Applied Sciences

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15 pages, 3870 KiB

Open AccessArticle

Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis

by Donggeun Kim and Younghwan Son

Appl. Sci. 2023, 13(18), 10484; https://doi.org/10.3390/app131810484 - 20 Sep 2023

Viewed by 1252

Thresholding is a digital image analysis method used to distinguish objects from the background in images and it is mainly used for void and density analysis in soil. It is important to select an appropriate thresholding method because the accuracy of void analysis [...] Read more.

Thresholding is a digital image analysis method used to distinguish objects from the background in images and it is mainly used for void and density analysis in soil. It is important to select an appropriate thresholding method because the accuracy of void analysis can vary significantly depending on the threshold value; however, there is currently no standard for soil density analysis. Therefore, this study proposes an image analysis method for soil density prediction. The experimental process involved collecting soil samples from agricultural lands, encompassing various soil textures including sandy loam, loam, silt loam, and silty clay loam. These samples were then meticulously prepared under controlled conditions, ensuring a comprehensive range of dry densities and water content levels. Digital images of the soil samples were acquired using a Canon EOS100d camera, employing a high-resolution setup that provided precise imaging capabilities. The porosity of the soil image is calculated by various thresholding methods. Based on the analysis results, we propose a void area curve, a new approach that can be applied to the soil density prediction. The void area curve is the relationship curve between the threshold value and porosity of the soil image. The standard deviation of the void area curve showed a high correlation with the dry density of the soil. The standard deviation of the void area curve was used to predict the dry density under various soil texture and water content conditions, and the RMSE was 0.037 t/m³. The method of estimating soil density with the standard deviation of the void area curve can be used more generally than the existing analysis method because there is no need to select a specific threshold value. Full article

(This article belongs to the Special Issue Climate Change on Water Resource)

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16 pages, 6690 KiB

Open AccessArticle

Comparative Sensitivity Analysis of Hydrology and Relative Corn Yield under Different Subsurface Drainage Design Using DRAINMOD

by Haribansha Timalsina, Soonho Hwang, Richard A. Cooke and Rabin Bhattarai

Appl. Sci. 2023, 13(16), 9252; https://doi.org/10.3390/app13169252 - 15 Aug 2023

Cited by 1 | Viewed by 1753

Abstract

DRAINMOD is a process-based hydrologic model used to analyze the effectiveness of various drainage systems and management strategies. In this study, a sensitivity analysis of DRAINMOD hydrologic parameters for two different field settings located at Champaign, Illinois, was performed to determine the most [...] Read more.

DRAINMOD is a process-based hydrologic model used to analyze the effectiveness of various drainage systems and management strategies. In this study, a sensitivity analysis of DRAINMOD hydrologic parameters for two different field settings located at Champaign, Illinois, was performed to determine the most sensitive parameters that affect the subsurface flow and relative productivity of corn. Latin-Hypercube One-Factor-at-a-Time (LH-OAT) was used to determine the sensitivity index of 17 parameters for six objective functions for daily flow, water balance, and relative yield for the productivity of corn. The results indicated that flow and yield were highly sensitive to drainage design parameters such as drainage depth and spacing. Winter flow and the water balance were sensitive to soil thermal conductivity parameters; however, they had no impact on the relative corn yield. The significant difference in sensitivity of the two fields was observed in the hydraulic conductivity of soil layers due to varying thicknesses for different soil types. This study highlights the need for more careful calibration of these sensitive parameters to reduce equifinality and model output uncertainty and appropriate drainage design for optimizing crop productivity and drainage outflow. Full article

(This article belongs to the Special Issue Climate Change on Water Resource)

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21 pages, 6387 KiB

Open AccessArticle

Performance Assessment of Bias Correction Methods for Precipitation and Temperature from CMIP5 Model Simulation

by Digambar S. Londhe, Yashwant B. Katpatal and Neeraj Dhanraj Bokde

Appl. Sci. 2023, 13(16), 9142; https://doi.org/10.3390/app13169142 - 10 Aug 2023

Cited by 7 | Viewed by 2549

Abstract

Hydrological modeling relies on the inputs provided by General Circulation Model (GCM) data, as this allows researchers to investigate the effects of climate change on water resources. But there is high uncertainty in the climate projections with various ensembles and variables. Therefore, it [...] Read more.

