# Surface runoff and sediment yield modelling under no-till: chiselling and crop management effects

Fabio José Andres Schneider, Jean Paolo Gomes Minella, Ana Lúcia Londero, Dinis Deuschle, Gustavo Henrique Merten, Olivier Evrard, Olivier Cerdan, Alice Prates Bisso Dambroz

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## To cite this version:

Fabio José Andres Schneider, Jean Paolo Gomes Minella, Ana Lúcia Londero, Dinis Deuschle, Gustavo Henrique Merten, et al.. Surface runoff and sediment yield modelling under no-till: chiselling and crop management effects. Hydrological Processes, 2025, 39, pp.e70354. 10.1002/hyp.70354. cea-05410401

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RESEARCH ARTICLE

OPEN ACCESS

# Surface Runoff and Sediment Yield Modelling Under No-Till: Chiselling and Crop Management Effects

Fabio José Andres Schneider<fnref n="1" /> ID | Jean Paolo Gomes Minella<fnref n="1" /> | Ana Lúcia Londero<fnref n="1" /> | Dinis Deuschle<fnref n="1" /> |
Gustavo Henrique Merten² ID | Olivier Evrard³ ID | Olivier Cerdan⁴ | Alice Prates Bisso Dambroz<fnref n="1" />

**Correspondence:** Fabio José Andres Schneider (fjas.schneider@gmail.com)

**Received:** 18 April 2025 | **Revised:** 26 November 2025 | **Accepted:** 27 November 2025

**Keywords:** apparent infiltration | hillslope hydrology | hydrological monitoring | LISEM | no-till agriculture

## ABSTRACT

No-till conserves the soil by minimizing disturbance and maintaining crop residue on the surface; however, runoff generation and propagation on no-till hillslopes are not well understood due to the high cost and labor requirements of field monitoring. Hydrological models can help advance this understanding by simulating runoff, sediment yield, and infiltration under different management conditions. This study evaluated the performance of the LISEM model in simulating runoff and sediment yield on hillslopes under no-till agriculture with different soil management practices (with and without chiseling) and cover crop levels, and compared model-simulated infiltration (Green-Ampt method) with apparent infiltration derived from field observations. Rainfall events were monitored from 2014 to 2018 in experimental units of 0.6 ha and 2.5 ha. Nineteen events covering a range of rainfall intensities and seasons were recorded using manual and automatic measurements (2–5 min intervals). LISEM was calibrated and validated for runoff and sediment yield at both spatial scales. For calibration events, Green-Ampt infiltration was directly compared with apparent infiltration estimated from rainfall-runoff data. Model performance was assessed using NSE and PBIAS. Runoff simulations showed good calibration (NSE = 0.99) and satisfactory validation (NSE > 0.5). Sediment yield was well reproduced during calibration but showed lower accuracy in validation, requiring reduced soil cohesion to match observed values. Infiltration simulated by the Green-Ampt method correlated strongly with apparent infiltration (NSE > 0.98), especially at the larger scale, where spatial variability is lower. Overall, the results demonstrate that LISEM can adequately represent runoff processes and infiltration in no-till systems with different management practices and surface cover conditions. The model provides a useful tool for evaluating and designing conservation practices, such as broad-based terraces and retention or detention structures, aimed at mitigating excess runoff and soil loss in agricultural landscapes.

## 1 | Introduction

No-till is widely adopted worldwide—especially in South America and Brazil—due to its well-documented benefits to soil quality, including increased biological activity, carbon accumulation, water storage, improved structure and fertility, reduced erosion, and enhanced biodiversity (Boddey et al. 2010; Derpsch et al. 2010; Schick et al. 2017; Kassam

et al. 2018; Dang et al. 2020; Ferreira et al. 2020; Fuentes-Llanillo et al. 2021; Pott et al. 2023). Crop residues protect the soil surface by reducing raindrop impact and runoff velocity (Merten et al. 2015; Govers et al. 2000; Bradford and Huang 1994). However, intense rainfall, low infiltration rates and saturated or compacted soils can still generate surface runoff capable of detaching soil particles (Londero, Minella, Schneider, et al. 2021, 2021; Qin et al. 2019). At the catchment

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Hydrological Processes* published by John Wiley & Sons Ltd.

<footnote n="1">¹Department of Soils, Federal University of Santa Maria, Santa Maria, Brazil | ²Department of Civil Engineering, University of Minnesota-Duluth, Duluth, Minnesota, USA | ³Laboratoire Des Sciences et de L'environnement, UMR 8212 (CEA/CNRS/UVSQ-IPSL), Université Paris-Saclay, Gif-sur-Yvette Cedex, France | ⁴Risk and Prevention Division, Bureau de Recherches Géologiques et Minières (BRGM), Orléans, France</footnote>
scale, runoff propagation is governed by topography and sur-
face roughness (Didoné et al. 2021; Barros et al. 2021; Silva
et al. 2021). In Southern Brazil, where long hillslopes and
convergent areas enhance runoff energy, disruption of soil
cover and rill formation remain widespread even under no-till
(Morais and Cogo 2001; Rieke-Zapp and Nearing 2005). Thus,
no-till alone is often insufficient to control runoff in steep ag-
ricultural landscapes, requiring complementary runoff man-
agement practices to mitigate erosion, pollutant transport,
and flooding.

Understanding the processes that trigger soil erosion requires long-term, continuous, and often costly monitoring efforts, which provides the field-derived data necessary for model calibration and validation, used for evaluating land and runoff management scenarios (Werle et al. 2025). Hydrological monitoring of no-till agricultural hillslopes is particularly important to assess how precipitation is partitioned between infiltration and surface runoff, the latter being a major driver of soil erosion (Hawkins and Cundy 1987). Infiltration at the soil surface determines the fraction of rainfall that becomes overland flow and is influenced by soil texture, structure, antecedent moisture content, compaction, and land management (dos Santos et al. 2018; Rauber et al. 2025). Although no-till tends to enhance infiltration through residue cover and improved aggregation (Rauber et al. 2025), high spatial and temporal variability requires site-specific monitoring and modelling approaches. Infiltration can be assessed at the point scale using infiltrometers, or inferred at the hillslope or catchment scale through rainfall-runoff relationships, such as apparent infiltration, allowing for an integrated assessment over heterogeneous surfaces (de Barros, Minella, Tassi, et al. 2014; Koppe et al. 2022).

Various empirical and physically based models have been devel-
oped to represent infiltration (Assouline 2013). Horton's (1933)
empirical model describes the decay of infiltration capacity
over time, while physically conceptual models, such as Green
and Ampt's (1911), assume a sharp wetting front and accounts
for capillary suction and gravitational forces. Despite simplify-
ing assumptions, the Green-Ampt model provides a reasonable
balance between physical realism and computational simplicity,
and is widely used in hydrological and erosion modelling (Van
den Putte et al. 2013; Werle et al. 2025). Accurate representa-
tion of infiltration is essential for simulating runoff (and, conse-
quently, sediment transport and nutrient losses) in agricultural
catchments, which are therefore used for designing conservation
practices that protect soil and water resources (Silva et al. 2021;
Werle et al. 2025).

Quantifying these field-observed processes is essential for
improved modelling, avoiding soil and water degradation,
and minimising losses (Cerdan et al. 2002; Hessel and van
Asch 2003; Jetten et al. 2003; Nearing et al. 2005; Starkloff
and Stolte 2014; Pandey et al. 2016). Physically based models,
such as LISEM, WEPP, ANSWERS, and SHETRAN, offer de-
tailed descriptions of hydrological and erosive processes, but
require numerous spatially distributed parameters related to
soil and land use conditions (de Vente et al. 2013; Dymond
and Vale 2018). These models explicitly incorporate properties
such as hydraulic conductivity, antecedent moisture, texture,

soil properties, and surface characteristics for each cell,
using equations that describe infiltration, runoff, and ero-
sion processes (Jetten et al. 2003; Le Bissonnais et al. 2005).
Although soil detachment processes are similar across scales,
re-infiltration, deposition, connectivity, terrain, riparian veg-
etation, and cropland characteristics introduce nonlinearities
(Carson and Kirkby 1972). Thus, larger catchment areas do
not necessarily yield proportionally greater sediment loads
(Walling 1999; de Vente et al. 2007). Hillslope-scale stud-
ies are therefore essential for identifying runoff and erosion
mechanisms that are often overlooked at broader scales and
for improving model parameterization, particularly in no-
till systems characterised by deep soils, with slopes ranging
from 5% to 10%, and hillslope lengths often exceeding 1000 m
(Bertol et al. 2000; Morais and Cogo 2001; Tondello Barbosa
et al. 2021). However, the limited number of hillslope-scale
monitoring studies and evaluation of model efficiency for
representing runoff under no-till, make it difficult to incor-
porate this management system into hydrological and erosion
models.

Model calibration is often based on limited hydrological data
collected exclusively at the catchment outlet, which can intro-
duce substantial uncertainties, particularly in large catchments
where re-infiltration, deposition, and sediment delivery ratios
vary widely. Additionally, sediment yield measured at outlet
typically represents only a fraction of the total gross erosion that
occurs across hillslopes (Minella et al. 2014; de Vente et al. 2007;
Walling 1983). Proposing effective soil and water conserva-
tion practices for no-till crop systems requires accurate model
parametrization that reflect different soil surface conditions at
the hillslope scale.

Therefore, this study has two main objectives: (i) to model run-
off and sediment yield on hillslopes representative of no-till
agriculture in Southern Brazil using the LISEM model; and (ii)
to evaluate the accuracy of LISEM-estimated infiltration by
comparing it with field-measured apparent infiltration. These
analyses aim to improve the understanding of hydrological and
erosive processes under conservation agriculture and to support
the development of more reliable modelling approaches for sim-
ilar conditions.

