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Hydrological and Environmental Modeling: from Observations to Predictions

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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 December 2019) | Viewed by 74081

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Special Issue Editors

Prof. Dr. Félix Francés

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Guest Editor

Research Institute of Water and Environmental Engineering, Universitat Politècnica de València, Spain
Interests: hydrological and environmental modelling, ecohydrology, flood forecasting systems, flood hazard and risk, flood frequency analysis, soil erosion and sediment yield

Dr. Salvatore Manfreda

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Guest Editor

Department of Civil, Architectural and Environmental Engineering, University of Naples "Federico II", Napoli, Italy
Interests: stochastic processes; hydrological modelling; model calibration; flood risk; geomorphology; ecohydrology; UAS monitoring
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Prof. Dr. Zhongbo Su

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Guest Editor

University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Department of Water Resources Hengelosestraat 99, P.O.Box 217, 7500 AE Enschede, The Netherlands
Interests: spatial hydrology; earth observation; water cycle and climate; land–atmosphere interaction; water resource management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mathematical modelling plays a central role in science, offering increasing predictive capabilities for several hydrological processes. Such reliability is strongly controlled by the tradeoff between model complexity and data availability. Therefore, there is a clear need to improve our monitoring systems to increase observations in space and time.

In operational hydrology, traditional observations (such as streamflow measurements, chemical or sediment concentration, and aquifer level) are often limited. However, there is an increasing availability of data provided by remote sensing observations from satellites and more recently from drones. Such systems are helping to achieve a better description of several state variables (e.g., river basin morphology, soil moisture, vegetation state, river stage, etc.) and improve our knowledge of the hydrological cycle. An increasing number of studies are trying to exploit such alternative forms of information to cope with the limited availability of traditional data.

Within this framework, this Special Issue focuses on the difficulties/methodologies/advances in the implementation of hydro-environmental models at different scales (from plot to continents) and on the understanding of the interactions between water, vegetation, sediments, and compounds (traditional as N or new as emerging pollutants), in both cases exploiting different sources of information. Within this context, we welcome contributions dealing with, but not limited to, the following topics:

The potential of remote sensing observations to improve our hydro-environmental knowledge;
The use of remote sensing observations for data assimilation and model calibration;
Model calibration using a combination of spatial and point observations with different characteristics (support, spacing, extension, and reliability);
Multi-objective calibration using different state variables;
Models up-scaling and down-scaling;
The definition of new model performance metrics and statistics;
Uncertainty propagation from observations to estimated parameters and/or model results.

Prof. Dr. Félix Francés
Prof. Dr. Salvatore Manfreda
Prof. Dr. Zhongbo Su
Guest Editors

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Keywords

hydrological and environmental modelling
multi-objective model calibration
spatial information
remote sensing
satellite observations
UAV
model performance metrics
up-scaling and down-scaling
model uncertainty

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Published Papers (14 papers)

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17 pages, 5785 KiB

Open AccessArticle

UAV-DEMs for Small-Scale Flood Hazard Mapping

by Antonio Annis, Fernando Nardi, Andrea Petroselli, Ciro Apollonio, Ettore Arcangeletti, Flavia Tauro, Claudio Belli, Roberto Bianconi and Salvatore Grimaldi

Water 2020, 12(6), 1717; https://doi.org/10.3390/w12061717 - 16 Jun 2020

Cited by 92 | Viewed by 8774

Abstract

Devastating floods are observed every year globally from upstream mountainous to coastal regions. Increasing flood frequency and impacts affect both major rivers and their tributaries. Nonetheless, at the small-scale, the lack of distributed topographic and hydrologic data determines tributaries to be often missing in inundation modeling and mapping studies. Advances in Unmanned Aerial Vehicle (UAV) technologies and Digital Elevation Models (DEM)-based hydrologic modeling can address this crucial knowledge gap. UAVs provide very high resolution and accurate DEMs with low surveying cost and time, as compared to DEMs obtained by Light Detection and Ranging (LiDAR), satellite, or GPS field campaigns. In this work, we selected a LiDAR DEM as a benchmark for comparing the performances of a UAV and a nation-scale high-resolution DEM (TINITALY) in representing floodplain topography for flood simulations. The different DEMs were processed to provide inputs to a hydrologic-hydraulic modeling chain, including the DEM-based EBA4SUB (Event-Based Approach for Small and Ungauged Basins) hydrologic modeling framework for design hydrograph estimation in ungauged basins; the 2D hydraulic model FLO-2D for flood wave routing and hazard mapping. The results of this research provided quantitative analyses, demonstrating the consistent performances of the UAV-derived DEM in supporting affordable distributed flood extension and depth simulations. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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20 pages, 7183 KiB

