Due to the influence of climate change, the range of change in precipitation and regional variation have increased over the past 10 years, and the occurrence of local drought is increasing. The existing water supply and demand analysis system in Korea is produced by each management department, so there are limitations in data collection and decision-making on water distribution. For efficient water management, integration of water information should be prioritized. Based on this, actual water use monitoring, evaluation and water shortage prediction technology can be developed.

In this study, the DB of water-cycle system was constructed focusing on domestic water and transfer function model was developed. DB construction was classified into 3 stages (pre-preparation/investigation and analysis/application and evaluation), and the first stage was defined as the concept of water inflow/delivery/outflow from the urban perspective and the current status of data by point was identified. In the second stage, research directions were established through expert consultation and undisclosed data were collected through cooperation with related organizations. The third stage was applied to Gongju-si and Nonsan-si in Korea, which are the study sites, and the supplementations were reviewed. A transfer function model was developed using the constructed DB. It is expected that it will be possible to construct a useful transfer function model when analyzing the performance index by learning the outflow of the Singwan sewage treatment equipment based on the water intake amount of the Hyeondo intake station and confirming the autocorrelation of the non-significant residual.

In the future, additional considerations (outlet location, service area, and sewage treatment area subdivision) are required in national reports on river basins and droughts, and precipitation is also considered as a major input factor for outflow.

(This work was supported by a grant from the Korea Environmental Industry & Technology Institute (KEITI), funded by the Ministry of Environment (ME) of the Republic of Korea (2022003610003))

How to cite: Lee, S. and Lee, S.: Construction of integrated DB for domestic water-cycle system and development of transfer function model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2201, https://doi.org/10.5194/egusphere-egu23-2201, 2023.

According to past observations and long-term forecasts, the Carpathian Basin is distinguished by two precipitation trends. The frequency, length, and severity of periods of precipitation deficit and drought are increasing. Furthermore, as small-scale convective updrafts intensify, heavy thunderstorms become more intense. Both trends pose significant risks from an anthropogenic perspective. The former increases food insecurity due to intensifying droughts, which damages agricultural yields, while the latter mainly increases property damage via heavy hailstorms.

The 2022 drought year demonstrated that effective use of available water is the foundation for sustainable growth, which may be supported by well-designed infrastructure investments and smart water management technologies. A rainfall radar system with a high spatial and temporal resolution that contributes to near real-time machine decision-making is one conceivable component of such a complex system.

The Furuno WR-2100 precipitation radar, which was deployed on the outskirts of Debrecen in 2020 for benchmarking purposes, is the first component of such an intelligent decision-making system in Hungary. The radar's range comprises both urban and rural areas, allowing it to continually gather high-resolution test data for both urban hydrology and agricultural irrigation system developments.

The research presented in the article was carried out within the framework of the Széchenyi Plan Plus program with the support of the RRF 2.3.1 21 2022 00008 project.

How to cite: Fehér, Z. Z., Budayné-Bódi, E., Nagy, A., Magyar, T., and János, T.: Hydrological decision-making systems using high-resolution weather radar observations –  a case study from Hungary, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16292, https://doi.org/10.5194/egusphere-egu23-16292, 2023.

Radar-based rainfall estimation represents an effective tool for hydrological modelling. Nevertheless, this data type is subject to systemic and natural perturbations that need to be considered before to use it. Because of that and to improve data quality, corrections based on raingauge observations are frequently adopted. Here, we compared the efficacy of different radar-raingauge merging procedures over a regional raingauge-radar network focusing on a selected number of rainfalls events.
We adopted the methods: 1) Kriging with External Drift (KED) interpolation (Wackernagel 1998), 2) Probability-Matching-Method (PMM, Rosenfeld et al., 1994), and 3) a kriging mixed method exploiting the Conditional Merging (CM) process (Sinclair-Pegram, 2005) based on elaborations available at DPCN (Italian National Civil Protection Department). These methods have been applied on the Tuscany Regional Territory using raingauge recorded rainfalls at 15’ time-step and CAPPI (Constant altitude plan position indicator) reflectivity data at 2000/3000/5000 m at 5’ and 10’.
Relationships describing precipitation VS radar reflectivity were integrated and elaborated as part of the development of the merging procedures, while the comparison involved the analysis of variance and diversity coefficients. Kriging-based elaborations showed different spatial patterns depending on the applied procedure, with patterns closer to radar variability when using DPCN, and more reflecting the gauge data structure when adopting KED. The probabilistic method (PMM), instead, had the advantage of integrating the gauge data while preserving the spatial radar patterns, confirming interesting perspectives.

