scholarly journals Volumetric Analysis of Reservoirs in Drought-Prone Areas Using Remote Sensing Products

2019 ◽  
Vol 11 (17) ◽  
pp. 1974 ◽  
Author(s):  
Tejas Bhagwat ◽  
Igor Klein ◽  
Juliane Huth ◽  
Patrick Leinenkugel
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Hydrological Drought ◽  
Water Volume ◽  
Lake Mead ◽  
Reservoir Water ◽  
Reservoir Volume ◽  
Volume Fluctuations ◽  
Lake Powell

Globally, the number of dams increased dramatically during the 20th century. As a result, monitoring water levels and storage volume of dam-reservoirs has become essential in order to understand water resource availability amid changing climate and drought patterns. Recent advancements in remote sensing data show great potential for studies pertaining to long-term monitoring of reservoir water volume variations. In this study, we used freely available remote sensing products to assess volume variations for Lake Mead, Lake Powell and reservoirs in California between 1984 and 2015. Additionally, we provided insights on reservoir water volume fluctuations and hydrological drought patterns in the region. We based our volumetric estimations on the area–elevation hypsometry relationship, by combining water areas from the Global Surface Water (GSW) monthly water history (MWH) product with corresponding water surface median elevation values from three different digital elevation models (DEM) into a regression analysis. Using Lake Mead and Lake Powell as our validation reservoirs, we calculated a volumetric time series for the GSWMWH–DEMmedian elevation combinations that showed a strong linear ‘area (WA) – elevation (WH)’ (R2 > 0.75) hypsometry. Based on ‘WA-WH’ linearity and correlation analysis between the estimated and in situ volumetric time series, the methodology was expanded to reservoirs in California. Our volumetric results detected four distinct periods of water volume declines: 1987–1992, 2000–2004, 2007–2009 and 2012–2015 for Lake Mead, Lake Powell and in 40 reservoirs in California. We also used multiscalar Standardized Precipitation Evapotranspiration Index (SPEI) for San Joaquin drainage in California to assess regional links between the drought indicators and reservoir volume fluctuations. We found highest correlations between reservoir volume variations and the SPEI at medium time scales (12–18–24–36 months). Our work demonstrates the potential of processed, open source remote sensing products for reservoir water volume variations and provides insights on usability of these variations in hydrological drought monitoring. Furthermore, the spatial coverage and long-term temporal availability of our data presents an opportunity to transfer these methods for volumetric analyses on a global scale.

Multi-scale analysis of landslide occurrence and evolution using optical and radar time series

2020 ◽  
Author(s):  
Sigrid Roessner ◽  
Robert Behling ◽  
Mahdi Motagh ◽  
Hans Ulrich-wetzel
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
High Resolution ◽  
Intense Rainfall ◽  
Large Area ◽  
Landslide Occurrence ◽  
Multi Scale ◽  
Multi Temporal ◽  
Hazard And Risk

