Spatiotemporal Data Analysis, Visualization, and Modelling in Water Resources

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (10 October 2020) | Viewed by 11390

Special Issue Editors


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Guest Editor
School of Mineral Resources Engineering, Technical University of Crete, 73100 Crete, Greece
Interests: space–time geostatistics; geosciences; stochastic methods; water resources; groundwater
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Guest Editor
Department of Hydroinformatics and Socio-Technical Innovation, IHE Delft, Institute for Water Education, 2611 AX Delft, The Netherlands
Interests: artificial intelligence; space–time models; hydrological modelling; hybrid modelling; flow forecasting; drought; data science

Special Issue Information

Dear Colleagues,

Water resources management problems have important characteristics in their spatial and temporal dimensions. In recent years, due to technological advancements, new research efforts have included data in water resources model representation and analysis from remote sensing and satellite sources. The study of water resources-associated problems requires advanced spatiotemporal methods for their analysis and prediction, including estimating the probability of their occurrence and the associated risk. Key issues are the management and mitigation of extreme hydrological phenomena (e.g., precipitation, runoff), floods, low flows, droughts, groundwater, as well as modelling the fate of pollution sources both in onshore and offshore environment. The spatiotemporal study of key topics aids the understanding of the relationship between their magnitude and the probability of these events occurring. This Special Issue aims to provide spatiotemporal methods to study and mitigate major problems associated with water resources based on space–time geostatistics, machine learning, statistical theory, hydrological modelling, risk assessment, etc.

Dr. Emmanouil Varouchakis
Dr. Gerald Corzo
Guest Editors

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Keywords

  • Spatiotemporal analysis
  • Spatial data analysis and visualization
  • Geostatistics
  • Machine learning
  • Water resources
  • Groundwater
  • Hydrology
  • Water pollution
  • Remote sensing
  • Weather data

Published Papers (4 papers)

