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South American Hydrology and Remote Sensing (South America Water from Space)

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (10 September 2021) | Viewed by 39797

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Special Issue Editors

Dr. Rodrigo Abarca Del Rio

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Guest Editor

Departamento de Geofísica (DGEO), Universidad de Concepción (UDEC), Casilla: 160-C, Barrió Universitario S/N, Concepción, Chile
Interests: satellite remote sensing for hydrology; data analysis; lakes; hydroclimate
Special Issues, Collections and Topics in MDPI journals

Dr. Daniel Moreira

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Guest Editor

Companhia De Pesquisa de Recursos Minerais (CPRM) - Geological Survey of Brazil, Department of Hydrology (DEHID), Avenida Pasteur, 404, UrcA, Rio de Janeiro (RJ) 22290-040, Brazil
Interests: geodesy; remote sensing and hydrology

Dr. Fabrice Papa

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Guest Editor

Institut de Recherche pour le Développement (IRD), LEGOS (Laboratoire d’Etudes en Géophysique et Océanographie Spatiales), Observatoire Midi-Pyrénées (OMP), 14, Avenue Edouard Belin, 31400 Toulouse, France
Interests: remote sensing; hydrology; water cycle; tropical climate variability
Special Issues, Collections and Topics in MDPI journals

Dr. Rodrigo Paiva

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Grupo Hidrologia de Grande Escala (HGE), Instituto de Pesquisas Hidráulicas –Universidade Federal do Rio Grande do Sul (IPH –UFRGS), Av. Bento Gonçalves, 9500 –Caixa Postal 15029CEP 91501-970 –Porto Alegre –RS, Brazil
Interests: hydrology; hydrodynamics; remote sensing; continental scale hydrology; modeling water systems

Dr. Marielle Gosset

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Geoscience Environnement Toulouse (GET), OMP, 14 avenue Edouard Belin, 31400 Toulouse, France
Interests: hydrometeorology; remote sensing; weather radar; hydrology; innovative sensors

Dr. Waldo Lavado

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Peruvian National Service of Meteorology and Hydrology (SENAHMI), Jr. Cahuide 785, Lima, Peru
Interests: hydrological models; hydroclimatological data; remote sensing; landslides; water security; hydroclimatology
Special Issues, Collections and Topics in MDPI journals

Dr. Jean-Francois Cretaux

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CNES (Centre National d’Etudes Spatiales), CNES, 18 avenue Edouard Belin, 31400 Toulouse, France
Interests: satellite remote sensing for hydrology; geodesy

Dr. Oliver Saavedra

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Guest Editor

Centro de Investigaciones en Ingeniería Civil y Ambiental (CIICA), Universidad Privada Boliviana (UPB), Av. Víctor Ustariz Km 6.5, Campus UPB, Cochabamba, Bolivia
Interests: applied hydrology; water resource management; satellite-based precipitation; optimal dam operation; groundwater flow

Dr. Philippe Maillard

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Department of Geography, Universidade federal de Minas Gerais (UFMG), Av. Antonio Carlos, 6627, Belo Horizonte, MG 31270-901, Brasil
Interests: SAR image processing; satellite radar altimetry; waveform processing; water extraction from images; dry bathymetry

Dr. Daniel Vila

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Divisão de Satélites e Sistemas Ambientais, Centro de Previsão de Tempo e Estudos Climáticos, Instituto Nacional de Pesquisas Espaciais (CPTEC/INPE), Rod Pres. Dutra km 40, Cachoeira Paulista, SP, Brasil
Interests: satellite rainfall estimation; severe weather nowcasting

Dr. Juan Leon

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Guest Editor

Universidad Nacional de Colombia (UNAL), Carrera 32 No 12 00, Vía Candelaria, Palmira, Valle del Cauca, Colombia
Interests: spatial hydrology

Dr. Vanessa Yael Bohn

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Guest Editor

Departamento de Geografía y Turismo (DGyT)- Universidad Nacional del Sur (UNS) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), DGyT – UNS, 12 de octubre y San Juan – Bahía Blanca (8000), Pcia. Buenos Aires, Argentina
Interests: hydrological vulnerability; remote sensing; climate variability; shallow lakes

Dr. Stéphane Calmant

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IRD (Institut de Recherche pour le Développement), IRD center, 275 route de Montabo, 97300 Cayenne, French Guiana
Interests: Earth observation from space (geophysics, hydrology)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Earth has a limited amount of water that recycles itself in what is called the 'water cycle'. Climate, weather, and human life and activities are profoundly affected by the variability and changes in this continuous, interconnected cycle. Therefore, observing, monitoring, and predicting the key variables governing the global and regional water cycle is essential to our understanding of the Earth’s climate, forecasting our weather, predicting floods and droughts, and improving water resource management.