<p>Landslides represent a worldwide natural hazard and often occur as cascading effects related to triggering events, such as earthquakes and hydrometeorological extremes. Recent examples are the Kaikoura earthquake in New Zealand (November 2016), the Gorkha earthquake in Nepal (April/May 2015), and the Typhoon Morakot in Taiwan (August 2009) as well as less intense rainfall events persisting over unusually long periods of time as observed for Central Asia (spring 2017) and Iran (spring 2019). Each of these events has caused thousands of landslides that account substantially to the primary disaster’s impact. Moreover, their initial failure usually represents the onset of long-term progressing slope destabilization leading to multiple reactivations and thus to long-term increased hazard and risk. Therefore, regular systematic high-resolution monitoring of landslide prone regions is of key importance for characterization, understanding and modelling of spatiotemporal landslide evolution in the context of different triggering and predisposing settings. Because of the large extent of the affected areas of up to several ten thousands km<sup>2</sup>, the use of multi-temporal and multi-scale remote sensing methods is of key importance for large area process analysis. In this context, new opportunities have opened up with the increasing availability of satellite remote sensing data of suitable spatial and temporal resolution (Sentinels, Planet) as well as the advances in UAV based very high resolution monitoring and mapping.</p><p>During the last decade, we have been pursuing extensive methodological developments in remote sensing based time series analysis including optical and radar observations with the goal of performing large area and at the same time detailed spatiotemporal analysis of landslide prone regions. These developments include automated post-failure landslide detection and mapping as well as assessment of the kinematics of pre- and post-failure slope evolution.  Our combined optical and radar remote sensing approaches aim at an improved understanding of spatiotemporal dynamics and complexities related to evolution of landslide prone slopes at different spatial and temporal scales.  In this context, we additionally integrate UAV-based observation for deriving volumetric changes also related to globally available DEM products, such as SRTM and ALOS.  </p><p>We present results for selected settings comprising large area co-seismic landslide occurrence related to the Kaikoura 2016 and the Nepal 2015 earthquakes. For the latter one we also analyzed annual pre- and post-seismic monsoon related landslide activity contributing to a better understanding of the interplay between these main triggering factors. Moreover, we report on ten years of large area systematic landslide monitoring in Southern Kyrgyzstan resulting in a multi-temporal regional landslide inventory of so far unprecedented spatiotemporal detail and completeness forming the basis for further analysis of the obtained landslide concentration patterns. We also present first results of our analysis of landslides triggered by intense rainfall and flood events in spring of 2019 in the North of Iran. We conclude that in all cases, the obtained results are crucial for improved landslide prediction and reduction of future landslide impact. Thus, our methodological developments represent an important contribution towards improved hazard and risk assessment as well as rapid mapping and early warning</p>

scholarly journals Hydrochemistry and trophic state change in a large reservoir in the Brazilian northeast region under intense drought conditions

2016 ◽  
Author(s):  
Janaina A. Santos ◽  
Rozane V. Marins ◽  
José E. Aguiar ◽  
Guillermo Challar ◽  
Francisco A.T.F. Silva ◽  
...  
Keyword(s):  
Water Column ◽  
Trophic State ◽  
Anthropogenic Activities ◽  
Water Volume ◽  
Semiarid Region ◽  
Trophic State Index ◽  
Reservoir Water ◽  
Worst Case ◽  
Large Reservoir ◽  
Reservoir Volume

<p>The study shows changes on physical and chemical water parameters and of trophic state in a large reservoir in the Brazilian semiarid region following decreasing reservoir volume due to rainfall shortage during four consecutive years. The monitoring period, between November 2011 and May 2014, assessed approximately 50% water volume reduction and 10 meters’ decrease of reservoir water level that degraded water quality. Decrease in reservoir volume, strong evaporation and the permanent influence of anthropogenic activities, favored the concentration of salts and accumulation of nutrients and of increasing pH. Thermal stratification of the water column occurred when volume was maximum and lead to a significant reduction in dissolved oxygen in the hypolimnion (0.07 to 2.62 mg L<sup>-1</sup>). Diminishing volume resulted in mixing of the hypolimnion nutrient-rich and oxygen-poor waters in the entre water column and changed the initial oligotrophic condition to eutrophic. However, the temporal scale of the response of the reservoir's trophic state differs in the different areas of the reservoir. Whereas deeper areas accumulating nutrients from aquaculture and agriculture progressively became mesotrophic and eventually eutrophic; shallower regions far from direct anthropogenic influences, changed their trophic sate much later, but rapidly turned into super-eutrophic conditions, probably due to more intense sediment resuspension and water mixing. Trophic State Index followed nutrient increase during most of the period. However, it also responded to an increase in chlorophyll <em>a </em>concentrations when the reservoir achieved its minimum volume, in particular in the shallower areas. The results suggest that this type of reservoir systems are vulnerable to eutrophication during extended drought periods and that a better assessment of the maximum support capacity for reservoir activities, particularly aquaculture, must be re-assessed taking into consideration worst case scenarios forecasted by global climate change. </p>