Open AccessArticle

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

by Nazmus Sazib, John Bolten and Iliana Mladenova

Water 2020, 12(5), 1371; https://doi.org/10.3390/w12051371 - 12 May 2020

Cited by 19 | Viewed by 5143

Abstract

An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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21 pages, 8436 KiB

Open AccessFeature PaperArticle

Identifying Spatially Correlated Patterns between Surface Water and Frost Risk Using EO Data and Geospatial Indices

by Panagiota Louka, Ioannis Papanikolaou, George P. Petropoulos, Kleomenis Kalogeropoulos and Nikolaos Stathopoulos

Water 2020, 12(3), 700; https://doi.org/10.3390/w12030700 - 4 Mar 2020

Cited by 7 | Viewed by 3614

Abstract

Frost is one the most significant hazards affecting various aspects of human lives including infrastructure, agriculture, economy and biodiversity. Water bodies are one of the key factors controlling temperature fluctuations and frost. This study introduces a contemporary method for identifying and spatially analyzing frost risk and also explores its spatial correlation with water bodies. The proposed technique is based on coupling freely distributed geospatial data with a time series of land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. A region located in NW Greece, which annually suffers from very low temperatures and frost conditions and hosts important infrastructure and other human activities, was selected as a case study. In total, 5944 images were processed covering a 14-year long period. A frost frequency map of the study area was compiled along with two geospatial indices associated to distance from rivers/lakes (Hydro Distance Index—HDI) and from the seashore (Sea Distance Index—SDI). Their combined statistical and spatial correlation analysis indicated a protective buffer zone from frost at distances up to 20 km from sea and up to 5 km from lakes and rivers respectively, suggesting that the protective buffer zone depends on the water volume. Statistically, frost frequency was found to be positively correlated with both SDI (rs = 0.527) and HDI (rs = 0.145). Also, the effect of topography was examined in our analysis. Results showed that altitude and slope were moderately correlated to frost frequency; yet, the significance of the correlation was reported to be lower to SDI. Furthermore, the spatial autocorrelation analysis revealed a correspondence in the clustering of frost frequency maps with the HDI and SDI. Our findings demonstrate that water bodies are a major controlling factor for frost occurrence, by lowering frost frequency in water surrounding areas. Furthermore, it highlights the promising potential of our proposed methodology in quantifying frost effects, which can form a potentially useful tool assisting effective planning of protection measures and frost hazard mitigation in general. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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24 pages, 4041 KiB

Open AccessArticle

The GEOframe-NewAge Modelling System Applied in a Data Scarce Environment

by Marialaura Bancheri, Riccardo Rigon and Salvatore Manfreda

Water 2020, 12(1), 86; https://doi.org/10.3390/w12010086 - 25 Dec 2019

Cited by 9 | Viewed by 5014

Abstract

In this work, the semi-distributed hydrological modeling system GEOframe-NewAge was integrated with a web-based decision support system implemented for the Civil Protection Agency of the Basilicata region, Italy. The aim of this research was to forecast in near real-time the most important hydrological variables at 160 control points distributed over the entire region. The major challenge was to make the system operational in a data-scarce region characterized by a high hydraulic complexity, with several dams and infrastructures. In fact, only six streamflow gauges were available for the calibration of the model parameters. Reliable parameter sets were obtained by simulating the hydrological budget and then calibrating the rainfall-runoff parameters. After the extraction of the flow-rating curves, six sets of parameters were obtained considering the different streamflow components (i.e., the baseflow and surface runoff) and using a multi-site calibration approach. The results show a good agreement between the measured and modeled discharges, with a better agreement in the sections located upstream of the dams. Moreover, the results were validated using the inflows measured at the most important dams (Pertusillo, San Giuliano and Monte Cotugno). For rivers without monitoring points, parameters were assigned using a principle of hydrological similarity in terms of their geology, lithology, and climate. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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19 pages, 6443 KiB