2 | Materials and Methods

2.1 | Study Site

The monitoring units are located in a region representative of
both soils and climate of the grain production system in south-
ern Brazil (29°13′39″ S, 53°40′38″ W) (Figure 1). The climate
is classified as Cfa (humid subtropical with no dry season
and hot summers), characterized by well-distributed monthly
rainfall (100–170 mm) (Alvares et al. 2013) and a mean daily
reference evapotranspiration around 3–4 mm.day⁻¹ (Ferreira
et al. 2019). Rainfall erosivity is considered very strong, around
10 657 MJ.mm.ha⁻¹h⁻¹ y⁻¹, based on records from 1976 to
2018 and calculated using the equation proposed by Ramon
et al. (2017). The soils are highly weathered, deep, and con-
tain more than 30% clay. They are classified as Rhodic Nitisol
(Dystric) (IUSS Working Group WRB 2014), formed by extrusive
<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Location of the study area in Southern America with the monitored units. Where: NTC is the no-till zero order catchment; CLF is the monitoring unit with chiselling and with low phytomass amount; NCLF is the monitoring unit without chiselling and with low phytomass amount; CHF is the monitoring unit with chiselling and with high phytomass amount; and, NCHF is the monitoring unit without chiselling and with high phytomass amount. “Phytomass” refers to plant biomass.</figcaption></figure>

volcanic rocks (basalt), and are representative of those found across the Brazilian meridional plateau.

The monitoring units were paired and installed over a gentle hillslope with slopes ranging between 3% and 20%. Five units were monitored: four macroplots (~0.6 ha each) and one zero-order catchment (NTC, with an area of 2.4 ha) (Figure 1).

The NTC catchment measured 200 m in length and 135 m in width. Its topography is characterised by a negative plan curvature (convergent flow), forming a thalweg, and a negative profile curvature (convex). In contrast, the macroplots were ~100 m long and ~65 m wide, with zero plan curvature (no thalweg), and slightly negative profile curvature. The area where the monitoring units were installed was characterised by a topographical survey with a total station. This allowed for the optimal allocation of the units and the generation of a 1 × 1 m Numerical Elevation Model.

For each unit, soil physical properties (texture, saturated hydraulic conductivity, porosity, and bulk density) were analyzed at nine sampling points and three depths (5, 15, and 30 cm) to ensure homogeneity of the soil physical-hydric properties

within each unit and among them (Londero, Minella, Schneider, et al. 2021; Deuschle et al. 2019).

## 2.2 | Soil and Crop Management

All units were cultivated under a no-till system; however three management factors varied among them and were expected to influence hydrological and erosion processes:

* Chiselling: with chiselling (C) or without chiselling (NC);

* Soil cover input: with low phytomass (LF) or high phytomass (HF) inputs;

* Scale: long-length slope with a thalweg (NTC) or short-length slope without a thalweg (CLF, NCLF, CHF, and NCHF).

The four macroplots combined various chiselling and soil cover inputs: (1) CLF, (2) NCLF, (3) CHF, and (4) NCHC. The zero-order catchment (NTC) corresponded to the macroplot (2) NCLF condition in terms of absence of chiselling and soil cover input, and was therefore used to evaluate scale effects (in terms of slope
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Crop management throughout the monitored years. Where: NTC is the no-till zero-order catchment; CLF is the monitoring unit with chiselling and with low phytomass amount; NCLF is the monitoring unit without chiselling and with low phytomass amount; CHF is the monitoring unit with chiselling and with high phytomass amount; and, NCHF is the monitoring unit without chiselling and with high phytomass amount.</figcaption></figure>

length and thalweg). The combination of these management factors has the potential to alter infiltration, re-infiltration, surface runoff generation and velocity, splash and flow detachment, and sediment deposition. Thus, the hydrological behaviour of each unit was analysed in the models to evaluate their respective sensitivity to these no-till configurations.

Chiselling was performed twice during the entire monitoring period: at the beginning of the experiment (2014) and again in 2016.

Crop management followed different crop rotations. The LF condition (~4 tha⁻¹ year⁻¹ of dry matter) reflects the most commonly used phytomass amount by local farmers, whereas the HF condition (~12 tha⁻¹ year⁻¹) represents phytomass amounts achieved under improved crop rotation without fallow periods. In the NTC catchment, the LF condition was used from 2014 to 2017, after which the system transitioned to HF (Figure 2).

All units received the same fertiliser applications, seeding, pesticide treatments, and harvesting operations, and were managed using the same equipment and field layout. This ensures that the surface roughness was not influenced by differences in agricultural operations.

## 2.3 | Hydrological Monitoring

The entire monitoring period included 61 rainfall-runoff events in the NTC catchment (July 2014 to April 2018) and 27 events

in the macroplots (July 2015 to April 2018). For this study, 15 events from the NTC and 11 from the macroplots were selected (Table 1). Not all rainfall events were monitored in all units; thus, some events were monitored in both the NTC and macroplots, while others were recorded only in one scale. The selected events represent a range of rainfall magnitudes and seasonal conditions. Smaller magnitude events were not considered because they generated minimal runoff and sediment losses.

Rainfall measurements (mm and mm.h⁻¹) were obtained using daily manual rain gauges and an automatic rain gauge recording at 5-min intervals. Each monitoring unit was delineated with soil ridges to isolate its contributing area, and surface runoff was directed toward an H-type flume for measuring the streamflow rate and suspended sediment concentration.

At the H-type flumes (Figure 3), pressure transducers recorded continuous water level data during each rainfall event. Before their installation, water levels were measured manually every 10 min. From 2014 to 2016, only the NTC catchment had a pressure transducer, with readings every 2 min. In 2016, pressure transducers were installed in the macroplots, recording at 5-min intervals for all the monitoring units. Water depth was converted to surface runoff flow (q-l.s⁻¹) using specific rating curves for each H-type flume. The flume in the NTC was 0.6096 m wide and 0.4572 m wide in the macroplots. Manual water level measurements were taken at 10-min intervals or whenever notable changes occurred within the flume, using a staff gauge. These manual measurements were used as a reference to validate the automatic records, ensuring their accuracy and reliability. This
<table><caption>TABLE 1 | Antecedent 3-day and 24-h rainfall, total rainfall depth, mean rainfall intensity ($I_{mean}$), and maximum 30-min rainfall intensity ($I_{30}$) for all monitored events used in the calibration and validation of the LISEM model.</caption><thead><tr><th rowspan="2">Date</th><th rowspan="2">3-day<br>antecedent<br>rainfall</th><th rowspan="2">24-h antecedent<br>rainfall</th><th rowspan="2">Rainfall<br>depth (mm)</th><th rowspan="2">I<sub>mean</sub><br>(mm.<br>h<sup>-1</sup>)</th><th rowspan="2">I<sub>30</sub><br>(mm.<br>h<sup>-1</sup>)</th><th colspan="2">Monitoring unit</th></tr><tr><th>NTC</th><th>Macroplots</th></tr></thead><tbody><tr><td>06/24/2015</td><td>52</td><td>17</td><td>52</td><td>5</td><td>8</td><td>X</td><td></td></tr><tr><td>07/14/2015</td><td>29</td><td>29</td><td>19</td><td>3</td><td>19</td><td>X</td><td>X</td></tr><tr><td>07/20/2015</td><td>16</td><td>12</td><td>49</td><td>6</td><td>18</td><td></td><td>X</td></tr><tr><td>09/20/2015</td><td>85</td><td>69</td><td>20</td><td>3</td><td>19</td><td>X</td><td></td></tr><tr><td>10/08/2015</td><td>36</td><td>35</td><td>160</td><td>8</td><td>58</td><td>X</td><td>X</td></tr><tr><td>11/19/2015</td><td>13</td><td>1</td><td>73</td><td>47</td><td>107</td><td>X</td><td></td></tr><tr><td>12/14/2015</td><td>43</td><td>43</td><td>48</td><td>3</td><td>11</td><td>X</td><td></td></tr><tr><td>12/23/2015</td><td>60</td><td>31</td><td>58</td><td>35</td><td>52</td><td>X</td><td>X</td></tr><tr><td>12/24/2015</td><td>133</td><td>103</td><td>84</td><td>8</td><td>26</td><td>X</td><td>X</td></tr><tr><td>12/29/2015</td><td>2</td><td>2</td><td>41</td><td>9</td><td>31</td><td>X</td><td></td></tr><tr><td>10/17/2016</td><td>44</td><td>12</td><td>66</td><td>10</td><td>46</td><td>X</td><td></td></tr><tr><td>10/18/2016</td><td>162</td><td>118</td><td>44</td><td>6</td><td>22</td><td>X</td><td></td></tr><tr><td>05/26/2017</td><td>42</td><td>12</td><td>36</td><td>14</td><td>56</td><td></td><td>X</td></tr><tr><td>06/08/2017</td><td>6</td><td>0</td><td>98</td><td>5</td><td>21</td><td></td><td>X</td></tr><tr><td>10/11/2017</td><td>56</td><td>51</td><td>27</td><td>24</td><td>45</td><td></td><td>X</td></tr><tr><td>10/12/2017</td><td>99</td><td>72</td><td>22</td><td>13</td><td>26</td><td></td><td>X</td></tr><tr><td>01/21/2018</td><td>45</td><td>20</td><td>46</td><td>4</td><td>9</td><td>X</td><td>X</td></tr><tr><td>02/10/2018</td><td>20</td><td>20</td><td>92</td><td>59</td><td>91</td><td>X</td><td>X</td></tr><tr><td>02/20/2018</td><td>6</td><td>6</td><td>60</td><td>10</td><td>27</td><td>X</td><td></td></tr><tr><td>03/15/2018</td><td>0</td><td>0</td><td>82</td><td>13</td><td>83</td><td>X</td><td></td></tr></tbody></table>

Note: In the "Monitoring unit" columns, X indicates that the event was included in the LISEM simulations.

<figure><img src="image_6.png" /><figcaption>FIGURE 3 | Measurement techniques at the gauge station: NTC boundary and H-Type flume. Where: NTC is the no-till zero-order catchment.</figcaption></figure>
cross-verification process allowed for the identification and correction of potential errors in data collection or interpretation, thereby improving the overall quality of the dataset.

