scholarly journals Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil

2020 ◽  
Vol 12 (24) ◽  
pp. 4095
Author(s):  
Augusto Getirana ◽  
Dalia Kirschbaum ◽  
Felipe Mandarino ◽  
Marta Ottoni ◽  
Sana Khan ◽  
...  
Keyword(s):  
Natural Disaster ◽  
Extreme Events ◽  
Land Surface ◽  
Urban Areas ◽  
Rio De Janeiro ◽  
Temporal Dynamics ◽  
Extreme Rainfall ◽  
Surface Model ◽  
The City ◽  
Rainfall Estimates

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001–2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4 mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12–35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

ECLand: an ECMWF land surface modelling platform

2021 ◽  
Author(s):  
Gianpaolo Balsamo ◽  
Souhail Boussetta
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Land Surface Model ◽  
Open Water ◽  
Surface Fluxes ◽  
Surface Model ◽  
New Developments ◽  
Surface Modelling ◽  
Land Surface Modelling ◽  
New Research

<p>The ECMWF operational land surface model, based on the Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) is the baseline for global weather, climate and environmental applications at ECMWF. In order to expedite its progress and benefit from international collaboration, an ECLand platform has been designed to host advanced and modular schemes. ECLand is paving the way toward a land model that could support a wider range of modelling applications, facilitating global kilometer scales testing as envisaged in the Copernicus and Destination Earth programmes. This presentation introduces the CHTESSEL and its recent new developments that aims at hosting new research applications.</p><p>These new improvements touch upon different components of the model: (i) vegetation, (ii) snow, (iii) soil hydrology, (iv) open water/lakes (v) rivers and (vi) urban areas. The developments are evaluated separately with either offline simulations or coupled experiments, depending on their level of operational readiness, illustrating the benchmarking criteria for assessing process fidelity with regards to land surface fluxes and reservoirs involved in water-energy-carbon exchange, and within the Earth system prediction framework, as foreseen to enter upcoming ECMWF operational cycles.</p><p>Reference: Souhail Boussetta, Gianpaolo Balsamo*, Anna Agustì-Panareda, Gabriele Arduini, Anton Beljaars, Emanuel Dutra, Glenn Carver, Margarita Choulga, Ioan Hadade, Cinzia Mazzetti, Joaquìn Munõz-Sabater, Joe McNorton, Christel Prudhomme, Patricia De Rosnay, Irina Sandu, Nils Wedi, Dai Yamazaki, Ervin Zsoter, 2021: ECLand: an ECMWF land surface modelling platform, MDPI Atmosphere, (in prep).</p>

scholarly journals Data analysis reveals that extreme events have increased the flood inundations in the Taquari River´s Valley, southern Brazil

2021 ◽  
Vol 1 (1) ◽  
pp. 16-25
Author(s):  
Francisco Manoel Wohnrath Tognoli ◽  
Sabrina Deconti Bruski ◽  
Thiago Peixoto de Araujo
Keyword(s):  
Extreme Events ◽  
Extreme Rainfall ◽  
Maximum Level ◽  
Data Series ◽  
Rainfall Time Series ◽  
Southern Brazil ◽  
Human Occupation ◽  
Extreme Rainfall Events ◽  
Starting Point ◽  
The City

Flood inundations represent more than 62% of the deaths caused by natural disasters in Brazil. The dataset comprises the records of the Encantado´s pluviometric station, a municipality located beside the margin of the Taquari River in southern Brazil, which comprises the rainfall time series (n = 36,466) over 78 years, from April 1943 to December 2020. Complementary datasets also include the annual volume of precipitation per year and the level reached by the Taquari River during 44 flood inundations since 1941. The number of events is subsampled because only 32 years have the complete record of the river level. Three of the five major flood inundations at Encantado occurred after 2001, and the more severe flood recorded the maximum level of the Taquari River (20,27 meters) on July 8th, 2020. Thirty-four percent of all flood inundations in the city were recorded between 2011 and 2020. The months of July to October record 70% of all the events, but there is no record of floods in February and December throughout the data series. The human occupation of the floodplain has been fast in the last decades, and most of the urban area has a potential risk of being affected by flood inundations. Moreover, extreme rainfall events and flood events have been more frequent in the last 30 years. This database can contribute as a starting point for developing predictive models and verifying a possible correlation of floods with extreme events and global climatic changes.