Open AccessArticle

Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation

by Carlos Echeverría, Guiomar Ruiz-Pérez, Cristina Puertes, Luis Samaniego, Brian Barrett and Félix Francés

Water 2019, 11(12), 2613; https://doi.org/10.3390/w11122613 - 11 Dec 2019

Cited by 5 | Viewed by 4383

Abstract

The aim of this study was to implement an eco-hydrological distributed model using only remotely sensed information (soil moisture and leaf area index) during the calibration phase. Four soil moisture-based metrics were assessed, and the best alternative was chosen, which was a metric based on the similarity between the principal components that explained at least 95% of the soil moisture variation and the Nash-Sutcliffe Efficiency (NSE) index between simulated and observed surface soil moisture. The selected alternative was compared with a streamflow-based calibration approach. The results showed that the streamflow-based calibration approach, even presenting satisfactory results in the calibration period (NSE = 0.91), performed poorly in the validation period (NSE = 0.47) and Leaf Area Index (LAI) and soil moisture were neither sensitive to the spatio-temporal pattern nor to the spatial correlation in both calibration and validation periods. Hence, the selected soil moisture-based approach showed an acceptable performance in terms of discharges, presenting a negligible decrease in the validation period (ΔNSE = 0.1) and greater sensitivity to the spatio-temporal variables’ spatial representation. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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27 pages, 3884 KiB

Open AccessArticle

Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed

by Frezer Seid Awol, Paulin Coulibaly, Ioannis Tsanis and Fisaha Unduche

Water 2019, 11(11), 2201; https://doi.org/10.3390/w11112201 - 23 Oct 2019

Cited by 9 | Viewed by 3914

Abstract

Accurate and reliable flow forecasting in complex Canadian prairie watersheds has been one of the major challenges faced by hydrologists. In an attempt to improve the accuracy and reliability of a reservoir inflow forecast, this study investigates structurally different hydrological models along with ensemble precipitation forecasts to identify the most skillful and reliable model. The key goal is to assess whether short- and medium-range ensemble flood forecasting in large complex basins can be accurately achieved by simple conceptual lumped models (e.g., SACSMA with SNOW17 and MACHBV with SNOW17) or it requires a medium level distributed model (e.g., WATFLOOD) or an advanced macroscale land-surface based model (VIC coupled with routing module (RVIC)). Eleven (11)-member precipitation forecasts from second-generation Global Ensemble Forecast System reforecast (GEFSv2) were used as inputs. Each of the ensemble members was bias-corrected by Empirical Quantile Mapping method using the Canadian Precipitation Analysis (CaPA) as a training/verification dataset. Forecast evaluation is performed for 1-day up to 8-days forecast lead times in a 6-month hindcast period. Results indicate that bias-correcting precipitation forecasts using verifying datasets (such as CaPA) for a training period of at least two years before the forecast time, produces skillful ensemble hydrological forecasts. A comparison of models in forecast mode shows that the two lumped models (SACSMA and MACHBV) can provide better overall forecast performance than the benchmark WATFLOOD and the macroscale Variable Infiltration Capacity (VIC) model. However, for shorter lead-times, particularly up to day 3, the benchmark distributed model provides competitive reliability, as compared to the lumped models. In general, the SACSMA model provided better forecast quality, reliability and differentiation skill than other considered models at all lead times. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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19 pages, 2559 KiB

Open AccessArticle

Prediction of Suspended Sediment Load Using Data-Driven Models

by Rana Muhammad Adnan, Zhongmin Liang, Ahmed El-Shafie, Mohammad Zounemat-Kermani and Ozgur Kisi

Water 2019, 11(10), 2060; https://doi.org/10.3390/w11102060 - 2 Oct 2019

Cited by 60 | Viewed by 5831

Abstract

Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China—Guangyuan and Beibei—were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The data period covers 01/04/2007–12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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18 pages, 60094 KiB