High temporal resolution precipitation (P) and surface runoff flow (q) measurements (2–5 min intervals) were essential for describing runoff generation and propagation and for improving model parameterization. The resulting hydrographs and hyetographs, from the combination of high temporal resolution and in-field monitoring, were critical for interpreting hydrological responses and for accurate modeling at this scale.

Suspended sediment concentration (SSC—mg.l⁻¹) was analyzed from manually collected water samples throughout the monitoring period. Sampling occurred every 15–30 min or according to water level changes, ensuring adequate representation of both rising and falling limbs of the hydrograph.

Samples were analysed in the laboratory using the evaporation method (Shreve and Downs 2005). Results were linearly interpolated to obtain 1-min SSC records. Sediment yield (SY) was calculated using SSC of the samples collected during the rainfall event. Sediment discharge (SD—g.s⁻¹) was determined by multiplying SSC and q, with appropriate unit conversions.

## 2.4 | Hydrological Modelling

The no-till conditions of the five monitoring units were implemented in the Limburg Soil Erosion Model (LISEM) version 5.97, with a temporal resolution of 20 s and a spatial resolution of 5 m.

LISEM is a physically based, distributed erosion model that includes two modules, one for runoff and a second for erosion. The model requires spatially distributed input parameters that describe hydraulic and mechanical properties of the soil surface and information derived from relief. Accurate characterization of soil hydraulic properties for each unit was essential due to the uncertainties associated with model parameterization under different no-till configurations.

All inputs were provided in a spatially distributed form. The digital elevation model (DEM) was used to propagate the fluxes and to define hydrograph characteristics. More detailed information on LISEM's input parameters and equations used in the runoff and erosion modules is provided in Appendix 2.

### 2.4.1 | Runoff Module

The input variables for the runoff module included rainfall intensity (mm h⁻¹), surface cover, leaf area index, plant height, roughness (Manning's n and micro-depression), saturated hydraulic conductivity (Ksat), soil porosity, and initial soil moisture.

Vegetation related parameters were measured in different seasons, considering plant growth stages and events for each monitoring unit and modelled event. These included litter storage (amount of cover provided by crops, expressed in millimetres), vegetation height (vertical distance from the ground to the top

of the vegetation canopy), fractional vegetation cover (proportion of the ground covered by vegetation), obtained from field visual observations, and leaf area index (LAI—total leaf area per unit ground area), which was obtained from the literature and used to determine the maximum canopy storage (Hamada and Pinto 2001; Romano 2005).

To determine micro-depression storage capacity, a pin-profilometer (with a length of 1 m and a 2 cm resolution) was used in two directions at six points in each macroplot and nine points in the NTC.

The Green and Ampt method was selected to simulate soil infiltration. Physical property parameters were obtained by analyzing undisturbed soil samples. Ksat and soil porosity were measured in the laboratory with three replicates at three depths (5, 15, 30 cm) at eight points in the NTC and six points in the macroplots.

Antecedent soil moisture content was determined by dividing the water retention curve into three categories: saturated soil (0–0.1 mca), field capacity (0.1–1 mca), and dry soil (> 1 mca). For each modelled event, the 3-day antecedent rainfall (as listed in Table 1) was used to estimate the soil moisture deficit following the Curve Number (NRCS 2009) approach. Rainfall depth > 60 mm indicated saturated soil; 20–60 mm indicated field capacity; and < 20 mm indicated dry soil. Based on the calibrated initial soil moisture content, the soil water potential was then estimated.

Considering that infiltration simulation is crucial for determining the runoff magnitude, infiltration results generated by the LISEM during the calibration were compared with the directly measured apparent infiltration. This ensured that LISEM's infiltration estimates were consistent with the observed hillslope-scale infiltration. Apparent infiltration in the monitored units was studied by Koppe et al. (2022), following the method of Hawkins and Cundy (1987). This technique estimates apparent infiltration by subtracting runoff from rainfall, as shown in Equation (1).

$$I_a = R - Q \quad (1)$$

where Ia is apparent infiltration (mm or mm.h⁻¹); R is rainfall (mm or mm.h⁻¹); Q is runoff (mm or mm.h⁻¹).

In this study, apparent infiltration for each time interval of the selected rainfall events was calculated. Because this method evaluates the monitoring units as a whole, the resulting apparent infiltration is assumed to closely represent actual conditions, with minimal evapotranspiration losses during rainfall events. However, the method does not account for water retained in the vegetation canopy or crop residues, which can temporarily intercept a portion of the precipitation. Nevertheless, under high-magnitude rainfall events and high humidity, this fraction is minimal and considered negligible. Apparent infiltration was adjusted using Equation (2), where maximum infiltration rate (Im) was determined as a function of precipitation (Rose 2004).

$$I_a(R) = I_m \times \left[ 1 - e^{-\frac{R}{Im}} \right] \quad (2)$$
where $I_a$ is apparent infiltration rate (mm.h⁻¹); $R$ is rainfall intensity (mm.h⁻¹); $I_m$ is maximum apparent infiltration (mm.h⁻¹).

The value of $I_m$ was determined using all events monitored in each unit. With the defined $I_m$, $I_a$ was calculated for each time interval, rainfall event, and monitoring unit.

Apparent infiltration was then compared with LISEM simulated infiltration to verify the model's ability to represent the water infiltration process using the Green and Ampt method.

The one-dimensional kinematic wave method (1D) was used to simulate flow propagation processes, based on the local drain direction (LDD) network. Surface runoff propagation along the hillslope was represented using Manning's equation, with Manning's n coefficient representing the surface conditions. Given the no-tillage adopted in the study area, crop residue was considered a key factor in determining this coefficient. For this purpose, we adopted Manning's n coefficient values proposed by Engman (1986), which were derived from USDA monitoring units. For phytomass amounts between 0.6 and 2.4 Mg.ha⁻¹, a Manning's n value of 0.07 was used, and 0.3 for phytomass greater than 2.4 Mg.ha⁻¹ (Engman 1986).

## 2.4.2 | Erosion Module

The erosion module required information on soil aggregate stability, rainfall kinetic energy, characteristic particle diameters ($D_{50}$ and $D_{90}$), and soil cohesion. Aggregate stability was measured following the water-drop method described by Moro (2010), in which the number of drops required to reduce a soil aggregate by half is recorded. In the study, aggregate stability was set at 200 drops. Particle size was determined using a laser particle size analyser, and the $D_{50}$ and $D_{90}$ corresponded to 50 and 110 µm, respectively.

Soil cohesion, which is related to soil detachment by overland flow, was measured in the field using a Torvane Shear Device under saturated conditions. Six points were analysed in each monitoring unit, with three replicates, yielding maximum, average, and minimum values of 67, 52, and 46 kPa, respectively. Random roughness values were adjusted according to the crop growth stage. Higher random roughness was measured during events shortly after sowing, whereas roughness decreased over time with mulch degradation, influenced by temperature and rainfall.

## 2.5 | Calibration and Validation

For the NTC catchment, 15 events were used for calibration, and 11 were used for the macroplots. Some events occurred in both monitoring units, while others were unique to either the NTC or the macroplots.

Calibration of rainfall events in LISEM was based on the model's ability to accurately represent surface runoff volume and to simulate the shape of the hydrograph and sediment discharge. The individually analysed variables were peak runoff, runoff volume, and sediment yield. Model performance was evaluated

using Percent Bias (PBIAS), in which values between 15% and 25% are considered satisfactory for peak runoff and runoff volume, and values below 10% are considered excellent. For sediment yield, performance criteria are more flexible, with PBIAS values up to 55% considered satisfactory (Moriasi et al. 2015). Hydrograph and sediment discharge performance were evaluated using the Nash–Sutcliffe efficiency (NSE), where values below 0.5 are unsatisfactory, values between 0.5 and 0.65 are satisfactory, values between 0.65 and 0.75 are good, and values between 0.75 and 1 are very good (Moriasi et al. 2015).

To validate the model, we applied K-fold cross-validation, dividing the dataset into three groups according to $I_{30}$, ensuring that all rainfall events were used once during the validation step. Cross-validation improved validation robustness (Wong and Yang 2017; Arabameri et al. 2021). This approach systematically partitions and rotates events between calibration and validation sets and is particularly effective in scenarios where the number of rainfall events is limited. The rainfall events selected for the procedure are shown in the Tables 2 and 3 for the NTC and macroplots, respectively. For the validation runs, hydrological parameters were set to the median of the calibrated parameter values ($K_{sat}$, Manning's n coefficient, random roughness, and soil cohesion), while soil moisture was adjusted according to antecedent rainfall conditions.

The main parameters used for LISEM calibration were antecedent moisture, $K_{sat}$, Manning's n coefficient, random roughness, and soil cohesion. These parameters were selected because they play a critical role in the model's hydrological and erosion processes and exhibit high sensitivity, directly affecting simulated runoff, erosion, and sediment yield. All remaining parameters were kept as measured in the field made during each rainfall event. Given the high spatial and temporal variability of $K_{sat}$, this parameter was adjusted to improve runoff volume estimation, due to its direct influence on infiltration capacity. Manning's n coefficient, which directly affects flow velocity, was calibrated to better reproduce the hydrograph, particularly peak runoff. Manning's n also shows significant temporal variability due to changes in surface conditions, driven by variations in crop cover and residue levels over time.

# 3 | Results and Discussion

## 3.1 | Results of the Calibrated Events With the LISEM Model

The results for runoff peak ($q_{peak}$), runoff volume ($Q$), and sediment yield (SY), along with their respective PBIAS values, are presented in Table 4. Overall, nearly all results for the three variables were satisfactory, with most events having excellent performance.