Lake water volume calculation with time series remote-sensing images

2013 ◽  
Vol 34 (22) ◽  
pp. 7962-7973 ◽  
Author(s):  
Shanlong Lu ◽  
Ninglei Ouyang ◽  
Bingfang Wu ◽  
Yongping Wei ◽  
Zelalem Tesemma
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Lake Water ◽  
Water Volume ◽  
Remote Sensing Images ◽  
Volume Calculation

Estimation of River Discharge using Multi-Mission Satellite Altimetry and Optical Remote Sensing Imagery

2020 ◽  
Author(s):  
Daniel Scherer ◽  
Christian Schwatke ◽  
Denise Dettmering
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Water Level ◽  
River Discharge ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Remote Sensing Data ◽  
Sensing Data

&lt;p&gt;Despite increasing interest in monitoring the global water cycle, the availability of in-situ discharge time series is decreasing. However, this lack of ground data can be compensated by using remote sensing techniques to observe river discharge.&lt;/p&gt;&lt;p&gt;In this contribution, a new approach for estimating the discharge of large rivers by combining various long-term remote sensing data with physical flow equations is presented. For this purpose, water levels derived from multi-mission satellite altimetry and water surface extents extracted from optical satellite images are used, both provided by DGFI-TUM&amp;#8217;s &amp;#8220;Database of Hydrological Time series of Inland Waters&amp;#8221; (DAHITI, https://dahiti.dgfi.tum.de). The datasets are combined by fitting a hypsometric curve in order to describe the stage-width relation, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is computed based on a linear adjustment of river surface slope using all altimetry-observed water level differences between synchronous measurements at various virtual stations along the river. The roughness coefficient is set based on geomorphological features quantified by adjustment factors. These are chosen using remote sensing data and a literature decision guide.&lt;/p&gt;&lt;p&gt;Within this study, all parameters are estimated purely based on remote sensing data, without using any ground data. In-situ data is only used for the validation of the method at the Lower Mississippi River. It shows that the presented approach yields best results for uniform and straight river sections. The resulting normalized root mean square error for those targets varies between 10% to 35% and is comparable with other studies.&lt;/p&gt;

scholarly journals Phytoplankton Phenology in the Coastal Zone of Cyprus, Based on Remote Sensing and In Situ Observations

2021 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Monica Demetriou ◽  
Dionysios E. Raitsos ◽  
Antonia Kournopoulou ◽  
Manolis Mandalakis ◽  
Spyros Sfenthourakis ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Phytoplankton Biomass ◽  
Trophic Levels ◽  
Data Availability ◽  
Ocean Colour ◽  
Chl A ◽  
In Situ Data

Alterations in phytoplankton biomass, community structure and timing of their growth (phenology), are directly implicated in the carbon cycle and energy transfer to higher trophic levels of the marine food web. Due to the lack of long-term in situ datasets, there is very little information on phytoplankton seasonal succession in Cyprus (eastern Mediterranean Sea). On the other hand, satellite-derived measurements of ocean colour can only provide long-term time series of chlorophyll (an index of phytoplankton biomass) up to the first optical depth (surface waters). The coupling of both means of observations is essential for understanding phytoplankton dynamics and their response to environmental change. Here, we use 23 years of remotely sensed, regionally tuned ocean-colour observations, along with a unique time series of in situ phytoplankton pigment composition data, collected in coastal waters of Cyprus during 2016. The satellite observations show an initiation of phytoplankton growth period in November, a peak in February and termination in April, with an overall mean duration of ~4 months. An in-depth exploration of in situ total Chl-a concentration and phytoplankton pigments revealed that pico- and nano-plankton cells dominated the phytoplankton community. The growth peak in February was dominated by nanophytoplankton and potentially larger diatoms (pigments of 19’ hexanoyloxyfucoxanthin and fucoxanthin, respectively), in the 0–20 m layer. The highest total Chl-a concentration was recorded at a station off Akrotiri peninsula in the south, where strong coastal upwelling has been reported. Another station in the southern part, located next to a fish farm, showed a higher contribution of picophytoplankton during the most oligotrophic period (summer). Our results highlight the importance of using available in situ data coupled to ocean-colour remote sensing, for monitoring marine ecosystems in areas with limited in situ data availability.

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