Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series

In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China’s second largest freshwater lake in the middle reaches of the Yangtze River—the Dongting Lake—and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA’s Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake’s wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km2, indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km2. Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km2, which is consistent with the Global WaterPack, deviating by only 4%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers.

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A linear/non-linear hybrid time-series model to investigate the depletion of inland water bodies

Environment Development and Sustainability ◽

10.1007/s10668-020-01081-6 ◽

2020 ◽

Author(s):

Babak Zolghadr-Asli ◽

Maedeh Enayati ◽

Hamid Reza Pourghasemi ◽

Mojtaba Naghdyzadegan Jahromi ◽

John P. Tiefenbacher

Keyword(s):

Time Series ◽

Time Series Model ◽

Water Bodies ◽

Inland Water ◽

Non Linear ◽

Inland Water Bodies

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China’s inland water dynamics: The significance of water body types

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2005584117 ◽

2020 ◽

Vol 117 (25) ◽

pp. 13876-13878 ◽

Cited By ~ 3

Author(s):

Jingying Zhu ◽

Chunqiao Song ◽

Jida Wang ◽

Linghong Ke

Keyword(s):

Water Body ◽

Water Dynamics ◽

Inland Water

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Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of earth observation data

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-11847-2015 ◽

2015 ◽

Vol 12 (11) ◽

pp. 11847-11903 ◽

Cited By ~ 3

Author(s):

V. Heimhuber ◽

M. G. Tulbure ◽

M. Broich

Keyword(s):

Time Series ◽

Surface Water ◽

River Basin ◽

Earth Observation ◽

Water Dynamics ◽

Observation Data ◽

Grid Cells ◽

Modeling Framework ◽

Basin Scale ◽

Lag Times

Abstract. The usage of time series of earth observation (EO) data for analyzing and modeling surface water dynamics (SWD) across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWD from a unique validated Landsat-based time series (1986–2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWD as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET) and rainfall (P). To enable a consistent modeling approach across space, we modeled SWD separately for hydrologically distinct floodplain, floodplain-lake and non-floodplain areas within eco-hydrological zones and 10 km × 10 km grid cells. We applied this spatial modeling framework (SMF) to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWD. Based on these automatically quantified flow lag times and variable combinations, SWD on 233 (64 %) out of 363 floodplain grid cells were modeled with r2 ≥ 0.6. The contribution of P, ET and SM to the models' predictive performance differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The SMF presented here is suitable for modeling SWD on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.

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Uncertainty Estimation for a Global Inland Surface Water Time-Series

10.5194/egusphere-egu21-6399 ◽

2021 ◽

Author(s):

Stefan Mayr ◽

Igor Klein ◽

Martin Rutzinger ◽

Claudia Kuenzer

Keyword(s):

Remote Sensing ◽

Time Series ◽

Surface Water ◽

Fresh Water ◽

Water Dynamics ◽

Landsat 8 ◽

Spatiotemporal Resolution ◽

Observational Uncertainty ◽

Moderate Resolution ◽

Sentinel 2

Fresh water is vital for life on the planet. Satellite remote sensing time-series are well suited to monitor global surface water dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on inland surface water. However, operating on diurnal- and global spatiotemporal resolution comes with certain drawbacks. As the time-series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, data gaps due to cloud coverage or invalid observations have to be interpolated. Furthermore, the moderate resolution of 250 m merely allows coarse pixel based areal estimations of surface water extent. To unlock the full potential of this dataset, information on associated uncertainty is essential. Therefore, we introduce several auxiliary layers aiming to address interpolation and quantification uncertainty. The probability of interpolated pixels to be covered by water is given by consideration of different temporal and spatial characteristics inherent to the time-series. Resulting temporal probability layers are evaluated by introducing artificial gaps in the original time-series and determining deviations to the known true state. To assess observational uncertainty in case of valid observations, relative datapoint (pixel) locations in feature space are utilized together with previously established temporal information in a linear mixture model. The hereby obtained classification probability also reveals sub-pixel information, which can enhance the product&#8217;s quantitative capabilities. Functionality is evaluated in 32 regions of interest across the globe by comparison to reference data derived from Landsat 8 and Sentinel-2 images. Results show an improved accuracy for partially water covered pixels (6.21 %), and that by uncertainty consideration, more comprehensive and reliable time-series information is achieved.Keywords: Fresh water, Landsat 8, MODIS, remote sensing, probability, Sentinel-2, sub-pixel scale, validation, water fraction.