The progress of Earth observation satellite technologies (EO) over the past few decades has made it possible to survey several of these variables from space. In the coming years, an increasing number of satellite missions will offer an unprecedented capacity to observe the Earth’s surface, its interior, and the atmosphere, ushering in a new era in the science of the Earth Environment and the water cycle.

It is within this perspective that we are pleased to invite you to participate in this issue. Our goal is for this issue to be the first of many, an issue regularly carried out in which all those using remote sensing technologies for monitoring waters (in all its forms) in Latin America find a receptacle.

We will welcome studies focusing on applications of remote sensing techniques to investigate water cycle studies, water management issues, liquid and solid discharge in rivers, hydrometeorological risks, precipitation, the cryosphere, soil moisture, water levels and surface waters, lakes, wetlands, rivers (including calibration/validation of current satellite missions), turbulent energy fluxes and evapotranspiration, irrigation, floods, and droughts, among others. Contributions dealing with modeling of the regional water cycle in synergy with the use of remote sensing observations will also be considered. Special contributions dealing with South American regional thematic (rivers such as the Amazon, the Orinoco, La Plata, Nordeste, Sao Francisco, Biobío, arid areas like the TPDS, etc.) are a plus, but contributions dealing with tropical large river basins, in general, are also welcome.

Studies devoted to the possibilities provided by the current South America CBERS, SAOCOM, and SABIA-MAR and results from other south American satellite missions (such as SSOT-FASAT CHARLIE), PERU-SAR1, VRSS1-2 etc ) and the European COPERNICUS space program, or to the advent of the new capabilities of the Surface Water Ocean Topography (SWOT) Mission (NASA, CNES, CSA, and UKSA) are most appreciated.

Dr. Rodrigo Abarca Del Rio
Dr. Daniel Moreira
Dr. Fabrice Papa
Dr. Rodrigo Paiva
Dr. Marielle Gosset
Dr. Waldo Lavado
Dr. Jean-Francois Cretaux
Dr. Oliver Saavedra
Dr. Philippe Maillard
Dr. Daniel Vila
Dr. Juan Leon
Dr. Vanessa Yael Bohn
Dr. Stephane Calmant
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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

South America
Earth observation satellite technologies
Continental waters
Water cycle
Discharge
Hydrological models
Hydrometeorology
Climate change
Anthropic impact
Geodesy

Published Papers (11 papers)

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Research

18 pages, 2854 KiB

Open AccessArticle

A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile

by María Pedreros-Guarda, Rodrigo Abarca-del-Río, Karen Escalona, Ignacio García and Óscar Parra

Remote Sens. 2021, 13(22), 4544; https://doi.org/10.3390/rs13224544 - 12 Nov 2021

Cited by 7 | Viewed by 3178

Abstract

Lake surface water temperature (LSWT) is a crucial water quality parameter that modulates many lake and reservoir processes. Therefore, it is necessary to monitor it from a long-term perspective. Over the last decades, many methods to retrieve LSWT fields from satellite imagery have been developed. This work aims to test, implement and automate six methods. These are performed in the Google Earth Engine (GEE) platform, using 30 m spatial resolution images from Landsat 7 and 8 satellites for 2000–2020. Automated methods deliver long-term time series. Series are then calibrated with in situ data. Two-dimensional (2D) × time data fields are built on the lakes with the calibration, and a subsequent LSWT climatology is derived. Our study area is two urban lagoons with areas smaller than two (2) km

^{2}

of the city of San Pedro de la Paz, South-Central Chile. The six methods describe the seasonal variation of LSWT (Willmott’s index of agreement > 0.91,

R^{2}

> 0.67). The main difference between series is their bias. Thus, after a simple calibration, all series adequately describe the LSWT. We utilized the Pedro de la Paz lagoons to demonstrate the method’s utility. Our research demonstrates that these adjacent lagoons exhibit comparable LSWT spatial (15.5–17

^{\circ}

C) and temporal (7–25

^{\circ}

C) trends throughout the year. Differences in geographical pattern might result from the northern island’s heat impact and the existence of the Biobío river to the east. Our work represents an efficient alternative for obtaining LSWT in particular lakes and reservoirs, especially useful in medium and small-sized ones. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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19 pages, 3741 KiB