Hydrological modeling relies on the inputs provided by General Circulation Model (GCM) data, as this allows researchers to investigate the effects of climate change on water resources. But there is high uncertainty in the climate projections with various ensembles and variables. Therefore, it is very important to carry out bias correction in order to analyze the impacts of climate change at a regional level. The performance evaluation of bias correction methods for precipitation, maximum temperature, and minimum temperature in the Upper Bhima sub-basin has been investigated. Four bias correction methods are applied for precipitation viz. linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Three bias correction methods are applied for temperature viz. linear scaling (LS), variance scaling (VS), and distribution mapping (DM). The evaluation of the results from these bias correction methods is performed using the Kolmogorov–Smirnov non-parametric test. The results indicate that bias correction methods are useful in reducing biases in model-simulated data, which improves their reliability. The results of the distribution mapping bias correction method have been proven to be more effective for precipitation, maximum temperature, and minimum temperature data from CMIP5-simulated data. Full article

(This article belongs to the Special Issue Climate Change on Water Resource)

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14 pages, 21519 KiB

Open AccessArticle

Soil-Surface-Image-Feature-Based Rapid Prediction of Soil Water Content and Bulk Density Using a Deep Neural Network

by Donggeun Kim, Taejin Kim, Jihun Jeon and Younghwan Son

Appl. Sci. 2023, 13(7), 4430; https://doi.org/10.3390/app13074430 - 30 Mar 2023

Cited by 6 | Viewed by 2002

Abstract

This study aimed to develop a deep neural network model for predicting the soil water content and bulk density of soil based on features extracted from in situ soil surface images. Soil surface images were acquired using a Canon EOS 100d camera. The [...] Read more.

This study aimed to develop a deep neural network model for predicting the soil water content and bulk density of soil based on features extracted from in situ soil surface images. Soil surface images were acquired using a Canon EOS 100d camera. The camera was installed in the vertical direction above the soil surface layer. To maintain uniform illumination conditions, a dark room and LED lighting were utilized. Following the acquisition of soil surface images, soil samples were collected using a metal cylinder to obtain measurements of soil water content and bulk density. Various features were extracted from the images, including color, texture, and shape features, and used as inputs for both a multiple regression analysis and a deep neural network model. The results show that the deep neural network regression model can predict soil water content and bulk density with root mean squared error of 1.52% and 0.78 kN/m³. The deep neural network model outperformed the multiple regression analysis, achieving a high accuracy for predicting both soil water content and bulk density. These findings suggest that in situ soil surface images, combined with deep learning techniques, can provide a fast and reliable method for predicting important soil properties. Full article

(This article belongs to the Special Issue Climate Change on Water Resource)

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15 pages, 8759 KiB

Open AccessArticle

Convolutional Neural Network-Based Soil Water Content and Density Prediction Model for Agricultural Land Using Soil Surface Images

by Donggeun Kim, Taejin Kim, Jihun Jeon and Younghwan Son

Appl. Sci. 2023, 13(5), 2936; https://doi.org/10.3390/app13052936 - 24 Feb 2023

Cited by 6 | Viewed by 3107

Abstract

For appropriate managing fields and crops, it is essential to understand soil properties. There are drawbacks to the conventional methods currently used for collecting a large amount of data from agricultural lands. Convolutional neural network is a deep learning algorithm that specializes in [...] Read more.

For appropriate managing fields and crops, it is essential to understand soil properties. There are drawbacks to the conventional methods currently used for collecting a large amount of data from agricultural lands. Convolutional neural network is a deep learning algorithm that specializes in image classification, and developing soil property prediction techniques using this algorithm will be extremely beneficial to soil management. We present the convolution neural network models for estimating water content and dry density using soil surface images. Soil surface images were taken with a conventional digital camera. The range of water content and dry density were determined considering general upland soil conditions. Each image was divided into segmented images and used for model training and validation. The developed model confirmed that the model can learn soil features through appropriate image argumentation of few of original soil surface images. Additionally, it was possible to predict the soil water content in a situation where various soil dry density conditions were considered. Full article

(This article belongs to the Special Issue Climate Change on Water Resource)

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20 pages, 1961 KiB

Open AccessArticle

The Hydrological Balance in Micro-Watersheds Is Affected by Climate Change and Land Use Changes

by Víctor H. Ruiz-García, Carlos Asensio-Grima, A. Guillermo Ramírez-García and Alejandro Ismael Monterroso-Rivas

Appl. Sci. 2023, 13(4), 2503; https://doi.org/10.3390/app13042503 - 15 Feb 2023

Cited by 3 | Viewed by 2478

Abstract

Temperate forests are key to the balance and provision of hydrological and environmental services. Currently, these forests are subject to human alterations as well as to the effects of global change, including warming, variability, deforestation, and forest fires. As a consequence, the hydrological [...] Read more.

