Research Article

Estimation of Irrigation Induced Drainable Excess water from Sugarcane Field using a Hydrus-1D Model: a Case of Omo-Kuraz Irrigation Project, Ethiopia

Nigatu Toma1*, Samuel Dagalo1 and Tena Alamirew2

1Faculty of Water Resources and Irrigation Engineering, Arbamich University, Ethiopia; 1Department of Water and Irrigation Engineering, Arbamich University, Ethiopia; 2Water and Land Resource Center, Addis Ababa University, Ethiopia.

Received | Dec 30 2023; Accepted | Jun 24 2025; Published | November 25, 2025

*Correspondence | Nigatu Toma, Faculty of Water Resources and Irrigation EngineeringIrrigation and Drainage Engineering, Arbamich University, arbamich, Ethiopia. Email: [email protected]

Citation | Toma, N., S. Dagalo and T. Alamirew. 2025.Estimation of irrigation induced drainable excess water from sugarcane field using a hydrus-1D model: a case of omo-kuraz irrigation project, Ethiopia. Sarhad Jurnal of Agriculture, 41(4): 1860-1873.

DOI | https://dx.doi.org/10.17582/journal.sja/2025/41.4.1860.1873

Keywords | Hydrus-1D model, Irrigation-induced Drainable excess, Sugarcane field, Omo-kuraz irrigation project, Ethiopia

Copyright: 2025 by the authors. Licensee ResearchersLinks Ltd, England, UK.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Introduction

Agriculture has been a key sector for Ethiopian society for centuries and is a crucial part of country’s economy, providing employment and livelihoods for over 85% population (Gebul, 2021; Gebrehiwot, 2018; Neglo et al., 2021). The goal of irrigated agriculture is to enhance irrigation water management and minimize risks associated with soil water stress (Minhas et al., 2020). Controlling and predicting plant water availability requires an analysis of the temporal and spatial distribution of soil water (Subbaiah, R. 2013; MeiXian et al., 2013). Predicting the occurrence of soil moisture in crop root zone, it is necessary for optimizing agricultural water use, designing irrigation regimes, and managing irrigation systems (Amarasinghe et al., 2021; MeiXian et al., 2013). Soil moisture value is a crucial parameter for estimating water balance at plot and catchment scales, as well as monitoring plant water intake for crop growth (Abdelbaset et al., 2023; Sanchezo et al., 2012) and water loss from the root zone. Therefore, it is essential to obtain high-quality soil moisture data over time is necessary to improve our acceptance of soil moisture changing aspects (Gabiri et al., 2018) in a given root zone in a cropped area. Direct monitoring of soil water in the field can be challenging, expensive, and time-consuming to implement due to factors such as soil heterogeneity and fluctuating atmospheric conditions (Li et al., 2021; Jurik et al.,2012; Lia et al., 2014; Khanh et al., 2022). Therefore, modeling approaches are being employed to better understanding of soil water dynamics for improved irrigation scheduling, water management, and evaluating irrigation losses.

There are several methods available in agricultural fields that can be used to determine the drainable excess. Some of the commonly used methods include Lysimtere, water balance, soil moisture monitoring, and tracer techniques. Lysimtere method involves collecting water that drains from a specific area and soil profile using a Lysimtere while the soil water balance method comprises measuring the amount of water that enters and exits a specific area of the root zone over a given period of time, and using this information to calculate the amount of drainable excess. However, the method of monitoring soil moisture entails monitoring the moisture content of soil at different depths and enabling to determine the quantity of water that is being held in the soil versus the amount that is draining below the root zone (Li et al., 2021). The tracer technique involves adding a tracer substance to the irrigation water and then measuring its concentration in the drainage water which can help determine the quantity of water that is draining below the root zone. Each of these methods has its advantages and disadvantages, and choice of method will depend on factors such as specific soil type, crop type, and available resources (Yang et al., 2020). Nowadays, the soil water balance (numerical) models together with soil water monitoring are being frequently used to determine drainable excess from agricultural area since these methods are cheaper to employ, accurate and easy to manage. Among the several types of models, Hydrus-1D (Khan et al., 2018) is the most frequently employed. HYDRUS-1D is a widely used numerical model to simulate water in one dimension flow, heat transport, and solute passage in unevenly saturated and unsaturated permeable material. The model is according to Richards’ equation, which describes the movement of moisture in soil, and it incorporates various hydraulic and physical characteristics of soil to simulate water dynamics accurately (Yang et al., 2020). In agricultural and environmental studies, HYDRUS-1D is frequently used to analyze water movement in the soil profile. Assess effects of various land management practices, evaluate irrigation strategies, and optimize management of water resources (Gabiri et al., 2018; Kuhl et al., 2018).

