Integrated system for control of soil moisture and local weather conditions for remote sensing data interpretation

Changing climate is expected to cause more extreme weather patterns in many parts of the world. In the Carpathian Basin, it is expected that the frequency of intensive precipitation will increase causing inland excess water (IEW) in parts of the plains more frequently, while currently the phenomenon already causes great damage. This research presents and validates a new methodology to determine the extent of these floods using a combination of passive and active remote sensing data. The method can be used to monitor IEW over large areas in a fully automated way based on freely available Sentinel-1 and Sentinel-2 remote sensing imagery. The method is validated for two IEW periods in 2016 and 2018 using high-resolution optical satellite data and aerial photographs. Compared to earlier remote sensing data-based methods, our method can be applied under unfavorite weather conditions, does not need human interaction and gives accurate results for inundations larger than 1000 m2. The overall accuracy of the classification exceeds 99%; however, smaller IEW patches are underestimated due to the spatial resolution of the input data. Knowledge on the location and duration of the inundations helps to take operational measures against the water but is also required to determine the possibilities for storage of water for dry periods. The frequent monitoring of the floods supports sustainable water management in the area better than the methods currently employed.

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Land-Cover Dependent Relationships between Fire and Soil Moisture

Fire ◽

10.3390/fire2040055 ◽

2019 ◽

Vol 2 (4) ◽

pp. 55 ◽

Cited By ~ 1

Author(s):

Alexander J. Schaefer ◽

Brian I. Magi

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Cover ◽

Remote Sensing Data ◽

Sensing Data ◽

Fire Activity ◽

Steep Decline ◽

Human Use ◽

Moisture Conditions ◽

In Fire

For this study, we characterized the dependence of fire counts (FCs) on soil moisture (SM) at global and sub-global scales using 15 years of remote sensing data. We argue that this mathematical relationship serves as an effective way to predict fire because it is a proxy for the semi-quantitative fire–productivity relationship that describes the tradeoff between fuel availability and climate as constraints on fire activity. We partitioned the globe into land-use and land-cover (LULC) categories of forest, grass, cropland, and pasture to investigate how the fire–soil moisture (fire–SM) behavior varies as a function of LULC. We also partitioned the globe into four broadly defined biomes (Boreal, Grassland-Savanna, Temperate, and Tropical) to study the dependence of fire–SM behavior on LULC across those biomes. The forest and grass LULC fire–SM curves are qualitatively similar to the fire–productivity relationship with a peak in fire activity at intermediate SM, a steep decline in fire activity at low SM (productivity constraint), and gradual decline as SM increases (climate constraint), but our analysis highlights how forests and grasses differ across biomes as well. Pasture and cropland LULC are a distinctly human use of the landscape, and fires detected on those LULC types include intentional fires. Cropland fire–SM curves are similar to those for grass LULC, but pasture fires are evident at higher SM values than other LULC. This suggests a departure from the expected climate constraint when burning is happening at non-optimal flammability conditions. Using over a decade of remote sensing data, our results show that quantifying fires relative to a single physical climate variable (soil moisture) is possible on both cultivated and uncultivated landscapes. Linking fire to observable soil moisture conditions for different land-cover types has important applications in fire management and fire modeling.

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Large-Eddy Simulations of Real-World Episodes in Complex Terrain Based on ERA-Reanalysis and Validated by Ground-Based Remote Sensing Data

Monthly Weather Review ◽

10.1175/mwr-d-19-0016.1 ◽

2019 ◽

Vol 147 (12) ◽

pp. 4325-4343 ◽

Cited By ~ 1

Author(s):

Cornelius Hald ◽

Matthias Zeeman ◽

Patrick Laux ◽

Matthias Mauder ◽

Harald Kunstmann

Keyword(s):

Remote Sensing ◽

Boundary Conditions ◽

Complex Terrain ◽

Computing Time ◽

Remote Sensing Data ◽

Large Eddy Simulations ◽

Small Scale ◽

Sensing Data ◽

Large Eddy ◽

Local Weather

Abstract A computationally efficient and inexpensive approach for using the capabilities of large-eddy simulations (LES) to model small-scale local weather phenomena is presented. The setup uses the LES capabilities of the Weather Research and Forecasting Model (WRF-LES) on a single domain that is directly driven by reanalysis data as boundary conditions. The simulated area is an example for complex terrain, and the employed parameterizations are chosen in a way to represent realistic conditions during two 48-h periods while still keeping the required computing time around 105 CPU hours. We show by evaluating turbulence characteristics that the model results conform to results from typical LES. A comparison with ground-based remote sensing data from a triple Doppler-lidar setup, employed during the “ScaleX” campaigns, shows the grade of adherence of the results to the measured local weather conditions. The representation of mesoscale phenomena, including nocturnal low-level jets, strongly depends on the temporal and spatial resolution of the meteorological boundary conditions used to drive the model. Small-scale meteorological features that are induced by the terrain, such as katabatic flows, are present in the simulated output as well as in the measured data. This result shows that the four-dimensional output of WRF-LES simulations for a real area and real episode can be technically realized, allowing a more comprehensive and detailed view of the micrometeorological conditions than can be achieved with measurements alone.

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