How to cite: Ciampalini, R., Antonini, A., Mazza, A., Melani, S., Ortolani, A., Rosi, A., Segoni, S., and Moretti, S.: Comparing different radar-raingauge precipitation merging methods for Tuscany region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12475, https://doi.org/10.5194/egusphere-egu23-12475, 2023.

In general, water level prediction models using deep learning techniques have been developed using time-series water level observation data from upstream water level stations and target water level stations even though many of physical data are necessary to predict water level. The changes of the water level are greatly affected by rainfall in the basin, therefore rainfall information is needed to more accurately predict the water level. In particular, radar data has the advantage of being able to directly acquire the amount of rainfall occurring within a watershed. This study aims to develop the multimodal deep learning model to predict the water level using 2D grid radar rainfall data and 1D time-series water level observation data. This study proposed two multimodal deep learning models which have different structures. Both multimodal deep learning models predict the water level by simultaneously using the observed water level data up to the present time and the radar rainfall data that affects the water level in the future. The first proposed model consists of a deep learning network that links 2D Average Pooling (AvgPool2D), which compresses 2D radar data to 1D data, and Long Short-Term Memory (LSTM), which predicts 1D time series water level data. The second proposed model consists of a deep learning network that predicts water levels by linking Conv2DLSTM and LSTM, which can reflect the characteristics of 2D radar data without deformation.  The two proposed multimodal deep learning models were learned and evaluated in the upper basin of Hantan River. In addition, it was compared with the results of single-modal LSTM using only water level data. There are three water level stations in the study area, and the objective was to predict the water level of the downstream station up to 180 minutes in advance. For learning and verification of the deep learning model, 10-minute water level and radar rainfall data were collected from May 2019 to October 2021. For the radar data used as input, the grid data included in the target watershed were extracted and used among composite radar data with a resolution of 1 km operating by Ministry of Environment. As a result of evaluating each learned deep learning model, two multimodal models had higher prediction accuracy than the single-modal using only water level data. In particular, second proposed model (Conv2dLSTM+LSTM) had better predictive performance than first proposed model (AvgPool2D+LSTM) at the time of the sudden rise in water level due to rainfall.

Acknowledgments

Research for this paper was carried out under the KICT Research Program (project no. 202200175-001, Development of future-leading technologies solving water crisis against to water disasters affected by climate change) funded by the Ministry of Science and ICT.

How to cite: Yoon, S., Kim, S., and Bae, S.: Application of multimodal deep learning using radar and water level data for water level prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5818, https://doi.org/10.5194/egusphere-egu23-5818, 2023.

The Korean Peninsula (KP) located in the Northwest Pacific have different topographic features. West coast of the KP has large tidal variations. If storm surge occurred at high tide, the west coast is vulnerable to flooding. The south coast has a complex coastline with hundreds of islands. Its complex topography can amplify storm surge height (SSH) and it also makes it difficult to conduct numerical modeling for storm surge. Moreover, as the KP is located in the pathways of typhoons, it has been affected by an average of three typhoons every year. The KP has actually suffered from storm surge-induced disaster several times in the past. In order to plan efficient and effective countermeasures against storm surge disasters, it is required to identify the vulnerability of coastal regions in the KP. Therefore, this study quantitatively analyzed the frequency and cause of occurrence of storm surges that occurred along the Korean coast in the past.