During the active growth phase of sugar-cane, a rise in the water table negatively impacts stalk weight and tiller production resulting in yield losses. Studies have shown that for every one-inch increase in water level, there can be a reported decrease of about one ton/acre in yield (Misra et al., 2016). Waterlogging also leads to elevated stalk mortality rates, reduced relative growth rate, and poor juice quality (Jain et al., 2023). The effect of excess water has been investigated, revealing that nutrient uptake is significantly affected under waterlogged conditions due to poor aerobic respiration (Misra et al., 2016). The Ethiopian government has been embarked on sugarcane plantation for producing sugar for various development prospects of the nation. Omo Kuraz is one of the sugarcane plantation and sugar milling factories among others. However, the irrigation project has been facing acute problems of water logging due to acute drainage problems. A decrease in sugarcane production has been observed due to waterlogging in certain irrigation plots. However, there has been a lack of comprehensive research utilizing numerical methods to investigate this problem, highlighting the need for further study. The primary goal of the research titled “Estimation of Irrigation Induced Drainable Excess water from Sugarcane Field using a Hydrus-1D Model: a case of Omo-Kuraz Irrigation Project, Ethiopia” is to assess the amount of excess water that drains from sugarcane fields due to irrigation practices. The Hydrus-1D model will be used to simulate the movement of water through the soil profile and calculate the amount of water that drains beyond the root zone. By understanding the quantity of excess water being drained from the field, potential waterlogging and salinization issues can be identified and appropriate management strategies can be implemented to improve the efficiency of water use in the Omo-Kuraz Irrigation Project in Ethiopia.

Materials and Methods

Research area description

This research was conducted in Omo Kuraz irrigation project, situated in the lower Omo-Gibe basin in the South Omo Administrative zone in southern Ethiopia. The project is part of Agricultural Growth Program (AGP) and supports nearby communities. The area spans are 100,000 hectares of land with elevations fluctuating from 300 to 500 m above the sea level. The project is situated in the Bench-Maji Zone (Surma and Mieinitshasha Districts), Keffa Zone (Diecha District), and South Omo Zone (Selamago and Gnanegatom Districts), which are located in the Southern Ethiopia and South Western Ethiopia Region. It is approximately 825-954 km from Addis Ababa via Hosaena-Arbaminch-Jinka or 859-988 km via Shashemenne-Arbaminch-Jinka. The study area is located 356 km southwest of Wolaita Sodo, the capital city of the Southern Ethiopia region, between 34°0’ to 38°0’E longitude and 4°0’ to 8°0’N latitude (Figure 1). The study area falls within the Omo Kuraz sugar cane development TC-5 commend area. Posturalism is the base for reginals economy, and the evaluation site’s flat topography has inclinations lower than 2% in most places makes it model for industrialized profitable agriculture. The soil of the command area is mostly clay. The area experiences temperatures range from 22.97 to 34.39°C, wind speed varying from 10.4 to 15.5 km/h, annual rainfall of 974.13 mm, daylight hours between 6.9 and 10.2 hours, and humidity between 58.32 and 68.07 percent. The average monthly evapotranspiration ranges from 151 to 205mm, with an yearly value of 2070mm (Gebregziabhare, 2019; Mogiso, 2020).

