Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.0
Journals
Water
Special Issues
Land–Water System Management: Simulations, Socio-Economic Analyses and Artificial Intelligence Techniques
water-logo
Submit to Water Review for Water Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Land–Water System Management: Simulations, Socio-Economic Analyses and Artificial Intelligence Techniques

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

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 26130

Special Issue Editors

Dr. Lei Gao

E-Mail Website
Guest Editor
Commonwealth Scientific and Industrial Research Organization (CSIRO), Glen Osmond, SA 5064, Australia
Interests: complex environmental system modelling; environmental policy assessment; sustainable development goals and health; decision-making and risk analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jeff Connor

E-Mail Website
Guest Editor
UniSA Business, University of South Australia, Adelaide, SA 5000, Australia
Interests: water economics
Special Issues, Collections and Topics in MDPI journals
Dr. Dirk Mallants

E-Mail Website
Guest Editor
Land and Water - CSIRO, Private Bag 2, Glen Osmond, SA 5064, Australia
Interests: groundwater hydrogeology; radionuclide transport; engineered barrier performance; safety assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land and water are fundamental resources to sustain human life and maintain environmental goods and services. However, land and water systems at global, regional, and local scales are under unprecedented pressures due to intensive use and inadequate management of these two resources. Land and water systems are often naturally coupled together and need to be managed as an integrated system to create benefits for human society and nature. Methods, analysis, and tools are urgently required that can help stakeholders understand the dynamics within and between the two systems, identify factors that drive the dynamics, assess cumulative impacts of these factors on the systems, better project possible future changes of the systems, and design management strategies to utilise the resources sustainably.

For this Special Issue, we invite papers developing frameworks/models for monitoring and quantification land-water system stressors/pressures, their synergistic and antagonistic effects, system‐scale interactions, coupled cycles and socio‐ecological impacts, as well as examining policy implications and management challenges of land-water systems. The topics include the development of land–water system models, the analysis of social, economic, institutional, technical and environmental measures/instruments and their effects on land and water resources at different scales, the exploration of land-water system dynamics and dynamics under external forces (such as climate change), the application of artificial intelligence in land-water system management, and the evaluation of land and water policy/management strategies. Theoretical discussions and case studies concerning the land–water system management are also welcomed.

Dr. Lei Gao
Prof. Jeff Connor
Dr. Dirk Mallants
Guest Editors

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. Water 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 2600 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

  • Land–water system management
  • Integrated system modelling
  • Socio-economic analyses
  • Complex systems modelling
  • Simulations
  • Policy evaluation
  • Cumulative impacts assessment
  • Artificial intelligence

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 2339 KiB  
Article
Groundwater Recharge Prediction Using Linear Regression, Multi-Layer Perception Network, and Deep Learning
by Xin Huang, Lei Gao, Russell S. Crosbie, Nan Zhang, Guobin Fu and Rebecca Doble
Water 2019, 11(9), 1879; https://doi.org/10.3390/w11091879 - 10 Sep 2019
Cited by 70 | Viewed by 7626
Abstract
As the largest freshwater storage in the world, groundwater plays an important role in maintaining ecosystems and helping humans adapt to climate change. However, groundwater dynamics, such as groundwater recharge, cannot be measured directly and is influenced by spatially and temporally complex processes, [...] Read more.
As the largest freshwater storage in the world, groundwater plays an important role in maintaining ecosystems and helping humans adapt to climate change. However, groundwater dynamics, such as groundwater recharge, cannot be measured directly and is influenced by spatially and temporally complex processes, models are therefore required to capture the dynamics and provide scientific advice for decision-making. This paper developed, estimated and compared the performance of linear regression, multi-layer perception (MLP) and Long Short-Term Memory (LSTM) models in predicting groundwater recharge. The experimental dataset consists of time series of annual recharge from the year 1970 to 2012, based on water table fluctuation estimates from 465 bores in the states of South Australia and Victoria, Australia. We identified the factors that influenced groundwater recharge and found that the correlation between rainfall and groundwater recharge was strongest. The linear regression model had the poorest fitting performance, with the root mean squared error (RMSE) being greater than 0.19 when various proportions of training data were considered. The MLP model outperformed the linear regression in the prediction capability, achieving RMSE = 0.11 when 80% of training data was considered. The LSTM model was found to have the best performance, whose root mean squared errors were less than 0.12 when various proportions of training data were applied. The relative importance of influential predictors was evaluated using the above three models. Full article
(This article belongs to the Special Issue Land–Water System Management: Simulations, Socio-Economic Analyses and Artificial Intelligence Techniques)
►▼ Show Figures