Temperate forests are key to the balance and provision of hydrological and environmental services. Currently, these forests are subject to human alterations as well as to the effects of global change, including warming, variability, deforestation, and forest fires. As a consequence, the hydrological balance has been modified. The present study simulates the effects of climate change and land use change on the hydrological balance of micro-watersheds in Mexico using the hydrological model Water Evaluation and Planning (WEAP). The land use change between 1995 and 2021 was estimated to establish a baseline. Climate scenario SSP585 was projected using three global models, MPI-ESM1-2-LR, HadGEM3-GC31-LL, and CNRM-CM6-1 by the 2081–2100 horizon, along with two scenarios of land use change: one with forest permanence and another with loss of forest cover and increased forest fires. Results indicate that future climatic conditions will modify the hydrological balance at the microbasin level. Even with positive conditions of forest permanence, increases in surface runoff of 124% (CNRM), 35% (HadGEM3), and 13% (MPI) are expected. The projections of coverage loss and fires showed surface runoff increases of 338% (CNRM), 188% (HadGEM3), and 143% (MPI). In the high areas of the microbasins where temperate forest predominates, climatic variations could be contained. If the forest is conserved, surface runoff decreases by −70% (CNRM), −87% (HadGEM3), and −89% (MPI). Likewise, the moisture in the soil increases. In areas with temperate forests, there will be modifications of the hydrological balance mainly due to the increase in evapotranspiration (due to the increase in temperature and precipitation). This will cause a significant decrease in flow and interflow. The alteration of these flows will decrease water availability in soil for infiltration. It is expected that the availability of hydrological and environmental services will be compromised in the entire study area due to climate change. Full article

(This article belongs to the Special Issue Climate Change on Water Resource)

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12 pages, 1561 KiB

Open AccessArticle

Impact of Construction and Functioning of a Newly Built Ski Slope on the Quality of Nearby Stream Water

by Anna Lenart-Boroń, Anna Bojarczuk and Mirosław Żelazny

Appl. Sci. 2023, 13(2), 763; https://doi.org/10.3390/app13020763 - 5 Jan 2023

Cited by 1 | Viewed by 2401

Abstract

The construction of new, artificially snowed, ski slopes and the accompanying infrastructure changes the natural environment and exerts pressure on the ecosystems. This study examined the impact of the construction and operation of a new ski slope, with its infrastructure and artificial snow [...] Read more.

The construction of new, artificially snowed, ski slopes and the accompanying infrastructure changes the natural environment and exerts pressure on the ecosystems. This study examined the impact of the construction and operation of a new ski slope, with its infrastructure and artificial snow production, on the quality of nearby stream waters. The research period covered the time before, during and after the slope construction. Electrolytic conductivity (EC) and pH were measured on-site, chemical analyses included the determination of Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃⁻, SO₄²⁻, Cl⁻, NO₃⁻, and microbiological analysis comprised mesophilic and psychrophilic bacteria, total and fecal coliforms, and E. coli. As a result of intensive environmental transformations, the examined parameters varied significantly over the study period, as shown by the coefficient of variation. Due to land cover changes, concentrations of all the examined parameters increased during the ski slope construction due to ions and bacteria leaching from the soil. However, when construction works were finished, all bacterial and some chemical indicators returned to the state observed before the construction, most probably due to the recovery of vegetation and self-purification of water. Supply of melt water from artificial snow, produced from water containing higher concentrations of ions, increased pH, EC, Ca²⁺, Mg²⁺ and HCO₃⁻ in the stream. Providing that the development of ski stations is unavoidable in the considered region, conducting studies assessing the impact of new ski slope construction is an important step that should be conducted prior to undertaking such investments. Full article

(This article belongs to the Special Issue Climate Change on Water Resource)

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16 pages, 4933 KiB

Open AccessArticle

Monthly Precipitation over Northern Middle Atlas, Eastern Morocco: Homogenization and Trends

by Rachid Addou, Mohamed Hanchane, Khalid Obda, Nir Y. Krakauer, Bouchta El Khazzan, Ridouane Kessabi and Hassan Achiban

Appl. Sci. 2022, 12(23), 12496; https://doi.org/10.3390/app122312496 - 6 Dec 2022

Cited by 4 | Viewed by 2902

Abstract

The lack of a complete and reliable data series often represents the main difficulty in carrying out climate studies. Diverse causes, such as human and instrumental errors, false and incomplete records, and the use of obsolete equipment in some meteorological stations, give rise [...] Read more.

The lack of a complete and reliable data series often represents the main difficulty in carrying out climate studies. Diverse causes, such as human and instrumental errors, false and incomplete records, and the use of obsolete equipment in some meteorological stations, give rise to inhomogeneities that do not represent climatic reality. This work in the northern part of the Moroccan Middle Atlas used 22 meteorological stations with sometimes-incomplete monthly precipitation data from 1970 to 2019. The homogenization and estimation of the missing data were carried out with the R software package Climatol version 3.1.1. The trends in the series were quantified by the Mann–Kendall nonparametric test. The results obtained show a low root mean square error (RMSE), between the original and homogenized data, of between 0.5 and 38.7 mm per month, with an average of 8.5 mm. Rainfall trends for the months of December through June are generally downward. These negative trends are significantly stronger in the southern and eastern parts of the study area, especially during the month of April (the wettest month). On the other hand, July shows positive trends, with 71% of stations having an increasing precipitation tendency, although only five (or 1/3) of these are statistically significant. From August to November, generally positive trends were also observed. For these months, the percentage of series with a positive and significant trend varied between 55 and 77%. Full article

(This article belongs to the Special Issue Climate Change on Water Resource)

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