Open AccessArticle

Assessing the Potential of Upcoming Satellite Altimeter Missions in Operational Flood Forecasting Systems

by Aline Falck, Javier Tomasella and Fabrice Papa

Remote Sens. 2021, 13(21), 4459; https://doi.org/10.3390/rs13214459 - 06 Nov 2021

Cited by 4 | Viewed by 2227

Abstract

This study investigates the potential of observations with improved frequency and latency time of upcoming altimetry missions on the accuracy of flood forecasting and early warnings. To achieve this, we assessed the skill of the forecasts of a distributed hydrological model by assimilating different historical discharge time frequencies and latencies in a framework that mimics an operational forecast system, using the European Ensemble Forecasting system as the forcing. Numerical experiments were performed in 22 sub-basins of the Tocantins-Araguaia Basin. Forecast skills were evaluated in terms of the Relative Operational Characteristics (ROC) as a function of the drainage area and the forecasts’ lead time. The results showed that increasing the frequency of data collection and reducing the latency time (especially 1 d update and low latency) had a significant impact on steep headwater sub-basins, where floods are usually more destructive. In larger basins, although the increased frequency of data collection improved the accuracy of the forecasts, the potential benefits were limited to the earlier lead times. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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22 pages, 5097 KiB

Open AccessArticle

Improving Hourly Precipitation Estimates for Flash Flood Modeling in Data-Scarce Andean-Amazon Basins: An Integrative Framework Based on Machine Learning and Multiple Remotely Sensed Data

by Juseth E. Chancay and Edgar Fabian Espitia-Sarmiento

Remote Sens. 2021, 13(21), 4446; https://doi.org/10.3390/rs13214446 - 05 Nov 2021

Cited by 5 | Viewed by 4085

Abstract

Accurate estimation of spatiotemporal precipitation dynamics is crucial for flash flood forecasting; however, it is still a challenge in Andean-Amazon sub-basins due to the lack of suitable rain gauge networks. This study proposes a framework to improve hourly precipitation estimates by integrating multiple satellite-based precipitation and soil-moisture products using random forest modeling and bias correction techniques. The proposed framework is also used to force the GR4H model in three Andean-Amazon sub-basins that suffer frequent flash flood events: upper Napo River Basin (NRB), Jatunyacu River Basin (JRB), and Tena River Basin (TRB). Overall, precipitation estimates derived from the framework (BC-RFP) showed a high ability to reproduce the intensity, distribution, and occurrence of hourly events. In fact, the BC-RFP model improved the detection ability between 43% and 88%, reducing the estimation error between 72% and 93%, compared to the original satellite-based precipitation products (i.e., IMERG-E/L, GSMAP, and PERSIANN). Likewise, simulations of flash flood events by coupling the GR4H model with BC-RFP presented satisfactory performances (KGE* between 0.56 and 0.94). The BC-RFP model not only contributes to the implementation of future flood forecast systems but also provides relevant insights to several water-related research fields and hence to integrated water resources management of the Andean-Amazon region. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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22 pages, 4609 KiB

Open AccessArticle

Synergistic Calibration of a Hydrological Model Using Discharge and Remotely Sensed Soil Moisture in the Paraná River Basin

by Ayan Santos Fleischmann, Ahmad Al Bitar, Aline Meyer Oliveira, Vinícius Alencar Siqueira, Bibiana Rodrigues Colossi, Rodrigo Cauduro Dias de Paiva, Yann Kerr, Anderson Ruhoff, Fernando Mainardi Fan, Paulo Rógenes Monteiro Pontes and Walter Collischonn

Remote Sens. 2021, 13(16), 3256; https://doi.org/10.3390/rs13163256 - 18 Aug 2021