Data collection and analysis

Data collected

In this investigation, soil moisture evaluation on-field was created to assess soil water changing aspects in a few irrigated agricultural fields. Because the research area’s water resources would determine how the field size would be manageable at 100m by 200 m. As a result, three locations,upper, middle, and lower field of selected early-stage sugarcane crop were selected at random during the first irrigation session. Each location was then subjected to a second irrigation at two different depths (0-30, 30-60 cm). Site’s irrigation schedule was set for 15 days for sugarcane cropland’s early growth phase as well as harvest phase. In order to collect soil moisture data for model calibration and validation in first and second seasons, respectively, early-stage sugarcane crop fields were chosen.

 

To ascertain whether or not irrigation scheduling at the site is dependent on soil moisture and how soil moisture dynamics affect the ground water table, daily measurements of soil moisture are made using the TDR-150 instrument for each season. The Hydrus-1D model is used to manage irrigation-induced waterlogging in research area and irrigation schedule options. Thus, four months’ worth of daily soil moisture data for sample size 121 reading were measured for calibration (January 3, 2015 to may 2, 2015), and five months’ worth of daily data for sample size 150 were measured for validation (September 8, 2013 to march1, 2014). Using a Time Domain Reflectometer (TDR 150), regular soil moisture content was evaluated at the above 30 cm depth at three locations (30 days times four months) and below 30 cm depth at thirty days times four months) from a selected field. Soil samples were gathered at each horizon to quantify the physical characteristics of the soil, such as organic matter, bulk density, texture, field capacity (FC), permanent wilting point (PWP), and pH, in order to estimate model variable. The bulk density was computed using a 4.38 cm diameter by 5.14 cm length core sampler and undisturbed soil samples taken at each layer. Soil samples were examined at South Ethiopia Design and Supervision Works Enterprise Soil Laboratory. Sugarcane has 30–60cm depth of effective roots and an extreme root depth is 50–100cm (Akbar et al., 2022). The soil moisture measurements were taken from early stage TC-5 fields. There is a significant amount of clay in these areas reaching to a depth of 90 cm with surface soils having a clay texture ranging from 0 to 60 cm. The site was divided into three (upper, middle, and lower fields) and the soil moisture was measured (0-30 and 30-60 cm) throughout the course of two seasons in order to calibrate and validate the model. Groundwater level fluctuation was not observed during the simulation periods because groundwater level was too deep at the time of data collection to have any bearing on crop soil-water use or soil moisture variation surrounding the plant root region. Waterlogging was made as a result of irrigation site selection and manual water diversion from the canal irrigation system.

Material used

Time domain reflector meter (TDR-150) was used to measure soil moisture content for two seasons. Auger instruments were used to open the upper soil layer with 30cm to insert TDR 150 for 60cm depth. Using TDR, volumetric water content was calibrated directly by oven-dry soil taken from the field.

Data analysis and procedures

A modeling environment for Microsoft Windows called HYDRUS-1D is used to analyze solute transport and water flow in porous material that is variably saturated. The model platform includes the one-sided finite element model HYDRUS for simulating heat, moisture, and different solutes in erratically waterlogged media. An collaborative graphics-based interface for discretizing the soil profile, preprocessing data, and presenting results graphically supports the model. A limited element model called HYDRUS is used to simulate the one- Sided movement of heat, water, and many solutes in variable saturation media. The Fickians-based advection-dispersion equations for heat and solute transfer, as well as Richards’ equation for saturated-unsaturated water flow, are numerically solved by the program.The analytical functions of (Khan et al., 2018), and the revised van Genuchten style are used to characterize the unsaturated soil hydraulic properties.

Hydrus-1D’s output

Hydrus-1D is a computer program that uses various meteorological and hydrologic circumstances as well as different water management options to predict soil moisture content and drain discharge or bottom flux in irrigated agricultural regions.

 

Description of hydrus-1D model

To simulate soil water change in investigational plots, the Richards equation-based one- Sided Hydrus-1D software application was chosen (Khan et al., 2018). The PC PROGRESS website offered a free download of the Hydrus-1D model. It wasn’t necessary to employ a model like 2D or 3D Hydrus that would consider several dimensions because the majority of flow of water in the profile of subsurface happens vertically, between the soil and table of groundwater (unsaturated zone) (Li et al., 2015). Thus, change of soil moisture can be studied using the Hydrus-1D model. Many researchers, including (Li et al., 2015), selected Hydrus-1D and effectively used it for unsaturated zone soil moisture change. The revised Richards equation is used to define the dominant one-dimension equation for soil water flow for moderately saturated permeable Media. It is believed that air phase has minimal impact on liquid movement and water movement caused by thermal gradients.

