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Spatial Modelling in Water Resources Management

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 40603

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Special Issue Editors

Dr. Hamid Reza Pourghasemi

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Guest Editor

Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
Interests: landslide modeling; natural and man-made hazards; spatial modeling in GIS and R; data mining/machine learning techniques; multihazard modeling, remote sensing applications
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Prof. Dr. Artemi Cerdà

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Guest Editor

Soil Erosion and Degradation Research Group, Departament de Geografia, Universitat de València, Blasco Ibàñez, 28, 46010-Valencia, Spain
Interests: desertification; land degradation; perception; forest fires
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

This Special Issue will consider spatial modeling in water resources management and researches of related to it. At present, there are different challenges and uncertainties due of climate change and man-made interferences, so it is very difficult to decide and select a better decision. By contrast, mismanagement and also sustainability of the current and future water resource allocation are other concerns. Thus, it is important to use the newest technologies and tools to improve and properly develop sustainable managements. In this regard, a relationship among three tools, including remote sensing (RS), GIS, and R statistical packages, could be very effective. Land sustainability strategies and water resources management along with corrected policies must be based on accurate surveys and assessment, and the three techniques (RS, R and GIS) will contribute to achieving the right management.

Today, the use of open source software is developing in different spatial modeling cases, such as geology, natural resources, environment, natural hazards, water resource, soil erosion, hydrology, geography, ecology, agriculture, etc. Additionally, remote sensing and GIS tools have been used to help toward a better description of phenomena and objects and even in forecasting and prediction of future analyses that occur in the real world and facilitate problem solving. We are sure that this issue could attract different researchers and scientists worldwide because it will combine remote sensing, GIS, and R statistical packages in various fields. We therefore look forward to your submissions of papers that cover these aspects.

Dr. Hamid Reza Pourghasemi
Prof. Artemi Cerda
Guest Editors

Manuscript Submission Information

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Keywords

Environmental hazard modeling, including landslides, flash flood, land subsidence, forest fires, drought, soil erosion, and dust
Metaheuristic and machine learning algorithms in water resources management
Climate change and land use/ land cover modeling
Modeling of wetland changes
Hydrological and groundwater modeling
Unmanned aerial vehicle (UAV) in environment and hydrology
Species distribution modeling and climate change
Urban hydrology
Underground dam site selection

Published Papers (9 papers)

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Research

22 pages, 6168 KiB

Open AccessArticle

A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing

by Matin Rahnamay Naeini, Tiantian Yang, Ahmad Tavakoly, Bita Analui, Amir AghaKouchak, Kuo-lin Hsu and Soroosh Sorooshian

Water 2020, 12(9), 2373; https://doi.org/10.3390/w12092373 - 24 Aug 2020

Cited by 7 | Viewed by 2582

Abstract

Data-driven algorithms have been widely used as effective tools to mimic hydrologic systems. Unlike black-box models, decision tree algorithms offer transparent representations of systems and reveal useful information about the underlying process. A popular class of decision tree models is model tree (MT), which is designed for predicting continuous variables. Most MT algorithms employ an exhaustive search mechanism and a pre-defined splitting criterion to generate a piecewise linear model. However, this approach is computationally intensive, and the selection of the splitting criterion can significantly affect the performance of the generated model. These drawbacks can limit the application of MTs to large datasets. To overcome these shortcomings, a new flexible Model Tree Generator (MTG) framework is introduced here. MTG is equipped with several modules to provide a flexible, efficient, and effective tool for generating MTs. The application of the algorithm is demonstrated through simulation of controlled discharge from several reservoirs across the Contiguous United States (CONUS). Full article

(This article belongs to the Special Issue Spatial Modelling in Water Resources Management)

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22 pages, 5760 KiB

Open AccessArticle

Benefits of Combining Satellite-Derived Snow Cover Data and Discharge Data to Calibrate a Glaciated Catchment in Sub-Arctic Iceland

by Julia de Niet, David Christian Finger, Arvid Bring, David Egilson, David Gustafsson and Zahra Kalantari