Towards reproducible pluvial flood simulation in urban areas with TELEMAC-2D

2021 ◽  
Author(s):  
Tobias Pilz
Keyword(s):  
Urban Areas ◽  
Extreme Rainfall ◽  
Local Information ◽  
Flood Simulation ◽  
Pluvial Flooding ◽  
Severe Flood ◽  
Analysis Of Results ◽  
Model Setup ◽  
Available Information ◽  
The City

<p>The megacity of Lagos, Nigeria, is subject to recurrent severe flood events as a consequence of extreme rainfall. In addition, climate change might exacerbate this problem by increasing rainfall intensities. To study the hazard of pluvial flooding in urban areas, several complex hydraulic models exist with a high demand in terms of required input data, manual preprocessing, and computational power. However, for many regions in the world only insufficient local information is available. Moreover, the complexity of model setup prevents reproducible model initialisation and application. This conference contribution addresses these issues by an example application of the complex hydrodynamic model TELEMAC-2D for the city of Lagos. The complex initialisation procedure is simplified by the new package ‘telemac’ for the statistical environment R. A workflow will be presented that illustrates the functionality of the package and the use of publicly available information, such as free DEMs and Openstreetmap data to cope with the problem of insufficient local information. By further analysis and visualisation procedures along the workflow the increasing hazard of pluvial flooding for Lagos is shown. The workflow makes model initialisation, application, and the analysis of results reproducible and applicable to other regions with a relatively low need for manual user interventions and without additional software other than R and TELEMAC-2D.</p>

scholarly journals Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1470 ◽  
Author(s):  
Nan Xia ◽  
Liang Cheng ◽  
ManChun Li
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Urban Areas ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Human Life ◽  
Human Movement ◽  
Night Time ◽  
Urban Mapping ◽  
The City

Urban areas are essential to daily human life; however, the urbanization process also brings about problems, especially in China. Urban mapping at large scales relies heavily on remote sensing (RS) data, which cannot capture socioeconomic features well. Geolocation datasets contain patterns of human movement, which are closely related to the extent of urbanization. However, the integration of RS and geolocation data for urban mapping is performed mostly at the city level or finer scales due to the limitations of geolocation datasets. Tencent provides a large-scale location request density (LRD) dataset with a finer temporal resolution, and makes large-scale urban mapping possible. The objective of this study is to combine multi-source features from RS and geolocation datasets to extract information on urban areas at large scales, including night-time lights, vegetation cover, land surface temperature, population density, LRD, accessibility, and road networks. The random forest (RF) classifier is introduced to deal with these high-dimension features on a 0.01 degree grid. High spatial resolution land cover (LC) products and the normalized difference built-up index from Landsat are used to label all of the samples. The RF prediction results are evaluated using validation samples and compared with LC products for four typical cities. The results show that night-time lights and LRD features contributed the most to the urban prediction results. A total of 176,266 km2 of urban areas in China were extracted using the RF classifier, with an overall accuracy of 90.79% and a kappa coefficient of 0.790. Compared with existing LC products, our results are more consistent with the manually interpreted urban boundaries in the four selected cities. Our results reveal the potential of Tencent LRD data for the extraction of large-scale urban areas, and the reliability of the RF classifier based on a combination of RS and geolocation data.

scholarly journals Impact of Urbanization on Heavy Convective Precipitation under Strong Large-Scale Forcing: A Case Study over the Milwaukee–Lake Michigan Region

2014 ◽  
Vol 15 (1) ◽  
pp. 261-278 ◽  
Author(s):  
Long Yang ◽  
James A. Smith ◽  
Mary Lynn Baeck ◽  
Elie Bou-Zeid ◽  
Stephen M. Jessup ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Lake Michigan ◽  
Land Surface Model ◽  
Urban Land ◽  
Surface Model ◽  
Urban Representations ◽  
Simulation Results ◽  
Impact Of Urbanization ◽  
The City