First, this study collected observed tidal data at 48 tide stations which are installed along the coast of the KP and performed a hormonic analysis on the observed tidal data to build a database of SSH information that occurred along the coast of the KP from 1979 to 2021. Next, the cause of the storm surge was evaluated based on the occurrence time of the high-level SSH. If the storm surge occurred in winter season, it was treated as being caused by an extra-tropical cyclone, and if in summer season, by and tropical cyclone. Lastly, storm surge vulnerable areas were assessed based on frequency and level of the SSH. To this end, the coast of the KP was divided into five zones: the northwest coast, the southwest coast, Jeju island, the southeast coast and northeast coast. The frequency of the high-level SSH generated in those zones was calculated, and areas where storm surge occurred a lot were selected as vulnerable areas.

As a result of the study, it was found that the high-level SSH with more than 1 m in the KP are caused by tropical cyclone in summer, and the area most vulnerable to storm surge is the southeast coast.

However, the observed tidal data used in this study has a limitation in that the collection period differs depending on the zone: the observation period is longer for the southeast coast than for the southwest coast. Looking at the paths of past typhoons, many typhoons passed through the west coast, so the possibility that the southwest coast would have been judged to be more vulnerable than the southeast coast cannot be ignored if the observed tidal data for the southwest coast were more abundant. In addition, since storm surge is phenomenon that is affected not only by meteorological conditions but also by topographic conditions (e.g., complexity of coastline, water depth, etc.), spatio-temporal analysis of storm surge by topographic conditions is going to be conducted through future research.

Acknowledgement

This work was supported by the National Research Foundation of Korea grant funded by the Korea government(MSIT) (No. 2022R1C1C2009205).

How to cite: Yang, J.-A.: Spatio-temporal analysis of storm surge in the Korean Peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11419, https://doi.org/10.5194/egusphere-egu23-11419, 2023.

Floods are one of the most common and catastrophic natural events worldwide, making studies on the magnitude, severity and frequency of past events essential for risk management. On this wise, remote sensing techniques have been widely used in flooding diagnoses, where Sentinel-2 images are one of the main resources employed in surface water mapping. These studies have developed single band, spectral indexes and machine learning-based methods, which have typically been applied to large water bodies. However, one of the issues in identifying water surfaces remains their size. When water surfaces have sizes close to the spatial resolution of satellite images, they are difficult to detect and map. To improve remotely sensed images' spatial resolution, an algorithm for super-resolving imagery has been developed, giving good results, especially in areas covered by agricultural land with large uniform surfaces. Although this method has proved effective on Sentinel-2 images, it has not been tested for enhancing flood mapping. Thus, flood mapping is still considered an open research topic, as no suitable method has been found for all datasets and all conditions. Consequently, the present study has developed a methodology for flood delineation in small-sized water bodies. The method leverages the advantages of Sentinel-2 MSI data, image preprocessing techniques, thresholding algorithms, spectral indexes and an unsupervised classification method. Thus, this framework includes a) the generation of super-resolved Sentinel-2 images, b) the application of seven spectral indexes to highlight flood surfaces and evaluation of their effectiveness, c) the application of fifteen methods for flood extent mapping, including fourteen thresholding algorithms and one unsupervised classification method and, d) the evaluation and comparison of the performance of flood mapping methods. The technique was applied in the Carrión River, located in the Duero basin, province of Palencia, Spain. This river is classified as a narrow water body, which presents recurrent flood events due to its morphometric characteristics, fluvial dynamics, and land uses. The results obtained show optimal performances when highlighting flood zones by combining AWE spectral indices with methods such as those of Huang and Wang, Li and Tam, Otsu, and momentum-preserving thresholding algorithms and EM cluster classification.

How to cite: Lombana, L.: Flood mapping in small-size water rivers: Analysis of spectral indexes using super-resolved Sentinel-2 images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-690, https://doi.org/10.5194/egusphere-egu23-690, 2023.