For the NTC catchment, the calibration performance ranged from satisfactory to very good, based on commonly accepted statistical criteria. Among the 11 calibrated rainfall events, runoff volume was rated as good (PBIAS < ±15) for four events (06/24/2015, 09/20/2015, 01/21/2018, and 03/15/2018), and as very good (PBIAS < ±10) for the remaining ones, following Moriasi et al. (2007). For peak runoff, only two events showed
<table><caption>TABLE 2 | Rainfall events assigned to each K-fold calibration and validation step for the NTC monitoring unit.</caption><thead><tr><th>Monitoring unit</th><th>K-fold</th><th>Simulation</th><th>Rainfall event</th><th>Rainfall depth (mm)</th><th>I<sub>30</sub> (mm.h<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="24">NTC</td><td rowspan="12">1</td><td rowspan="12">Calibration</td><td>07/14/2015</td><td>19</td><td>19</td></tr><tr><td>09/20/2015</td><td>20</td><td>19</td></tr><tr><td>10/08/2015</td><td>160</td><td>58</td></tr><tr><td>11/19/2015</td><td>73</td><td>107</td></tr><tr><td>12/14/2015</td><td>48</td><td>11</td></tr><tr><td>12/23/2015</td><td>58</td><td>52</td></tr><tr><td>12/24/2015</td><td>84</td><td>26</td></tr><tr><td>12/29/2015</td><td>15</td><td>27</td></tr><tr><td>01/21/2018</td><td>46</td><td>9</td></tr><tr><td>02/20/2018</td><td>60</td><td>27</td></tr><tr><td rowspan="6">Validation</td><td>06/24/2015</td><td>52</td><td>8</td></tr><tr><td>03/15/2018</td><td>82</td><td>83</td></tr><tr><td>02/10/2018</td><td>92</td><td>91</td></tr><tr><td>10/17/2016</td><td>66</td><td>46</td></tr><tr><td>10/18/2016</td><td>44</td><td>22</td></tr><tr><td>06/24/2015</td><td>52</td><td>8</td></tr><tr><td rowspan="12">2</td><td rowspan="12">Calibration</td><td>09/20/2015</td><td>20</td><td>19</td></tr><tr><td>10/08/2015</td><td>160</td><td>58</td></tr><tr><td>11/19/2015</td><td>73</td><td>107</td></tr><tr><td>12/23/2015</td><td>58</td><td>52</td></tr><tr><td>12/29/2015</td><td>15</td><td>27</td></tr><tr><td>10/17/2016</td><td>66</td><td>46</td></tr><tr><td>10/18/2016</td><td>44</td><td>22</td></tr><tr><td>02/10/2018</td><td>92</td><td>91</td></tr><tr><td>03/15/2018</td><td>82</td><td>83</td></tr><tr><td rowspan="6">Validation</td><td>02/20/2018</td><td>60</td><td>27</td></tr><tr><td>01/21/2018</td><td>46</td><td>9</td></tr><tr><td>12/14/2015</td><td>48</td><td>11</td></tr><tr><td>07/14/2015</td><td>19</td><td>19</td></tr><tr><td>12/24/2015</td><td>84</td><td>26</td></tr><tr><td>06/24/2015</td><td>52</td><td>8</td></tr><tr><td rowspan="12">3</td><td rowspan="12">Calibration</td><td>07/14/2015</td><td>19</td><td>19</td></tr><tr><td>12/14/2015</td><td>48</td><td>11</td></tr><tr><td>12/24/2015</td><td>84</td><td>26</td></tr><tr><td>10/17/2016</td><td>66</td><td>46</td></tr><tr><td>10/18/2016</td><td>44</td><td>22</td></tr><tr><td>01/21/2018</td><td>46</td><td>9</td></tr><tr><td>02/10/2018</td><td>92</td><td>91</td></tr><tr><td>02/20/2018</td><td>60</td><td>27</td></tr><tr><td>03/15/2018</td><td>82</td><td>83</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 2 | (Continued)</caption><thead><tr><th>Monitoring unit</th><th>K-fold</th><th>Simulation</th><th>Rainfall event</th><th>Rainfall depth (mm)</th><th>I<sub>30</sub> (mm.h<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="5">NTC</td><td rowspan="5">3</td><td rowspan="5">Validation</td><td>11/19/2015</td><td>73</td><td>107</td></tr><tr><td>09/20/2015</td><td>20</td><td>19</td></tr><tr><td>12/29/2015</td><td>41</td><td>31</td></tr><tr><td>12/23/2015</td><td>58</td><td>52</td></tr><tr><td>10/08/2015</td><td>160</td><td>58</td></tr></tbody></table>

Note: Where: NTC = no-till zero order catchment.

PBIAS values above ±15%, which is within the satisfactory range (±15 to ±25), while the remaining events achieved very good performance (PBIAS < ±10). Overall, the hydrological calibration demonstrated robust and consistent performance across all rainfall events.

Six rainfall events with available sediment yield data were calibrated for the NTC. Among these, only the events of 07/14/2015 and 10/17/2016 presented PBIAS values above 15% (classified as good), whereas the remaining events achieved very good performance (PBIAS < ±15), based on the thresholds proposed by Moriasi et al. (2007). Although these two events showed higher PBIAS values, the soil loss differences were minimal, 4 kg for the 07/14/2015 event and 3 kg for the 10/17/2016 event, considering the 2.4 ha area of the NTC catchment. These results suggest that the model effectively captured sediment yield dynamics within the catchment.

This performance can be largely attributed to the physical and environmental characteristics of the NTC catchment, particularly the role of vegetative cover and no-till practices in reducing sediment and water losses. The low sediment yield and water loss observed during these events can be attributed to the dense vegetative cover, which reduces detachment by stabilizing the soil and intercepting rainfall energy. No-till provides protection against raindrop impact, preventing soil crust formation and minimizing soil loss (Almeida et al. 2021). The limited water loss also suggests that runoff lacked sufficient erosive energy (Wolschick et al. 2021).

NSE values for both hydrographs and sediment discharge in the NTC catchment indicated very good model performance (0.75–1), typically around 0.90, demonstrating strong agreement between observed and simulated data. However, the event of 10/17/2016 yielded an NSE of 0.76 (satisfactory), while the events on 10/18/2016 and 01/21/2018 showed unsatisfactory performance (NSE < 0.50). These deviations may be attributed to uncertainties in input data, model parameterization, or atypical rainfall-runoff dynamics during these specific events.

Figure 4 illustrates the simulation of two high-intensity rainfall events, for which the model satisfactorily represented runoff propagation and sediment discharge.

Sediment discharge in the NTC catchment showed a very good result only for the rainfall event of 12/23/2015. Lower soil disturbance and higher aggregation increased soil cohesion, making soil detachment more difficult to simulate. Nonetheless, sediment yield calibration showed excellent results for the NTC catchment.

Calibration for the CLF and NCLF macroplots also resulted in good performance for $q_{peak}$, runoff volume, and sediment yield.

The event of 10/08/2015 showed good performance (±15 < PBIAS < ±30) for all three variables. Its high rainfall intensity (maximum 30 min intensity ($I_{30}$) of 58 mm.h$^{-1}$) combined with the small size of the monitoring unit (~0.6 ha) contributed to a shorter concentration time. Manual water level and SSC measurements (every 10 to 15 min) may have introduced uncertainty and underestimated observed water level values due to low temporal resolution, meaning that the simulated values could potentially be closer to the actual hydrological response.

The event of 01/21/2018 was of low magnitude as initial rainfall increased soil moisture and runoff was generated only after saturation. Although LISEM provides multiple options for simulating infiltration, the Green-Ampt method was applied here. This method assumes Hortonian flow and uniform contribution to surface runoff over the entire area, particularly under saturated soil conditions (Bronstert et al. 2014). When the soil is not saturated, LISEM struggles to reproduce heterogeneous runoff patterns, due to the heterogeneity of saturated zones.

For the NCLF macroplot, only one event (01/21/2018) had a PBIAS > ±25 for $q_{peak}$, while runoff volume for the 07/14/2015 event was unsatisfactory (PBIAS > ±55). Sediment yield PBIAS remained < ±30 across the four analysed events for both CLF and NCLF. $q_{peak}$ and runoff volume results were negatively affected by the 01/21/2018 and 07/14/2015 events, respectively. These poorer performances can be attributed to the units' small size and their distinct responses to rainfall events (De Roo and Jetten 1999). LISEM may not have accurately captured these processes. In a study conducted in southern Brazil, LISEM calibration performed well for runoff and sediment yield in small catchments; however, some parameters, such as saturated hydraulic conductivity ($K_{sat}$), initial soil moisture, and erosion-related parameters, required adjustments. Validation showed weaker agreement between the simulated and observed values, emphasising the need for long-term hydrosedimentological monitoring and detailed characterisation of soil parameters, which could be crucial for improving validation results (Ebling et al. 2024).