Abstract In this study, observational and numerical modeling analyses based on the Weather Research and Forecasting Model (WRF) are used to investigate the impact of urbanization on heavy rainfall over the Milwaukee–Lake Michigan region. The authors examine urban modification of rainfall for a storm system with continental-scale moisture transport, strong large-scale forcing, and extreme rainfall over a large area of the upper Midwest of the United States. WRF simulations were carried out to examine the sensitivity of the rainfall distribution in and around the urban area to different urban land surface model representations and urban land-use scenarios. Simulation results suggest that urbanization plays an important role in precipitation distribution, even in settings characterized by strong large-scale forcing. For the Milwaukee–Lake Michigan region, the thermodynamic perturbations produced by urbanization on the temperature and surface pressure fields enhance the intrusion of the lake breeze and facilitate the formation of a convergence zone, which create favorable conditions for deep convection over the city. Analyses of model and observed vertical profiles of reflectivity using contoured frequency by altitude displays (CFADs) suggest that cloud dynamics over the city do not change significantly with urbanization. Simulation results also suggest that the large-scale rainfall pattern is not sensitive to different urban representations in the model. Both urban representations, the Noah land surface model with urban land categories and the single-layer urban canopy model, adequately capture the dominant features of this storm over the urban region.

scholarly journals Aumento das temperaturas extremas na cidade do Rio de Janeiro e o desvio ocasionado durante um evento de El Niño intenso (Thermal change in the city of Rio de Janeiro and the deviation caused during an intense El Niño event)

2019 ◽  
Vol 12 (4) ◽  
pp. 1291
Author(s):  
Henderson Silva Wanderley ◽  
Ronabson Cardoso Fernandes ◽  
André Luiz De Carvalho
Keyword(s):  
Air Temperature ◽  
El Niño ◽  
Urban Areas ◽  
Rio De Janeiro ◽  
El Nino ◽  
Pearson Correlation ◽  
Temperature Data ◽  
Kolmogorov Smirnov ◽  
The City ◽  
Maximum Air Temperature

O processo de urbanização tem o potencial de alterar a característica térmica e aerodinâmica da superfície dos grandes centros urbanos, possibilitando o aumento da temperatura do ar. No entanto, a correlação da intensificação da temperatura do ar em áreas urbanas em resposta a um evento extremo de El Niño é escassa, principalmente no que se refere à cidade do Rio de Janeiro. Assim, o objetivo deste estudo visa quantificar as mudanças ocorridas na temperatura do ar (máxima e mínima) na cidade do Rio de Janeiro e o desvio ocasionado às temperaturas extremas durante um evento de El Niño intenso. Os dados de temperatura do ar utilizados referem-se às normais climatológicas nos períodos climatológicos de 1961-1990 e 1980-2010, comparados entre si, e posteriormente, comparou-se as normais climatológicas do período de 1980-2010 com as do El Niño intenso de 2015-2016. Para a análise, dados de temperatura mínima e máxima do ar em uma escala mensal foram comparados. As médias mensais das temperaturas em análise foram submetidas ao ajuste do coeficiente de correlação de Pearson, ao teste t de Student e ao teste de Kolmogorov-Smirnov. Os resultados mostraram um aumento médio na temperatura do ar mínima (máxima) de +0,66 °C e +0,73 °C (+1,21 °C e +0,90 °C), respectivamente entre os períodos climatológicos e o último período climatológico com o evento El Niño intenso, entretanto, sem diferença estatística para o aumento da média e de sua distribuição.   A B S T R A C TUrbanization process has potential to change the thermal and aerodynamic characteristics of large urban centers surface, allowing the increase of air temperature. However, correlation of air temperature intensification in urban areas in response to an extreme event of El Niño is scarce, especially in relation to the city of Rio de Janeiro. Thus, the objective of this study is to quantify the changes occurred in the air temperature (maximum and minimum) in the city of Rio de Janeiro and the deviation caused to extreme temperatures during an intense event of El Niño. Data of air temperature data refer to the climatological normals in the periods of 1961-1990 and 1980-2010, and intense event of El Niño occurred in 2015-2016. For the analysis, minimum and maximum air temperature data on a monthly scale were compared. Monthly mean values of the air temperature under analysis were adjusted to the Pearson correlation coefficient, Student's t-test and Kolmogorov-Smirnov test. The results showed a mean increase in minimum (maximum) air temperature of +0.66 °C and +0.73 °C (+1.21 °C and +0.90 °C), respectively between the climatological periods and the last climatological period with the intense event of El Niño, however, with no statistical difference for the increase of the mean and its distribution.Keywords: Urban climate, ENSO, air temperature.