Figure 1

31 pages, 5480 KiB  
Article
Prediction of Seasonal Frost Heave Behavior in Unsaturated Soil in Northeastern China Using Interactive Factor Analysis with Split-Plot Experiments and GRNN
by Hanli Wan, Jianmin Bian, Juanjuan Wu, Xiaoqing Sun, Yu Wang and Zhuo Jia
Water 2019, 11(8), 1587; https://doi.org/10.3390/w11081587 - 31 Jul 2019
Cited by 12 | Viewed by 3072
Abstract
Few studies of frost heave mechanisms have considered multifactor interactions, particularly in unsaturated saline soils typical of northeastern China. We collected soil samples in western Jijin Province and assessed their potential frost heave behavior with reference to four controllable factors: soluble salt content [...] Read more.
Few studies of frost heave mechanisms have considered multifactor interactions, particularly in unsaturated saline soils typical of northeastern China. We collected soil samples in western Jijin Province and assessed their potential frost heave behavior with reference to four controllable factors: soluble salt content (CSS), compactness (C), temperature (T), and water content (WC) using a two-level split-plot experiment. The resulting frost heave ratio was between −0.6% and 2.1%. Analysis of variance showed that water content, compactness, and temperature had significant effects on frost heave behavior, with water content having the strongest correlation (factor coefficient of 0.82), while content of soluble salt (CSS) had no significant effect. The interaction factors (products of single factors) CSS × WC and C × WC had significant effects on frost heave behavior. A correlation analysis using these interaction factors with experimental data drawn from previous research showed results consistent with the improved frost heave experiment as the significant effects of single factors on frost heave behavior ranked from WC > C > T and the interaction factors CSS × WC and C × WC gain had significant effects. We then established two generalized regression neural network (GRNN) models based on the single and interaction factors in order to predict frost heave behavior, showing that adding the latter to the input dataset improved the model accuracy. Thus, future research on predicting frost heave behavior in unsaturated saline soils should consider multiple interacting factor for greater accuracy. Full article
(This article belongs to the Special Issue Land–Water System Management: Simulations, Socio-Economic Analyses and Artificial Intelligence Techniques)
►▼ Show Figures

Graphical abstract

20 pages, 3835 KiB  
Article
Assessing the Impacts of Best Management Practices on Nonpoint Source Pollution Considering Cost-Effectiveness in the Source Area of the Liao River, China
by Yu Wang, Jianmin Bian, Wangmei Lao, Yongsheng Zhao, Zeyu Hou and Xiaoqing Sun
Water 2019, 11(6), 1241; https://doi.org/10.3390/w11061241 - 14 Jun 2019
Cited by 14 | Viewed by 3715
Abstract
Agricultural nonpoint source pollution has been a major influential factor on the deterioration of water quality in the Liao River source area. Best management practices (BMPs), as a comprehensive pollution prevention system designed to reduce the impacts of agricultural activities and improve water [...] Read more.
Agricultural nonpoint source pollution has been a major influential factor on the deterioration of water quality in the Liao River source area. Best management practices (BMPs), as a comprehensive pollution prevention system designed to reduce the impacts of agricultural activities and improve water quality, has been considered one of the most effective solutions for nonpoint source pollution control. However, economic cost has been an important element for screening the implementation of BMPs. Both pollution reduction and capital expenditure need to be resolved with the actual situation. A water quality model such as the Soil and Water Assessment Tool (SWAT) and empirical cost algorithm are important tools to assess the cost-effectiveness of the effects of BMPs on nonpoint source pollution. In this study, BMP scenarios including buffer strips (BSs), fertilizer reduction (FR), forest land increase (FLI), grassland increase (GLI), and their combination were implemented using the SWAT model; furthermore, the efficiency of their pollutants reduction and costs benefit were estimated in the watershed. The results showed that combined BMPs have better control effects than a single BMP, with “BS20 (widths 20 m) + FR15 (fertilization reduction 15%) + FLI (forest land increase)” arriving at the greatest loads reduction in the critical periods. From environmental and economic perspectives, the cost-effectiveness of interception measures is higher than that of the source control measures. The results indicated that BS was the most environmentally friendly practice, and FR was the most economically efficient out of all the BMPs. Regarding land-use changes, FLI was more environmentally friendly, and GLI was more economically efficient. The most economical and effective BMPs can be designated as follows: BS1.5 (widths 1.5 m) and FR15 (fertilization reduction 15%). Therefore, due to possible differences in government policies, it is important to consider an integrated approach for all the relevant actors and seek sustainable environmental and economic development. Full article
(This article belongs to the Special Issue Land–Water System Management: Simulations, Socio-Economic Analyses and Artificial Intelligence Techniques)
►▼ Show Figures