....(1)

Where, x is spatial direct [cm] positive upward direction, t is time [day], h is water pressure head [L], θ is volumetric water content [cm3cm-3], S is drop term [cm3cm-3day-1], α is angle between the movement direction and vertical axis (i.e., α = 00 for vertical movement, 900 for horizontal movement, and 00 < α < 900 for inclined movement), and K is function of unsaturated hydraulic conductivity [cmhour-1] provided by Eq.2.

.....(2)

In this case, Ks is hydraulic conductivity saturated [cm/day], and what hydraulic conductivity relative is Kr [-]. The amount of water that plants absorb from a unit volume of soil in a unit amount of time is referred to as the sink term, or S. (Feddas et al., 1978) S was described as:

....(3)

Where; Sp is the crop’s potential rate of water uptake [1/day], and Root-water absorption and water stress response mechanism in crops α (h) is a predefined dimensionless function of the soil water pressure head (0 ≤ α ≤ 1). When α (h) = 1, variable Sp in (Eq.3) represents the water intake rate during times when there is no water stress. Sp becomes when the root zone’s potential water uptake rate is evenly distributed.

.....(4)

where LR is depth of root zone [cm] and Tp is crop potential transpiration rate [cm/day]. A non-uniform distribution of the potential water uptake rate across a root zone of any shape can be added to equation 4 to make it more general: Ritchie (1974) states that potential evapotranspiration can be calculated by applying law of Beers (Equation 5) to determine partitioning of evaporation and potential transpiration.

....(5)

Where, definition, ETp is crop potential evapotranspiration (cm day−1), e is a mathematical constant, and LAI is the leaf area index of the crops. The dimensionless coefficient of emission attenuation, k, was determined by (Wang et al., 2021), to be equal to 0.4. Maulem’s statistical pore-size distribution model was utilized by Van Genuchten et al. (1991) to predict the unsaturated hydraulic conductivity function based on the characteristics of soil water retention. These soil-hydraulic processes are also applied by the HYDRUS -1D model. Van Genuchten states that θ(h) is computed as follows:

.....(6)

.....(7)

.....(8)

The aforementioned five independent parameters are present in equations: θr, θs, α, n, and Ks. Moulem (1976) found that average value of parameter for pore-connectivity (l) within the hydraulic conductivity function was roughly 0.5 across a wide range of soils. Here, k (h) stands for the unsaturated hydraulic conductivity theorem’s [cm day−1]. The sink term (S) in Eq. 4 is the amount of water that plant roots draw out of a unit volume of soil in a unit amount of time. Soylu et al., (2011) state that the model uses Feddes equation, which is comparable to transpiration to determine the actual root water uptake.

Model sensitivity

Sensitivity analysis was used to determine which settings were the most sensitive by first examining the impact of each parameter modification on model outputs (Simunek et al., 2012). Local sensitivity analysis (LSA) using a one-at-a-time (OAT) technique was used for this inquiry to evaluate the influence of each parameter on model output because this method allows a strong identification of particular parameter influences. The results showed that most essential hydraulic parameters influencing the saturated soil moisture content, saturated hydraulic conductivity, and pore size distribution parameters are the model’s outputs.

Model calibration and validation of hydrus-1D

Since sugarcane’s effective root depth was close to 60 cm, the hydraulic constraints of soil, θs, θr, n, m and α van Genuchten parameter, were fixed during calibration of the Hydrus-1D model. Four months’ observed soil moisture data was collected from early-stage irrigated sugar cane fields at three different locations (upper, middle, and lower) at two depths (0-30, 30-60cm) . The Hydrus-1D model was utilized to test irrigation scheduling alternatives by optimizing retention data through usage of the retention curve constraint optimization (RETC) model. Once the expected data was fitted, (n, α, m, θr and θs) the unsaturated soil hydraulic parameters, were determined using RETC program. Retention curves (Van Genuchten et al., 1991) used nonlinear least-squares optimization to estimate unknown model parameters using observed retention data. For many types of soils, it was expected that pore connectivity parameter (l) would always have an average value of 0.5 (Van Genuchten et al., 1991).

