Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Edit a Special Issue

Journal Menu

Journal Browser

Special Issue "Climate Change on Water Resource"

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: 30 June 2023 | Viewed by 3984

Share This Special Issue

Special Issue Editor

Dr. Soonho Hwang

E-Mail Website
Guest Editor

Department of Agricultural and Biological Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Interests: land use; land change; climate change; hydrology

Special Issue Information

Dear Colleagues,

This Special Issue, titled Climate Change on Water Resource, seeks the most recent works on the adaptations or responses of the water resources to climate change and its influence on the aquatic environment. The consequences of climate change can significantly influence the issues with the water resources related to the frequent occurrence of severe droughts, water scarcity, flooding, rising sea levels, and aquatic biodiversity. In the earth system, every element relates to each other, and water resources play a major role for all others in an environment. Thus, water resources in general, as well as the variation of water resources and its impact on other environmental elements, represent a crucial issue.

The scope of this Special Issue covers all aspects of the water resources research from all analyses of the anthropogenic climate change. In this Special Issue, researchers are encouraged to target all the fields related to water resources, such as agricultural water, urban water, lake, river, reservoir, coastal environment, and even biological and chemical changes in the aquatic system. This Special Issue also emphasizes the issues of the uncertainty problem of water resources to climate change.

Dr. Soonho Hwang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

droughts
floods
water quality
agricultural water
agricultural reservoir
urban water
lake
costal environment
biodiversity
uncertainty assessment

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Open AccessArticle

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

and

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

Viewed by 584

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 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)

► Show Figures

Figure 1

Open AccessArticle

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

and

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

Viewed by 699

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 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)

► Show Figures

Figure 1

Open AccessArticle

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

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

Viewed by 571

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 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)

► Show Figures

Figure 1

Open AccessArticle

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

Anna Lenart-Boroń

Anna Bojarczuk

and

Mirosław Żelazny

Appl. Sci. 2023, 13(2), 763; https://doi.org/10.3390/app13020763 - 05 Jan 2023

Viewed by 783

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 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)

► Show Figures

Figure 1

Open AccessArticle

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

and

Appl. Sci. 2022, 12(23), 12496; https://doi.org/10.3390/app122312496 - 06 Dec 2022

Cited by 1 | Viewed by 952

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 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)

► Show Figures

Journal Menu

Journal Browser

Special Issue "Climate Change on Water Resource"

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI