Special Issue "Integration of Remote Sensing Information in the Analyses of the Dynamic of Water Resources Systems"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 7261

Special Issue Editors

Dr. David Pulido-Velázquez
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Chief Guest Editor
Department of Research on Geological Resources, Geological Survey of Spain, 18006 Granada, Spain
Interests: climate change impacts; adaptation strategies; water resources; hydrology; groundwater; remote sensing; droughts; stream-aquifer interaction; conjunctive use; management models; decision support systems
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Juan Collados-Lara
E-Mail Website
Guest Editor
Department of Civil Engineering, University of Granada, Water Institute, 18003 Granada, Spain
Interests: climate change; hydrology; snow; groundwater; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Simon Stisen
E-Mail Website
Guest Editor
Department of Hydrology, Geological Survey of Denmark and Greenland (GEUS), Øster Voldgade 10, 1350 Copenhagen, Denmark
Interests: hydrological modeling; remote sensing; groundwater; spatial patterns in hydrology; climate change

Special Issue Information

Dear Colleagues,

Satellite information can provide useful information for monitoring many climatic and hydrological variables. For example, it can provide information about soil moisture (Demirel et al., 2018), areas covered by snow (Collados-Lara et al., 2019, Pardo-Igúzquiza et al., 2017), water (Zhou et al., 2017, Colditz et al., 2018), and different land uses (Pflugmacher et al., 2019) in water resource systems.

There are several platforms or satellite missions that include different sensors based on, e.g., visible, infrared, and/or radar radiation providing data with different spatial and temporal resolution. Some studies make a combined use of field and satellite data with different resolutions, to study, for example, the dynamics of lakes (Xiao et al., 2018; Che et al., 2017). For the study of some variables (e.g., snow depth and/or water equivalent) and/or when clouds significantly reduce the information coming from the visible or infrared spectrum, the use of radar information (Salcedo et al., 2014; Ya-Lun et al., 2019) is required.

Although there are some operational products made from satellite information that can provide complete data to approach the dynamics of some systems (e.g., snow cover area products for some areas), in many cases, there is not enough information with the necessary spatial–temporal resolution, or there are no operational products available for some variables of interest. In these cases, raw data processing and treatment of the sensors are required, which requires a greater investment of resources (Verpoorter et al., 2012; Mueller et al., 2016). For example, from a methodological point of view, surface water extension mapping from satellite data has been approached using different indices and spectral sensors related to water (Zhou et al., 2017; Colditz et al., 2018). On the other hand, the integration of satellite data within system modeling procedures may help to define more reliable approaches (Collados-Lara et al., 2020).

This Special Issue aims to collect research works on the integration of remote sensing information in the analyses of the dynamics of water resources systems. Potential topics included (but not limited to them) are:

  • Dynamics of hydroclimatic variables (humidity, evapotranspiration, soil moisture, etc.);
  • Dynamics of lakes and flooding (surface and/or volume of water);
  • Dynamics of snow cover area and/or snow depth and water equivalent;
  • Dynamics of land uses and irrigation.

Dr. David Pulido-Velázquez
Dr. Antonio Juan Collados-Lara
Dr. Simon Stisen
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 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

  • remote sensing
  • dynamics
  • hydrology
  • climate
  • flooding
  • snow
  • land use

Published Papers (5 papers)