The model was validated using field data seen during the second season.Two categories were identified from observed soil moisture content data: HYDRUS-1D’s inverse modeling method was utilized to calibrate the model over the first four months of the season in order to ascertain the factors related to water retention and soil hydraulic properties. After that, the calibrated model was used to simulate the remaining soil water content data for a period of five months (Li et al., 2014).

Models performance evaluation

The following definitions apply the mean bias error (MBE) and root mean square error (RMSE), respectively, were used to calculate the models’ performance.

.....(9)

.....(10)

Where; N is number of evaluated data points and di is Variation between the measured and anticipated values (Akbar et al., 2022). As per their suggestion, we will employ an additional t-test.

....(11)

to ascertain whether the variation in simulated and observed soil water content is statistically significant, the absolute value of computed t has to be less than the critical t value (tcrit) for the specified significance level. Assuming a significance level of α = 0.05, the value of tcrit for N-1 units of freedom is 2.05.

Hydrus-1D’s initial and boundary conditions

SWC recorded during the crop planting period was used to characterize the initial state. The surface of soil is exposed to atmosphere at predetermined irrigation and evaporation levels. Potential evapotranspiration (ETo) is used in an atmospheric boundary condition with surface runoff that defines the top boundary condition in the model. Using meteorological data (Figures 3 and 4), ETo was calculated using the Penman-Monteith equation (Khanh et al., 2022). The depth of ground water level eliminated its impact on the capillary rise-induced water transport in the plant root zone. Due to open drainage, irrigation water percolates below the plant root zone, this process was used to characterize lower soil profile boundary conditions. Regular meteorological information gathered from Kuraz irrigation site stations situated in the Omo- Kuraz irrigation site study area, was utilized as an input for Hydrus-1D. This information included maximum and minimum air temperatures, relative humidity, daylight hours, and wind speed. Measured depths of irrigation were taken into account as time-varying boundary conditions during the simulation. For simulation, two soil layers with thicknesses of 0–30 cm and 30–60 cm were identified.

 

 

Table 1: Hydrus-1D soil hydraulic characteristics that are optimized

Depth	Soil	Qr(cm3/cm3)	Qs(cm3/cm3)	α(cm-1)	n	Ks(cm/day)	R2	RMSE	L
0-30cm	Clay	0.18	0.54	0.016	1.36	35.8	0.84	0.022	0.5
30-60cm	Clay	0.2	0.52	0.016	1.36	35.6	0.845	0.021	0.5

 

Results and Discussion

Hydraulic settings for the hydrus-1D model

In order to evaluate soil water dynamics, the Hydrus-1D soil hydraulic parameters (θr, θs, a, n, and Ks) were calculated using the K-nearest neighborhood pedotransfer function based on the observed soil physical properties, as indicated in Table 1. The retention curve parameter optimization tool (RETC) was used to match the reported soil water contents in order to calibrate Hydrus-1D. Regression result which means R2 above 0.6 was acceptable then the value sensitivity parameter was fixed for validation of data.

Where; Saturated hydraulic conductivity is denoted by Ks, R2 is the coefficient of determination, SSQ is the sum square of error, θr is the remaining water content in soil, θs is saturated soil water content, and features of the pore size distribution are α-shape parameters.

Analysis of model parameter sensitivity

In order to assess the impact of each parameter on model output, local sensitivity analysis (LSA) employing a one-at-a-time (OAT) technique was employed for this investigation. This method enables a clear identification of single parameter impacts. Results showed that most important hydraulic parameters influencing model output are saturated soil moisture content, characteristics of the pore size distribution and saturation hydraulic conductivity. For the four-month daily soil moisture data, model’s calibration result would fit the actual data quite well, with regression result from R2 was 0.84, The p-value was less than 0.05, indicating that there is no significant difference between the predicted and observed values for each independent variable (observed value). Consequently, using observed data that compares to simulated or predicted values during validation and the simple size for calibration was 120 (Table 2).

