HS7.3

Air pollution has always been one of the serious issues around the world, not only related to the large-scale climate environment, but also related to local-scale vehicles-caused air pollutants in the city. Generally, diesel-burning vehicles emit NO_X, SO_2, CO; gasoline burning vehicles emit CO, CO₂, NO_X respectively. The common air pollutants CO and NO_X are widely regarded as the primary traffic-caused air pollutants. Therefore in this study, we take vehicle detector data including car speed, car volume, lane occupy as well as meteorological data and the air pollutants concentration in consider. Firstly, we use the Stepwise Regression Model(SRM) to select the significant factors for the target air pollutants and predict them with multivariate linear regression. Secondly, we also combine Long Short-Terms Memory (LSTM) Model to simulate the highly nonlinear and unstationary complex chemical interaction between air pollutants. In this study we got high model accuracy performance in primary pollutants prediction (CO,NO_X) by including the vehicle detector data with both Multivariate linear regression Model and LSTM model which conclude that the vehicle detector data can significantly improve the quality of model prediction. This process select the statistically significant factors of the pollutants, and also establishes a neural network model including traffic, meteorological factors and air quality which contribute to the air pollutants risk management of government agency.

Keywords: traffic pollutants, air quality, stepwise regression, LSTM model

How to cite: Lin, Y.-T. and Lin, Y.-C.: The impact analysis of traffic and meteorological factors on air pollution risk in Taiwan, Taipei City, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7446, https://doi.org/10.5194/egusphere-egu21-7446, 2021.

Water scarcity, which is a critical issue worldwide, is exacerbated by geomorphic characteristics and highly uneven spatiotemporal distribution of rainfall in Taiwan. The annual water availability per capita in Taiwan is less than one-fifth of the world average despite the high annual rainfall. Hence, stable water supply and efficient water resources management are challenging tasks for related authorities, and a decision support tool is required for the optimal decision. This study proposes a risk assessment framework for water shortage based upon a dynamic Bayesian network. Standardized precipitation index (SPI), standardized runoff index (SRI) and long-term weather forecasts are included in the framework. Taoyuan, a northern city in Taiwan with rapid growth of population and industries, is particularly vulnerable to water shortage and thereby chosen as our study site.

How to cite: Lee, S.-Y., Tsai, M., and Yu, H.-L.: Integrating drought indices and long-term weather forecasts with a dynamic Bayesian network for assessing water shortage risk – a case study in Taoyuan, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5753, https://doi.org/10.5194/egusphere-egu21-5753, 2021.

Each year, average of Earth’s temperature rises and the urbanized cities, are warming at a significant rate than the surrounding rural areas. This phenomenon is called Urban Heat Island (UHI). UHI is a consequence of human activities in urban area and it has possibilities to impact weather and climate on regional or global scale. Precipitation is one of the basic hydro-meteorological phenomena that could be affected by UHI trend with thunderstorm as a part of precipitation. As the UHI level rises from year to year, the pattern of precipitation could change. However, this issue is still underdeveloped, thus, this work tries to comprehensively understand the hydrological response to UHI.

This research selects Taipei city as the study area and explores the connection between UHI and precipitation pattern’s change. The data used here are hourly temperature and precipitation data collected from 21 Taipei weather stations collected from Central Weather Bureau (CWB) Taiwan. In order to reveal specific details and trend of non-linear relation from both time domain and frequency, Hilbert-Huang Transform (HHT) is adopted in this study. The HHT results are compared between each station. Later, empirical orthogonal function (EOF) also being used to extract main spatial pattern of precipitation in Taipei city.

The results show that the urbanization in Taipei city contribute to increasing trend of 0.5 – 1 ^oC in daily UHI and also increase of 27% in the afternoon thunderstorm frequency for this past 20 years. The increase of thunderstorm would result into a bigger rain water flow to the river and a fewer time for it to percolate to the ground. If there are more thunderstorms in the future, it is possible the phenomenon could lead to the lack of groundwater discharge and depletion of groundwater reserve. This result could be utilized in the future to understand more about UHI mitigation and thunderstorm in Taipei.