The units were bordered by embankments to prevent external water inflow. At the base of the plot, runoff was directed toward the H-flume, but the embankment system can introduce delays, especially in the units of linear shape, meaning that
<table><caption>TABLE 3 | Rainfall events assigned to each K-fold calibration and validation step for the macroplots.</caption><thead><tr><th>Monitoring unit</th><th>K-fold</th><th>Simulation</th><th>Rainfall event</th><th>Rainfall depth (mm)</th><th>I<sub>30</sub> (mm.h<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="24">Macroplots</td><td rowspan="12">1</td><td rowspan="12">Calibration</td><td>07/20/2015</td><td>49</td><td>18</td></tr><tr><td>10/08/2015</td><td>160</td><td>58</td></tr><tr><td>12/24/2015</td><td>84</td><td>26</td></tr><tr><td>05/26/2017</td><td>36</td><td>56</td></tr><tr><td>06/08/2017</td><td>98</td><td>21</td></tr><tr><td>10/12/2017</td><td>22</td><td>26</td></tr><tr><td>02/10/2018</td><td>92</td><td>91</td></tr><tr><td>01/21/2018</td><td>46</td><td>9</td></tr><tr><td>10/11/2017</td><td>27</td><td>45</td></tr><tr><td>07/14/2015</td><td>19</td><td>19</td></tr><tr><td>12/23/2015</td><td>58</td><td>52</td></tr><tr><td rowspan="12">2</td><td rowspan="12">Calibration</td><td>07/14/2015</td><td>19</td><td>19</td></tr><tr><td>07/20/2015</td><td>49</td><td>18</td></tr><tr><td>12/23/2015</td><td>58</td><td>52</td></tr><tr><td>10/11/2017</td><td>27</td><td>45</td></tr><tr><td>10/12/2017</td><td>22</td><td>26</td></tr><tr><td>01/21/2018</td><td>46</td><td>9</td></tr><tr><td>02/10/2018</td><td>92</td><td>91</td></tr><tr><td>05/26/2017</td><td>36</td><td>56</td></tr><tr><td>06/08/2017</td><td>98</td><td>21</td></tr><tr><td>10/08/2015</td><td>160</td><td>58</td></tr><tr><td>12/24/2015</td><td>84</td><td>26</td></tr><tr><td rowspan="12">3</td><td rowspan="12">Calibration</td><td>07/14/2015</td><td>19</td><td>19</td></tr><tr><td>10/08/2015</td><td>160</td><td>58</td></tr><tr><td>12/23/2015</td><td>58</td><td>52</td></tr><tr><td>12/24/2015</td><td>84</td><td>26</td></tr><tr><td>05/26/2017</td><td>36</td><td>56</td></tr><tr><td>06/08/2017</td><td>98</td><td>21</td></tr><tr><td>10/11/2017</td><td>27</td><td>45</td></tr><tr><td>01/21/2018</td><td>46</td><td>9</td></tr><tr><td>02/10/2018</td><td>92</td><td>91</td></tr><tr><td>10/12/2017</td><td>22</td><td>26</td></tr><tr><td>07/20/2015</td><td>49</td><td>18</td></tr></tbody></table>

the runoff first reaches the embankment before being directed to the flume. In contrast, in the NTC catchment, runoff naturally converges toward the thalweg, where the H-flume is located, improving model performance at this scale. Accurate representation of flow pathways depends on the DEM and the LDD, which determine runoff propagation (Dávila-Hernández et al. 2022).

Hydrograph modeling for CLF and NCLF showed similar difficulties. The 07/14/2015 event exhibited delays possibly due to inconsistencies in manually collected data. The 01/21/2018 event was complex because runoff was not generated initially, and only occurred later, resulting in a PBIAS > ±25. Sediment discharge in the CFL macroplot is difficult to compare with NCLF, partly due to differences in SSC sampling frequency. Manual sampling
<table><caption>TABLE 4 | PBIAS statistics for runoff peak ($q_{peak}$), runoff volume (Q), and sediment yield (SY) for each calibrated rainfall event.</caption><thead><tr><th rowspan="2">Monitoring unit</th><th rowspan="2">Event</th><th rowspan="2">q<sub>peako</sub><br>(L s<sup>-1</sup>)</th><th rowspan="2">q<sub>peaks</sub><br>(L s<sup>-1</sup>)</th><th rowspan="2">Q<sub>o</sub> (m<sup>3</sup>)</th><th rowspan="2">Q<sub>s</sub> (m<sup>3</sup>)</th><th rowspan="2">SY<sub>o</sub> (kg)</th><th rowspan="2">SY<sub>s</sub> (kg)</th><th colspan="3">PBIAS (%)</th></tr><tr><th>q<sub>peak</sub></th><th>Q</th><th>SY</th></tr></thead><tbody><tr><td rowspan="10">NTC</td><td>06/24/2015</td><td>18</td><td>18</td><td>321</td><td>357</td><td>NA</td><td>NA</td><td>-3.4</td><td>11.1</td><td>NA</td></tr><tr><td>07/14/2015</td><td>13</td><td>16</td><td>50</td><td>45</td><td>22</td><td>26</td><td>23.4</td><td>-8.3</td><td>21.3</td></tr><tr><td>09/20/2015</td><td>6</td><td>6</td><td>18</td><td>15</td><td>NA</td><td>NA</td><td>2.6</td><td>-15.7</td><td>NA</td></tr><tr><td>10/08/2015</td><td>354</td><td>370</td><td>1723</td><td>1722</td><td>79</td><td>74</td><td>4.7</td><td>0</td><td>-6.9</td></tr><tr><td>11/19/2015</td><td>457</td><td>472</td><td>905</td><td>822</td><td>NA</td><td>NA</td><td>3.2</td><td>-9.2</td><td>NA</td></tr><tr><td>12/14/2015</td><td>29</td><td>30</td><td>203</td><td>199</td><td>NA</td><td>NA</td><td>4.4</td><td>-2.3</td><td>NA</td></tr><tr><td>12/23/2015</td><td>283</td><td>290</td><td>1205</td><td>1102</td><td>448</td><td>428</td><td>2.5</td><td>-8.5</td><td>-4.5</td></tr><tr><td>12/24/2015</td><td>138</td><td>141</td><td>1023</td><td>1007</td><td>108</td><td>109</td><td>2.1</td><td>-1.6</td><td>0.7</td></tr><tr><td>12/29/2015</td><td>51</td><td>50</td><td>74</td><td>77</td><td>NA</td><td>NA</td><td>-0.5</td><td>3.5</td><td>NA</td></tr><tr><td>10/17/2016</td><td>19</td><td>22</td><td>41</td><td>39</td><td>14</td><td>11</td><td>21.4</td><td>-6</td><td>-19.1</td></tr><tr><td>10/18/2016</td><td>22</td><td>22</td><td>119</td><td>131</td><td>2</td><td>3</td><td>2.7</td><td>9.6</td><td>14.2</td></tr><tr><td rowspan="10">CLF</td><td>01/21/2018</td><td>198</td><td>189</td><td>330</td><td>380</td><td>182</td><td>174</td><td>-5</td><td>15.1</td><td>-4.7</td></tr><tr><td>02/10/2018</td><td>360</td><td>357</td><td>766</td><td>761</td><td>NA</td><td>NA</td><td>-1</td><td>-0.7</td><td>NA</td></tr><tr><td>02/20/2018</td><td>49</td><td>44</td><td>115</td><td>130</td><td>NA</td><td>NA</td><td>-9.7</td><td>13.2</td><td>NA</td></tr><tr><td>03/15/2018</td><td>18</td><td>18</td><td>321</td><td>357</td><td>NA</td><td>NA</td><td>-3.4</td><td>11.1</td><td>NA</td></tr><tr><td>07/14/2015</td><td>3</td><td>3</td><td>8</td><td>6</td><td>11</td><td>11</td><td>6.1</td><td>-16.8</td><td>-6.4</td></tr><tr><td>07/20/2015</td><td>10</td><td>10</td><td>47</td><td>47</td><td>19</td><td>16</td><td>-8.7</td><td>-0.2</td><td>-16.8</td></tr><tr><td>10/08/2015</td><td>92</td><td>110</td><td>318</td><td>399</td><td>25</td><td>20</td><td>19.5</td><td>25.2</td><td>-18.6</td></tr><tr><td>12/23/2015</td><td>70</td><td>67</td><td>284</td><td>265</td><td>103</td><td>103</td><td>-4.1</td><td>-6.6</td><td>0.6</td></tr><tr><td>12/24/2015</td><td>48</td><td>38</td><td>287</td><td>313</td><td>69</td><td>67</td><td>-21.2</td><td>9</td><td>-3.2</td></tr><tr><td>05/26/2017</td><td>29</td><td>29</td><td>49</td><td>49</td><td>NA</td><td>NA</td><td>2.1</td><td>-0.1</td><td>NA</td></tr><tr><td>06/08/2017</td><td>6</td><td>6</td><td>43</td><td>42</td><td>NA</td><td>NA</td><td>2.9</td><td>-1.8</td><td>NA</td></tr><tr><td rowspan="10">NCLF</td><td>10/11/2017</td><td>27</td><td>26</td><td>35</td><td>37</td><td>NA</td><td>NA</td><td>-4.9</td><td>6.7</td><td>NA</td></tr><tr><td>10/12/2017</td><td>18</td><td>18</td><td>39</td><td>40</td><td>NA</td><td>NA</td><td>-3.4</td><td>2.5</td><td>NA</td></tr><tr><td>01/21/2018</td><td>11</td><td>5</td><td>25</td><td>16</td><td>NA</td><td>NA</td><td>-53.5</td><td>37.6</td><td>NA</td></tr><tr><td>02/10/2018</td><td>88</td><td>87</td><td>122</td><td>119</td><td>NA</td><td>NA</td><td>-0.8</td><td>-2.8</td><td>NA</td></tr><tr><td>07/14/2015</td><td>3</td><td>3</td><td>8</td><td>6</td><td>26</td><td>24</td><td>7.4</td><td>-26</td><td>-8.5</td></tr><tr><td>07/20/2015</td><td>11</td><td>12</td><td>60</td><td>63</td><td>41</td><td>36</td><td>2.3</td><td>4.7</td><td>-12.4</td></tr><tr><td>10/08/2015</td><td>92</td><td>104</td><td>386</td><td>369</td><td>48</td><td>37</td><td>12.5</td><td>-4.3</td><td>-23.4</td></tr><tr><td>12/23/2015</td><td>70</td><td>77</td><td>307</td><td>285</td><td>158</td><td>151</td><td>9.1</td><td>-6.9</td><td>-4.8</td></tr><tr><td>12/24/2015</td><td>52</td><td>38</td><td>307</td><td>313</td><td>129</td><td>136</td><td>-27.5</td><td>1.8</td><td>5.5</td></tr><tr><td>05/26/2017</td><td>70</td><td>68</td><td>92</td><td>98</td><td>NA</td><td>NA</td><td>-2.9</td><td>5.7</td><td>NA</td></tr><tr><td>06/08/2017</td><td>9</td><td>9</td><td>80</td><td>67</td><td>NA</td><td>NA</td><td>0.7</td><td>-16.3</td><td>NA</td></tr><tr><td>10/11/2017</td><td>38</td><td>39</td><td>49</td><td>48</td><td>NA</td><td>NA</td><td>3.4</td><td>-1.9</td><td>NA</td></tr><tr><td>10/12/2017</td><td>26</td><td>27</td><td>49</td><td>45</td><td>NA</td><td>NA</td><td>4.5</td><td>-9.7</td><td>NA</td></tr><tr><td>01/21/2018</td><td>20</td><td>5</td><td>40</td><td>16</td><td>NA</td><td>NA</td><td>-73.1</td><td>-59.8</td><td>NA</td></tr><tr><td>02/10/2018</td><td>150</td><td>139</td><td>184</td><td>194</td><td>NA</td><td>NA</td><td>-6.9</td><td>5.6</td><td>NA</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 4 | (Continued)</caption><thead><tr><th rowspan="2">Monitoring unit</th><th rowspan="2">Event</th><th rowspan="2">q<sub>peako</sub><br>(L s<sup>-1</sup>)</th><th rowspan="2">q<sub>peaks</sub><br>(L s<sup>-1</sup>)</th><th rowspan="2">Q<sub>o</sub> (m<sup>3</sup>)</th><th rowspan="2">Q<sub>s</sub> (m<sup>3</sup>)</th><th rowspan="2">SY<sub>o</sub> (kg)</th><th rowspan="2">SY<sub>s</sub> (kg)</th><th colspan="3">PBIAS (%)</th></tr><tr><th>q<sub>peak</sub></th><th>Q</th><th>SY</th></tr></thead><tbody><tr><td rowspan="12">CHF</td><td>07/14/2015</td><td>2</td><td>2</td><td>6</td><td>3</td><td>1</td><td>1</td><td>22.5</td><td>-48.7</td><td>-9.2</td></tr><tr><td>07/20/2015</td><td>6</td><td>6</td><td>34</td><td>23</td><td>5</td><td>5</td><td>8.4</td><td>-32.3</td><td>-3.9</td></tr><tr><td>10/08/2015</td><td>81</td><td>108</td><td>329</td><td>334</td><td>12</td><td>8</td><td>33.9</td><td>1.5</td><td>-32.6</td></tr><tr><td>12/23/2015</td><td>61</td><td>74</td><td>271</td><td>265</td><td>90</td><td>87</td><td>22.4</td><td>-2.5</td><td>-4.1</td></tr><tr><td>12/24/2015</td><td>37</td><td>34</td><td>235</td><td>235</td><td>54</td><td>55</td><td>-6.9</td><td>0</td><td>1.5</td></tr><tr><td>05/26/2017</td><td>46</td><td>45</td><td>82</td><td>71</td><td>NA</td><td>NA</td><td>-3.2</td><td>-13.6</td><td>NA</td></tr><tr><td>06/08/2017</td><td>3</td><td>4</td><td>37</td><td>31</td><td>NA</td><td>NA</td><td>11.4</td><td>-16</td><td>NA</td></tr><tr><td>10/11/2017</td><td>21</td><td>21</td><td>35</td><td>33</td><td>NA</td><td>NA</td><td>2.6</td><td>-4.1</td><td>NA</td></tr><tr><td>10/12/2017</td><td>17</td><td>19</td><td>38</td><td>38</td><td>NA</td><td>NA</td><td>10.1</td><td>0.3</td><td>NA</td></tr><tr><td>01/21/2018</td><td>3</td><td>3</td><td>7</td><td>8</td><td>NA</td><td>NA</td><td>1.3</td><td>14.8</td><td>NA</td></tr><tr><td>02/10/2018</td><td>57</td><td>57</td><td>80</td><td>82</td><td>NA</td><td>NA</td><td>-0.4</td><td>2.4</td><td>NA</td></tr><tr><td rowspan="12">NCHF</td><td>07/14/2015</td><td>2</td><td>3</td><td>6</td><td>4</td><td>0</td><td>0</td><td>53.8</td><td>-39.2</td><td>22</td></tr><tr><td>07/20/2015</td><td>7</td><td>7</td><td>39</td><td>32</td><td>3</td><td>1</td><td>-1.9</td><td>-17.2</td><td>-5.2</td></tr><tr><td>10/08/2015</td><td>86</td><td>122</td><td>364</td><td>341</td><td>13</td><td>8</td><td>41.2</td><td>-6.4</td><td>-36.2</td></tr><tr><td>12/23/2015</td><td>65</td><td>74</td><td>275</td><td>263</td><td>32</td><td>35</td><td>14</td><td>-4.5</td><td>8.5</td></tr><tr><td>12/24/2015</td><td>33</td><td>32</td><td>207</td><td>224</td><td>21</td><td>21</td><td>-4.9</td><td>8.3</td><td>-3.9</td></tr><tr><td>05/26/2017</td><td>94</td><td>94</td><td>132</td><td>123</td><td>NA</td><td>NA</td><td>0.8</td><td>-6.4</td><td>NA</td></tr><tr><td>06/08/2017</td><td>6</td><td>6</td><td>63</td><td>58</td><td>NA</td><td>NA</td><td>5.7</td><td>-7.8</td><td>NA</td></tr><tr><td>10/11/2017</td><td>41</td><td>43</td><td>59</td><td>58</td><td>NA</td><td>NA</td><td>5.1</td><td>-2</td><td>NA</td></tr><tr><td>10/12/2017</td><td>25</td><td>24</td><td>51</td><td>70</td><td>NA</td><td>NA</td><td>-3.9</td><td>36.8</td><td>NA</td></tr><tr><td>01/21/2018</td><td>2</td><td>2</td><td>4</td><td>4</td><td>NA</td><td>NA</td><td>10.2</td><td>10.8</td><td>NA</td></tr><tr><td>02/10/2018</td><td>89</td><td>90</td><td>104</td><td>113</td><td>NA</td><td>NA</td><td>1.3</td><td>8.6</td><td>NA</td></tr></tbody></table>