This indicates that the observed (independent) variable is equal to 0.03311 with a 95% confidence level and standard error. In order to assess model’s accuracy, the anticipated soil water content derived from the observation point (at 0-30, 30-60cm soil profundity) compared to the measured soil moisture content (SMC) by Hydrus-1D for all locations of the

 

Table 2: Regression result during calibration

Summary output
Regression statistics
R square	0.845745
Observation	120
	coefficient	Standard error	T stat	p-value	Lower 95%	Upper95%
Intercept	0.055817	0.012946	4.311473	3.36E-05	0.030182	0.081451
Observed 2	0.845732	0.03311	25.5431	3.97E-05	0.780171	0.911293

 

 

field with depth (see Figure. 5 a, b).

Additionally, a graphical method that shows a comparison between measured and simulated soil moisture content validates the model. As a result, model’s estimation of soil moisture (the upper, middle, and lower) of the field was 0.39652 cm3cm−3 during the upper layer (0-30cm) simulation time and 0.392 cm3cm−3 during the lower layer(30-60cm) simulation time respectively. The Hydrus-1D model lacks parameters, particularly considered for the upper layer alongside the initial state and soil hydraulic characteristics. Because of this, Hydrus-1D undervalues the amount of soil moisture on a daily basis in the upper layer of the field. For both the upper and lower levels of the field, the simulated and observed soil moisture contents matched fairly well and displayed patterns that were somewhat similar. There was a good correlation between the simulated and actual soil moisture content at various points throughout crop growth in 2022 and 2023. High R2 (0.66–0.85), low RMSE (0.021–0.024), and ME (0.0013–0.0167) values all pointed to a strong association. The R2, RMSE, and ME between the simulated and observed soil moisture content show that the model achieved a decent job of simulating soil water dynamics.

Furthermore, Hydrus-1D could accurately simulate soil moisture dynamics (SMD) during the growing season of sugar cane. During this season, changes to the soil moisture content corresponded well between the simulated and actual values. Daily Soil Water Content Reacted Favorably to Rainfall and Irrigation Events. Irrigation scheduling in the study site was not managed well so to reduce irrigation-induced water logging application of water under deficit was recommended in the area.

 

 

During the two seasons of 2022 and 2023, flooding water depths were observed and simulated. Model simulations and observed values can also be compared using percolation functions. In contrast, the second season of 2023 saw a total of 264.5 cm of simulated percolation at a depth of 60 cm before 150 DAS (or an average flux of 1.76 cm/day) (Figure 6). These show that the value of water is downward to recharge groundwater and affects the crop harvesting time due to water logging in the crop root zone. The effective root zone of the sugar cane crop was 60cm therefore water flux nod at 60 was drainable water that improves the ground water table rise and also increases water logging under the crop root zone due to capillary rise.

Using the Hydrus-1D model, irrigation scheduling choices were set based on the simulated and actual soil water content. During the simulation, soil moisture sensitivity parameters were evaluated for the study area soil textures. Using this soil hydraulic parameter optimizes the reduction of water logging due to excess water application by surface irrigation. Once the expected data was fitted, the unsaturated soil hydraulic parameters (θr, θs, m, α, and n) were determined using the RETC program. Retention curves use a nonlinear least-squares optimization technique to calculate unknown model parameters from retention data that has been observed (Van Genuchten et al., 1991). During the validation of the hydrus1D model, the soil hydraulic parameters were observed there for simulated residual water content θsr approaches to (0.3) which means that the next irrigation would be taken but for the optimization using the RETC model, it takes some lower so we recommend the rogation would take some few days daily for next irrigation. (Figure 7) hydraulic capacity and water content relationship show that residual water contents (0.2cm) therefore, schedule irrigation soil water content approach to 0.2.