Cited by 4 | Viewed by 2560

Abstract

Hydrological models are useful tools for water resources studies, yet their calibration is still a challenge, especially if aiming at improved estimates of multiple components of the water cycle. This has led the hydrologic community to look for ways to constrain models with multiple variables. Remote sensing estimates of soil moisture are very promising in this sense, especially in large areas for which field observations may be unevenly distributed. However, the use of such data to calibrate hydrological models in a synergistic way is still not well understood, especially in tropical humid areas such as those found in South America. Here, we perform multiple scenarios of multiobjective model optimization with in situ discharge and the SMOS L4 root zone soil moisture product for the Upper Paraná River Basin in South America (drainage area > 900,000 km²), for which discharge data for 136 river gauges are used. An additional scenario is used to compare the relative impacts of using all river gauges and a small subset containing nine gauges only. Across the basin, the joint calibration (CAL-DS) using discharge and soil moisture leads to improved precision and accuracy for both variables. The discharges estimated by CAL-DS (median KGE improvement for discharge was 0.14) are as accurate as those obtained with the calibration with discharge only (median equal to 0.14), while the CAL-DS soil moisture retrieval is practically as accurate (median KGE improvement for soil moisture was 0.11) as that estimated using the calibration with soil moisture only (median equal to 0.13). Nonetheless, the individual calibration with discharge rates is not able to retrieve satisfactory soil moisture estimates, and vice versa. These results show the complementarity between these two variables in the model calibration and highlight the benefits of considering multiple variables in the calibration framework. It is also shown that, by considering only nine gauges instead of 136 in the model optimization, the model is able to estimate reasonable discharge and soil moisture, although relatively less accurately and with less precision than for the entire dataset. In summary, this study shows that, for poorly gauged tropical basins, the joint calibration of SMOS soil moisture and a few in situ discharge gauges is capable of providing reasonable discharge and soil moisture estimates basin-wide and is more preferable than performing only a discharge-oriented optimization process. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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Graphical abstract

19 pages, 70458 KiB

Open AccessArticle

The Development of a Combined Satellite-Based Precipitation Dataset across Bolivia from 2000 to 2015

by Jhonatan Ureña, Oliver Saavedra and Takuji Kubota

Remote Sens. 2021, 13(15), 2931; https://doi.org/10.3390/rs13152931 - 26 Jul 2021

Cited by 2 | Viewed by 2583

Abstract

This study proposes the use of satellite-based precipitation (SBP) products in combination with local rain gauges in Bolivia. Using this approach, the country was divided into three major hydrographic basins: the Altiplano, La Plata, and Amazon. The selected SBP products were Global Satellite Mapping of Precipitation (GSMaP) and Climate Hazards Group Infrared Precipitations with Stations (CHIRPS). The correlation coefficients of SBP were found to be from 0.94 to 0.98 at monthly temporal scale. The applied methodology iterates correction factors, taking advantage of surface measurements from the national rain gauge network; five iterations showed stability in the convergence. Once the improved SBP product was obtained, validation was performed by reducing ten percent the number of rain gauges randomly. After applying the correction factors, the combined products improved their correlation coefficient values by up to 0.99. The validation of the methodology showed that with a combination of products using 90% of the rain gauges, correlation coefficients ranged from 0.98 to 0.99. Among the three basins, the Amazon basin presented the poorest results; this fact may be related to low rain gauge density compared to the other two basins. The validation approach shows that the methodology has an acceptable performance. The database generated in this study, now open to the public, is ready to be used for different hydrological applications such as precipitation time-series analysis, water balance, and water assessment at the sub-basin scale within Bolivia. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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23 pages, 4168 KiB

Open AccessArticle

Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

by Celina Aznarez, Patricia Jimeno-Sáez, Adrián López-Ballesteros, Juan Pablo Pacheco and Javier Senent-Aparicio

Remote Sens. 2021, 13(10), 2014; https://doi.org/10.3390/rs13102014 - 20 May 2021

Cited by 40 | Viewed by 5913

Abstract

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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18 pages, 24376 KiB

Open AccessArticle

Assessing Near Real-Time Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Peruvian Andes

by Harold Llauca, Waldo Lavado-Casimiro, Karen León, Juan Jimenez, Kevin Traverso and Pedro Rau