Keywords: urban heat island, thunderstorm, Hilbert-Huang Transform, empirical orthogonal function

How to cite: Rachma, S. T. and Lin, Y.-C.: An analysis of urban heat island impact toward increasing of afternoon thunderstorm frequency in Taipei, Taiwan, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10522, https://doi.org/10.5194/egusphere-egu21-10522, 2021.

Sub-Saharan Africa is one of the most food-insecure regions in the world, and is particularly vulnerable to the impacts of extreme climate events and climate change.  To gain a better understanding of the present-day likelihood of extreme seasonal temperature and rainfall events, and their joint occurrence, we apply the UNprecedented Simulated Extremes using ENsembles (UNSEEN) approach  to a large ensemble of high-resolution initialised climate simulations in three countries of Sub-Saharan Africa: Tanzania, Zambia and South Africa. We assess the annual likelihood of unprecedented seasonal temperature and precipitation extremes during the maize growing season (October-April), as key variables for maize productivity , and investigate the large-scale dynamics of the climate system that govern their occurrence. We estimate that there is a 3-4% chance per year of exceeding the present-day seasonal temperature records in the maize growing regions of these countries, and a 1-3.5% chance per year of exceeding the present-day seasonal precipitation records.  Conversely, whilst we find a 2% and 5% chance per year of subceeding the present-day seasonal precipitation records in Zambia and Tanzania respectively, we find a very low chance (0-1% per year) of subceeding the present-day seasonal precipitation records in South Africa.  We also use the large ensemble to investigate the large-scale dynamics of the climate extremes, finding that high temperature extremes tend to be associated with El Niño and positive IOD/SIOD events and low temperature extremes with La Niña and negative IOD/SIOD events. The drivers of precipitation extremes, however, differ between the countries. In South Africa, high (low) precipitation extremes are associated with La Niña (El Niño) events but otherwise the influence on extremes of ENSO, and even more so the IOD/SIOD, is weak or not seen in the ensemble, which invites further investigation. To explore implications for growing maize in these regions, we convert our unprecedented seasonal temperature estimates to daily maximum temperatures and our seasonal precipitation estimates to monthly precipitation indices and compare to climatic thresholds for maize. Combined with projected changes to crop suitability in much of sub-Saharan Africa, our analysis suggests the need for significant adaptation strategies that build food system resilience in the near and longer term.

How to cite: Bradshaw, C., Pope, E., Kay, G., Davie, J., Cottrell, A., Bacon, J., Jennings, S., Challinor, A., Chapman, S., Birch, C., and Sallu, S.: Unprecedented climate extremes in Sub-Saharan Africa and implications for maize production, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15485, https://doi.org/10.5194/egusphere-egu21-15485, 2021.

Groundwater is a reliable freshwater resource in many areas, and it is also an important source of backup water during the drought. Therefore, understanding the characteristics of groundwater resources is crucial and can be explored by building correct hydrogeological models for simulation. To build a perfect hydrogeological model, it is necessary to grasp the correct geological conditions and hydrogeological parameters to establish an effective numerical simulation of groundwater flow. However, geological conditions always contain some uncertainties, which may cause a certain impact on the spatiotemporal changes of groundwater.

Therefore, this study uses the groundwater flow numerical model, MODFLOW, to build the groundwater simulation model. The ideal case and real case at Touqiao Minshiung Industrial Zone in Chiayi is built from 2009 to 2013. The results show that under different hydrogeological parameters, geology, and other conditions, groundwater will have different patterns of variation. The Empirical Orthogonal Function (EOF) method is also used to compare the dominated patterns. The simulation results show the R² can all reach 0.9 compare with the groundwater real observation data. This study can further explore the drought-resistant availability of groundwater in various regions under different geological conditions, it will help relevant agencies and local governments to better manage groundwater resources.

Keywords: groundwater simulation, MODFLOW, uncertainty, hydrogeology, EOF

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*Department of Civil Engineering, National Central University

How to cite: Lin, H.-S. and Lin, Y.-C.: The simulation of groundwater spatiotemporal changes under the uncertainty of hydrogeological conditions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4770, https://doi.org/10.5194/egusphere-egu21-4770, 2021.