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Constructing Mediterranean wetland open water dynamics using a new 18-year MODIS-derived surface water fraction dataset

10.5194/egusphere-egu2020-19945 ◽

2020 ◽

Author(s):

Linlin Li ◽

Anton Vrieling ◽

Andrew Skidmore ◽

Tiejun Wang

Keyword(s):

Time Series ◽

Surface Water ◽

Temporal Dynamics ◽

Open Water ◽

Water Area ◽

Water Bodies ◽

Water Dynamics ◽

Water Fraction ◽

Wide Range

Wetlands are among the most biodiverse ecosystems in the world, due largely to their dynamic hydrology. Frequent observations by satellite sensors such as the Moderate Resolution Imaging Spectrometer (MODIS) allow for monitoring the seasonal, inter-annual and long-term dynamics of surface water extent. However, existing MODIS-based studies have only demonstrated this for large water bodies despite the ecological importance of smaller-sized wetland systems. In this paper, we constructed the temporal dynamics of surface water extent for 340 individual water bodies in the Mediterranean region between 2000 and 2017, using a previously developed 8-day 500 m MODIS surface water fraction (SWF) dataset. These water bodies has a wide range of size, specifically 0.01 km2 and larger. We then compared the water extent time series derived from MODIS SWF with those derived from a Landsat-based dataset. Results showed that MODIS- and Landsat-derived water extent time series showed a high correlation (r = 0.81) for more dynamic water bodies. Our MODIS SWF dataset can also effectively monitor the variability of very small water bodies (<1 km2) when comparing with Landsat data as long as the temporal variability in their surface water area was high. We conclude that MODIS SWF is a useful product to help understand hydrological dynamics for both small and larger-sized water bodies, and to monitor their seasonal, intermittent, inter-annual and long-term changes.

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Monitoring of water dynamics in rice ecosystems based on remote sensing time series

10.20868/upm.thesis.48173 ◽

2017 ◽

Author(s):

Lucía Tornos Castillo

Keyword(s):

Remote Sensing ◽

Time Series ◽

Water Dynamics ◽

Rice Ecosystems ◽

Sensing Time

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Multivariate statistical modelling of extreme coastal water levels and the effect of climate variability: a case study in the Netherlands

10.5194/hess-2020-536 ◽

2020 ◽

Author(s):

Victor M. Santos ◽

Mercè Casas-Prat ◽

Benjamin Poschlod ◽

Elisa Ragno ◽

Bart van den Hurk ◽

...

Keyword(s):

Time Series ◽

Water Level ◽

Climate Model ◽

Regional Climate ◽

Water Levels ◽

Multivariate Statistical ◽

Inland Water ◽

Impact Function ◽

The Impact

Abstract. The co-occurrence of (not necessarily extreme) precipitation and surge can lead to extreme inland water levels in coastal areas. In a previous work the positive dependence between the two meteorological drivers was demonstrated in a case study in the Netherlands by empirically investigating an 800-year time series of water levels, which were simulated via a physical-based hydrological model driven by a regional climate model large ensemble. In this study, we present and test a multivariate statistical framework to replicate the demonstrated dependence and the resulting return periods of inland water levels. We use the same 800-year data series to develop an impact function, which is able to empirically describe the relationship between high inland water levels (the impact) and its driving variables (precipitation and surge). In our study area, this relationship is complex because of the high degree of human management affecting the dynamics of the water level. By event sampling and conditioning the drivers, an impact function was created that can reproduce the water levels maintaining an unbiased performance at the full range of simulated water levels. The dependence structure between the driving variables is modeled using two- and three-dimensional copulas. These are used to generate paired synthetic precipitation and surge events, transformed into inland water levels via the impact function. The compounding effects of surge and precipitation and the return water level estimates fairly well reproduce the earlier results from the empirical analysis of the same regional climate model ensemble. The proposed framework is therefore able to produce robust estimates of compound extreme water levels for a highly managed hydrological system. In addition, we present a unique assessment of the uncertainty when using only 50 years of data (what is typically available from observations). Training the impact function with short records leads to a general underestimation of the return levels as water level extremes are not well sampled. Also, the marginal distributions of the 50-year time series of the surge show high variability. Moreover, compounding effects tend to be underestimated when using 50 year slices to estimate the dependence pattern between predictors. Overall, the internal variability of the climate system is identified as a major source of uncertainty in the multivariate statistical model.

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The Role of Respiration in Estimation of Net Carbon Cycle: Coupling Soil Carbon Dynamics and Canopy Turnover in a Novel Version of 3D-CMCC Forest Ecosystem Model

10.20944/preprints201703.0141.v2 ◽

2017 ◽

Author(s):

Sergio Marconi ◽

Tommaso Chiti ◽

Angelo Nole ◽

Riccardo Valentini ◽

Alessio Collalti

Keyword(s):

Time Series ◽

Soil Carbon ◽

Ecosystem Respiration ◽

Constitutive Relations ◽

Carbon Mineralization ◽

Random Parameter ◽

Carbon Dynamics ◽

Water Dynamics ◽

Soil Carbon Dynamics ◽

Temperature State

Understanding the dynamics of Organic Carbon mineralization is fundamental in forecasting biosphere to atmosphere Net Carbon Ecosystem Exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid Process Based Model 3D‐CMCC FEM where also heterotrophic respiration (Rh) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting Ecosystem Respiration (Reco) to Gross Primary Productivity (GPP). To do so, we developed a simplification of the Soil Carbon dynamics routine proposed in DNDC [1]. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameters distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.

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