Open AccessArticle

Transient Flow and Transport Modelling of an Historical CHC Source in North-West Milano

by Loris Colombo, Luca Alberti, Pietro Mazzon and Giovanni Formentin

Water 2019, 11(9), 1745; https://doi.org/10.3390/w11091745 - 22 Aug 2019

Cited by 17 | Viewed by 4025

Abstract

Legislative Decree 152/2006 requires Public Authorities to identify the subjects who are responsible for soil and groundwater contamination. In highly urbanized areas with a long industrial history and an elevated number of potential contaminant sources, as in N-W Milano Functional Urban Area (FUA), their identification can be difficult. Since the groundwater flow has showed consistent changes in the last 30 years as in Milan, the problem became even more complicate. The Public Authorities put in charge by the law, i.e., Regione Lombardia and Città Metropolitana Milanese, need new methodologies to assist them in finding the source locations and implementing remediation actions. The aim of this study is, coupling unsteady flow with fate and transport model of Chlorinated Hydrocarbons, to reconstruct the potential impact of a former chemical plant on public wells in the N-W area of Milano. The proposed methodology consists in (a) reconstruction of the piezometric trend over time (1980–2018) by means of a transient flow model (MODFLOW2005 + Parameter Estimation - PEST) and (b) simulation of transport as a function of the flow variations in time. The obtained results were compared with the previous ones obtained with a quasi-steady model (no changes in time-dependent parameters). Finally, a predictive scenario was performed to assess the potential evolution of tetrachloroethylene (PCE) in groundwater; on this frame, strategies to monitor and remediate the contamination were proposed. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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15 pages, 4675 KiB

Open AccessArticle

A New National Water Quality Model to Evaluate the Effectiveness of Catchment Management Measures in England

by Barry Hankin, Johan Strömqvist, Chris Burgess, Charlotta Pers, Sally Bielby, Beatriz Revilla-Romero and Linda Pope

Water 2019, 11(8), 1612; https://doi.org/10.3390/w11081612 - 3 Aug 2019

Cited by 13 | Viewed by 5946

Abstract

This investigation reports on a new national model to evaluate the effectiveness of catchment sensitive farming in England, and how pollution mitigation measures have improved water quality between 2006 and 2016. An adapted HYPE (HYdrological Predictions for the Environment) model was written to use accurate farm emissions data so that the pathway impact could be accounted for in the land phase of transport. Farm emissions were apportioned into different runoff fractions simulated in surface and soil layers, and travel time and losses were taken into account. These were derived from the regulator’s ‘catchment change matrix’ and converted to monthly load time series, combined with extensive point source load datasets. Very large flow and water quality monitoring datasets were used to calibrate the model nationally for flow, nitrogen, phosphorus, suspended sediments and faecal indicator organisms. The model was simulated with and without estimated changes to farm emissions resulting from catchment measures, and spatial and temporal changes to water quality concentrations were then assessed. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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19 pages, 5612 KiB

Open AccessArticle

A New Digital Lake Bathymetry Model Using the Step-Wise Water Recession Method to Generate 3D Lake Bathymetric Maps Based on DEMs

by Siyu Zhu, Baojian Liu, Wei Wan, Hongjie Xie, Yu Fang, Xi Chen, Huan Li, Weizhen Fang, Guoqing Zhang, Mingwei Tao and Yang Hong

Water 2019, 11(6), 1151; https://doi.org/10.3390/w11061151 - 31 May 2019

Cited by 26 | Viewed by 6935 | Correction

Abstract

The availability of lake bathymetry maps is imperative for estimating lake water volumes and their variability, which is a sensitive indicator of climate. It is difficult, if not impossible, to obtain bathymetric measurements from all of the thousands of lakes across the globe due to costly labor and/or harsh topographic regions. In this study, we develop a new digital lake bathymetry model (DLBM) using the step-wise water recession method (WRM) to generate 3-dimensional lake bathymetric maps based on the digital elevation model (DEM) alone, with two assumptions: (1) typically, the lake’s bathymetry is formed and shaped by geological processes similar to those that shaped the surrounding landmasses, and (2) the agent rate of water (the thickness of the sedimentary deposit proportional to the lake water depth) is uniform. Lake Ontario and Lake Namco are used as examples to demonstrate the development, calibration, and refinement of the model. Compared to some other methods, the estimated 3D bathymetric maps using the proposed DLBM could overcome the discontinuity problem to adopt the complex topography of lake boundaries. This study provides a mathematically robust yet cost-effective approach for estimating lake volumes and their changes in regions lacking field measurements of bathymetry, for example, the remote Tibetan Plateau, which contains thousands of lakes. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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18 pages, 3441 KiB

