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2021 ◽  
Vol 13 (23) ◽  
pp. 4949
Author(s):  
Liis Sipelgas ◽  
Age Aavaste ◽  
Rivo Uiboupin

The current study presents a methodology for water mapping from Sentinel-1 (S1) data and a flood extent analysis of the three largest floodplains in Estonia. The automatic processing scheme of S1 data was set up for the mapping of open-water flooding (OWF) and flooding under vegetation (FUV). The extremely mild winter of 2019/2020 resulted in several large floods at floodplains that were detected from S1 imagery with a maximal OWF extent up to 5000 ha and maximal FUV extent up to 4500 ha. A significant correlation (r2 > 0.6) between the OWF extent and the closest gauge data was obtained for inland riverbank floodplains. The outcome enabled us to define the water level at which the water exceeds the shoreline and flooding starts. However, for a coastal river delta floodplain, a lower correlation (r2 < 0.34) with gauge data was obtained, and the excess of river coastline could not be related to a certain water level. At inland riverbank floodplains, the extent of FUV was three times larger compared to that of OWF. The correlation between the water level and FUV was <0.51, indicating that the river water level at these test sites can be used as a proxy for forest floods. Relating conventional gauge data to S1 time series data contributes to flood risk mitigation.


Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1501
Author(s):  
Chung-Chieh Wang ◽  
Chih-Sheng Chang ◽  
Yi-Wen Wang ◽  
Chien-Chang Huang ◽  
Shih-Chieh Wang ◽  
...  

In this study, 24 h quantitative precipitation forecasts (QPFs) by a cloud-resolving model (with a grid spacing of 2.5 km) on days 1–3 for 29 typhoons in six seasons of 2010–2015 in Taiwan were examined using categorical scores and rain gauge data. The study represents an update from a previous study for 2010–2012, in order to produce more stable and robust statistics toward the high thresholds (typically with fewer sample points), which is our main focus of interest. This is important to better understand the model’s ability to predict such high-impact typhoon rainfall events. The overall threat scores (TS, defined as the fraction among all verification points that are correctly predicted to reach a given threshold to all points that are either observed or predicted to reach that threshold, or both) were 0.28 and 0.18 on day 1 (0–24 h) QPFs, 0.25 and 0.16 on day 2 (24–48 h) QPFs, and 0.15 and 0.08 on day 3 (48–72 h) QPFs at 350 mm and 500 mm, respectively, showing improvements over 5 km models. Moreover, as found previously, a strong dependence of higher TSs for larger rainfall events also existed, and the corresponding TSs at 350 and 500 mm for the top 5% of events were 0.39 and 0.25 on day 1, 0.38 and 0.21 on day 2, and 0.25 and 0.12 on day 3. Thus, for the top typhoon rainfall events that have the highest potential for hazards, the model exhibits an even higher ability for QPFs based on categorical scores. Furthermore, it is shown that the model has little tendency to overpredict or underpredict rainfall for all groups of events with different rainfall magnitude across all thresholds, except for some tendency to under-forecast for the largest event group on day 3. Some issues associated with categorical statistics to be aware of are also demonstrated and discussed.


2021 ◽  
Author(s):  
Nikolaos Skliris ◽  
Robert Marsh ◽  
Ivan D. Haigh ◽  
Melissa Wood ◽  
Joel Hirschi ◽  
...  

Abstract Observational rain gauge/satellite and reanalysis datasets since the 1950s are evaluated for trends in mean and extreme rainfall over Mainland Southeast Asia (MSEA). Rain gauge data indicate strong increases exceeding 50% in both annual mean precipitation and various extreme precipitation indices over Vietnam and the northwestern part of the peninsula since 1979. Increasing precipitation in MSEA is associated with increased monsoon intensity in southeast Asia and a northward shift of the monsoon activity centre towards MSEA over 1979-2018. Re-analysis data reveal warming-driven increases in evaporation over the seas adjacent to MSEA, including the main oceanic source regions feeding precipitation over the peninsula - the Arabian Sea, the Bay of Bengal, and the South China Sea. Furthermore, moisture budget analysis based on the ERA5 re-analysis also showed increasing oceanic moisture transport along the typical winter and summer moisture pathways towards the MSEA. However, the majority of the increased moisture from these oceanic sources ends up as summer precipitation over the oceanic regions adjacent to the MSEA. In contrast, the ERA5 data do reveal pronounced increases in winter precipitation over the MSEA, in accordance with rain-gauge data, that are associated with increased transport of moisture that originated from the western tropical Pacific and the South China Sea. Long-term amplification of the regional hydrological cycle is further investigated, through analysis of CMIP5 coupled climate models in historical and RCP4.5/8.5 21st century scenario simulations. The CMIP5 ensemble mean shows robust wide-spread trends in wet season precipitation with increasing frequency and intensity of extreme precipitation events over the MSEA, following strong increases in evaporation in the oceanic moisture sources.


