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Flood Prediction Using ML Models
Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction of the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists.Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task.
21 Jan 2021
Atmospheric Correction for Landsat 8
Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as well as sensor calibration. In this study, the performances of four atmospheric correction (AC) algorithms, the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI), Atmospheric Correction for OLI ‘lite’ (ACOLITE), Landsat 8 Surface Reflectance (LSR) Climate Data Record (Landsat CDR), herein referred to as LaSRC (Landsat 8 Surface Reflectance Code), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), implemented for Landsat 8 Operational Land Imager (OLI) data, were evaluated. The OLI-derived remote sensing reflectance (Rrs) products (also known as Level-2 products) were tested against near-simultaneous in-situ data acquired from the OC component of the Aerosol Robotic Network (AERONET-OC). Analyses of the match-ups revealed that generic atmospheric correction methods (i.e., ARCSI and LaSRC), which perform reasonably well over land, provide inaccurate Level-2 products over coastal waters, in particular, in the blue bands. Between water-specific AC methods (i.e., SeaDAS and ACOLITE), SeaDAS was found to perform better over complex waters with root-mean-square error (RMSE) varying from 0.0013 to 0.0005 sr−1 for the 443 and 655 nm channels, respectively. An assessment of the effects of dominant environmental variables revealed AC retrieval errors were influenced by the solar zenith angle and wind speed for ACOLITE and SeaDAS in the 443 and 482 nm channels. Recognizing that the AERONET-OC sites are not representative of inland waters, extensive research and analyses are required to further evaluate the performance of various AC methods for high-resolution imagers like Landsat 8 and Sentinel-2 under a broad range of aquatic/atmospheric conditions.
30 Oct 2020
This entry reviews atmospheric processes affecting pollutant transport and diffusion over complex terrain, focusing in particular on the peculiarities of processes over mountains when compared to flat terrain. In fact, pollutant dispersion processes over complex terrain are much more complicated than over flat areas, as they are affected by atmospheric interactions with the orography at different spatial scales. In particular, atmospheric flows over complex terrain are characterized by a continuous and interacting range of scales, from synoptic forcing to mesoscale circulations and turbulence fluctuations. In complex terrain, the mechanical and thermal influence of the orography can modify the large-scale flow and produce smaller-scale motions which would not exist on flat terrain, thus enhancing the spatial and temporal variability of atmospheric processes relevant for pollutant dispersion.
26 Oct 2020
Viable Bacteria in Dust-Generating Area
The distribution of microorganisms in the atmospheric circulation affects the animals that inhabit the area. Also, many organisms that share the environment also influence the distribution of environmental bacteria. In this paper, we focused on microbes that survive on the surface of Asian Dust, and clarified their topographical features and distribution. The characteristics of microorganisms that are easily influenced by environmental factors, and their effects on the atmospheric circulation are considered as issues of the One Health Concept.
16 Sep 2020
Analytical solutions of groundwater dynamics in an elongated aquifer subjected to general time-dependent recharge are presented. The lateral boundaries are specified heads with the head variations governed by general time-dependent functions. General recharge function was not considered in previous works of this kind. Both single and double-porosity aquifers are considered. The solution is obtained using Fourier sine and Laplace transformations, followed by an inverse Fourier-Laplace transform involving residue theorem and convolutional integral. For the unconfined single-porosity aquifer case, the exact time-domain solution is obtained using the residue theorem; for the unconfined double-porosity aquifer case, the time-domain head is calculated using the de Hoog inverse Laplace algorithm. The presented solution can be used to estimate the hydraulic parameters of 1) groundwater head variation of a river basin aquifer subjected to general lateral head variation and recharge; 2) groundwater head variation in a double-porosity elongated fractured anticline; 3) groundwater depletion of an elongated fractured anticline subjected to recharge due to rainfall or snowmelt to its adjacent alluvial aquifer. In addition, the presented solution can be utilized to optimize the irrigation pattern in a cropland between two trench drains to control the groundwater mound.
23 Dec 2020
Atmospheric Influence on Grapevine Development
In many European regions, viticulture and winemaking play a major socioeconomic role in local economies, with climate being a central component of the terroirs, governing vineyard microclimate, vine development and growth, phenology, yield, and grape berry composition, which ultimately control attributes and typicity of the produced wines. Nonetheless, climate change is already affecting the viticultural suitability of many wine regions throughout the continent and is expected to continue along this same path in the upcoming decades. These climate-driven shifts may lead to a redesign of the geographical distribution of wine regions, while wine typicity may also be threatened in most cases. Climate change does require the implementation of well-timed, appropriate, and economically efficient adaptation strategies, while respecting local specificities for an effective reduction of the risks to which this vulnerable sector is exposed. However, knowledge on the adaptation potential of a range of measures is still incipient and will need more research in the near future.