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Research

24 pages, 5335 KiB  
Article
Spatio-Temporal Hydrological Model Structure and Parametrization Analysis
by Mostafa Farrag, Gerald Corzo Perez and Dimitri Solomatine
J. Mar. Sci. Eng. 2021, 9(5), 467; https://doi.org/10.3390/jmse9050467 - 26 Apr 2021
Cited by 5 | Viewed by 2742
Abstract
Many grid-based spatial hydrological models suffer from the complexity of setting up a coherent spatial structure to calibrate such a complex, highly parameterized system. There are essential aspects of model-building to be taken into account: spatial resolution, the routing equation limitations, and calibration [...] Read more.
Many grid-based spatial hydrological models suffer from the complexity of setting up a coherent spatial structure to calibrate such a complex, highly parameterized system. There are essential aspects of model-building to be taken into account: spatial resolution, the routing equation limitations, and calibration of spatial parameters, and their influence on modeling results, all are decisions that are often made without adequate analysis. In this research, an experimental analysis of grid discretization level, an analysis of processes integration, and the routing concepts are analyzed. The HBV-96 model is set up for each cell, and later on, cells are integrated into an interlinked modeling system (Hapi). The Jiboa River Basin in El Salvador is used as a case study. The first concept tested is the model structure temporal responses, which are highly linked to the runoff dynamics. By changing the runoff generation model description, we explore the responses to events. Two routing models are considered: Muskingum, which routes the runoff from each cell following the river network, and Maxbas, which routes the runoff directly to the outlet. The second concept is the spatial representation, where the model is built and tested for different spatial resolutions (500 m, 1 km, 2 km, and 4 km). The results show that the spatial sensitivity of the resolution is highly linked to the routing method, and it was found that routing sensitivity influenced the model performance more than the spatial discretization, and allowing for coarser discretization makes the model simpler and computationally faster. Slight performance improvement is gained by using different parameters’ values for each cell. It was found that the 2 km cell size corresponds to the least model error values. The proposed hydrological modeling codes have been published as open-source. Full article
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26 pages, 9186 KiB  
Article
Spatiotemporal Drought Risk Assessment Considering Resilience and Heterogeneous Vulnerability Factors: Lempa Transboundary River Basin in The Central American Dry Corridor
by Ali Khoshnazar, Gerald A. Corzo Perez and Vitali Diaz
J. Mar. Sci. Eng. 2021, 9(4), 386; https://doi.org/10.3390/jmse9040386 - 5 Apr 2021
Cited by 7 | Viewed by 3031
Abstract
Drought characterization and risk assessment are of great significance due to drought’s negative impact on human health, economy, and ecosystem. This paper investigates drought characterization and risk assessment in the Lempa River basin in Central America. We applied the Standardized Evapotranspiration Deficit Index [...] Read more.
Drought characterization and risk assessment are of great significance due to drought’s negative impact on human health, economy, and ecosystem. This paper investigates drought characterization and risk assessment in the Lempa River basin in Central America. We applied the Standardized Evapotranspiration Deficit Index (SEDI) for drought characterization and drought hazard index (DHI) calculation. Although SEDI’s applicability is theoretically proven, it has been rarely applied. Drought risk is generally derived from the interactions between drought hazard (DHI) and vulnerability (DVI) indices but neglects resilience’s inherent impact. Accordingly, we propose incorporating DHI, DVI, and drought resilience index (DREI) to calculate drought risk index (DRI). Since system factors are not equally vulnerable, i.e., they are heterogeneous, our methodology applies the Analytic Hierarchy Process (AHP) to find the weights of the selected factors for the DVI computation. Finally, we propose a geometric mean method for DRI calculation. Results show a rise in DHI during 2006–2010 that affected DRI. We depict the applicability of SEDI via its relationship with El Nino-La Nina and El Salvador’s cereal production. This research provides a systematic drought risk assessment approach that is useful for decision-makers to allocate resources more smartly or intervene in Drought Risk Reduction (DRR). This research is also useful for those interested in socioeconomic drought. Full article
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17 pages, 5293 KiB  
Article
Impact of the Mean Areal Rainfall Calculation on a Modular Rainfall-Runoff Model
by Jose Valles, Gerald Corzo and Dimitri Solomatine
J. Mar. Sci. Eng. 2020, 8(12), 980; https://doi.org/10.3390/jmse8120980 - 2 Dec 2020
Cited by 3 | Viewed by 1789
Abstract
Hydrological models are based on the relationship between rainfall and discharge, which means that a poor representation of rainfall produces a poor streamflow result. Typically, a poor representation of rainfall input is produced by a gauge network that is not able to capture [...] Read more.
Hydrological models are based on the relationship between rainfall and discharge, which means that a poor representation of rainfall produces a poor streamflow result. Typically, a poor representation of rainfall input is produced by a gauge network that is not able to capture the rainfall event. The main objective of this study is to evaluate the impact of the mean areal rainfall on a modular rainfall-runoff model. These types of models are based on the divide-and-conquer approach and two specialized hydrological models for high and low regimes were built and then combined to form a committee of model that takes the strengths of both specialized models. The results show that the committee of models produces a reasonable reproduction of the observed flow for high and low flow regimes. Furthermore, a sensitivity analysis reveals that Ilopango and Jerusalem rainfall gauges are the most beneficial for discharge calculation since they appear in most of the rainfall subset that produces low Root Mean Square Error (RMSE) values. Conversely, the Puente Viejo and Panchimalco rainfall gauges are the least beneficial for the rainfall-runoff model since these gauges appear in most of the rainfall subset that produces high RMSE value. Full article
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12 pages, 1936 KiB  
Article
Environmental Influence on the Spatiotemporal Variability of Fishing Grounds in the Beibu Gulf, South China Sea
by Yanfeng Wang, Lijun Yao, Pimao Chen, Jing Yu and Qia’er Wu
J. Mar. Sci. Eng. 2020, 8(12), 957; https://doi.org/10.3390/jmse8120957 - 24 Nov 2020
Cited by 15 | Viewed by 2729
Abstract
The spatiotemporal distribution of fishing grounds in the Beibu Gulf and its relationship with marine environment were analyzed using the survey data of light falling-net vessels and satellite remote sensing data including sea surface temperature (SST), chlorophyll a concentration (Chl a) and [...] Read more.
The spatiotemporal distribution of fishing grounds in the Beibu Gulf and its relationship with marine environment were analyzed using the survey data of light falling-net vessels and satellite remote sensing data including sea surface temperature (SST), chlorophyll a concentration (Chl a) and net primary production (NPP), based on the generalized additive model (GAM) and the center of gravity (COG) of fishing grounds. The results showed that the total deviance explained by GAM for the catch per unit effort (CPUE) in the Beibu Gulf was 42.9%, in which SST was the most important influencing factor on CPUE, with a relative contribution of 40%; followed by latitude, Chl a, month and NPP, with relative contributions of 25.2%, 19%, 10.4% and 5.4%, respectively. Fishing grounds in the Beibu Gulf were mainly distributed in waters with SST of 27–29 °C, Chl a of 0.5–1.5 mg m−3 and NPP of 500–700 mg m−2 d−1. Light falling-net fishing grounds were concentrated in waters with latitude of 18.5° N and 20–20.25° N. There was a significant correlation between the mean latitude of optimum NPP and the latitudinal COG of CPUE, with the R2 being 0.91. These were connected with environmental factors such as the northeast monsoon that began in autumn and winter, warm pools near 19° N and local upwelling in the Beibu Gulf. Full article
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