Remote Sens. 2021, 13(4), 826; https://doi.org/10.3390/rs13040826 - 23 Feb 2021

Cited by 25 | Viewed by 4641

Abstract

This study investigates the applicability of Satellite Precipitation Products (SPPs) in near real-time for the simulation of sub-daily runoff in the Vilcanota River basin, located in the southeastern Andes of Peru. The data from rain gauge stations are used to evaluate the quality of Integrated Multi-satellite Retrievals for GPM–Early (IMERG-E), Global Satellite Mapping of Precipitation–Near Real-Time (GSMaP-NRT), Climate Prediction Center Morphing Method (CMORPH), and HydroEstimator (HE) at the pixel-station level; and these SPPs are used as meteorological inputs for the hourly hydrological modeling. The GR4H model is calibrated with the hydrometric station of the longest record, and model simulations are also verified at one station upstream and two stations downstream of the calibration point. Comparing the sub-daily precipitation data observed, the results show that the IMERG-E product generally presents higher quality, followed by GSMaP-NRT, CMORPH, and HE. Although the SPPs present positive and negative biases, ranging from mild to moderate, they do represent the diurnal and seasonal variability of the hourly precipitation in the study area. In terms of the average of Kling-Gupta metric (KGE), the GR4H_GSMaP-NRT’ yielded the best representation of hourly discharges (0.686), followed by GR4H_IMERG-E’ (0.623), GR4H_Ensemble-Mean (0.617) and GR4H_CMORPH’ (0.606), and GR4H_HE’ (0.516). Finally, the SPPs showed a high potential for monitoring floods in the Vilcanota basin in near real-time at the operational level. The results obtained in this research are very useful for implementing flood early warning systems in the Vilcanota basin and will allow the monitoring and short-term hydrological forecasting of floods by the Peruvian National Weather and Hydrological Service. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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18 pages, 9093 KiB

Open AccessArticle

The Performance of the Diurnal Cycle of Precipitation from Blended Satellite Techniques over Brazil

by Ricardo Almeida de Siqueira, Daniel Alejandro Vila and João Maria de Sousa Afonso

Remote Sens. 2021, 13(4), 734; https://doi.org/10.3390/rs13040734 - 17 Feb 2021

Cited by 4 | Viewed by 2233

Abstract

The knowledge of the diurnal cycle of precipitation is of extreme relevance to understanding the physical/dynamic processes associated with the spatial and temporal distribution of precipitation. The main difficulty of this task is the lack of surface precipitation information over certain regions on an hourly time scale and the low spatial representativeness of these data (normally surface gauges). In order to overcome these difficulties, the main objective of this study is to create a 3-h precipitation accumulation database from the gauge-adjusted daily regional precipitation products to resolve the diurnal cycle properly. This study also proposes to evaluate different methodologies for partitioning gauge-adjusted daily precipitation products, i.e., a product made by the combination of satellite estimates and surface gauge observations, into 3-h precipitation accumulation. Two methodologies based on the calculation of a conversion factor F between a daily gauge-adjusted product, combined scheme (CoSch, hereafter), and a non-gauge-adjusted one, the integrated multi-satellite retrievals for GPM (IMERG)-Early (IMERG, hereafter) were tested for this research. Hourly rain gauge stations for the period of 2015–2018 over Brazil were used to assess the performance of the proposed methodologies over the whole region and five sub-regions with homogeneous precipitation regimes. Standard statistical metrics and categorical indices related with the capability to detect rainfall events were used to compare the ability of each product to represent the diurnal cycle. The results show that the new 3-h CoSch products show better agreement with rainfall gauge stations when compared with IMERG, better capturing the diurnal cycle of precipitation. The biggest improvement was over northeastern region close to the coast, where IMERG was not able to capture the diurnal cycle properly. One of the proposed methodologies (CoSchB) performed better on the critical success index and equitable threat score metrics, suggesting that this is the best product over the two. The downside, when compared with the other methodology (CoSchA), was a slight increase in the values of bias and mean absolute error, but still at acceptable levels. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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Graphical abstract

19 pages, 3515 KiB

Open AccessArticle

Exploring the Fingerprints of Past Rain-on-Snow Events in a Central Andean Mountain Range Basin Using Satellite Imagery

by D. Ocampo Melgar and F.J. Meza

Remote Sens. 2020, 12(24), 4173; https://doi.org/10.3390/rs12244173 - 20 Dec 2020

Cited by 6 | Viewed by 2682

Abstract

Rain-on-snow (ROS) events can alter nival regimes and increase snowmelt, peak river flow, and reduce water storage. However, detection of ROS events is challenging and only the most intense and obvious cases are identified. Rain is known to reduce snow cover and decrease near-infrared reflectance due to increased grain size. This study explored the fingerprints of ROS events on mountain snowpack with a simple typology that classifies changes in snow reflectance using fifteen years of MODIS imagery, reanalysis, and surface hydrometeorological data. The Maipo River Basin, with strong nival regime and a steep topography, in the western Andean mountain range was selected as a case study. Statistical analysis showed two distinct and opposite responses in the near infrared reflectance distribution of snow-covered pixels after precipitation, consistent with the typology for rain or snow events. For the probable ROS events, the daily maximum and minimum temperature increased in the days preceding the event and subsequently decreased, in some cases followed by a less consistent response in river flow. Although much remains to be studied, this approach can be used to expand historical records and improve modelling and detection schemes. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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19 pages, 1807 KiB