Open AccessArticle

Water Balance Assessment under Different Glacier Coverage Scenarios in the Hunza Basin

by Saroj Shrestha and Santosh Nepal

Water 2019, 11(6), 1124; https://doi.org/10.3390/w11061124 - 29 May 2019

Cited by 21 | Viewed by 5780

Abstract

The potential impact of glacier recession on river discharge from the Hunza river basin was estimated as an indicator for downstream changes in the Indus river system. The J2000 model was used to analyze the water balance in the basin and simulate the contribution of snow and ice melt to total discharge at present and under three scenarios of glacier recession. Precipitation was corrected using virtual weather stations created at a higher elevation and a precipitation gradient. Snowmelt from the whole basin contributed, on average, 45% of the total river discharge during the modeling period and 47% of the ice melt from the glacier area. Total ice melt declined by 55%, 81%, and 96% under scenarios of glacier recession to 4000, 4500, and 5000 masl, respectively. The contribution of ice melt to river discharge decreased to 29%, 14%, and 4% under the three scenarios, while total discharge from the Hunza river decreased by 28%, 40%, and 46%. The results suggest that glacier recession in the Hunza river basin could have serious implications for downstream water availability. Understanding melt contribution in the basin based on ongoing and projected future climatic change can play a crucial role in future water resource management. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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20 pages, 2382 KiB

Open AccessReview

Intertwining Observations and Predictions in Vadose Zone Hydrology: A Review of Selected Studies

by Nunzio Romano

Water 2020, 12(4), 1107; https://doi.org/10.3390/w12041107 - 13 Apr 2020

Cited by 2 | Viewed by 3058

Abstract

Observing state variables, fluxes, and key properties in terrestrial ecosystems should not be seen as disjointed, but rather as fruitfully complementary to ecosystem dynamics modeling. This intertwined view should also take the organization of the monitoring equipment into due account. This review paper explores the value of the interplay between observations and predictions by presenting and discussing some selected studies dealing with vadose zone hydrology. I argue for an advanced vision in carrying out these two tasks to tackle the issues of ecosystem services and general environmental challenges more effectively. There is a recognized need to set up networks of critical zone observatories in which strategies are developed and tested that combine different measurement techniques with the use of models of different complexity. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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36 pages, 5498 KiB

Open AccessConcept Paper

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

by Zhongbo Su, Yijian Zeng, Nunzio Romano, Salvatore Manfreda, Félix Francés, Eyal Ben Dor, Brigitta Szabó, Giulia Vico, Paolo Nasta, Ruodan Zhuang, Nicolas Francos, János Mészáros, Silvano Fortunato Dal Sasso, Maoya Bassiouni, Lijie Zhang, Donald Tendayi Rwasoka, Bas Retsios, Lianyu Yu, Megan Leigh Blatchford and Chris Mannaerts

Water 2020, 12(5), 1495; https://doi.org/10.3390/w12051495 - 23 May 2020

Cited by 12 | Viewed by 7677

Abstract

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results. Full article

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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1 pages, 146 KiB

Open AccessCorrection

Correction: Zhu, S., et al. A New Digital Lake Bathymetry Model Using the Step-Wise Water Recession Method to Generate 3D Lake Bathymetric Maps Based on DEMs. Water 2019, 11, 1151

by Siyu Zhu, Baojian Liu, Wei Wan, Hongjie Xie, Yu Fang, Xi Chen, Huan Li, Weizhen Fang, Guoqing Zhang, Mingwei Tao and Yang Hong

Water 2019, 11(11), 2419; https://doi.org/10.3390/w11112419 - 19 Nov 2019

Viewed by 2293

Abstract

In the published article [1], the authors realized some errors in the affiliation and email address of Yang Hong, and thus wish to make the following revisions: Add the Affiliation 5 “School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK [...] Read more.

(This article belongs to the Special Issue Hydrological and Environmental Modeling: from Observations to Predictions)

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