scholarly journals Impact of urban canopy meteorological forcing on aerosol concentrations

2018 ◽  
Author(s):  
Peter Huszar ◽  
Michal Belda ◽  
Jan Karlický ◽  
Tatsiana Bardachova ◽  
Tomas Halenka ◽  
...  
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Urban Canopy ◽  
Specific Humidity ◽  
Surface Model ◽  
Meteorological Forcing ◽  
Turbulent Diffusion Coefficient ◽  
Temperature Impact ◽  
The Impact ◽  
Meteorological Effects

Abstract. The regional climate model RegCM4 extended with the land-surface model CLM4.5 was coupled to the chemistry transport model CAMx to analyze the impact of urban meteorological forcing on the surface fine aerosol (PM2.5) concentrations for summer conditions over the 2001–2005 period focusing on the area of Europe. Starting with the analysis of the meteorological modifications caused by urban canopy forcing we found significant increases of urban surface temperatures (up to 2–3 K), decrease of specific humidity (by up to 0.4–0.6 g/kg) reduction of wind speed (up to −1 m/s) and enhancement of vertical turbulent diffusion coefficient (up to 60–70 m2/s). These modifications translated into significant changes in surface aerosol concentrations that were calculated by cascading experimental approach. First, none of the urban meteorological effects were considered. Than, the temperature effect was added, than the humidity, the wind and finally, the enhanced turbulence was considered in the chemical runs. This facilitated the understanding of the underlying processes acting to modify urban aerosol concentrations. Moreover, we looked at the impact of the individual aerosol components as well. The urban induced temperature changes resulted in decreases of PM2.5 by −1.5 to −2 μg/m3, while decreased urban winds resulted in increases by 1–2 μg/m3. The enhanced turbulence over urban areas results in decreases of PM2.5 by −2 μg/m3. The combined effect of all individual impact depends on the competition between the partial impacts and can reach up to −3 μg/m3 for some cities, especially were the temperature impact was stronger in magnitude than the wind impact. The effect of changed humidity was found to be minor. The main contributor to the temperature impact is the modification of secondary inorganic aerosols, mainly nitrates, while the wind and turbulence impact is most pronounced in case of primary aerosol (primary black and organic carbon and other fine particle matter). The overall as well as individual impacts on secondary organic aerosol is very small with the increased turbulence acting as the main driver. The analysis of the vertical extend of the aerosol changes showed that the perturbations caused by urban canopy forcing, besides being large near the surface, have a secondary maximum for turbulence and wind impact over higher model levels, which is attributed to the vertical extend of the changes in turbulence over urban areas. The validation of model data with measurements showed good agreement and we could detect a clear model improvement at some areas when including the urban canopy meteorological effects in our chemistry simulations.

scholarly journals Changes in Urbanization and Urban Heat Island Effect in Dhaka City

2021 ◽  
Author(s):  
A S M Shanawaz Uddin ◽  
Najeebullah Khan ◽  
Abu Reza Md. Towfiqul I ◽  
Mohammad Kamruzzaman ◽  
Shamsuddin Shahid
Keyword(s):  
Urban Heat Island ◽  
Land Surface ◽  
Urban Areas ◽  
Clustering Algorithm ◽  
Planning Process ◽  
Urban Expansion ◽  
Heat Island ◽  
Dhaka City ◽  
Urban Heat ◽  
The City