Water 2020, 12(4), 975; https://doi.org/10.3390/w12040975 - 30 Mar 2020

Cited by 7 | Viewed by 3259

Abstract

The benefits of fractional snow cover area, as an additional dataset for calibration, were evaluated for an Icelandic catchment with a low degree of glaciation and limited data. For this purpose, a Hydrological Projections for the Environment (HYPE) model was calibrated for the Geithellnaá catchment in south-east Iceland using daily discharge (Q) data and satellite-retrieved MODIS snow cover (SC) images, in a multi-dataset calibration (MDC) approach. By comparing model results using only daily discharge data with results obtained using both datasets, the value of SC data for model calibration was identified. Including SC data improved the performance of daily discharge simulations by 7% and fractional snow cover area simulations by 11%, compared with using only the daily discharge dataset (SDC). These results indicate that MDC improves the overall performance of the HYPE model, confirming previous findings. Therefore, MDC could improve discharge simulations in areas with extra sources of uncertainty, such as glaciers and snow cover. Since the change in fractional snow cover area was more accurate when MDC was applied, it can be concluded that MDC would also provide more realistic projections when calibrated parameter sets are extrapolated to different situations. Full article

(This article belongs to the Special Issue Spatial Modelling in Water Resources Management)

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21 pages, 5781 KiB

Open AccessArticle

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

by Davoud Davoudi Moghaddam, Omid Rahmati, Ali Haghizadeh and Zahra Kalantari

Water 2020, 12(3), 679; https://doi.org/10.3390/w12030679 - 02 Mar 2020

Cited by 34 | Viewed by 3791

Abstract

In some arid regions, groundwater is the only source of water for human needs, so understanding groundwater potential is essential to ensure its sustainable use. In this study, three machine learning models (Genetic Algorithm for Rule-Set Production (GARP), Quick Unbiased Efficient Statistical Tree (QUEST), and Random Forest (RF)) were applied and verified for spatial prediction of groundwater in a mountain bedrock aquifer in Piranshahr Watershed, Iran. A spring location dataset consisting of 141 springs was prepared by field surveys, and from this three different sample datasets (S1–S3) were randomly generated (70% for training and 30% for validation). A total of 10 groundwater conditioning factors were prepared for modeling, namely slope percent, relative slope position (RSP), plan curvature, altitude, drainage density, slope aspect, topographic wetness index (TWI), terrain ruggedness index (TRI), land use, and lithology. The area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) were used to evaluate the accuracy of models. The results indicated that all models had excellent goodness-of-fit and predictive performance, but that RF (AUC_mean = 0.995, TSS_mean = 0.89) and GARP (AUC_mean = 0.957, TSS_mean = 0.82) outperformed QUEST (AUC_mean = 0.949, TSS_mean = 0.74). In robustness analysis, RF was slightly more sensitive than GARP and QUEST, making it necessary to consider several random partitioning options for preparing training and validation groups. The outcomes of this study can be useful in sustainable management of groundwater resources in the study region. Full article

(This article belongs to the Special Issue Spatial Modelling in Water Resources Management)

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20 pages, 5590 KiB

Open AccessEditor’s ChoiceArticle

Hydrological Modeling to Assess the Efficiency of Groundwater Replenishment through Natural Reservoirs in the Hungarian Drava River Floodplain

by Ali Salem, József Dezső, Mustafa El-Rawy and Dénes Lóczy

Water 2020, 12(1), 250; https://doi.org/10.3390/w12010250 - 16 Jan 2020

Cited by 26 | Viewed by 4153

Abstract

Growing drought hazard and water demand for agriculture, ecosystem conservation, and tourism in the Hungarian Drava river floodplain call for novel approaches to maintain wetland habitats and enhance agricultural productivity. Floodplain rehabilitation should be viewed as a complex landscape ecological issue which, beyond water management goals to relieve water deficit, ensures a high level of provision for a broad range of ecosystem services. This paper explores the hydrological feasibility of alternative water management, i.e., the restoration of natural reservoirs (abandoned paleochannels) to mitigate water shortage problems. To predict the efficiency of the project, an integrated surface water (Wetspass-M) and groundwater model (MODFLOW-NWT) was developed and calibrated with an eight-year data series. Different management scenarios for two natural reservoirs were simulated with filling rates ranging from 0.5 m³ s⁻¹ to 1.5 m³ s⁻¹. In both instances, a natural reservoir with a feeding rate of 1 m³ s⁻¹ was found to be the best scenario. In this case 14 days of filling are required to reach the possible maximum reservoir stage of +2 m. The first meter rise increases the saturation of soil pores and the second creates an open surface water body. Two filling periods per year, each lasting for around 180 days, are required. The simulated water balance shows that reservoir–groundwater interactions are mainly governed by the inflow into and outflow from the reservoir. Such an integrated management scheme is applicable for floodplain rehabilitation in other regions with similar hydromorphological conditions and hazards, too. Full article

