scholarly journals Multi-Decadal Surface Water Dynamics in North American Tundra

2017 ◽  
Vol 9 (5) ◽  
pp. 497 ◽  
Author(s):  
Mark Carroll ◽  
Tatiana Loboda
Keyword(s):  
Surface Water ◽  
North American ◽  
Water Dynamics

scholarly journals Quantifying Surface Water Dynamics at 30 Meter Spatial Resolution in the North American High Northern Latitudes 1991–2011

2016 ◽  
Vol 8 (8) ◽  
pp. 622 ◽  
Author(s):  
Mark Carroll ◽  
Margaret Wooten ◽  
Charlene DiMiceli ◽  
Robert Sohlberg ◽  
Maureen Kelly
Keyword(s):  
Surface Water ◽  
Spatial Resolution ◽  
North American ◽  
Water Dynamics ◽  
The North ◽  
Northern Latitudes

Using Satellite Imagery to Examine the Relationship between Surface-Water Dynamics of the Salt Lakes of Western Texas and Ogallala Aquifer Depletion

Wetlands ◽  
2017 ◽  
Vol 37 (6) ◽  
pp. 1055-1065 ◽  
Author(s):  
L. J. Heintzman ◽  
S. M. Starr ◽  
K. R. Mulligan ◽  
L. S. Barbato ◽  
N. E. McIntyre
Keyword(s):  
Surface Water ◽  
Satellite Imagery ◽  
Water Dynamics ◽  
Salt Lakes ◽  
Ogallala Aquifer ◽  
Aquifer Depletion ◽  
The Relationship

scholarly journals Monitoring monthly surface water dynamics of Dongting Lake using Sentinal-1 data at 10 m

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4992 ◽  
Author(s):  
Liwei Xing ◽  
Xinming Tang ◽  
Huabin Wang ◽  
Wenfeng Fan ◽  
Guanghui Wang
Keyword(s):  
Surface Water ◽  
Water Distribution ◽  
Remote Sensing Data ◽  
Water Area ◽  
Dongting Lake ◽  
Water Dynamics ◽  
High Temporal Resolution ◽  
Backscattering Coefficient ◽  
Distribution Maps ◽  
Lower Accuracy

High temporal resolution water distribution maps are essential for surface water monitoring because surface water exhibits significant inner-annual variation. Therefore, high-frequency remote sensing data are needed for surface water mapping. Dongting Lake, the second-largest freshwater lake in China, is famous for the seasonal fluctuations of its inundation extents in the middle reaches of the Yangtze River. It is also greatly affected by the Three Gorges Project. In this study, we used Sentinel-1 data to generate surface water maps of Dongting Lake at 10 m resolution. First, we generated the Sentinal-1 time series backscattering coefficient for VH and VV polarizations at 10 m resolution by using a monthly composition method. Second, we generated the thresholds for mapping surface water at 10 m resolution with monthly frequencies using Sentinel-1 data. Then, we derived the monthly surface water distribution product of Dongting Lake in 2016, and finally, we analyzed the inner-annual surface water dynamics. The results showed that: (1) The thresholds were −21.56 and −15.82 dB for the backscattering coefficients for VH and VV, respectively, and the overall accuracy and Kappa coefficients were above 95.50% and 0.90, respectively, for the VH backscattering coefficient, and above 94.50% and 0.88, respectively, for the VV backscattering coefficient. The VV backscattering coefficient achieved lower accuracy due to the effect of the wind causing roughness on the surface of the water. (2) The maximum and minimum areas of surface water were 2040.33 km2in July, and 738.89 km2in December. The surface water area of Dongting Lake varied most significantly in April and August. The permanent water acreage in 2016 was 556.35 km2, accounting for 19.65% of the total area of Dongting Lake, and the acreage of seasonal water was 1525.21 km2. This study proposed a method to automatically generate monthly surface water at 10 m resolution, which may contribute to monitoring surface water in a timely manner.

Remote Sensing of Surface Water Dynamics Between 2015 and 2020 in the Prairie Pothole Region 

2021 ◽  
Author(s):  
Stefan Schlaffer ◽  
Marco Chini ◽  
Wouter Dorigo
Keyword(s):  
Surface Water ◽  
Prairie Pothole Region ◽  
Water Area ◽  
Water Bodies ◽  
Water Dynamics ◽  
Drought Indices ◽  
Prairie Pothole ◽  
Small Water ◽  
Wide Range ◽  
Small Water Bodies

<p>The North American Prairie Pothole Region (PPR) consists of millions of wetlands and holds great importance for biodiversity, water storage and flood management. The wetlands cover a wide range of sizes from a few square metres to several square kilometres. Prairie hydrology is greatly influenced by the threshold behaviour of potholes leading to spilling as well as merging of adjacent wetlands. The knowledge of seasonal and inter-annual surface water dynamics in the PPR is critical for understanding this behaviour of connected and isolated wetlands. Synthetic aperture radar (SAR) sensors, e.g. used by the Copernicus Sentinel-1 mission, have great potential to provide high-accuracy wetland extent maps even when cloud cover is present. We derived water extent during the ice-free months May to October from 2015 to 2020 by fusing dual-polarised Sentinel-1 backscatter data with topographical information. The approach was applied to a prairie catchment in North Dakota. Total water area, number of water bodies and median area per water body were computed from the time series of water extent maps. Surface water dynamics showed strong seasonal dynamics especially in the case of small water bodies (< 1 ha) with a decrease in water area and number of small water bodies from spring throughout summer when evaporation rates in the PPR are typically high. Larger water bodies showed a more stable behaviour during most years. Inter-annual dynamics were strongly related to drought indices based on climate data, such as the Palmer Drought Severity Index. During the extremely wet period of late 2019 to 2020, the dynamics of both small and large water bodies changed markedly. While a larger number of small water bodies was encountered, which remained stable throughout the wet period, also the area of larger water bodies increased, partly due to merging of smaller adjacent water bodies. The results demonstrate the potential of Sentinel-1 data for long-term monitoring of prairie wetlands while limitations exist due to the rather low temporal resolution of 12 days over the PPR.</p>

scholarly journals Surface Water Dynamics in the North America Arctic Based on 2000–2016 Landsat Data

Water ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 824 ◽  
Author(s):  
Yijie Sui ◽  
Dongjie Fu ◽  
Xuefeng Wang ◽  
Fenzhen Su
Keyword(s):  
Surface Water ◽  
North America ◽  
Water Dynamics ◽  
Landsat Data ◽  
The North

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

2015 ◽  
Vol 12 (11) ◽  
pp. 11847-11903 ◽  
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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