Open AccessArticle

Precipitation Diurnal Cycle Assessment of Satellite-Based Estimates over Brazil

by João Maria de Sousa Afonso, Daniel Alejandro Vila, Manoel Alonso Gan, David Pareja Quispe, Naurinete de Jesus da Costa Barreto, Joao Henry Huamán Chinchay and Rayana Santos Araujo Palharini

Remote Sens. 2020, 12(14), 2339; https://doi.org/10.3390/rs12142339 - 21 Jul 2020

Cited by 13 | Viewed by 3400

Abstract

The main objective of this study is to assess the ability of several high-resolution satellite-based precipitation estimates to represent the Precipitation Diurnal Cycle (PDC) over Brazil during the 2014–2018 period, after the launch of the Global Precipitation Measurement satellite (GPM). The selected algorithms are the Global Satellite Mapping of Precipitation (GSMaP), The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data from different national and regional networks were used as the reference dataset after going through rigid quality control tests. All datasets were interpolated to a common 0.1° × 0.1° grid every 3 h for comparison. After a hierarchical cluster analysis, seven regions with different PDC characteristics (amplitude and phase) were selected for this study. The main results of this research could be summarized as follow: (i) Those regions where thermal heating produce deep convective clouds, the PDC is better represented by all algorithms (in term of amplitude and phase) than those regions driven by shallow convection or low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms the rest of the algorithms with lower bias and less dispersion. In this case, the gauge-adjusted version improves the satellite-only retrievals of the same algorithm suggesting that daily gauge-analysis is useful to reduce the bias in a sub-daily scale; (iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F)) overestimates rainfall for almost all times and all the regions, while the satellite-only version provide better results than the final version; (iv) CMORPH has the better performance for a transitional regime between a coastal land-sea breeze and a continental amazonian regime. Further research should be performed to understand how shallow clouds processes and convective/stratiform classification is performed in each algorithm to improve the representativity of diurnal cycle. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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24 pages, 5366 KiB

Open AccessArticle

Assessment of the Extreme Precipitation by Satellite Estimates over South America

by Rayana Santos Araujo Palharini, Daniel Alejandro Vila, Daniele Tôrres Rodrigues, David Pareja Quispe, Rodrigo Cassineli Palharini, Ricardo Almeida de Siqueira and João Maria de Sousa Afonso

Remote Sens. 2020, 12(13), 2085; https://doi.org/10.3390/rs12132085 - 29 Jun 2020

Cited by 35 | Viewed by 4154

Abstract

In developing countries, accurate rainfall estimation with adequate spatial distribution is limited due to sparse rain gauge networks. One way to solve this problem is the use of satellite-based precipitation products. These satellite products have significant spatial coverage of rainfall estimates and it is of fundamental importance to investigate their performance across space–time scales and the factors that affect their uncertainties. In the open literature, some studies have already analyzed the ability of satellite-based rain estimation products to estimate average rainfall values. These investigations have found very close agreement between the estimates and observed data. However, further evaluation of the satellite precipitation products is necessary to improve their reliability to estimate extreme values. In this scenario, the main goal of this work is to evaluate the ability of satellite-based precipitation products to capture the characteristics of extreme precipitation over the tropical region of South America. The products evaluated in this investigation were 3B42 RT v7.0, 3B42 RT v7.0 uncalibrated, CMORPH V1.0 RAW, CMORPH V1.0 CRT, GSMAP-NRT-no gauge v6.0, GSMAP-NRT- gauge v6.0, CHIRP V2.0, CHIRPS V2.0, PERSIANN CDR v1 r1, CoSch and TAPEER v1.5 from Frequent Rainfall Observations on GridS (FROGS) database. Some products considered in this investigation are adjusted with rain gauge values and others only with satellite information. In this study, these two sets of products were considered. In addition, gauge-based daily precipitation data, provided by Brazil’s National Institute for Space Research, were used as reference in the analyses. In order to compare gauge-based daily precipitation and satellite-based data for extreme values, statistical techniques were used to evaluate the performance the selected satellite products over the tropical region of South America. According to the results, the threshold for rain to be considered an extreme event in South America presented high variability, ranging from 20 to 150 mm/day, depending on the region and the percentile threshold chosen for analysis. In addition, the results showed that the ability of the satellite estimates to retrieve rainfall extremes depends on the geographical location and large-scale rainfall regimes. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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