 

Table 3 : Test analysis results (t-test: assuming unequal variances in a two-sample)

	Observed-lower	Simulated
Mean	0.393070748	0.391713333
Variance	0.002248027	0.002119665
Observations	150	150
Hypothesized mean difference	0
df	298
T Stat	0.251554604
P(T<=t) one-tail	0.400779368
T Critical one-tail	1.649982976
P(T<=t) two-tail	0.801558736
T Critical two-tail	1.967956506

 

Since tobs = 0.2515 < 1.968 = tcrit (or p-value = 0.8015 > 0.05 = α). We maintain the null hypothesis, meaning that there is a 95% likelihood that any differences observed between the two groups are the result of chance. From the above table homogenate of the mean shows how the observed and simulated value was fit so the model was well performed (Table 4).

The performance of the model

Finding the root mean square error (RMSE), which indicates the average difference between the model’s predicted values and the dataset’s actual values, is one technique to evaluate how well a regression model fits a dataset. Root mean square error < 10% is a good model, and RMSE<5% is a very good model (C1hai, T. et al., 2014). The variation in the mistakes in a group of forecasts can be diagnosed by combining the RMSE and the MAE. The variation in the individual mistakes in the sample is higher the more the difference between the RMSE and MAE is; the RMSE will always be bigger than or equal to the MAE. In this study the RMSE 0.021 and MAE 0.01585 this show observed and simulated data well fits. For the validation mean homogenate and ANOVA test value they well correlated with each other (Table 3).

 

Table 4: Regression results of the simulated data

Regrassion statistics
Multiple R	0.89878828
R Square	0.80782038
Adjusted R square	0.80651303
Standard error	0.02014093
Observations	150
P-value	0.001

 

Conclusions and Recommendations

In conclusion, the Hydrus-1D model has proven to be an effective tool for simulating soil moisture dynamics. The model accurately predicted the temporal and spatial distribution of soil moisture in various scenarios, allowing for a better understanding of water movement in the soil profile. Additionally, the model was able to simulate the impacts of different management practices and environmental conditions on soil moisture, providing valuable insights for optimizing irrigation strategies and enhancing crop productivity.

Soil moisture content calculated by the model closely matched the value that was observed at the upper surface of 30 cm and below 30 cm soil depth. The stage of crops and a fixed day were to determine project’s watering plan without taking soil moisture into account. Findings of the study led to the conclusion that the water content residual condition should not be precisely matched by the amount of water remaining in the crop root zone during irrigation application time. It was implied that too much water had been poured into the field and it had seeped into the groundwater. The seasonal deep percolation of sites was greater than water’s upward flow following irrigation. The higher layer’s evaporation loss, however, was more substantial.

Based on the findings of this study, several recommendations can be made for future research and practical applications of the Hydrus-1D model. Firstly, further validation of the model using field data from different soil types and climatic conditions would help enhance the accuracy of the simulations. Additionally, incorporating more detailed information on soil properties, crop characteristics, and management practices into the model inputs would improve the reliability of the results. Lastly, integrating the Hydrus-1D model with other models or tools for crop growth prediction and water resource management could facilitate a more comprehensive analysis of the impacts of soil moisture dynamics on agricultural systems.The Omo Kuraz irrigation technique was generally employed with care for the water content of the soil and had greater actual residual water; it was advised for agronomist irrigation practice in the research region.

Acknowledgements

The anonymous reviewers’ comments, which undoubtedly enhanced this publication, are greatly appreciated by the authors.

Novelty Statement

In order to enhance water logging in irrigated agricultural land, this study use quantitative technique(s) to assess the effects of the hydrus1D model as an irrigation scheduling tool. It also offers implications for reformulating integrated rural irrigation water management development policies.

Author’s Contribution

Nigatu Toma: Research, formal analysis, data curation, inquiry, technique, software, and original draft writing.

Samuel Dagalo: Formal analysis, approach, oversight, verification, composition, editing, and review.

Tena Alamirew: data collecting validation, oversight, and editing.

Generative AI or AI assisted technology statement

The authors declare that no Genrative AI was used in the creation of this manuscript.

Conflict of interest

There is no conflict of interest disclosed by the authors.

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