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Research

Article
Evaluation of CMORPH, PERSIANN-CDR, CHIRPS V2.0, TMPA 3B42 V7, and GPM IMERG V6 Satellite Precipitation Datasets in Arabian Arid Regions
Water 2023, 15(1), 92; https://doi.org/10.3390/w15010092 - 27 Dec 2022
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Abstract
Rainfall depth is a crucial parameter in water resources and hydrological studies. Rain gauges provide the most reliable point-based rainfall estimates. However, they do not have a proper density/distribution to provide sufficient rainfall measurements in many areas, especially in arid regions. To evaluate [...] Read more.
Rainfall depth is a crucial parameter in water resources and hydrological studies. Rain gauges provide the most reliable point-based rainfall estimates. However, they do not have a proper density/distribution to provide sufficient rainfall measurements in many areas, especially in arid regions. To evaluate the adequacy of satellite datasets as an alternative to the rain gauges, the Kingdom of Saudi Arabia (KSA) is selected for the current study as a representative of the arid regions. KSA occupies most of the Arabian Peninsula and is characterized by high variability in topographic and climatic conditions. Five satellite precipitation datasets (SPDSs)—CMORPH, PERSIANN-CDR, CHIRPS V2.0, TMPA 3B42 V7, and GPM IMERG V6—are evaluated versus 324 conventional rain-gauges’ daily precipitation measures. The evaluation is conducted based on nine quantitative and categorical metrics. The evaluation analysis is carried out for daily, monthly, yearly, and maximum yearly records. The daily analysis revealed a low correlation for all SPDSs (<0.31), slightly improved in the yearly and maximum yearly analysis and reached its highest value (0.58) in the monthly analysis. The GPM IMERG V6 and PERSIANN-CDR have the highest probability of detection (0.55) but with a high false alarm ratio (>0.8). Accordingly, in arid regions, the use of daily SPDSs in rainfall estimation will lead to high uncertainty in the obtained results. The best performance for all statistical metrics was found at 500–750 m altitudes in the central and northern parts of the study area for all satellites except minor anomalies. CMORPH dataset has the lowest centered root mean square error (RMSEc) for all analysis periods with the best results in the monthly analyses. Full article
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Article
Product- and Hydro-Validation of Satellite-Based Precipitation Data Sets for a Poorly Gauged Snow-Fed Basin in Turkey
Water 2022, 14(17), 2758; https://doi.org/10.3390/w14172758 - 05 Sep 2022
Cited by 2 | Viewed by 1018
Abstract
Satellite-based Precipitation (SBP) products are receiving growing attention, and their utilization in hydrological applications is essential for better water resource management. However, their assessment is still lacking for data-sparse mountainous regions. This study reveals the performances of four available PERSIANN family products of [...] Read more.
Satellite-based Precipitation (SBP) products are receiving growing attention, and their utilization in hydrological applications is essential for better water resource management. However, their assessment is still lacking for data-sparse mountainous regions. This study reveals the performances of four available PERSIANN family products of low resolution near real-time (PERSIANN), low resolution bias-corrected (PERSIANN-CDR), and high resolution real-time (PERSIANN-CCS and PERSIANN-PDIR-Now). The study aims to apply Product-Validation Experiments (PVEs) and Hydro-Validation Experiments (HVEs) in a mountainous test catchment of the upper Euphrates Basin. The PVEs are conducted on different temporal scales (annual, monthly, and daily) within four seasonal time periods from 2003 to 2015. HVEs are accomplished via a multi-layer perceptron (MLP)-based rainfall-runoff model. The Gauge-based Precipitation (GBP) and SBP are trained and tested to simulate daily streamflows for the periods of 2003–2008 and 2009–2011 water years, respectively. PVEs indicate that PERSIANN-PDIR-Now comprises the least mean annual bias, and PERSIANN-CDR gives the highest monthly correlation with the GBP data. According to daily HVEs, MLP provides a compromising alternative for biased data sets; all SBP models show reasonably high Nash–Sutcliffe Efficiency for the training (above 0.80) and testing (0.62) periods, while the PERSIANN-CDR-based MLP (0.88 and 0.79) gives the highest performance. Full article
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Article
Integrating Meteorological Forcing from Ground Observations and MSWX Dataset for Streamflow Prediction under Multiple Parameterization Scenarios
Water 2022, 14(17), 2721; https://doi.org/10.3390/w14172721 - 01 Sep 2022
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Abstract
Precipitation and near-surface air temperatures are significant meteorological forcing for streamflow prediction where most basins are partially or fully data-scarce in many parts of the world. This study aims to evaluate the consistency of MSWXv100-based precipitation, temperatures, and estimated potential evapotranspiration (PET) by [...] Read more.
