Special Issue "Satellite Remote Sensing and Analyses of Climate Variability"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (15 February 2019).

Special Issue Editors

Guest Editor
Prof. Dr. Gerd Wendler Website E-Mail
Alaska Climate Research Center, University of Alaska Fairbanks, 2158 N Koyukuk St., Fairbanks, AK 99709, USA
Interests: climatology; meteorology; radiation; micro-meteorology; hydrology; satellite meteorology; glacio-meteorology
Guest Editor
Dr. Gilberto J. Fochesatto Website E-Mail
Department of Atmospheric Sciences, Geophysical Institute and College of Natural Science and Mathematics, University of Alaska Fairbanks. 2156 Koyukuk Drive. P.O. Box 757320 Fairbanks, AK 99775-7320, USA
Interests: remote sensing; LiDAR; polar meteorology; atmospheric boundary layer; surface fluxes; evapotranspiration

Special Issue Information

Dear Colleagues,

Before satellite remote sensed data became available, climate and climate change was limited to ground based observations and a few of such ground based observations went back in time for more than a century. The aerial coverage varied widely, was relatively good in populated areas, e.g. Europe, but was mostly missing in the Polar Regions and over the oceans. Early in the 20th century, the available station data were used  to develop climatology for different continents,  the climate classification developed by Köppen in 1936 being the most sophisticated, as it was not only based on temperature, but also on precipitation..

Over the last few decades, remotely sensed data became available with satellites being dominantly used as platform. The first satellite based sensors were relative fairly simple, both in resolution and sophistication. By now sensors measure in various regions of the visible spectrum as well as in infrared and microwave region, the latter one being able to look through clouds.  In addition, the resolution has increased drastically, and can be as low as a few meters. These data allow us to observe over different time scales and over various areas from small watersheds to continents “Climate Variability”.

In this Special Issue of Water we welcome papers based on observations, from small watersheds to continents, from diurnal variations to multi-year changes, and from polar to tropical

climate conditions. Further, water will be considered in its three phases, from changes in the solid (glaciers, sea ice, snow fall), changes in the liquid form (rain fall, sea level change, run-off, basins), to water vapor (evapotranspiration, changes in water content of the atmosphere).

Prof. Dr. Gerd Wendler
Dr. Gilberto J. Fochesatto
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 papers will be 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. Water is an international peer-reviewed open access monthly 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 1600 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

  • surface temperature
  • evapotranspiration
  • cloud macrophysics
  • precipitation
  • radiation sensing
  • aerosols
  • sea ice
  • ice-sheet
  • glaciers
  • basins, from small watersheds to continents

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
A 60-Year Time Series Analyses of the Upwelling along the Portuguese Coast
Water 2019, 11(6), 1285; https://doi.org/10.3390/w11061285 - 20 Jun 2019
Abstract
Coastal upwelling has a significant local impact on marine coastal environment and on marine biology, namely fisheries. This study aims to evaluate climate and environmental changes in upwelling trends between 1950 and 2010. Annual, seasonal and monthly upwelling trends were studied in three [...] Read more.
Coastal upwelling has a significant local impact on marine coastal environment and on marine biology, namely fisheries. This study aims to evaluate climate and environmental changes in upwelling trends between 1950 and 2010. Annual, seasonal and monthly upwelling trends were studied in three different oceanographic areas of the Portuguese coast (northwestern—NW, southwestern—SW, and south—S). Two sea surface temperature datasets, remote sensing (RS: 1985–2009) and International Comprehensive Ocean—Atmosphere Data Set (ICOADS: 1950–2010), were used to estimate an upwelling index (UPWI) based on the difference between offshore and coastal sea surface temperature. Time series analyses reveal similar yearly and monthly trends between datasets A decrease of the UPWI was observed, extending longer than 20 years in the NW (1956–1979) and SW (1956–1994), and 30 years in the S (1956–1994). Analyses of sudden shifts reveal long term weakening and intensification periods of up to 30 years. This means that in the past 60 years a normal climate UPWI occurred along the Portuguese coast. An intensification of UPWI was recorded in recent decades regardless of the areas (RS: 1985–2009). Such an intensification rate (linear increase in UPWI) is only significant in S in recent decades (increase rate: ICOADS = 0.02 °C decade-1; RS = 0.11 °C decade-1) while in NW and SW the increase rate is meaningless. In NW more stable UPWI conditions were recorded, however average UPWI values increased in autumn and winter in NW in recently decades (RS: 1985–2009). An intensification rate of UPWI was recorded during summer (July, August and September) in SW and S in latter decades (RS: 1985–2009). The average UPWI values increased in recent decades in autumn in S. Marked phenological changes were observed in S in summer (before downwelling conditions prevail whilst recently when UPWI regimes prevail) with UPWI seasonal regime in S in recent decades becoming similar to those found in SW and NW. Results of this work can contribute to a better understanding of how upwelling dynamics affect/are correlated with biological data. Full article
(This article belongs to the Special Issue Satellite Remote Sensing and Analyses of Climate Variability)
Show Figures