(This article belongs to the Special Issue Spatial Modelling in Water Resources Management)

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16 pages, 2964 KiB

Open AccessArticle

Using OCO-2 Satellite Data for Investigating the Variability of Atmospheric CO₂ Concentration in Relationship with Precipitation, Relative Humidity, and Vegetation over Oman

by Foroogh Golkar, Malik Al-Wardy, Seyedeh Fatemeh Saffari, Kathiya Al-Aufi and Ghazi Al-Rawas

Water 2020, 12(1), 101; https://doi.org/10.3390/w12010101 - 27 Dec 2019

Cited by 22 | Viewed by 5373

Abstract

Recognition of the carbon dioxide (CO₂) concentration variations over time is critical for tracing the future changes in climate both globally and regionally. In this study, a time series analysis of atmospheric CO₂ concentration and its relationship with precipitation, relative humidity (RH), and vegetation is investigated over Oman. The daily XCO₂ data from OCO-2 satellite was obtained from September 2014 to March 2019. The daily RH and precipitation data were also collected from the ground weather stations, and the Normalized Difference Vegetation Index was obtained from MODIS. Oman was studied in four distinct regions where the main emphasis was on the Monsoon Region in the far south. The CO₂ concentration time series indicated a significant upward trend over different regions for the study period, with annual cycles being the same for all regions except the Monsoon Region. This is indicative of RH, precipitation, and consequently vegetation cover impact on atmospheric CO₂ concentration, resulting in an overall lower annual growth in the Monsoon Region. Simple and multiple correlation analyses of CO₂ concentration with mentioned parameters were performed in zero to three-month lags over Oman. They showed high correlations mainly during the rainfall period in the Monsoon Region. Full article

(This article belongs to the Special Issue Spatial Modelling in Water Resources Management)

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30 pages, 8666 KiB

Open AccessFeature PaperArticle

Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms

by Vali Vakhshoori, Hamid Reza Pourghasemi, Mohammad Zare and Thomas Blaschke

Water 2019, 11(11), 2292; https://doi.org/10.3390/w11112292 - 01 Nov 2019

Cited by 40 | Viewed by 4416

Abstract

The aim of this study was to apply data mining algorithms to produce a landslide susceptibility map of the national-scale catchment called Bandar Torkaman in northern Iran. As it was impossible to directly use the advanced data mining methods due to the volume of data at this scale, an intermediate approach, called normalized frequency-ratio unique condition units (NFUC), was devised to reduce the data volume. With the aid of this technique, different data mining algorithms such as fuzzy gamma (FG), binary logistic regression (BLR), backpropagation artificial neural network (BPANN), support vector machine (SVM), and C5 decision tree (C5DT) were employed. The success and prediction rates of the models, which were calculated by receiver operating characteristic curve, were 0.859 and 0.842 for FG, 0.887 and 0.855 for BLR, 0.893 and 0.856 for C5DT, 0.891 and 0.875 for SVM, and 0.896 and 0.872 for BPANN that showed the highest validation rates as compared with the other methods. The proposed approach of NFUC proved highly efficient in data volume reduction, and therefore the application of computationally demanding algorithms for large areas with voluminous data was feasible. Full article

(This article belongs to the Special Issue Spatial Modelling in Water Resources Management)

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13 pages, 1993 KiB

Open AccessArticle

Using RothC Model to Simulate Soil Organic Carbon Stocks under Different Climate Change Scenarios for the Rangelands of the Arid Regions of Southern Iran