Precipitation and near-surface air temperatures are significant meteorological forcing for streamflow prediction where most basins are partially or fully data-scarce in many parts of the world. This study aims to evaluate the consistency of MSWXv100-based precipitation, temperatures, and estimated potential evapotranspiration (PET) by direct comparison with observed measurements and by utilizing an independent combination of MSWXv100 dataset and observed data for streamflow prediction under four distinct scenarios considering model parameter and output uncertainties. Initially, the model is calibrated/validated entirely based on observed data (Scenario 1), where for the second calibration/validation, the observed precipitation is replaced by MSWXv100 precipitation and the daily observed temperature and PET remained unchanged (Scenario 2). Furthermore, the model calibration/validation is done by considering observed precipitation and MSWXv100-based temperature and PET (Scenario 3), and finally, the model is calibrated/validated entirely based on the MSWXv100 dataset (Scenario 4). The Kling–Gupta Efficiency (KGE) and its components (correlation, ratio of bias, and variability ratio) are utilized for direct comparison, and the Hanssen–Kuiper (HK) skill score is employed to evaluate the detectability strength of MSWXv100 precipitation for different precipitation intensities. Moreover, the hydrologic utility of MSWXv100 dataset under four distinct scenarios is tested by exploiting a conceptual rainfall-runoff model under KGE and Nash–Sutcliffe Efficiency (NSE) metrics. The results indicate that each scenario depicts high streamflow reproducibility where, regardless of other meteorological forcing, utilizing observed precipitation (Scenario 1 and 3) as one of the model inputs, shows better model performance (KGE = 0.85) than MSWXv100-based precipitation, such as Scenario 2 and 4 (KGE = 0.78–0.80). Full article
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Article
Analysis of the Potential Impact of Climate Change on Climatic Droughts, Snow Dynamics, and the Correlation between Them
Water 2022, 14(7), 1081; https://doi.org/10.3390/w14071081 - 29 Mar 2022
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Abstract
Climate change is expected to increase the occurrence of droughts, with the hydrology in alpine systems being largely determined by snow dynamics. In this paper, we propose a methodology to assess the impact of climate change on both meteorological and hydrological droughts, taking [...] Read more.
Climate change is expected to increase the occurrence of droughts, with the hydrology in alpine systems being largely determined by snow dynamics. In this paper, we propose a methodology to assess the impact of climate change on both meteorological and hydrological droughts, taking into account the dynamics of the snow cover area (SCA). We also analyze the correlation between these types of droughts. We generated ensembles of local climate scenarios based on regional climate models (RCMs) representative of potential future conditions. We considered several sources of uncertainty: different historical climate databases, simulations obtained with several RCMs, and some statistical downscaling techniques. We then used a stochastic weather generator (SWG) to generate multiple climatic series preserving the characteristics of the ensemble scenario. These were simulated within a cellular automata (CA) model to generate multiple SCA future series. They were used to calculate multiple series of meteorological drought indices, the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and a novel hydrological drought index (Standardized Snow Cover Index (SSCI)). Linear correlation analysis was applied to both types of drought to analyze how they propagate and the time delay between them. We applied the proposed methodology to the Sierra Nevada (southern Spain), where we estimated a general increase in meteorological and hydrological drought magnitude and duration for the horizon 2071–2100 under the RCP 8.5 emission scenario. The SCA droughts also revealed a significant increase in drought intensity. The meteorological drought propagation to SCA droughts was reflected in an immediate or short time (1 month), obtaining significant correlations in lower accumulation periods of drought indices (3 and 6 months). This allowed us to obtain information about meteorological drought from SCA deficits and vice versa. Full article
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Article
Impact Assessment of Gridded Precipitation Products on Streamflow Simulations over a Poorly Gauged Basin in El Salvador
Water 2021, 13(18), 2497; https://doi.org/10.3390/w13182497 - 11 Sep 2021
Cited by 2 | Viewed by 1565
Abstract
In this study, five open access gridded precipitation (GP) products (CFSR, MSWEPv1.1, PERSIANN-CDR, CMORPH, and CHIRPSv2.0) and local climate data were evaluated over the Grande de San Miguel (GSM) River Basin in El Salvador. The main purpose was to identify optional data sources [...] Read more.
In this study, five open access gridded precipitation (GP) products (CFSR, MSWEPv1.1, PERSIANN-CDR, CMORPH, and CHIRPSv2.0) and local climate data were evaluated over the Grande de San Miguel (GSM) River Basin in El Salvador. The main purpose was to identify optional data sources of precipitation for hydrological modelling given that ground-based precipitation gauges in El Salvador are scarce and their data includes important temporal and spatial gaps. Firstly, a direct comparison was made between the precipitation data from the five GP products and from the rain gauges. Secondly, the SWAT model was used to simulate the streamflow regimen based on the precipitation datasets. The analysis of results showed that the models produced correct predictions, and the accuracy increased as models were calibrated to each specific precipitation product. Overall, PERSIANN-CDR produced the best simulation results, including streamflow predictions in the GSM basin, and outperformed other GP products and also the results obtained from data precipitation gauges. The findings of this research support the hydrological modelling based on open-access GP products, particularly when the data from precipitation gauges are scarce and poor. Full article
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