28 Oct 2020
Climate Change and Water Resources
Water resources are highly dependent on climatic variations. The quantification of climate change impacts on surface water availability is critical for agriculture production and flood management. The current study focuses on the projected streamflow variations in the transboundary Mangla Dam watershed. Precipitation and temperature changes combined with future water assessment in the watershed are projected by applying multiple downscaling techniques for three periods (2021–2039, 2040–2069, and 2070–2099). Streamflows are simulated by using the Soil and Water Assessment Tool (SWAT) for the outputs of five global circulation models (GCMs) and their ensembles under two representative concentration pathways (RCPs). Spatial and temporal changes in defined future flow indexes, such as base streamflow, average flow, and high streamflow have been investigated in this study. Results depicted an overall increase in average annual flows under RCP 4.5 and RCP 8.5 up until 2099. The maximum values of low flow, median flow, and high flows under RCP 4.5 were found to be 55.96 m3/s, 856.94 m3/s, and 7506.2 m3/s and under RCP 8.5, 63.29 m3/s, 945.26 m3/s, 7569.8 m3/s, respectively, for these ensembles GCMs till 2099. Under RCP 4.5, the maximum increases in maximum temperature (Tmax), minimum temperature (Tmin), precipitation (Pr), and average annual streamflow were estimated as 5.3 °C, 2.0 °C, 128.4%, and 155.52%, respectively, up until 2099. In the case of RCP 8.5, the maximum increase in these hydro-metrological variables was up to 8.9 °C, 8.2 °C, 180.3%, and 181.56%, respectively, up until 2099. The increases in Tmax, Tmin, and Pr using ensemble GCMs under RCP 4.5 were found to be 1.95 °C, 1.68 °C and 93.28% (2021–2039), 1.84 °C, 1.34 °C, and 75.88%(2040–2069), 1.57 °C, 1.27 °C and 72.7% (2070–2099), respectively. Under RCP 8.5, the projected increases in Tmax, Tmin, and Pr using ensemble GCMs were found as 2.26 °C, 2.23 °C and 78.65% (2021–2039), 2.73 °C, 2.53 °C, and 83.79% (2040–2069), 2.80 °C, 2.63 °C and 67.89% (2070–2099), respectively. Three seasons (spring, winter, and autumn) showed a remarkable increase in streamflow, while the summer season showed a decrease in inflows. Based on modeling results, it is expected that the Mangla Watershed will experience more frequent extreme flow events in the future, due to climate change. These results indicate that the study of climate change's impact on the water resources under a suitable downscaling technique is imperative for proper planning and management of the water resources.
23 Oct 2020
NOx Emission Reduction and Recovery
Since its first confirmed case at the end of 2019, COVID-19 has become a global pandemic in three months with more than 1.4 million confirmed cases worldwide, as of early April 2020. Quantifying the changes of pollutant emissions due to COVID-19 and associated governmental control measures is crucial to understand its impacts on economy, air pollution, and society. We used the WRF-GC model and the tropospheric NO2 column observations retrieved by the TROPOMI instrument to derive the top-down NOx emission change estimation between the three periods: P1 (January 1st to January 22nd, 2020), P2 (January 23rd, Wuhan lockdown, to February 9th, 2020), and P3 (February 10th, back-to-work day, to March 12th, 2020). We found that NOx emissions in East China averaged during P2 decreased by 50% compared to those averaged during P1. The NOx emissions averaged during P3 increased by 26% compared to those during P2. Most provinces in East China gradually regained some of their NOx emissions after February 10, the official back-to-work day, but NOx emissions in most provinces have not yet to return to their previous levels in early January. NOx emissions in Wuhan, the first epicenter of COVID-19, had no sign of emission recovering by March 12. A few provinces, such as Zhejiang and Shanxi, have recovered fast, with their averaged NOx emissions during P3 almost back to pre-lockdown levels.
29 Oct 2020
ESA Round Robin Exercise
Motivated by the experience acquired in the ESA promoted Round Robin exercise aimed at comparing cloud detection algorithms for PROBA-V sensor, we investigate specific issues related to cloud detection when remotely sensed images comprise only a few spectral bands in the visible and near-infrared by considering a bunch of well-known classification methods. First, we demonstrate the feasibility of using a training dataset semi-automatically obtained from other accurate algorithms. In addition, we investigate the effect of ancillary information, e.g., surface type or climate, on accuracy. Then we compare the different classification methods using the same training dataset under different configurations. We also perform a consensus analysis aimed at estimating the degree of mutual agreement among classification methods in detecting Clear or Cloudy sky conditions. Results are also compared on a high-quality test dataset of 1350 reflectances and Clear/Cloudy corresponding labels prepared by ESA for the mentioned exercise.
02 Feb 2021
Optimal Interpolation for infrared satellite data
Thermal infrared remote sensing measurements are blinded to surface emissions under cloudiness because infrared sensors cannot penetrate thick cloud layers. Therefore, surface and atmospheric parameters can be retrieved only in clear sky conditions giving origin to spatial fields flagged with missing pieces of information. Motivated by this we present a methodology to retrieve missing values of some interesting geophysical variables retrieved from spatially scattered infrared satellite observations in order to yield level 3 (L3), regularly gridded, data. The technique is based on a 2-Dimensional (2D) Optimal Interpolation (OI) scheme. The goodness of the approach has been tested on 15-min temporal resolution Spinning Enhanced Visible and Infrared Imager (SEVIRI) emissivity and surface temperature (ST) products over South Italy (land and sea), on Infrared Atmospheric Sounding Interferometer (IASI) atmospheric ammonia (NH3) concentration over North Italy and carbon monoxide (CO), sulfur dioxide (SO2) and NH3 concentrations over China. Sea surface temperature (SST) retrievals have been compared with gridded data from MODIS (Moderate-resolution Imaging Spectroradiometer) observations. For gases concentration, we have considered data from 3 different emission inventories, that is, Emissions Database for Global Atmospheric Research v3.4.2 (EDGARv3.4.2), the Regional Emission inventory in ASiav3.1 (REASv3.1) and MarcoPolov0.1, plus an independent study.
30 Oct 2020
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