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Hydroinformatic Tools and Spatial Analyses for Water Resources and Extreme Water Events

Department of Environmental Engineering, Faculty of Environmental Engineering and Food Science, Valahia University of Targoviste, Aleea Sinaia No. 13, 130004 Targoviste, Romania
Faculty of Geography, Babes-Bolyai University, Clinicilor Street, 5–7, 400006 Cluj-Napoca, Romania
Department of Geography, Faculty of Humanities, Valahia University of Targoviste, 130105 Targoviste, Romania
Author to whom correspondence should be addressed.
Water 2023, 15(3), 463;
Submission received: 29 December 2022 / Accepted: 18 January 2023 / Published: 23 January 2023
In recent years, the frequency of flooding has increased due to population growth and climate change worldwide [1]. An increase in the volume of data accumulated from hydrological stations, combined with the information provided by remote sensing, is a challenge but also an opportunity for the scientific community. Hydroinformatics research, using different techniques for the selection, processing, and visualization of data, using modern analytical and modeling tools [2], is the solution for understanding the dynamics, trends, and uncertainties of different hydrological processes and parameters [3].
The domain of hydroinformatics is relatively new, usually relying on visual computing techniques combined with advanced computational methods and human reasoning to determine solutions for engineering and socioeconomic problems, i.e., (1) supporting complex data-driven research problems and (2) supporting the communication as well as decision-making processes in the water resources management sector. Such advanced approaches are frequently integrated with geographic information systems (GISs) and novel cyberinfrastructures to provide new opportunities and methods for enhancing water resource management [4].
Additionally, in the context of global climate change, atypical and dangerous weather episodes with high intensities have been recorded in various parts of the world [5]. The negative effects of dangerous hydrometeorological processes were amplified by massive deforestation, which resulted in quick accumulations of streamflow on slopes and lead to excessive soil erosion, landslides, and significant alluvial material transport in streams or arable land [1]. All hydrological processes are accentuated by anthropogenic influences, whether we are talking about the phases of runoff (floods/drying of rivers) or the physicochemical/biological parameters of water.
Information on the timing and magnitude of floods/water shortages is required in many practical applications of water resource engineering for the local, seasonal, and regional frequency analyses required in engineering design, reservoir management, and operation of water infrastructure.
Unfortunately, water is not a priority in public policy despite the high short-term costs of extreme natural events. It is important to ensure the resilience of water systems in the long run through useful reforms, long-term improvements, and public outreach as well as information dissemination [6].
This Special Issue focuses on the assessment of various hydroinformatic tools and associated case studies useful for establishing trends in the intensity of annual extreme hydrological flow processes but also for different hydrological parameters in basins ranging from a medium to large scale. The evaluation of the impact of climate change and human-induced environmental changes on water resources in the watershed is also envisaged based on long-term hydrometeorological time series. Furthermore, the evaluation of the performance of models, trend detection algorithms, and the application of hydroinformatic tools in planning water resource strategies and policies are key aspects.
In this context, Ha et al. [7] managed to develop a novel hybrid approach based on bald eagle search (BES), support vector machine (SVM), random forest (RF), bagging (BA), and multi-layer perceptron (MLP) to generate a flood susceptibility map in Thua Thien Hue province, Vietnam. They found that the integration of the BES algorithm with an individual model can significantly improve the performance of the individual model and that the correct selection of conditioning factors plays an important role in improving the model’s performance, since data redundancy can make the model more complex and affect the model’s predictability. They noticed that the use of ASTER DEMs should provide better results because they better reflect the land area, particularly regarding the characteristics of buildings, vegetation, flow direction, and slope.