Note: Where: q<sub>peako</sub> = observed runoff peak; q<sub>peaks</sub> = simulated runoff peak; Q<sub>o</sub> = observed runoff volume; Q<sub>s</sub> = simulated runoff volume; SY<sub>o</sub> = observed sediment yield; SY<sub>s</sub> = simulated sediment yield; NA = no data available. Monitoring units: NTC = no-till zero order catchment; CLF = chiselling with low phytomass amount; NCLF = no chiselling and low phytomass amount; CHF = chiselling and high phytomass amount; NCHF = no chiselling and high phytomass amount.

limited the temporal representation of SSC. Although automatic samplers could improve SSC resolution, they may face issues with suction in small-scale runoff collection, risking sediment overestimation; however they are useful for water quality studies determining nutrient concentrations (Wilson et al. 2024).

For the CHF and NCHF macroplots, calibration resulted in lower runoff peak and volume for the 01/21/2018 and 07/14/2015 events. The event of 10/08/2015 showed good performance for runoff volume (±10 < PBIAS < ±55), despite the runoff peak being overestimated. Sediment yield for the same event was underestimated due to the low frequency of SSC sampling, which was also observed in the low phytomass macroplots (CLF and NCLF). Hydrographs for the 07/14/2015 and 01/21/2018 events were difficult to model because of delayed and initially absent runoff. For sediment discharge, only the 12/23/2015 event achieved good performance in CHF and NCHF (±15 < PBIAS < ±30), due to more frequent SSC sampling.

Overall, calibration showed good performance (PBIAS < ±30) for the NTC catchment. For the macroplots, however, the model faced difficulties representing hydrological and erosive processes. Although LISEM was not designed for small plots (De Roo and Jetten 1999), it performs well at the hillslope scale, which is encouraging for catchment scale modelling. Accurate simulation at the hillslope scale suggests reliable performance at larger scales, although caution is needed regarding rainfall magnitude (de Vente et al. 2007). LISEM performs better under high-intensity and high-volume rainfall, when surface conditions are less significant. During such events, vegetation and soil management become less effective in controlling surface runoff (Deuschle et al. 2019; Londero, Minella, Schneider, et al. 2021; Fuentes-Guevara et al. 2024), and relief becomes the dominant factor (Liu et al. 2019). Parameters such as K<sub>sat</sub>, Manning's n, and phytomass amount strongly influence the model, especially in a no-till system with deep soils, such as this study. However, no significant reduction in runoff was observed with increasing phytomass.
<figure><img src="image_7.png" /><figcaption>**FIGURE 4** | Observed and simulated hydrographs and sediment discharge for the rainfall events of 12/23/2015 (a) and 03/15/2018 (b) in the NTC catchment. Where: $Q_o$ is the observed runoff volume; $Q_s$ is the simulated runoff volume; $SY_o$ is the observed sediment yield; $SY_s$ is the simulated sediment yield.</figcaption></figure>

Phytomass plays a key role in enhancing aggregate stability, infiltration, productivity while reducing soil loss (Merten et al. 2015). These benefits, however, diminish during high-intensity rainfall or under high soil moisture antecedent conditions (Deuschle et al. 2019; Londero, Minella, Schneider, et al. 2021, 2021), when rainfall intensity becomes the controlling factor. In LISEM, vegetation interception is represented by parameters such as canopy openness, litter storage, LAI, cover height, and cover surface (Appendix 2).

LISEM showed good calibration performance for short-duration, high-intensity events at both studied scales, the NTC with convergent topography and the four macroplots with linear/planar curvature. The comparison between observed and calibrated peak runoff, runoff volume, and sediment yield produced NSE values >0.98, confirming very good calibration quality, as shown in Figure 5.

## 3.2 | Hydrological Component Calibration

Table 5 presents the average measured and calibrated values of $K_{sat}$, initial moisture content ($\theta_i$), surface roughness (RR), and n for each monitoring unit. The calibrated $K_{sat}$ values were lower than the measured ones in the macroplots (74% for CLF, 66% for NCLF, 54% for CHF, and 82% for NCHF), with the exception being the NTC, where the calibrated average was 57% higher than the measured value. In general, the $K_{sat}$ calibration tends to result in lower values than field measurements (de Barros, Minella, Dalbianco, and Ramon 2014).

Estimated and calibrated $\theta_i$ values did not differ, as the selected calibration events occurred under high antecedent moisture.