by Sayed Fakhreddin Afzali, Bijan Azad, Mohammad H. Golabi and Rosa Francaviglia

Water 2019, 11(10), 2107; https://doi.org/10.3390/w11102107 - 10 Oct 2019

Cited by 8 | Viewed by 5150

Abstract

Soil organic carbon (SOC) is strongly influenced by climate change, and it is believed that increased temperatures might enhance the release of CO₂ with higher emission into the atmosphere. Appropriate models may be used to predict the changes of SOC stock under projected future scenarios of climate change. In this investigation, the RothC model was run for a period of 36 years under climate scenarios namely: P (no climate change) as well as CCH1 and CCH2 (climate change scenarios) in the arid rangelands of Ghir–O-Karzin’s BandBast in southern Iran. Model results have shown that after 11 years (2014–25), SOC stock decreased by 3.05% under the CCH1 scenario (with a projected annual precipitation decrease by 6.69% and mean annual temperature increase by 9.96%) and by 0.23% under the P scenario. In CCH2, with further decreases in rainfall (10.93%) and increase in temperature (12.53%) compared to CCH1, the model predicted that the SOC stock during the 25 years (2025–50) was reduced by 2.36% and 3.53% under the CCH1 and CCH2 scenario respectively. According to model predictions, with future climatic conditions (higher temperatures and lower rainfall) the decomposition rate may increase resulting in higher losses of soil organic carbon from the soil matrix. The result from this investigation may also be used for developing management techniques to be practiced in the other arid rangelands of Iran with similar conditions. Full article

(This article belongs to the Special Issue Spatial Modelling in Water Resources Management)

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19 pages, 8914 KiB

Open AccessFeature PaperArticle

A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping

by Mohammadtaghi Avand, Saeid Janizadeh, Seyed Amir Naghibi, Hamid Reza Pourghasemi, Saeid Khosrobeigi Bozchaloei and Thomas Blaschke

Water 2019, 11(10), 2076; https://doi.org/10.3390/w11102076 - 05 Oct 2019

Cited by 82 | Viewed by 5524

Abstract

This research was conducted to determine which areas in the Robat Turk watershed in Iran are sensitive to gully erosion, and to define the relationship between gully erosion and geo-environmental factors by two data mining techniques, namely, Random Forest (RF) and k-Nearest Neighbors (KNN). First, 242 gully locations we determined in field surveys and mapped in ArcGIS software. Then, twelve gully-related conditioning factors were selected. Our results showed that, for both the RF and KNN models, altitude, distance to roads, and distance from the river had the highest influence upon gully erosion sensitivity. We assessed the gully erosion susceptibility maps using the Receiver Operating Characteristic (ROC) curve. Validation results showed that the RF and KNN models had Area Under the Curve (AUC) 87.4 and 80.9%, respectively. As a result, the RF method has better performance compared with the KNN method for mapping gully erosion susceptibility. Rainfall, altitude, and distance from a river were identified as the most important factors affecting gully erosion in this area. The methodology used in this research is transferable to other regions to determine which areas are prone to gully erosion and to explicitly delineate high-risk zones within these areas. Full article

(This article belongs to the Special Issue Spatial Modelling in Water Resources Management)

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17 pages, 6621 KiB

Open AccessArticle

Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

by Abdolrahman Rahimian Boogar, Hassan Salehi, Hamid Reza Pourghasemi and Thomas Blaschke

Water 2019, 11(10), 2049; https://doi.org/10.3390/w11102049 - 30 Sep 2019

Cited by 31 | Viewed by 5323

Abstract

Support vector machine (SVM) and maximum entropy (MaxEnt) machine learning techniques are well suited to model the habitat suitability of species. In this study, SVM and MaxEnt models were developed to predict the habitat suitability of Juniperus spp. in the Southern Zagros Mountains of Iran. In recent decades, drought extension and climate alteration have led to extensive changes in the geographical occurrence of this species and its growth and regeneration are extremely limited in this area. This study evaluated the habitat suitability of Juniperus through spatial modeling and predicts appropriate regions for future cultivation and resource conservation. We modeled the natural habitat of Juniperus for an area of 700 ha in Sepidan Area in the Fars province using (1) data regarding the presence of the species (295 samples) collected through field surveys and GPS, (2) habitat soil information and indices derived from 60 soil samples collected in the study area, and (3) climatic and topographic datasets collected from various sources. In total, 15 conditioning factors were used for this spatial modeling approach. Receiver operator characteristic (ROC) curves were applied to estimate the accuracy of the habitat suitability models produced by the SVM and MaxEnt techniques. Results indicated logical and similar area under the curve (AUC)-ROC values for the SVM (0.735) and MaxEnt (0.728) models. Both the SVM and MaxEnt methods revealed a significant relationship between the Juniperus spp. distribution and conditioning factors. Environmental factors played a vital role in evaluating the presence of Juniperus sp. as Max and Min temperatures and annual mean rainfall were the three most important factors for habitat suitability in the study area. Finally, an area with high and very high suitability for the future cultivation of Juniperus sp. and for landscape conservation was suggested based on the SVM model. Full article

(This article belongs to the Special Issue Spatial Modelling in Water Resources Management)

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