Drought is a devastating natural hazard that can be of diverse magnitude, duration and intensity. It leads to economic and social losses and ecological imbalances. Ascribing to climate change, drought has occurred more frequently with high intensity worldwide in recent decades, such as the striking droughts in the summer of year 2022. In water resource aspect, one direct consequence of drought is the decrease of water amount in the rivers, which could further develop into water shortage for irrigation and drinking water supply, and cargo shipping disruption. Therefore, in order to make management decisions that help mitigate the drought damage, it is important to monitor river water anomalies and identify the vulnerable shrinking sections along the river network. Traditional river gauging stations only provide us limited observations of particular spots. A proper utilization of spatially distributed remote sensing data is necessary and effective. In this work, we develop a novel framework to monitor river water shrinking anomaly by including image processing and machine learning approaches, based on earth observation data. The Rhine, a major cargo-route river, is selected as the pilot case, because it had huge water decrease and caused shipping disruption during the 2022 summer’s drought in Germany. The Modified Normalized Difference Water Index (MNDWI) is calculated from the green and Shortwave-Infrared bands of Sentinel-2 satellite images.  MNDWI images of a specific non-drought period is defined as the reference datasets representing normal conditions. Afterwards, a new water shrinking index is introduced to quantify the river water anomaly during drought periods.  Specifically, a python based algorithm which includes image processing and machine learning clustering methods is developed to scan along the MNDWI images to compute the water shrinking index with adjustable river section size. With the index datasets, river sections are further grouped into categories with drought vulnerable levels. By parameterizing the section size, the algorithm is able to quantify and identify the vulnerable shrinking river sections with varying scales. It provides classified references of drought affected hotspots for the regional water management plans in case of drought mitigation. Such a scalable framework can offer a timely distributed monitoring of the drought impacts on the water resource along the river network. As a long term benefit, numerical connections can be identified between the river shrinking status and the economic losses of cargo shipping disruption due to drought.  This is of great value to facilitate the drought impact analysis and forecasts.

How to cite: Tuo, Y., Zhu, X., and Disse, M.: An innovative data driven approach improves drought impact analysis using earth observation data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15389, https://doi.org/10.5194/egusphere-egu23-15389, 2023.

With pervasive access to mobile phones with powerful sensors and processors, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from traditional sensors. But there is a lack of a comprehensive application programming interface (API)-based framework that can collect data from multiple sources through user-friendly workflows. INDRA Reporter has been designed with a mobile-first approach geared towards real-time applications and an emphasis on user-interface/user-experience (UI/UX) to maximize collection of higher fidelity data. This paper details a comprehensive suite of tools for active and passive crowdsensing of natural hazards such as floods, storm, lightning, rain etc. Currently the framework includes mobile applications, telegram chatbots, and a publicly available dashboard. Most citizen science applications in flooding are quantitative, which makes it difficult for non-specialists to provide accurate scientific information along with providing user insight into prevailing situation within a single coherent workflow. It is imperative that workflows targeting dangerous situations emphasize on speed and visual acuity while collecting critical data.  The main objective of INDRA is to provide a simple and intuitive way of collecting qualitative and quantitative data from people. Since traditional data collection through ground-based sensors and satellites suffer from various limitations, measurements collected using INDRA will supplement these sources and form the basis of developing multi-sensor data products. We are reporting the development and release of four components of the framework – a) open INDRA API b) INDRA Reporter mobile application, c) Telegram Chat bot, and d) web dashboard. The API has been kept extensible in order to expand the data collection to other hydrologic and meteorological phenomenon.

How to cite: Saharia, M., Saharia, D., Gupta, S., and Singhal, S.: International Natural Disasters Research and Analytics (INDRA) Reporter: A multi-platform Citizen Science Framework and Tools for Disaster Risk Reduction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11217, https://doi.org/10.5194/egusphere-egu23-11217, 2023.

Hydrology is a data limited science, especially spatially distributed observations are lacking. Citizen science observations can complement existing monitoring networks and provide useful data. Engaging the public in data collection can also increase people’s interest and awareness about water-related topics. In this PICO, we will present the CrowdWater project, in which citizen scientists share, with the help of a smartphone app, hydrological observations on stream water levels, the presence of water in temporary streams, soil moisture conditions, plastic pollution, and general information on water quality. We will highlight the type of data that are collected, our quality control procedures, and the value of the data for hydrological model calibration. We will also discuss the motivations of the citizen scientists to join the project and to continue to contribute to the project. Although the majority of our frequent contributors are adults, we try to engage the youth in the project by giving presentations in schools and at science fairs. Therefore, we will end the PICO presentation with some examples of our outreach work and lessons learned.