High moisture improves both simulation and calibration performance because LISEM simulates infiltration using a Hortonian process (Koppe et al. 2022). Under wetter conditions, the model is therefore more effective at simulating surface runoff.

Manning's n coefficient reflects the effect of surface residues in no-till systems. In LISEM, Manning's n is treated as constant during each rainfall-runoff simulation; however, it is known that this coefficient varies with soil surface conditions and flow depth (Julien 2018). Therefore, a specific Manning's n value was calibrated for each rainfall-runoff event. Manning's coefficient values used for this work were obtained from studies and experiments conducted under controlled conditions, either with simulated rainfall or water flow generating uniform runoff (Engman 1986; Julien 2018). Lower Manning's coefficient values are found in plots with lower amounts of residue, while larger residue amounts require higher n values to account for increased surface friction. The final calibrated values remain within the range observed in previous studies by Engman (1986), Te Chow et al. (1988), and Julien (2018).

RR had to be increased during calibration to delay the onset of runoff. This was likely due to the model generating runoff earlier than observed, due to its representation of the Hortonian process. Increasing RR helps delay runoff onset. RR can vary with management practices, particularly before and after sowing and harvesting. Depending on the volume and intensity of rainfall, soil type, and the distribution of soil aggregates, RR can decrease from sowing to harvest (Dalla Rosa et al. 2012).

Figure 6 shows the calibrated distributions of $K_{sat}$, n, RR, $coh$, and $coh_{veg}$ for each K-fold. These parameters were adjusted to obtain satisfactory NSE and PBIAS values for peak runoff and runoff volume.
<figure><img src="image_8.png" /><figcaption>**FIGURE 5** | Linear model of observed and calibrated (a) runoff volume, (b) runoff peak and (c) sediment yield from the LISEM model. Where: the continuous line represents the plane 1:1; NTC is the no-till zero order catchment; CLF is the monitoring unit with chiselling and with low phytomass amount; NCLF is the monitoring unit without chiselling and with low phytomass amount; CHF is the monitoring unit with chiselling and with high phytomass amount; and, NCHF is the monitoring unit without chiselling and with high phytomass amount.</figcaption></figure>

<table><caption>**TABLE 5** | Average calibrated and measured values of Ksat, θi, RR, and Manning's n for each monitoring unit.</caption><thead><tr><th></th><th>NTC<br>(n=11)</th><th>CLF<br>(n=8)</th><th>NCLF<br>(n=8)</th><th>CHF<br>(n=8)</th><th>NCHF<br>(n=8)</th></tr><tr><td></td><td colspan="5" style="text-align: center;">K<sub>sat</sub> (mm.<br>h<sup>-1</sup>)</td></tr></thead><tbody><tr><td>Calibrated</td><td>12.5</td><td>9.4</td><td>7.3</td><td>12.3</td><td>8.5</td></tr><tr><td>Measured</td><td>7.1</td><td>35.6</td><td>21.1</td><td>26.4</td><td>47.5</td></tr><tr><td></td><td colspan="5" style="text-align: center;">θ<sub>i</sub> (cm<sup>3</sup>.<br>cm<sup>-3</sup>)</td></tr><tr><td>Calibrated</td><td>0.4</td><td>0.38</td><td>0.38</td><td>0.38</td><td>0.38</td></tr><tr><td>Estimated</td><td>0.39</td><td>0.38</td><td>0.38</td><td>0.38</td><td>0.38</td></tr><tr><td></td><td colspan="5" style="text-align: center;">n (s.m<br>-1/3)</td></tr><tr><td>Calibrated</td><td>0.27</td><td>0.21</td><td>0.17</td><td>0.20</td><td>0.18</td></tr><tr><td>Estimated</td><td>0.07</td><td>0.07</td><td>0.07</td><td>0.3</td><td>0.3</td></tr><tr><td></td><td colspan="5" style="text-align: center;">RR (cm)</td></tr><tr><td>Calibrated</td><td>1.6</td><td>1.4</td><td>1.7</td><td>1.5</td><td>2.0</td></tr><tr><td>Measured</td><td>1.1</td><td>1.1</td><td>1.1</td><td>1.1</td><td>1.1</td></tr></tbody></table>

Note: Where monitoring units: NTC = no-till zero order catchment; CLF = chiselling with low phytomass amount; NCLF = no chiselling and low phytomass amount; CHF = chiselling and high phytomass amount; NCHF = no chiselling and high phytomass amount.

The infiltration performance of LISEM after calibration was highly satisfactory when compared with apparent infiltration (Ia). The results of the calibration showed an improved accuracy and the calibrated parameters remained within the range of observed or determined data. As reported in previous studies (De Roo and Jetten 1999; Jetten et al. 2003; de Barros, Minella, Dalbianco, and Ramon 2014), Ksat often must be reduced substantially to achieve accurate simulations—a pattern

also observed here for the macroplots—although the values remained within the range of observed variations. Other studies required adjustments to antecedent moisture and matrix potential due to the sensitivity of the Green-Ampt method to these parameters (De Roo and Jetten 1999; de Barros, Minella, Dalbianco, and Ramon 2014) (Appendix 1).

Manning's n commonly requires adjustment in LISEM applications (de Barros, Minella, Dalbianco, and Ramon 2014), being increased compared with the values reported in the literature (Hessel et al. 2006). In this study, Manning's n values remained consistent with the assumptions used for determining friction coefficients; they were not excessively high, whereas sometimes they exceed 1 (Julien 2018). RR was also adjusted to better represent the delay in runoff onset. Event-by-event comparisons generally indicated that, following calibration, LISEM accurately reproduced infiltration processes across different no-till hillslopes.

Figure 7 presents a comparison between LISEM's infiltration based on the Green-Ampt method versus infiltration derived by apparent infiltration. Infiltration was better represented for the NTC, compared with the macroplots, supporting the hypothesis that Green-Ampt and apparent infiltration methods perform more effectively at larger spatial scales, where small-scale variability is averaged out (Dunne et al. 1991; Yu et al. 2000). Dunne et al. (1991) also noted that apparent infiltration stabilizes at hillslope lengths greater than 100 m, a condition that more closely matches the NTC, rather than the macroplots.

Soil water infiltration is influenced by a combination of soil physical properties (e.g., texture, structure, and porosity), surface conditions (e.g., micro-roughness and residue cover), and hydraulic characteristics (e.g., Ksat). Although physically based models seek to incorporate these factors, the inherently dynamic nature of infiltration, affected by rainfall intensity, antecedent moisture, and soil surface sealing, makes accurate
<figure><img src="image_9.png" /><figcaption>**FIGURE 6** | Boxplots of saturated soil conductivity ($K_{sat}$), Manning's n coefficient ($n$), random roughness (RR), soil cohesion (coh), and additional soil cohesion by vegetation ($coh_{veg}$) for each monitoring unit and K-fold used during calibration. Where: NTC is the no-till zero order catchment; CLF is the monitoring unit with chiselling and with low phytomass amount; NCLF is the monitoring unit without chiselling and with low phytomass amount; CHF is the monitoring unit with chiselling and with high phytomass amount; NCHF is the monitoring unit without chiselling and with high phytomass amount; and the 1, 2, 3 represent the K-folds.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**FIGURE 7** | Linear models comparing LISEM infiltration (Green-Ampt method) with apparent infiltration for each monitoring unit. Where: the continuous line represents the plane 1:1; NTC is the no-till zero order catchment; CLF is the monitoring unit with chiselling and with low phytomass amount; NCLF is the monitoring unit without chiselling and with low phytomass amount; CHF is the monitoring unit with chiselling and with high phytomass amount; and, NCHF is the monitoring unit without chiselling and with high phytomass amount.</figcaption></figure>

<table><caption>**TABLE 6** | Average and standard deviation (SD) of calibrated soil cohesion (coh) and cohesion of vegetation (coh<sub>veg</sub>) parameters for each monitoring unit.</caption><thead><tr><th></th><th>NTC</th><th>CLF</th><th>NCLF</th><th>CHF</th><th>NCHF</th></tr></thead><tbody><tr><td></td><td colspan="5" style="text-align: center;">Coh (kPa)</td></tr><tr><td>Average</td><td>19.37</td><td>12.08</td><td>4.25</td><td>4.25</td><td>12.25</td></tr><tr><td>SD</td><td>20.57</td><td>14.03</td><td>4.02</td><td>4.02</td><td>11.56</td></tr><tr><td></td><td colspan="5" style="text-align: center;">Coh<sub>veg</sub> (kPa)</td></tr><tr><td>Average</td><td>2.02</td><td>2.16</td><td>0.43</td><td>0.43</td><td>1.18</td></tr><tr><td>SD</td><td>2.24</td><td>2.16</td><td>0.40</td><td>0.40</td><td>1.20</td></tr></tbody></table>

Note: Where monitoring units: NTC = no-till zero order catchment; CLF = chiselling with low phytomass amount; NCLF = no chiselling and low phytomass amount; CHF = chiselling and high phytomass amount; NCHF = no chiselling and high phytomass amount.

representation challenging. This is critical at smaller monitoring scales, such as the macroplots, due to their high spatial variability (Dunne et al. 1991; Yu et al. 2000). Small differences in roughness or porosity can cause significant deviations between simulated and observed infiltration.

In contrast, apparent infiltration integrates these factors over larger areas, reducing micro-scale noise and providing more stable and representative infiltration estimates at the plot or catchment scale. This reinforces its suitability for model evaluation, particularly when assessing average hydrological behavior in heterogeneous agricultural landscapes (Koppe et al. 2022).

Manning's n was adjusted within literature-reported limits (Engman 1986) until the simulated hydrograph aligned with observations, ensuring consistency with established flow dynamics. High Manning's n values are commonly used in LISEM (De Roo and Jetten 1999), especially shortly after sowing when roughness is greatest. Higher roughness increases runoff delay and peak time. Manning's n also controls recession times, as lower values yield faster recessions.