In the applications and studies of subsurface flow, it is necessary to understand the geological lithological composition in the study area. So as to find out the lithological distribution in the study area, many geological spatial statistical methods used to analyze the lithological composition on unsampled points. One of the drawbacks in the traditional two-point based geostatistical methods(e.g., Kriging) is that they based on variogram, thus, inability to handle complex and heterogeneous spatial structures. Furthermore, they produce excessively smooth results. The goal of Multiple-point geostatistics is to overcome the limitations of the variogram. Multiple-point geostatistics is a general statistical framework to model spatial fields with complex structures. It uses training image(TI) instead of variogram to estimate the conditional probability at interpolation location by the observed data and the already interpolated data. Take advantage of TI helps extracting spatial structure information and precisely describing more complex structures. This study focuses on Choshui river alluvial fan, using multiple-point geostatistics method to do simulation of lithological classification.

How to cite: Lin, T.-A. and Yu, H.-L.: Simulation of Lithological Classification in Choshui river Alluvial Fan based on Muliple-point geostastics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7505, https://doi.org/10.5194/egusphere-egu21-7505, 2021.

Abstract

Groundwater, as a vital existence in human life and economic development, is also one of the stable sources of water resources. Therefore, how to properly utilize groundwater becomes a very important issue when faced with water shortages. However, most of the previous literature uses monthly data as the time scale, and usually uses the historical water level data of the area as the only input factor in the modeling process without considering pumping information and rainfall. This shows that the current studies of small-scale data which is based on the use of multiple factors with hydrological mechanisms to explore and predict the groundwater level is still quite lacking.

Therefore, this study proposed a novel framework combining wavelet analysis and deep learning models called wavelet-deep learning models and taking the Daliao area of ​​Kaohsiung as an example. From the historical hourly observation data during 2017/08/23-2020/01/30, including groundwater level, smart pumping measurement, tidal, and meteorological data. After abstracting important features of each factor with groundwater level by wavelet transform, using deep learning algorithms such as recurrent neural networks (RNN) and long short-term memory (LSTM) model to summarize and predict the impact of multiple variable factors on the groundwater level under different time lags. The results of hourly prediction show that the performance of the LSTM model and RNN model are both reliable in which values of the coefficient of determination () were obtained 0.813 and 0.784, respectively.

This study provides a feasible and accurate approach for groundwater level prediction by understanding and predicting different water level changes that may occur in the Daliao area in advance. As a result, the study will be an important reference for groundwater resources management and risk assessment, and achieve the goal of sustainable use of groundwater resources.

Keywords Groundwater prediction, Wavelet transform, Risk assessment, LSTM

How to cite: Weng, T.-N., Hsu, C.-C., and Lin, Y.-C.: Development of risk assessment model for groundwater level by wavelet-deep learning approach with smart pumping data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4624, https://doi.org/10.5194/egusphere-egu21-4624, 2021.

Climate change cause large, long-term impacts on human well-being and adds more pressure to terrestrial and marine ecosystems. The archipelago of the Azores is located in the subtropical region of the North Atlantic and is therefore highly influenced by the North Atlantic Subtropical Anticyclone. As it is an almost stationary high pressure system, whose development and orientation determine the nature and characteristics of the air masses that reach the region. The motivation for this research has two phases; the first was to study the effects of some meteorological parameters (temperature, radiation, wind speed, humidity, precipitation, evaporation, tank temperature and tank level) for the period 2010-2012, on the biodiversity of phytoplankton communities in relation to the abundance of these organisms in the lagoons of Fogo, Furnas, and Sete Cidades of the island of São Miguel - Azores, for the period 2010-2012, using an analysis in Principal  Components, which will allow correlating the meteorological parameters and the abundance of phytoplankton. The phytoplankton and meteorological community data were obtained from the website of the Regional Secretariat for the Environment and Climate Change of the Azores Government. In a second phase, the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis of the ERA5 project (ECMWF Re-Analyzes) was used for the 1979-2019 observation period and for the Azores region. For this region, the deviations of the surface air temperature, average annual precipitation and climatological extremes were calculated, this referring to the maximum number of consecutive days with precipitation <1 mm, and also, the number of tropical nights using the ERA5 reanalysis series in the period 1979-2019 with reference to 1961-1990. Projections were also estimated up to 2100 and according to scenarios RCP 2.6, 4.5 and 8.5 for the referred parameters. Finally, variations for the end of the century (2071-2100) were estimated with reference to the most recent situation of 1991-2020.