How to cite: van Meerveld, I., Schwarzenbach, F., Goebel, R., Scheller, M., Blanco Ramirez, S., and Seibert, J.: Water observations by the public- experiences from the CrowdWater project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13505, https://doi.org/10.5194/egusphere-egu23-13505, 2023.

Groundwater has provided a reliable source of high-quality water for human use. After India, USA and China, Pakistan is the fourth largest groundwater user in the world and around 60x10⁹ m³ of groundwater is extracted annually. The situation in Pakistan has further exacerbated when government subsidized electricity for agricultural purposes – paving the way for installation of myriad tube wells across the country which resulted in excessive withdrawal of groundwater. The major challenges in sustainable groundwater management system are twofold. First, increasing withdrawals to meet growing human needs have led to significant groundwater depletion, which is usually not monitored due to high cost of monitoring system. Second, data limitations and the application of regional groundwater models for future prediction.

In this research, Internet of Things (IoT) enabled smart groundwater monitoring system has been developed and tested to monitor in-situ real-time dynamics of groundwater depletion. Each groundwater monitoring sensor is connected to an embedded module that consists of a microcontroller and a wireless transceiver based on Long Range Radio (LoRa) technology. The readings from each LoRa enabled module is aggregated at one (or more) gateways which is then connected to a central server typically through an IP connection. Sensors of the smart groundwater monitoring system were calibrated in the lab by fluctuation water levels in a 3-meter water column. A network of the low-cost groundwater sensors was installed in managed aquifer recharge wells to provide real-time assessment of groundwater level measurement remotely. The smart and resource efficient groundwater monitoring system help to reduce number of physical visits to the field and also enhance stakeholders participation to get social benefits (promote equity among groundwater users), economic benefit (optimize pumping, which reduces energy cost) and technical benefit (better estimates of groundwater abstraction) for sustainable groundwater management.

How to cite: Jadoon, K. Z., Ali, M. Z., Yousafzai, H. U. K., Rehman, K. U., Shah, J. A., and Shiekh, N. A.: Smart Groundwater Monitoring System for Managed Aquifer Recharge Based on Enabled Real-Time Internet of Things, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12909, https://doi.org/10.5194/egusphere-egu23-12909, 2023.

Operating precise rainfall nowcasting with the help of observations from weather radar can give an effective warning before hydrometerological hazards occur. A common radar based rainfall nowcasting procedure includes: rain cell identification and tracking, spatial and temporal analysis of rain cell, rainfall nowcasting and nowcasting results evaluation.

In this study, an open source rainfall nowcasting tool - pyRCIT is designed and developed which is purely based on qualified weather radar data. It have four main modules: (1) weather radar data processing; (2) rainfall spatial and temporal analysis; (3) deterministic rainfall nowcasting and (4) ensemble rainfall nowcasting. In pyRCIT, rainfall is firstly obtained from weather radar data sets with a series of data quality adjustment procedures. Secondly, rain cells are identified and their spatial and temporal properties are analyzed by the RCIT algorithm. Thirdly, deterministic rainfall nowcasting is operated following the extrapolating schema using lagrangian persistence and semi-lagrangian methods separately, nowcasting results are evaluated by the object oriented verification method - SAL (Structure-Amplitude-Location). Finally, nowcasting uncertainties are analyzed by the random field theory and the quantified uncertainties are implemented as the aid of ensemble rainfall nowcasting.

Nowcasting quality of pyRCIT are evaluated by comparing it with some main rainfall nowcasting methods: TREC, SCOUT and pySTEPS. Comparative results showed that deterministic nowcasting score of pyRCIT were higher than the TREC and SCOUT methods but is nearly equal to the score of pySTEPS, for the ensemble nowcasting, score of pyRCIT is higher than all three methods for the selected cases. The pyRCIT can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and flash flood forecasting.