Ocean Science ◽  
2021 ◽  
Vol 17 (5) ◽  
pp. 1367-1384
Author(s):  
Igor A. Dmitrenko ◽  
Denis L. Volkov ◽  
Tricia A. Stadnyk ◽  
Andrew Tefs ◽  
David G. Babb ◽  
...  

Abstract. In recent years, significant trends toward earlier breakup and later freeze-up of sea ice in Hudson Bay have led to a considerable increase in shipping activity through the Port of Churchill, which is located in western Hudson Bay and is the only deep-water ocean port in the province of Manitoba. Therefore, understanding sea-level variability at the port is an urgent issue crucial for safe navigation and coastal infrastructure. Using tidal gauge data from the port along with an atmospheric reanalysis and Churchill River discharge, we assess environmental factors impacting synoptic to seasonal variability of sea level at Churchill. An atmospheric vorticity index used to describe the wind forcing was found to correlate with sea level at Churchill. Statistical analyses show that, in contrast to earlier studies, local discharge from the Churchill River can only explain up to 5 % of the sea-level variability. The cyclonic wind forcing contributes from 22 % during the ice-covered winter–spring season to 30 % during the ice-free summer–fall season due to cyclone-induced storm surges generated along the coast. Multiple regression analysis revealed that wind forcing and local river discharge combined can explain up to 32 % of the sea-level variability at Churchill. Our analysis further revealed that the seasonal cycle of sea level at Churchill appears to be impacted by the seasonal cycle in atmospheric circulation rather than by the seasonal cycle in local discharge from the Churchill River, particularly post-construction of the Churchill River diversion in 1977. Sea level at Churchill shows positive anomalies for September–November compared to June–August. This seasonal difference was also revealed for the entire Hudson Bay coast using satellite-derived sea-level altimetry. This anomaly was associated with enhanced cyclonic atmospheric circulation during fall, reaching a maximum in November, which forced storm surges along the coast. Complete sea-ice cover during winter impedes momentum transfer from wind stress to the water column, reducing the impact of wind forcing on sea-level variability. Expanding our observations to the bay-wide scale, we confirmed the process of wind-driven sea-level variability with (i) tidal-gauge data from eastern Hudson Bay and (ii) satellite altimetry measurements. Ultimately, we find that cyclonic winds generate sea-level rise along the western and eastern coasts of Hudson Bay at the synoptic and seasonal timescales, suggesting an amplification of the bay-wide cyclonic geostrophic circulation in fall (October–November), when cyclonic vorticity is enhanced, and Hudson Bay is ice-free.


Author(s):  
Sheetal Mali

Abstract: The tipping bucket system consists of funnel which collects the water of the rain in a container which is like a seesaw type module which tips side by side and collects the water. When the level of the water decreases below a preset level, the lever changes its side, causing the collected water to dump in a vessel and electrical signal is sent. By this system the high, medium or heavy rainfall character can be obtained. The rainfall character is calculated by the rainfall in 1 hour and corresponding number of pulses clicking in a period of 10 minutes. Various types of tipping bucket systems are reviewed by using rainfall and snow precipitation, using internet enabling, using rain drop imaging and artificial intelligence and also using wireless sensor network and GSM data transmission. Tipping Bucket is the most useful parameter for measuring the rainfall. In this way the rainfall is measured using the Tipping Bucket Rain Gauge System.


2021 ◽  
Vol 13 (18) ◽  
pp. 3609
Author(s):  
Sharon E. Nicholson ◽  
Douglas A. Klotter

This article examines the reliability of satellite and reanalysis estimates of rainfall in the Congo Basin and over Lake Victoria and its catchment. Nine satellite products and five reanalysis products are considered. They are assessed by way of inter-comparison and by comparison with observational data sets. The three locations considered include a region with little observational gauge data (the Congo), a region with extensive gauge data (Lake Victoria catchment), and an inland water body. Several important results emerge: for one, the diversity of estimates is generally very large, except for the Lake Victoria catchment. Reanalysis products show little relationship with observed rainfall or with the satellite estimates, and thus should not be used to assess rainfall in these regions. Most of the products either overestimate or underestimate rainfall over the lake. The diversity of estimates makes it difficult to assess the factors governing the interannual variability of rainfall in these regions. This is shown by way of correlation with sea-surface temperatures, particularly with the Niño 3.4 temperatures and with the Dipole Mode Index over the Indian Ocean. Some guidance is given as to the best products to utilize. Overall, any user must establish that the is product reliable in the region studied.


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