The thermal balance of a lagoon is associated with climatic and meteorological conditions. Much of the biological processes in the lagoons are directly affected by thermal changes in the water, and therefore, indirectly affected by climatic variation. Understanding the interaction between the lagoon-atmosphere system is important to predict the consequences of the effects of climate change on the abundance of phytoplankton. In this study, a positive correlation was verified between precipitation and abundance of Bacillariophyta, Dinophyta and Cryptophyta. From the calculations performed, the average of the models results in an increase in the maximum number of consecutive days with low rainfall (<1mm) from + 0.2 to 4.8 days / year until the year 2100, with a lower abundance of these algae being expected. On the other hand, Cyanophyta, Chlorophyta and Chrisophyta are well correlated with high values ​​of air temperature, lagoon water temperature and solar radiation. Thus, it is estimated an increase in the abundance of these algae, due to the forecasts of several models, that point to an increase in the average annual temperature in this region between 1 and 3 K until the year 2100, with reference to the period from 1961 to 1990.

How to cite: Meirelles, M.: Climate Change and the Evolution of Phytoplankton (Abundance) in Some Lagoons on the São Miguel Island – Azores, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1621, https://doi.org/10.5194/egusphere-egu21-1621, 2021.

Mosses (bryophyta) have the ability to absorb and retain large amounts of water. This property results from the specific way in which these organisms uptake, conduct, and store water.

The aim of the study was to investigate the water storage capacity (S) of red-stemmed feathermoss Pleurozium schreberi (Mitt.) in the fresh state (current capacity) and after drying (maximum capacity), depending on the initial moisture content, and depending on the percentage of the various structural parts of the moss sample which included soil.

Forty moss samples of equal size were used in the study; they were sprayed with a constant dose of water in laboratory conditions. The actual water capacity was obtained from the difference in the weight of the sample after spraying with a constant dose of water, and the weight of the sample in the fresh state. After the stimulated rainfall cycle, the samples were divided into individual fractions (part with green leaves, stalks and rhizoids, and soil) and dried in an airoven for 24 hours at 105^oC.

The weight of the dry sample, the initial moisture, the maximum water capacity, and the current water capacity were calculated. The analyses conducted led to the conclusion that water capacity of moss is extremely important for the water cycle as it retained, on average, as much as 29% of the total rainfall.

The initial moisture depends above all on the amount of soil that dominated the entire sample volume. Retention capacity of the moss must be higher than that of the soil, as each additional gram of soil reduced the initial moisture content of the samples.

Experiments have additionally shown that the higher the initial moisture, i.e. the more water in the fresh moss samples collected with the soil lump, the higher the maximum capacity. The calculated maximum water capacity relates to the dry weight of the entire sample. This conclusion can be compared to the water properties of soil where the wetter fresh soil is able to retain more water, and the excessively dry soil becomes hydrophobic.

In turn, the higher the initial moisture, the less water is retained in the fresh moss sample after rainfall. This observation is similar to the actual situation that occurs in natural conditions, e.g. in a forest. This may be due to the fact that the more water is contained in the moss assimilation apparatus, the higher the cell turgor pressure, which makes the surface tighter. The moss absorbs water from the atmosphere, and the largest increases in retained water are recorded for drier samples. This may also result from external and internal structure of moss, which is different than in vascular plants. The leaves of bryophytes have characteristic vertical rows of cells of the collenchyma on their upper surface. Such arrangement of cells promotes water absorption.

The obtained results remain in line with the research on the hydrological properties of forest ecosystems, and they show that the role of moss in the forest is very important but not yet fully understood.

How to cite: Klamerus-Iwan, A., Iwan, M., and Bigaj, K.: Water capacity of red-stemmed feathermoss (Pleurozium schreberi Mitt.), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3067, https://doi.org/10.5194/egusphere-egu21-3067, 2021.