Abstract Urbanization changes the local environment, resulting in urban heat island (UHI) effect and deteriorating human life quality. Knowledge of urban environments and temperature changes is important to outline the urban planning process for mitigation of UHI effect. The study aimed to assess the changes in urban areas and UHI effects in Dhaka city, Bangladesh from 2001to 2017, using Moderate Resolution Imaging Spectroradiometer (MODIS) daily day- and nighttime land surface temperature (LST) data from 2001to 2017. The expansion of the city was calculated using the city clustering algorithm (CCA). The temperature of the identified urbanized area was analyzed and compared with the adjacent regions. The changes in urban temperature were estimated using non-parametric statistical methods. The results showed that the Dhaka city area has grown by 19.12% and its inhabitants by 76.65% during 2001–2017. Urban expansion and dense settlements caused an increase in average temperature in some areas of Dhaka city nearly 3°C compared to that at its boundary. The day and night temperatures at Dhaka city's warmest location were nearly 7 and 5ºC, respectively, more than the coolest point outside the city. The city's annual average day- and nighttime temperature was increasing at a rate of 0.03° and 0.023°C/year over the period 2001–2017. The rising temperature would increase the UHI effect in the future, which combined with high humidity, may cause a significant increase in public health risk in the city if mitigation practices are not followed.

scholarly journals Dynamics and Heterogeneity of Microbial Communities in Urban Water Environments

2020 ◽  
Author(s):  
Yanqing Lian ◽  
Lisha Zhen ◽  
Xi Chen ◽  
Yang Li ◽  
Xiaona Li
Keyword(s):  
Microbial Communities ◽  
Urban Areas ◽  
Pathogenic Bacteria ◽  
Temporal Dynamics ◽  
Statistical Analyses ◽  
Polluted Water ◽  
Outer Side ◽  
Rrna Gene ◽  
The City ◽  
The 16S Rrna Gene

Abstract Water samples for the 16S rRNA gene and water quality analyses were collected from around 155 kilometers of river segments surrounding the urban areas in Xi’an of China. Multiple statistical analyses showed the temporal dynamics of microbial communities and heterogeneity in their spatial distributions. The dynamic shifts of microbial communities in the Chan, Ba, and Feng Rivers from the Spring to the Summer seasons were apparent, but little in the Zao River. The heterogeneity of microbial distributions was more due to the influence of hydrologic conditions and various sources of inflows in the rivers. The LEfSe analysis showed the Chan and Zao Rivers, both were more impacted by the sewage effluents, were more differentially abundant with bacteria related to polluted water, but the Ba and Feng Rivers, both on the outer side of the city, were more abundant with microbial communities in soil and freshwater environments in August. Multiple statistical analyses indicated that environmental variables had a significant impact on microbial communities. The GIS-based spatial analysis not only showed heterogeneity of microbial community distributions along the rivers, more importantly, could help identify locations where pathogenic bacteria presented.

Investigating vegetation role on UHI with mechanistic modelling worldwide

2021 ◽  
Author(s):  
Ziyan Zhang ◽  
Athanasios Paschalis ◽  
Ana Mijic ◽  
Naika Meili ◽  
Simone Fatichi
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Energy Demand ◽  
Land Surface Model ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Surface Model ◽  
Urban Fabric ◽  
Urban Energy ◽  
Set Up

<p>The urban heat island effect (UHI), defined as the temperature difference between urban areas and their surroundings, has been widely observed in many cities worldwide, impacting urban energy demand, citizen’s comfort and health. UHI intensities have been found to depend on background climate, and the urban fabric, including built (building thermal properties, heights, reflectance) and natural characteristics (vegetation cover, species composition, vegetation management). In this study, we focus on developing a global scale mechanistic understanding of how each of those properties alters the urban energy budget and leads to UHI development. To achieve this goal, we use the state-of-art urban ecohydrological and land-surface model (urban Tethys-Chloris) to perform a set of detailed UHI simulations for multiple large urban clusters across America, Europe and China in a 10-year time period (2009-2019), spanning a gradient of aridity, vegetation amount, and different compositions of the urban fabric. Model simulations were set up using the latest generation remote sensing data and climate reanalysis (ERA5). Using the simulations, we develop a paradigm of how UHIs develop worldwide, and propose viable solutions for sustainable UHI mitigation.</p>

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