Figure 1

19 pages, 1478 KiB  
Article
Value Stream Analysis and Emergy Evaluation of the Water Resource Eco-Economic System in the Yellow River Basin
by Danyang Di, Zening Wu, Xi Guo, Cuimei Lv and Huiliang Wang
Water 2019, 11(4), 710; https://doi.org/10.3390/w11040710 - 6 Apr 2019
Cited by 30 | Viewed by 4349
Abstract
Value accounting of water in the Yellow River Basin is a key issue in managing local water resources in an efficient, equitable, and sustainable way. In view of the dubious current theories of water resource value, the value transfer of water resources, based [...] Read more.
Value accounting of water in the Yellow River Basin is a key issue in managing local water resources in an efficient, equitable, and sustainable way. In view of the dubious current theories of water resource value, the value transfer of water resources, based on energy flow, is discussed from the perspective of eco-economics. An emergy analysis method is introduced to quantify both the sediment transportation value and social value, and a quantitative system of eco-economic value indicators is constructed. The water resource values of 66 cities in the Basin were calculated, and the GIS atlas was used to describe their spatial distribution. Eight typical cities were selected for the key analysis. The results show that: (1) Among the sub-items, the social value of water per unit is the largest, reaching 30.67 Chinese Yuan/m³, and the difference between the maximum and minimum is only 0.04%, which reflects the social equity of water resources. (2) The eco-environmental value inside the river is generally higher than that of industry, and it is verified that industrial water should not intrude the eco-environmental water in the river. (3) The unit agricultural value of water is the lowest among the sub-items, and the construction of water-saving agriculture should be carried out. Full article
(This article belongs to the Special Issue Land–Water System Management: Simulations, Socio-Economic Analyses and Artificial Intelligence Techniques)
►▼ Show Figures

Figure 1

18 pages, 3264 KiB  
Article
Immune Evolution Particle Filter for Soil Moisture Data Assimilation
by Feng Ju, Ru An and Yaxing Sun
Water 2019, 11(2), 211; https://doi.org/10.3390/w11020211 - 26 Jan 2019
Cited by 13 | Viewed by 3851
Abstract
Data assimilation (DA) has been widely used in land surface models (LSM) to improve model state estimates. Among various DA methods, the particle filter (PF) with Markov chain Monte Carlo (MCMC) has become increasingly popular for estimating the states of the nonlinear and [...] Read more.
Data assimilation (DA) has been widely used in land surface models (LSM) to improve model state estimates. Among various DA methods, the particle filter (PF) with Markov chain Monte Carlo (MCMC) has become increasingly popular for estimating the states of the nonlinear and non-Gaussian LSMs. However, the standard PF always suffers from the particle impoverishment problem, characterized by loss of particle diversity. To solve this problem, an immune evolution particle filter with MCMC simulation inspired by the biological immune system, entitled IEPFM, is proposed for DA in this paper. The merit of this approach is in imitating the antibody diversity preservation mechanism to further improve particle diversity, thus increasing the accuracy of estimates. Furthermore, the immune memory function refers to promise particle evolution process towards optimal estimates. Effectiveness of the proposed approach is demonstrated by the numerical simulation experiment using a highly nonlinear atmospheric model. Finally, IEPFM is applied to a soil moisture (SM) assimilation experiment, which assimilates in situ observations into the Variable Infiltration Capacity (VIC) model to estimate SM in the MaQu network region of the Tibetan Plateau. Both synthetic and real case experiments demonstrate that IEPFM mitigates particle impoverishment and provides more accurate assimilation results compared with other popular DA algorithms. Full article
(This article belongs to the Special Issue Land–Water System Management: Simulations, Socio-Economic Analyses and Artificial Intelligence Techniques)
►▼ Show Figures

Figure 1

22 pages, 12471 KiB  
Article
Modeling Hydro-Dynamics in a Harbor Area in the Daishan Island, China
by Yuting Li, Zhiyao Song, Guoqiang Peng, Xuwen Fang, Ruijie Li, Peng Chen and Haoyuan Hong
Water 2019, 11(2), 192; https://doi.org/10.3390/w11020192 - 23 Jan 2019
Cited by 10 | Viewed by 3099
Abstract
This study presents an incorporation and application of a two-dimensional, unstructured-grid hydrodynamic model with a suspended sediment transport module in Daishan, China. The model is verified with field measurement data from 2017: water level, flow velocities and suspended sediment concentration (SSC). In the [...] Read more.
This study presents an incorporation and application of a two-dimensional, unstructured-grid hydrodynamic model with a suspended sediment transport module in Daishan, China. The model is verified with field measurement data from 2017: water level, flow velocities and suspended sediment concentration (SSC). In the application on the Daishan, the performance of the hydrodynamic model has been satisfactorily validated against observed variations of available measurement stations. Coupled with the hydrodynamic model, a sediment transport model has been developed and tested. The simulations agreed quantitatively with the observations. The validated model was applied to the construction of breakwaters and docks under a different plan. The model can calculate the flow field and siltation situation under different breakwater settings. After we have analyzed the impact of existing breakwater layout schemes and sediment transport, a reasonable plan will be selected. The results show that the sea area near the north of Yanwo Shan and Dongken Shan has a large flow velocity exceeding 2.0 m/s and the flow velocity within the isobath of 5 m is small, within 0.6 m/s. According to the sediment calculation, the dock project is feasible. However, the designed width of the fairway should be increased to ensure the navigation safety of the ship according to variation characteristics of cross flow velocity in channel. Full article
(This article belongs to the Special Issue Land–Water System Management: Simulations, Socio-Economic Analyses and Artificial Intelligence Techniques)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop