Earth observation informed modelling of flash floods

Control of risks associated with floods in recent years has a central role in urban planning scientific research. This is related to the increased incidence of these phenomena whose the root cause was found in the greater frequency of extreme rainfall events as a result of climate change. In Italy the phenomenon became dramatic because of the numerous human casualties that occur each year. Without prejudice to the need for interventions that effectively act on climate change, there is a need to provide interventions to mitigate the consequences in the short-term. The Department of Urban Planning at Naples University has conducted a thorough research on the case, objectively verifying the impact that human activity has on the dramatic consequences of rainfall events, also highlighting the lack of efficiency of the traditional mitigation measures based on the construction of huge detention basins dug in the ground. This is due to three main reasons: such interventions require financial investments that local communities often do not have; the creation of detention basins cause the removal of large quantities of soil for agricultural use; the continuous increase in impervious surfaces due to urbanization implies the need to cope with the masses of water ever more impressive and therefore the dry ponds already created quickly become insufficient. The research proposes some interventions and best practices that, if adopted, can radically reduce the impact of extreme rainfall on public safety, enabling an efficient participation of private capital in investments through the use of leverage financial and fiscal incentives.

Monsoon rainfall has profound economic and societal impacts for more than two-thirds of the global population. Here we provide a review on past monsoon changes and their primary drivers, the projected future changes, and key physical processes, and discuss challenges of the present and future modeling and outlooks. Continued global warming and urbanization over the past century has already caused a significant rise in the intensity and frequency of extreme rainfall events in all monsoon regions (high confidence). Observed changes in the mean monsoon rainfall vary by region with significant decadal variations. Northern Hemisphere land monsoon rainfall as a whole declined from 1950 to 1980 and rebounded after the 1980s, due to the competing influences of internal climate variability and radiative forcing from greenhouse gases and aerosol forcing (high confidence); however, it remains a challenge to quantify their relative contributions. The CMIP6 models simulate better global monsoon intensity and precipitation over CMIP5 models, but common biases and large intermodal spreads persist. Nevertheless, there is high confidence that the frequency and intensity of monsoon extreme rainfall events will increase, alongside an increasing risk of drought over some regions. Also, land monsoon rainfall will increase in South Asia and East Asia (high confidence) and northern Africa (medium confidence), decrease in North America, and be unchanged in the Southern Hemisphere. Over the Asian–Australian monsoon region, the rainfall variability is projected to increase on daily to decadal scales. The rainy season will likely be lengthened in the Northern Hemisphere due to late retreat (especially over East Asia), but shortened in the Southern Hemisphere due to delayed onset.

Unimodal response of N2O flux to changing rainfall amount and frequency in a wet meadow in the Tibetan Plateau

The spatial and temporal variability of extreme rainfall events over India and their long‐term trend have been analysed during the southwest monsoon season from June to September (JJAS) by taking daily rainfall data for 55 years (1951–2005). The contribution of extreme rainfall events (Category iii with rainfall > 124.4 mm day−1) to the total seasonal rainfall is also analysed for the same period. The analysis shows that the occurrence of extreme rainfall events over India during JJAS shows spatial variations with preferred regions of occurrence over the west coast, central parts of India and northeast India. The average frequency of extreme rainfall events along with the contribution of extreme rainfall events to the seasonal rainfall shows a significant increasing trend (above the 98% confidence level) over the Indian region during JJAS, and also during June and July. It is also found that the increasing trend of contribution from extreme rainfall events during JJAS is balanced by a decreasing trend in Category i (rainfall ≤ 64.4 mm day−1) rainfall events. Using the filtered data it is found that the increasing trend of extreme rainfall over central India is mainly contributed by synoptic scale systems (periodicity between 3 and 7 days). The seasonal mean moist convective instability (CI) during JJAS averaged over central India indicates a significant increasing trend (99.9% confidence level) and is basically due to the increasing trend of the number of days with greater degree of moist CI during JJAS, which may be one possible cause for the increasing trend in the frequency of extreme rainfall events over the region. Copyright © 2009 Royal Meteorological Society

Trends in Australian precipitation from 1970 to 2006 are examined using a daily rainfall dataset. Results suggest a linkage between changes in the monsoon trough and rainfall trends over northwestern Australia. The late twentieth century drought observed along the Queensland coast is a response to changes in the atmospheric circulation that generates anomalous subsidence at high and middle levels of the atmosphere, thus inhibiting convection over the region. In addition, an anomalous anticyclonic circulation at low levels over Queensland tends to weaken the easterlies in the tropical western Pacific, thus diminishing the transport of moist air onto the coast. Trends in the frequency and magnitude of different rainfall events are also examined. This reveals that changes in total rainfall are dominated by trends in very heavy rainfall events across Australia. For example, some parts of western Australia reveal an increase in heavy rainfall events that are not accompanied by a rise in modest rainfall events, resulting in changes in the shape of the distribution towards a more skewed precipitation distribution. On the other hand, the frequency of extreme rainfall events along the Queensland coast has declined during summer and autumn consistently with the total rainfall decrease, indicating changes in the position of the precipitation distribution rather than its shape. Copyright © 2008 Royal Meteorological Society

In this study, the flood hazard of Corum province (Turkey) was investigated using the Analytic Hierarchy Process (AHP), which is one of the most popular Multi-criteria Decision Analysis (MCDA) methods, based on Geographic Information System (GIS). As a result of the AHP process, Corum province was categorized into five flood hazard classes: very high, high, medium, low, and very low. It was determined that 3% of the total area is under a very high flood hazard, and 25% is considered a high flood hazard. To assess the validity of the flood hazard map, the results were compared with the historical flood inventory. Our hazard map was compatible with the historical flood inventory, and our hazard map can now be used to estimate the areas that are threatened by possible floods. When the existing structural measures are overlapped with the hazard map in Corum, it is understood that a large part of the structural measures carried out to date have focused on the areas of very high and high flood hazard in the flood hazard map. Future structural measures and detailed studies should now address other areas identified as under threat in the flood hazard map. Our results suggest that the hazard assessment based on MCDA is suitable for flood hazard mapping.

Torrential and long-lasting rainfall often causes long-duration floods in flat and lowland areas in data-scarce Nyaungdon Area of Myanmar, imposing large threats to local people and their livelihoods. As historical hydrological observations and surveys on the impact of floods are very limited, flood hazard assessment and mapping are still lacked in this region, making it hard to design and implement effective flood protection measures. This study mainly focuses on evaluating the predicative capability of a 2D coupled hydrology-inundation model, namely the Rainfall-Runoff-Inundation (RRI) model, using ground observations and satellite remote sensing, and applying the RRI model to produce a flood hazard map for hazard assessment in Nyaungdon Area. Topography, land cover, and precipitation are used to drive the RRI model to simulate the spatial extent of flooding. Satellite images from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Phased Array type L-band Synthetic Aperture Radar-2 onboard Advanced Land Observing Satellite-2 (ALOS-2 ALOS-2/PALSAR-2) are used to validate the modeled potential inundation areas. Model validation through comparisons with the streamflow observations and satellite inundation images shows that the RRI model can realistically capture the flow processes (R2 ≥ 0.87; NSE ≥ 0.60) and associated inundated areas (success index ≥ 0.66) of the historical extreme events. The resultant flood hazard map clearly highlights the areas with high levels of risks and provides a valuable tool for the design and implementation of future flood control and mitigation measures.

This study investigated the impact of the Normalized Difference Vegetation Index (NDVI) on the Land Surface Temperature (LST) in the West Bank, Palestine, using multitemporal satellite imagery. Landsat 5 TM and Landsat 8 OLI data were used in the study. The Landsat 5 TM data was acquired on March 29, 2001, while the Landsat 8 OLI data was acquired on April 05, 2021. The NDVI and the LST for the two images were then calculated from the two images using ArcGIS 10.8 software. Results of the study showed that the NDVI of the 2021 image was much higher than that of the 2001 image. The mean NDVI of the 2001 image was 0.25, while that of the 2021 image was 0.32. As for the LST, the mean LST of the 2001 image was 29.85 °C, while that of the 2021 image was 26.98 °C. Around 74.7% of the West Bank lands recorded an inverse relationship between the NDVI change and the LST change between 2001 and 2021. Around 25.3% of the West Bank lands recorded a positive relationship. In other words, the study depicted the impact of the NDVI on the LST in the West Bank. The study recommends that policymakers need to increase the green areas in the West Bank to reduce the LST as this is seen to affect the climate conditions in the area. Keywords: LST, NDVI, West Bank, Palestine, Landsat data.

Flood is a natural calamity and causes damage of life and property devastation. The main objective of this study was to analyse flood hazard and inundation area mapping of Paravanar sub-Basin. Flood generating factors, like elevation, slope, drainage density, rainfall, soil type and land use were analyzed and delineated flood zones using Geospatial techniques and multi criteria evaluation. The flood generating factor was computed by data integration, floodplain depiction, mapping, and evaluation. Various parameters such as Normalized difference vegetation index (NDVI), Normalized difference build-up index (NDBI), Normalized difference water index (NDWI) were acquired for performing Regression Analysis. This information can be further utilized for hydro-meteorological figures and Surge forecast. These data can be used in building hazard and risk maps for pre-flood prediction. The flood hazard threats in the southeast and in between southeast and west part of the Paravanar River basin are high flood hazard threat zone.

ABSTRACTRecent studies show how geomorphic descriptors, retrieved from digital elevation models (DEMs), can be used for flood hazard mapping. As they strictly depend on the accuracy of the input DEMs and reference flood hazard maps used for training, DEM-based flood hazard models may display severe inconsistencies. Our study shows the application of two advanced DEM-based models to a large study area, and presents two main innovative points. First, the delicate tasks of appropriately selecting the input DEM and flood hazard map are specifically addressed with newly defined methods. Second, the ability of DEM-based models to exploit their natural features to enhance flood hazard mapping over the study region is investigated. Our results show (a) the benefits of considering multiple geomorphic descriptors, (b) the potential of DEM-based models for completing the information of imperfect reference flood hazard maps, and (c) the advantages of continuous representation of hazard over binary flood maps.

The frequency of extreme rainfall events in Southern Brazil is impacted by El Niño—Southern Oscillation (ENSO) episodes, especially in austral spring. There are two areas in which this impact is more significant: one is on the coast, where extreme events are more frequent during El Niño (EN) and the other one extends inland, where extreme events increase during EN and decrease during La Niña (LN). Atmospheric circulation patterns associated with severe rainfall in those areas are similar (opposite) to anomalous patterns characteristic of EN (LN) episodes, indicating why increase (decrease) of extreme events in EN (LN) episodes is favoured. The most recurrent precipitation patterns during extreme rainfall events in each of these areas are disclosed by Principal Component Analysis (PCA) and evidence the separation between extreme events in these areas: a severe precipitation event generally does not occur simultaneously in the coast and inland, although they may occur inland and in the coastal region in sequence. Although EN predominantly enhances extreme rainfall, there are EN years in which fewer severe events occur than the average of neutral years, and also the enhancement of extreme rainfall is not uniform for different EN episodes, because the interdecadal non‐ENSO variability also modulates significantly the frequency of extreme events in Southern Brazil. The inland region, which is more affected, shows increase (decrease) of extreme rainfall in association with the negative (positive) phase of the Atlantic Multidecadal Variability, with the negative (positive) phase of the Pacific Multidecadal Variability and with the positive (negative) phase of the Pacific Interdecadal Variability. Copyright © 2008 Royal Meteorological Society

Soil fertility and precipitation are major factors regulating transitions from grasslands to forests. Biotic regulation may influence the effects of these abiotic drivers. In this study, we examined the effects of extreme rainfall events, anthropogenic nutrient loading and insect herbivory on the ability of Chinese tallow tree (Sapium sebiferum) to invade coastal prairie to determine how these factors may influence woody invasion of a grassland. We manipulated soil fertility (NPK addition) and simulated variation in frequency of extreme rainfall events in a three growing season, full factorial field experiment. Adding water to or pumping water out of plots simulated increased and decreased rainfall frequencies. We added Sapium seeds and seedlings to each plot and manipulated insect herbivory on transplanted Sapium seedlings with insecticide. We measured soil moisture, Sapium performance, vegetation mass, and carbon and nitrogen in vegetation and soils (0–10 cm deep, 10–20 cm deep). Fertilization increased Sapium invasion intensity by increasing seedling survival, height growth and biomass. Insect damage was low and insect suppression had little effect in all conditions. Recruitment of Sapium from seed was very low and independent of treatments. Vegetation mass was increased by fertilization in both rainfall treatments but not in the ambient moisture treatment. The amount of carbon and nitrogen in plants was increased by fertilization, especially in modified moisture plots. Soil carbon and nitrogen were independent of all treatments. These results suggest that coastal tallgrass prairies are more likely to be impacted by nutrient loading, in terms of invasion severity and nutrient cycling, than by changes in the frequency of extreme rainfall events.

Flooding prediction for a large scale area has been popularly carried out, nevertheless flood hazard assessment for a small, complex terrain for planning industrial constructions needs more detailed studies. This study aims to: (1) determine forcing factors and their roles in the flood hazard using the Analytic Hierarchy Process (AHP), and (2) generate the flood hazard map using spatial analysis in Chu Lai peninsular, Vietnam. The study shows that 6 important forcing factors on flood hazard include drainage density, infiltration rate, soil saturation, elevation, slope, and land cover. The factor which most impacts on the flood hazard is the elevation with the detected weight of 0.438. The infiltration rate plays the least importance with a weight of 0.066. Consequently, a flood hazard map is generated with 5 levels very low, low, medium, high, and very high hazard of flood. The very high hazard occupies 7.16% area of the peninsula focusing on the regions covered by concrete surface, permanent and temporal water cover, bare soil, and very low slope from 0° to 8°. The map provides critical information to make a plan for one of the most important industrial complexes in the center of Vietnam.

This research aimed at assessing flood hazard areas and flood literacy of the elderly population in Bangkok, Thailand and analyzing their flood preparedness through SWOT analysis. Expert interviews and a community survey were conducted. Using the analytic hierarchy process (AHP) and GIS technique, the results indicated that land-use, drainage density, and annual maximum rainfall were the most heavily weighted factors in flood hazard mapping in Bangkok. About half (50.32%) of Bangkok’s total area was defined as high flood hazard area. A total of 736 questionnaires were distributed in flood-prone areas and in the areas with the highest percentage of elderly population. The results of both SWOT and survey analysis found that many senior citizens have low digital and media literacy and limited experience in using information technology for flood preparedness. Lack of integration of disaster risk reduction and aging population policy, ineffective warning system, and lack of access to disaster preparedness training were the key barriers in reducing vulnerability to flood hazard. The survey revealed that the majority of elderly respondents (75%) have neither used online applications for their flood hazard management both before and during flood disaster nor shared/communicated information via online platforms. Some respondents (13%) used Facebook and Line applications to obtain information before a flood event. Very few of the elderly respondents (<2%) accessed the national/provincial web-based platform to find out flood-related information. Almost all respondents, especially who are living in high-risk flood zones, had never participated in the community training of flood preparedness and management. Therefore, effective strategies in enhancing social engagement of the elderly and their literacy skills in flood risk preparedness and management are urgently needed.

This study presents a simple approach of spatiotemporal change detection of vegetation cover based on analysis of time series remotely sensed images. The study was carried out at Thathe Vondo Area, which is characterised by episodic variation of vegetation gain and loss. This variation is attributable to timber and tea plantations and their production cycles, which periodically result in either vegetation gain or loss. The approach presented here was implemented on two ASTER images acquired in 2007 and 2017. It involved the combined use of band combination, unsupervised image classification and Normalised Difference Vegetation Index (NDVI) techniques. True colour composite (TCC) images for 2007 and 2017 were created from combination of bands 1, 2 and 3 in red, blue and green, respectively. The difference image of the TCC images was then generated to show the inconsistencies of vegetation cover between 2007 and 2017. For analytical simplicity and interpretability, the difference image was subjected to ISODATA unsupervised classification, which clustered pixels in the difference image into eight classes. Two ISODATA derived classes were interpreted as vegetation gain and one as vegetation loss. These classes were confirmed as regions of vegetation gain and loss by NDVI values of 2007 and 2017. In addition, the polygons of vegetation gain and loss regions were created and superimposed over the TCC images to further demonstrate the spatiotemporal vegetation change in the area. The vegetation change statistics show vegetation gain and loss of 10.62% and 2.03%, respectively, implying a vegetation gain of 8.59% over the selected decade.Significance: Vegetation change detection is essential in environmental monitoring and management of an area. This study presents a simple approach for assessing vegetation change over time. The approach involveschange detection through the difference of spectral values of vegetation pixels of time series remotelysensed images.