Figure 1

Open AccessArticle
Integrating Landsat TM/ETM+ and Numerical Modeling to Estimate Water Temperature in the Tigris River under Future Climate and Management Scenarios
Water 2019, 11(5), 892; https://doi.org/10.3390/w11050892 - 28 Apr 2019
Cited by 1
Abstract
Modeling the water quality of rivers and assessing the effects of changing conditions is often hindered by a lack of in situ measurements for calibration. Here, we use a combination of satellite measurements, statistical models, and numerical modeling with CE-QUAL-W2 to overcome in [...] Read more.
Modeling the water quality of rivers and assessing the effects of changing conditions is often hindered by a lack of in situ measurements for calibration. Here, we use a combination of satellite measurements, statistical models, and numerical modeling with CE-QUAL-W2 to overcome in situ data limitations and evaluate the effect of changing hydrologic and climate conditions on water temperature (Tw) in the Tigris River, one of the largest rivers in the Middle East. Because few in situ estimates of Tw were available, remotely-sensed estimates of Tw were obtained from Landsat satellite images at roughly 2 week intervals for the year 2009 at the upstream model boundary (Mosul Dam) and two locations further downstream, Baeji and Baghdad. A regression was then developed between air temperature and Landsat Tw in order to estimate daily Tw. These daily Tw were then used for the upstream model boundary condition and for model calibration downstream. Modeled Tw at downstream locations agreed well with Landsat-based statistical estimates with an absolute mean error of <1 °C. A model sensitivity analysis suggested that altering upstream river discharge had little impact on downstream Tw. By contrast, a climate change scenario in which air temperatures were increased by 2 °C resulted in a 0.9 °C and 1.5 °C increase in Tw at Baeji and Baghdad, respectively. Since Tw is a fundamental state variable in water quality models, our approach can be used to improve water quality models when in situ data are scarce. Full article
(This article belongs to the Special Issue Satellite Remote Sensing and Analyses of Climate Variability)
Show Figures

Figure 1

Open AccessArticle
A Multi-Sourced Data Retrodiction of Remotely Sensed Terrestrial Water Storage Changes for West Africa
Water 2019, 11(2), 401; https://doi.org/10.3390/w11020401 - 25 Feb 2019
Cited by 1
Abstract
Remotely sensed terrestrial water storage changes (TWSC) from the past Gravity Recovery and Climate Experiment (GRACE) mission cover a relatively short period (≈15 years). This short span presents challenges for long-term studies (e.g., drought assessment) in data-poor regions like West Africa (WA). Thus, [...] Read more.
Remotely sensed terrestrial water storage changes (TWSC) from the past Gravity Recovery and Climate Experiment (GRACE) mission cover a relatively short period (≈15 years). This short span presents challenges for long-term studies (e.g., drought assessment) in data-poor regions like West Africa (WA). Thus, we developed a Nonlinear Autoregressive model with eXogenous input (NARX) neural network to backcast GRACE-derived TWSC series to 1979 over WA. We trained the network to simulate TWSC based on its relationship with rainfall, evaporation, surface temperature, net-precipitation, soil moisture, and climate indices. The reconstructed TWSC series, upon validation, indicate high skill performance with a root-mean-square error (RMSE) of 11.83 mm/month and coefficient correlation of 0.89. The validation was performed considering only 15% of the available TWSC data not used to train the network. More so, we used the total water content changes (TWCC) synthesized from Noah driven global land data assimilation system in a simulation under the same condition as the GRACE data. The results based on this simulation show the feasibility of the NARX networks in hindcasting TWCC with RMSE of 8.06 mm/month and correlation coefficient of 0.88. The NARX network proved robust to adequately reconstruct GRACE-derived TWSC estimates back to 1979. Full article
(This article belongs to the Special Issue Satellite Remote Sensing and Analyses of Climate Variability)
Show Figures

Graphical abstract

Open AccessArticle
Assimilation of Synthetic SWOT River Depths in a Regional Hydrometeorological Model
Water 2019, 11(1), 78; https://doi.org/10.3390/w11010078 - 04 Jan 2019
Abstract
The SWOT (Surface Water and Ocean Topography) mission, to be launched in 2021, will provide water surface elevations, slopes, and river width measurements for rivers wider than 100 m. In this study, synthetic SWOT data are assimilated in a regional hydrometeorological model in [...] Read more.
The SWOT (Surface Water and Ocean Topography) mission, to be launched in 2021, will provide water surface elevations, slopes, and river width measurements for rivers wider than 100 m. In this study, synthetic SWOT data are assimilated in a regional hydrometeorological model in order to improve the dynamics of continental waters over the Garonne catchment, one of the major French catchments. The aim of this paper is to demonstrate that the sequential assimilation of SWOT-like river depths allows the correction of river bed roughness coefficients and thus simulated river depths. An extended Kalman filter is implemented and the data assimilation strategy was applied to four experiments of gradually increasing complexity regarding observation and model error over the 1995–2000 period. With respect to a “true” river state, assimilating river depths allows the proper retrieval of constant and spatially distributed roughness coefficients with a root mean square error of 1 m1/3 s−1, and the estimation of associated river depths. It was also shown that river depth differences can be assimilated, resulting in a higher root mean square error for roughness coefficients with respect to the true river state. Finally, the last experiment shows how one can take into account more realistic sources of SWOT error measurements, in particular the importance of the estimation of the tropospheric water content in the process. Full article
(This article belongs to the Special Issue Satellite Remote Sensing and Analyses of Climate Variability)
Show Figures

Figure 1

Open AccessArticle
Variability of Arctic Sea Ice (1979–2016)
Water 2019, 11(1), 23; https://doi.org/10.3390/w11010023 - 23 Dec 2018
Abstract
This study is based on the daily sea ice concentration data from the National Snow and Ice Data Center (NSIDC; Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USA) from 1979 to 2016. The Arctic sea ice is analyzed from the [...] Read more.
This study is based on the daily sea ice concentration data from the National Snow and Ice Data Center (NSIDC; Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USA) from 1979 to 2016. The Arctic sea ice is analyzed from the total sea ice area, first year ice extent, multiyear ice area, and the variability of sea ice concentration in different ranges. The results show that the total sea ice area decreased by 0.0453 × 106 km2·year−1 (−0.55%/year) between 1979 and 2016, and the variability of the sea ice area from 1997 to 2016 is significantly larger than that from 1979 to 1996. The first-year ice extent increased by 0.0199 × 106 km2·year−1 (0.36%/year) from 1997 to 2016. The multiyear ice area decreased by 0.0711 × 106 km2·year−1 (−0.66%/year) from 1979 to 2016, of which in the last 20 years is about 30.8% less than in 1979–1996. In terms of concentration, we have focused on comparing 1979–1996 and 1997–2016 in different ranges. Sea ice concentration between 0.9–1 accounted for about 39.57% from 1979 to 1996, while from 1997–2016, it accounted for only 27.75%. However, the sea ice of concentration between 0.15–0.4 exhibits no significant trend changes. Full article
(This article belongs to the Special Issue Satellite Remote Sensing and Analyses of Climate Variability)
Show Figures