Bretcan et al. [8] quantitatively analyzed the small-scale pit lakes in the piedmont and subsidence plains from contact with the Getic and Curvature Subcarpathians from Romania by using the normalized difference water index (NDWI) and data series, with different resolutions, from Landsat 8, Google Earth, and Sentinel 2A. The problems encountered in extracting the contours of the gravel pit lakes were determined by the different resolutions of the images, the uneven qualities of the images exported from Google Earth, and an additional challenge that occurred due to the diversity of the analyzed land surfaces, the land use, and the optical properties of the lakes. The usefulness of a database with small gravel pit lakes is clear given the rapidity of their appearance and the increased modifications of landscape changes along the rivers. The advantages of identifying areas with gravel pits using remote sensing are free images, a good frequency of data series, and a high resolution, and thus a very good efficiency/cost ratio. The LAI maps derived from modern imagery are a reliable support for monitoring important biophysical properties of the vegetation near the gravel pits and thus assessing the negative impact to ensure sustainable wildlife and critical habitat management near river banks and at the basin level. The authors pointed out the necessity of future studies that will focus on existing biomes in conjunction with satellite products featuring land cover, such as MODIS Land Cover MCD12Q1 Version 6, for improved classification and proper data fusion as well as downscaling methods to enhance low-resolution data.
Chang et al. [9] pointed out that, despite recent developments in the field of radar rainfall monitoring and estimation, the use of radar QPE products for extreme rainfall analyses and the derivation of design frequency estimates has not yet been fully exploited. Therefore, they developed a methodology to incorporate the benefit of high-spatial-resolution radar rainfall data as well as temporal enhancement against limited historical records via regional frequency analyses. Since homogeneous regions are identified in regional frequency analyses based on the L-moments approach, the best-fit distributions are therefore selected in order to estimate the rainfall quantiles in each homogeneous region. The index flood method and regional approach to each grid across Taiwan could be applied to explore the spatial variations in rainfall extremes. Regional frequency analyses may yield more precise estimates of rainfall quantiles than at-site analyses, not only for larger areas but also for smaller-scale areas. According to the findings, the use of radar rainfall in the estimation of extreme rainfall when using a regional approach is highly recommended in other regions with unreliable or relatively few and uneven rain gauges, particularly in catchment areas that require frequency analyses for water-related infrastructure that is typically not covered by sufficient rain gauges.
Popescu et al. [10] reported new insights regarding the surface water quality of the Danube as it passes through the Romania–Serbia border in the Djerdap and Iron Gate nature reservations, focusing on sediment compositions, oxygen regimes, nutrients, and heavy metals in 48 sampling locations. Despite the relatively good water quality status, there are many stressors, such as wastewater treatment systems (poor treatment or their absence), agricultural land runoff, contributing to the pollution of surface waters with ammonia, phosphates, and nitrites, and industrial activity, with an emphasis on mining activity’s impact in Majdanpek on the water quality of the Pek River and in Moldova Noua on the Danube, directly. To preserve the landscape of this area, with important touristic potential, more focused attention should be devoted towards preventing organic pollution. The use of the SEM-EDAX method to characterize heavy metals’ deposition on the micropebble surface of sediments is addressed in this study.
The contributions published in this Special Issue signal several important issues that can be addressed with hydroinformatics that can improve the societal and political sense of urgency concerning preserving water resources and managing extreme events. Contrarily, society will confront costly and risky extreme events that may fully collapse the human–natural system of systems, as was underlined by Madani [6].

Author Contributions

D.D., G.Ș. and P.B. have equally contributed to the original draft preparation, review, editing, and proofreading. All authors have read and agreed to the published version of the manuscript.


This work was funded by the Romanian Ministry of Education, grant number CNFIS-FDI-2022-0283.


The Guest Editors would like to thank, all authors that contributed with articles, and the anonymous reviewers whose pertinent and valuable comments improved the overall quality of the current Special Issue.

Conflicts of Interest

The authors declare no conflict of interest.


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Dunea, D.; Șerban, G.; Brețcan, P. Hydroinformatic Tools and Spatial Analyses for Water Resources and Extreme Water Events. Water 2023, 15, 463.

AMA Style

Dunea D, Șerban G, Brețcan P. Hydroinformatic Tools and Spatial Analyses for Water Resources and Extreme Water Events. Water. 2023; 15(3):463.

Chicago/Turabian Style

Dunea, Daniel, Gheorghe Șerban, and Petre Brețcan. 2023. "Hydroinformatic Tools and Spatial Analyses for Water Resources and Extreme Water Events" Water 15, no. 3: 463.

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