After calibration, RR values were similar across monitoring units, especially those with the same soil management. However, the chiselled units (CLF and CHF) had lower average RR than the non-chiselled ones (NTC, NCLF, and NCHF). Mean Manning's n was also similar among monitoring units, although higher values were observed for the NTC, followed by the chiselled plots (CLF and CHF), and lower values in the non-chiselled ones (NCHF and NCLF). This is consistent with the theory of friction parameters, in which higher residue cover and micro-roughness result in increased Manning's n values (Cogo et al. 1983, 2003; Engman 1986; Julien 2018).

## 3.3 | Erosive Component Calibration

The values of D<sub>50</sub>, D<sub>90</sub>, and soil aggregate stability were not adjusted across events and monitoring units, as they exhibited low sensitivity within the model. In contrast, soil cohesion and the additional cohesion due to the presence of roots were calibrated.

Table 6 presents the average measured and calibrated values of soil and vegetation cohesion for each monitoring unit. Soil cohesion
<figure><img src="image_11.png" /><figcaption>**FIGURE 8** | Observed and simulated runoff volumes during the validation step for each K-fold and monitoring unit. Where: the continuous line represents the plane 1:1, NTC is the no-till zero order catchment; CLF is the monitoring unit with chiselling and with low phytomass amount; NCLF is the monitoring unit without chiselling and with low phytomass amount; CHF is the monitoring unit with chiselling and with high phytomass amount; and, NCHF is the monitoring unit without chiselling and with high phytomass amount.</figcaption></figure>

values ranged from 4.25 to 19.37 kPa, with the highest average value observed in the NTC and the lowest in NCLF. Vegetation cohesion refers to the resistance of plants to be pulled out of the soil due to root strength; higher values suggest the vegetation is more effective in protecting the soil against erosion (Baets et al. 2008). Vegetation cohesion ranged from 0.43 to 2.16 kPa, with the highest average in CLF and the lowest in NCLF.

The variation in cohesion parameters did not follow a consistent or physically justified pattern, as multiple adjustments were required during calibration. In many cases, final calibrated values differed considerably from field or laboratory measured values. LISEM calibration highlighted soil cohesion as the parameter requiring the most substantial modifications to accurately reproduce sediment yield, which was initially overestimated,
<figure><img src="image_12.png" /><figcaption>**FIGURE 9** | Observed and simulated runoff peak during the validation step for each K-fold and monitoring unit. Where: the continuous line represents the plane 1:1, NTC is the no-till zero order catchment; CLF is the monitoring unit with chiselling and low phytomass amount; NCLF is the monitoring unit without chiselling and low phytomass amount; CHF is the monitoring unit with chiselling and high phytomass amount; and, NCHF is the monitoring unit without chiselling and high phytomass amount.</figcaption></figure>

and consistent with results from de Barros, Minella, Dalbianco, and Ramon (2014) and Kværnø and Stolte (2012). While newer model versions, offering additional sediment yield calculation options, have shown improved performance in poorly

weathered soil conditions (Schlesner 2017), the highly weathered and cohesive soils in this study demanded large reductions in soil cohesion to achieve results similar to those obtained for less cohesive soils (Hessel et al. 2006).
<figure><img src="image_13.png" /><figcaption>**FIGURE 10** | Observed and simulated sediment yield during the validation step for each K-fold and monitoring unit. Where: the continuous line represents the plane 1:1, NTC is the no-till zero order catchment; CLF is the monitoring unit with chiselling and low phytomass amount; NCLF is the monitoring unit without chiselling and low phytomass amount; CHF is the monitoring unit with chiselling and high phytomass amount; and, NCHF is the monitoring unit without chiselling and high phytomass amount.</figcaption></figure>

## 3.4 | Validating LISEM

The median calibrated values of $K_{sat}$, n, RR, coh, and coh$_{veg}$ were used to validate the rainfall events for each K-fold (Figure 6).

Figure 8 shows the validation results for runoff volumes across K-folds and monitoring units. For the macroplots, all K-folds produced NSE values above 0.65, indicating good model performance; most exceeded 0.75, classified as very good according to Moriasi et al. (2007). However, in K-fold 3, one event (02/10/2018)
<figure><img src="image_14.png" /><figcaption>**FIGURE 11** | Observed and simulated runoff volume, runoff peak, and sediment yield during the validation step for each and monitoring unit. Where: the continuous line represents the plane 1:1; NTC is the no-till zero order catchment; CLF is the monitoring unit with chiselling and with low phytomass amount; NCLF is the monitoring unit without chiselling and with low phytomass amount; CHF is the monitoring unit with chiselling and with high phytomass amount; and, NCHF is the monitoring unit without chiselling and with high phytomass amount.</figcaption></figure>

diverged substantially from observed data, resulting in an unsatisfactory NSE of <0.5. This event featured high rainfall depth and intensity, and its performance reflects the strong sensitivity of LISEM to $K_{sat}$: during calibration $K_{sat}$ was twice as high as in validation. Similar findings have been reported by Jerszurki et al. (2022), who demonstrated a strong influence of $K_{sat}$ on

runoff volume, as also observed in our results, where $K_{sat}$, RR, and Manning's coefficient showed high sensitivity.

For the NTC, K-folds 2 and 3 produced the best fits. Poorly performing NTC events also had much higher $K_{sat}$ values during calibration than validation, reinforcing the sensitivity of this parameter.
<figure><img src="image_15.png" /><figcaption>**FIGURE 12** | Observed and simulated runoff volume, runoff peak, and sediment yield during the validation step for each K-fold. Where: the continuous line represents the plane 1:1, NTC is the no-till zero-order catchment; CLF is the monitoring unit with chiseling and with low phytomass amount; NCLF is the monitoring unit without chiseling and with low phytomass amount; CHF is the monitoring unit with chiseling and with high phytomass amount; and, NCHF is the monitoring unit without chiseling and with high phytomass amount.</figcaption></figure>
Figure 9 presents the validation of peak runoff for each K-fold and monitoring unit. Unlike runoff volume, peak runoff did not consistently show a good fit when analyzing each K-fold and monitoring unit. In addition to Ksat, Manning's coefficient had a strong influence on peak runoff, as small variations in n produced significant changes in the hydrograph. As shown in Figure 6, Manning's coefficient varied substantially across K-folds, yet median values were used in the simulations. Even small changes in n can lead to substantial differences; nevertheless, most validation events still achieved satisfactory performance (NSE > 0.5).

Figure 10 presents the validation results for sediment yield. NSE values were generally unsatisfactory (NSE < 0.5), except for the NTC in K-fold 1, which showed very good performance (NSE > 0.75). Sediment yield proved to be highly sensitive to cohesion and Manning's coefficient, both of which showed considerable variation during calibration. For K-fold 3, the macroplots (CLF, NCLF, CHF, and NCHF) recorded only one event with measurable sediment yield; therefore, NSE values could not be calculated.

Figure 11 shows the observed and simulated runoff volume, peak runoff, and sediment yield for each monitoring unit during validation. When aggregating the three folds, the K-fold statistical test indicates that relationships were not consistently strong. Similar patterns appear in Figure 12: Depending on the variable, performance was better for runoff volume (K-folds 2 and 3) or peak runoff (K-fold 3), whereas none of the folds produced satisfactory results for sediment yield.

These results confirm that LISEM is highly sensitive to the parameters used in validation, particularly Ksat, Manning's coefficient, RR, and soil cohesion. This sensitivity reflects field reality, as these parameters exhibit spatial and temporal variability and are strongly influenced by land management. Jetten et al. (1998) also reported a strong influence of these parameters in a northern catchment in France. Likewise, Ebling et al. (2024), working in paired catchments, found that Ksat had to be reduced up to 50% to simulate runoff volume and sediment yield. Our findings are consistent with these studies, highlighting LISEM's strong dependence on these key parameters.

## 4 | Conclusions

The results of this study indicate that LISEM adequately reproduced runoff and sediment yield across all monitoring units during the calibration step. Cross-validation improved the robustness of the results by reducing calibration bias: distinct groups of events were alternately used for calibration and validation, minimizing the influence of individual events and strengthening confidence in the results. A good fit was obtained for runoff peak and runoff volume, whereas sediment yield was reproduced less satisfactorily, indicating model limitations that warrant further refinement, particularly for highly cohesive soils. Model response was strongly influenced by a small set of parameters (Ksat, Manning's n, RR, and soil cohesion) during calibration and validation. Further modeling studies of cohesive soils under no-till are essential to better represent soil losses.

Regarding the spatial scale, calibration performance was not negatively affected. During validation, however, the model performed better for larger scale (NTC) compared with the macroplots. This result suggests that parameter adjustments and higher variability in smaller areas can substantially influence simulation outcomes, reflecting the high sensitivity of LISEM to parameters governing infiltration, flow propagation, and erosion processes.

Chiselling did not directly impact the simulation of rainfall-runoff events, although monitoring units under chiselling presented slightly higher Manning's n, Ksat, and RR values. Similarly, monitoring units with greater amounts of cover crops exhibited modest increases in these parameters, yet still within the same order of magnitude as the remaining units. Seasonal variability in surface conditions under no-till affects hydrological responses and must be accounted for. Together, these findings indicate that, for this set of simulated events, LISEM shows only moderate sensitivity to chiselling and to variations in phytomass or cover crops.

The calibrated events revealed a strong relationship between apparent infiltration and LISEM-simulated infiltration, demonstrating that the model reliably represents infiltration processes at the hillslope scale.

Overall, this study highlights the importance of considering soil properties and surface cover characteristics when predicting infiltration and runoff across different environments. It also emphasizes the need for continued refinement of process-based models used to simulate infiltration and runoff, particularly for complex environments with varying surface cover, and cohesive and highly weathered soils.

## Acknowledgements

This study was funded by the CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior). The authors acknowledge the financial support granted by the Maiságua Project (2012–2018), FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico). The Article Processing Charge for the publication of this research was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (ROR identifier: 00x0ma614).

## Funding

This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul.

## Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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## Supporting Information

Additional supporting information can be found online in the Supporting Information section. **Appendix S1:** Online Appendix.