Figure 1

Open AccessArticle
Evaluation of Evapotranspiration Estimates in the Yellow River Basin against the Water Balance Method
Water 2018, 10(12), 1884; https://doi.org/10.3390/w10121884 - 19 Dec 2018
Abstract
Evapotranspiration (ET), a critical process in global climate change, is very difficult to estimate at regional and basin scales. In this study, we evaluated five ET products: the Global Land Surface Evaporation with the Amsterdam Methodology (GLEAM, the EartH2Observe ensemble (E2O)), the Global [...] Read more.
Evapotranspiration (ET), a critical process in global climate change, is very difficult to estimate at regional and basin scales. In this study, we evaluated five ET products: the Global Land Surface Evaporation with the Amsterdam Methodology (GLEAM, the EartH2Observe ensemble (E2O)), the Global Land Data Assimilation System with Noah Land Surface Model-2 (GLDAS), a global ET product at 8 km resolution from Zhang (ZHANG) and a supplemental land surface product of the Modern-ERA Retrospective analysis for Research and Applications (MERRA_land), using the water balance method in the Yellow River Basin, China, including twelve catchments, during the period of 1982–2000. The results showed that these ET products have obvious different performances, in terms of either their magnitude or temporal variations. From the viewpoint of multiple-year averages, the MERRA_land product shows a fairly similar magnitude to the ETw derived from the water balance method, while the E2O product shows significant underestimations. The GLEAM product shows the highest correlation coefficient. From the viewpoint of interannual variations, the ZHANG product performs best in terms of magnitude, while the E2O still shows significant underestimations. However, the E2O product best describes the interannual variations among the five ET products. Further study has indicated that the discrepancies between the ET products in the Yellow River Basin are mainly due to the quality of precipitation forcing data. In addition, most ET products seem to not be sensitive to the downward shortwave radiation. Full article
(This article belongs to the Special Issue Satellite Remote Sensing and Analyses of Climate Variability)
Show Figures

Figure 1

Open AccessArticle
Object-Based Convolutional Neural Networks for Cloud and Snow Detection in High-Resolution Multispectral Imagers
Water 2018, 10(11), 1666; https://doi.org/10.3390/w10111666 - 15 Nov 2018
Cited by 1
Abstract
Cloud and snow detection is one of the most significant tasks for remote sensing image processing. However, it is a challenging task to distinguish between clouds and snow in high-resolution multispectral images due to their similar spectral distributions. The shortwave infrared band (SWIR, [...] Read more.
Cloud and snow detection is one of the most significant tasks for remote sensing image processing. However, it is a challenging task to distinguish between clouds and snow in high-resolution multispectral images due to their similar spectral distributions. The shortwave infrared band (SWIR, e.g., Sentinel-2A 1.55–1.75 µm band) is widely applied to the detection of snow and clouds. However, high-resolution multispectral images have a lack of SWIR, and such traditional methods are no longer practical. To solve this problem, a novel convolutional neural network (CNN) to classify cloud and snow on an object level is proposed in this paper. Specifically, a novel CNN structure capable of learning cloud and snow multiscale semantic features from high-resolution multispectral imagery is presented. In order to solve the shortcoming of “salt-and-pepper” in pixel level predictions, we extend a simple linear iterative clustering algorithm for segmenting high-resolution multispectral images and generating superpixels. Results demonstrated that the new proposed method can with better precision separate the cloud and snow in the high-resolution image, and results are more accurate and robust compared to the other methods. Full article
(This article belongs to the Special Issue Satellite Remote Sensing and Analyses of Climate Variability)
Show Figures

Figure 1

Back to TopTop