in soil type or antecedent moisture on established relationships. This paper reports the testing of eight soils from Alberta, Canada, under simulated rainfall on ten slopes from 3° to 30°. Material eroded was separated into that transported by rainsplash and that by sheetwash. In general, it was found that the influence of changes in slope angle on soil transport is best described by polynomial relationships, but these are shown to vary considerably between rainspash and sheetwash, between different soil types and for different antecedent moisture conditions. Despite careful control of all factors other than slope angle very high variability of results was experienced. Causes of variability are examined and the need for evaluation of the effect of test plot size on variability of results is suggested.