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Advanced Technologies and Methods for Soil Water Monitoring and Management

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 15565

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Dr. Jingyi Huang

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Guest Editor

Department of Soil Science, University of Wisconsin–Madison, Madison, WI 53706-1299, USA
Interests: soil physics; digital soil mapping; proximal soil sensing; geostatistics; inverse modeling

Dr. Brendan Malone

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Guest Editor

Agriculture and Food Commonwealth Scientific and Industrial Research Organisation, Bruce E Butler Laboratory, Clunies Ross Street, Black Mountain, ACT 2601, Australia
Interests: soil science; digital soil mapping; pedometrics; GIS
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Dear Colleagues,

Soil moisture is at the nexus of nexus of food, energy and water systems. The partitioning of precipitation into infiltration, ground water percolation, runoff, and evapotranspiration, as well as the partitioning of heat fluxes at the ground surface are influenced by soil moisture. Soil moisture varies in space and time across scales, and the variations are controlled by meteorological forcing, land cover, topographic features, soil texture, and human activities. To measure, monitor, map, and model soil moisture for enhanced understanding of water and energy cycles across scales and for improved water resource management for global food security under a changing climate and human activities, in situ, proximal, and remote sensing technologies and novel conceptual frameworks have been developed and increasling used in many disciplines, such as agronomy, hydrology, meteorology, ecology, and environmental sciences. This Special Issue collects original research and review articles on the latest advances in technologies and models developed to assist in monitoring, mapping, and modeling soil water dynamics across scales to understand the water cycles and sustainable water resource management.

Dr. Jingyi Huang
Dr. Brendan Malone
Guest Editors

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Keywords

water resource management
sensing technologies
Internet of Things
food security
climate change
water cycles

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Published Papers (4 papers)

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Research

14 pages, 3077 KiB

Open AccessArticle

Matrix Suction Evaluation of Soil-Rock Mixture Based on Electrical Resistivity

by Kui Wang, Zhengting Xia and Xue Li

Water 2021, 13(20), 2937; https://doi.org/10.3390/w13202937 - 19 Oct 2021

Cited by 6 | Viewed by 2349

Abstract

The soil-rock mixture is an important construction material in practical engineering. Its physical properties, especially soil-water properties, are particularly important for on-site construction, while both matrix suction and electrical resistivity in unsaturated soils depend on the magnitude of moisture content, and a certain relationship can be established between the two physical quantities. Therefore, in this paper, we designed a matrix suction–electrical resistivity synergistic test device and conducted indoor physical tests on common soil-rock mixtures in the Chongqing area to investigate the correlation between matrix suction and electrical resistivity in soil-rock mixtures. The electrical resistivity comprehensive parameter was used to represent the electrical resistivity characteristics of rock and soil. Based on the experimental results, a matrix suction–electrical resistivity comprehensive parameter model applicable to soil-rock mixtures was established, and the sensitivity analysis of the model parameters was carried out. The results show that the soil-water characteristic curve of the soil-rock mixture has a double-step shape, and the shape of the curve under different compaction degrees is similar. The matrix suction and mass moisture content of the soil-rock mixture were positively correlated with the compaction degree. There was good consistency between the experimentally measured matrix suction and the matrix suction reflected by the electrical resistivity in the model. Moreover, the model curve was able to reflect the drainage process of pores in the soil for soil–stone mixtures with a bimodal pore size distribution, thus providing a new way to measure the matrix suction of unsaturated soil–stone mixtures in practical engineering. Full article

(This article belongs to the Special Issue Advanced Technologies and Methods for Soil Water Monitoring and Management)

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15 pages, 23160 KiB

Open AccessEditor’s ChoiceArticle

Vertical Electrical Sounding (VES) for Estimation of Hydraulic Parameters in the Porous Aquifer

by Andreia de Almeida, Daiane Ferreira Maciel, Karen Félix Sousa, Carlos Tadeu Carvalho Nascimento and Sérgio Koide