The code of pyRCIT is available at https://github.com/greensubriane/PYRCIT.git

How to cite: He, T. and Einfalt, T.: pyRCIT - A rainfall nowcasting tool based on a synthetic approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2493, https://doi.org/10.5194/egusphere-egu23-2493, 2023.

The quality of mosaic QPE directly determines the accuracy of QPF products from nowcasting models. However, there is a common spatial discontinuity phenomenon caused by the biases of multiple radars in mosaic QPE. Consistency correction, a type of multi-radar quality control method, can be used to mitigate the spatial discontinuity of mosaic QPE, but its improving effect on QPF products should be analyzed.

For this consideration, a consistency correction method based on GPM KuPR proposed by Chu et al (2018a) was applied to the three S-band operational radars of China, and the improvement on QPE by Z-R relationship, deterministic QPF by S-SPROG (Spectral Prognosis), and ensemble QPF by STEPS (Short-Term Ensemble Prediction System) were analyzed. The results showed: 1) the raw reflectivity factors by the three operational radars over the same equidistance area were significantly different. After the consistency correction, the differences decreased to be less than 0.5 dB and the spatial discontinuity of mosaic products disappeared. 2) The precision of mosaic QPE was significantly improved after the correction, and the average RMSE of QPE decreased by 19.5%, and the TS of heavy rainfall and above rose by 44.8%. 3) The 0-1h deterministic QPF by S-SPROG, and ensemble QPF by STEPS were significantly improved after the correction. The deterministic (ensemble) TS of moderate rain and above rose by 11.9% (10.2%), and that of heavy rain and above increased by 34.2% (38.7%). 4) Furthermore, the consistency correction method contributed to precipitation velocity estimation, and decreased its RMSE by 25.0%. Clearly, the consistency correction method is significantly contributive to multi-radar mosaic QPE and precipitation nowcasting.

How to cite: Chu, Z.: Improvement of Multi-Radar Quantitative Precipitation Nowcasting with Consistency Correction Method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16647, https://doi.org/10.5194/egusphere-egu23-16647, 2023.

Population growth and economic development increase water demand, while human activities degrade the quality of available water resources along the adjacent rivers. The U.S. state of Alabama has been suffering from floods causing the degraded water quality by scouring pollutants into the water. In recent decades, Alabama has been experiencing persistent precipitation deficits and unusual severe droughts, resulting in limited economic and water-based recreation activities within downstream states. Since 2020, The COVID-19 pandemic aroused a series policies like quarantine and lock down, which slowed down the economic development and reduced chances of people going outside to witness the water pollution accidents.

In this study, we conducted a sentiment analysis of over 9,900 water pollution complaints (2012-2020) from residents in Alabama. Overall, it is found that complaints are dominated by negative and objective complaints no matter what extremes events or environmental accidents. Results show that sentiment alteration during climate extremes and COVID period was detected. Potential causes of the sentimental alteration in the public water pollution complaint reports were explored. Results show more complaints during summer seasons, which can be explained as higher temperature and intensive precipitation at that time. More complaints are distributed in the counties that are higher socioeconomically developed, to be more specific, counties with more population and higher GDP level. The severity of antecedent extreme events can affect the sentiment of environmental pollution complaints related to on-going extreme events due to limited human judgements. Key words extracted from the complaints point out the pollution resources and locations, which provide important clues from local government to resolved problems.

This study provides an example of how unstructured data such as public complaints can be used as a technology to improve the water pollution and public health monitoring with the help of big data and artificial intelligent technologies. While the results of this study were based water pollution complaints from residents of Alabama state, it is applicable to other environmental pollutions (like air and land) and other regions with available long-term textual data.

How to cite: Liu, A. and Kam, J.: Observed Sentimental Alteration in the Public Water Pollution Complaints during Climatic Extremes and the COVID-19 Pandemic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10886, https://doi.org/10.5194/egusphere-egu23-10886, 2023.