Water 2021, 13(2), 170; https://doi.org/10.3390/w13020170 - 13 Jan 2021

Cited by 23 | Viewed by 6417

Abstract

Similarities in both water and electric current flows allow the relation of hydraulic and geoelectric parameters of porous aquifers. Based on this assumption and the importance of the hydraulic parameters for groundwater analyses, this study aimed to estimate hydraulic conductivity (K) and transmissivity (T) with vertical electrical sounding (VES) in the porous aquifer at the experimental farm of the University of Brasilia, Brazil. VES is a geophysical technique that provides electrical resistivity (ρ, Ω m) and thickness (h) of the subsurface layers. The ρ and h aquifer data, associated with lithology, water table level (WTL), and groundwater electrical resistivity (ρ_w, Ω m), allowed the calculation of complementary geoelectric parameters (formation factor, F, and Dar Zarrouk parameters) and the relation with K and T, determined via slug test. VES data allowed the elaboration of geoelectric models, with mean absolute percentage error (MAPE) below 6% compared to field data, and the identification of the aquifer in each VES station. Significant exponential regression models (R² > 0.5 and p-value < 0.05) showed the possibility of using geoelectric parameters to estimate hydraulic parameters. This study allowed the verification of the applicability of consolidated models and the identification of appropriate empirical relationships for hydrogeological characterization in the Brazilian tropical porous aquifers. The results of this work, besides the rapid sampling and low cost of performing vertical electrical sounding (VES), may justify the use of this geophysical technique for preliminary porous aquifer characterization, especially in regions absent of or with insufficient monitoring wells. Full article

(This article belongs to the Special Issue Advanced Technologies and Methods for Soil Water Monitoring and Management)

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20 pages, 5417 KiB

Open AccessArticle

Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe

by Roïya Souissi, Ahmad Al Bitar and Mehrez Zribi

Water 2020, 12(11), 3109; https://doi.org/10.3390/w12113109 - 5 Nov 2020

Cited by 9 | Viewed by 3090

Abstract

This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) as a training dataset. The paper also addresses the transferability of the trained ANN across climatic and soil texture conditions. Data from the International Soil Moisture Network (ISMN) were collected for several networks with variable soil texture and climate classes. Several scaling, feature extraction, and training approaches were tested. An artificial neural network employing rolling averages (ANN_RAV) of SSM over 10, 30, and 90 days was developed. The results show that applying a standard scaling (SSCA) to the ANN input features improves the correlation, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) for 52%, 91%, and 87%, respectively, of the tested stations, compared to MinMax scaling (MMSCA). Different training sets are suggested, namely, training on data from all networks, data from one network, or data of all networks excluding one. Based on these trainings, new transferability (TranI) and contribution (ContI) indices are defined. The results show that one network cannot provide the best prediction accuracy if used alone to train the ANN. They also show that the removal of the less contributing networks enhances performance. For example, elimination of the densest network (SCAN) from the training enhances the mean correlation by 20.5% and the mean NSE by 42.5%. This motivates the implementation of a data filtering technique based on the ANN’s performance. A median, max, and min correlation of 0.77, 0.96, and 0.65, respectively, are obtained by the model after data filtering. The performances are also analyzed with respect to the covered climatic regions and soil texture, providing insights into the robustness and limitations of the approach, namely, the need for complementary information in highly evaporative regions. In fact, the ANN using only SSM to predict RZSM has low performance when decoupling between the surface and root zones is observed. The application of ANN to obtain spatialized RZSM will require integrating remote sensing-based surface soil moisture in the future. Full article

(This article belongs to the Special Issue Advanced Technologies and Methods for Soil Water Monitoring and Management)

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19 pages, 3987 KiB

Open AccessArticle

An Empirical Orthogonal Function-Based Approach for Spatially- and Temporally-Extensive Soil Moisture Data Combination

by Ying Zhao, Fei Li, Rongjiang Yao, Wentao Jiao and Robert Lee Hill

Water 2020, 12(10), 2919; https://doi.org/10.3390/w12102919 - 19 Oct 2020

Cited by 5 | Viewed by 3015

Abstract

Modeling and prediction of soil hydrologic processes require identifying soil moisture spatial-temporal patterns and effective methods allowing the data observations to be used across different spatial and temporal scales. This work presents a methodology for combining spatially- and temporally-extensive soil moisture datasets obtained in the Shale Hills Critical Zone Observatory (CZO) from 2004 to 2010. The soil moisture was investigated based on Empirical Orthogonal Function (EOF) analysis. The dominant soil moisture patterns were derived and further correlated with the soil-terrain attributes in the study area. The EOF analyses indicated that one or two EOFs of soil moisture could explain 76–89% of data variation. The primary EOF pattern had high values clustered in the valley region and, conversely, low values located in the sloping hills, with a depth-dependent correlation to which curvature, depth to bedrock, and topographic wetness index at the intermediate depths (0.4 m) exhibited the highest contributions. We suggest a novel approach to integrating the spatially-extensive manually measured datasets with the temporally-extensive automatically monitored datasets. Given the data accessibility, the current data merging framework has provided the methodology for the coupling of the mapped and monitored soil moisture datasets, as well as the conceptual coupling of slow and fast pedologic and hydrologic functions. This successful coupling implies that a combination of diverse and extensive moisture data has provided a solution of data use efficiency and, thus, exciting insights into the understanding of hydrological processes at multiple scales. Full article

(This article belongs to the Special Issue Advanced Technologies and Methods for Soil Water Monitoring and Management)

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