The accurate assessment of the relationship between reservoir outflow and downstream floods is often challenging in flood control scheduling of upstream reservoirs aimed at downstream flood protection. In this research, the Fengshuba Reservoir in the Dongjiang River Basin, China, is taken as the subject of study. Utilizing a dataset encompassing 62 years of daily measured flood processes, the MIC coefficient is employed to determine the correlation between the reservoir outflow process at different lag times and the flow at the downstream section. The flood propagation time is determined by identifying the lag time associated with the maximum MIC value. By utilizing the BPANN model, which incorporates the reservoir outflow process and the interval flood process as inputs, an accurate prediction of the downstream flood process is achieved, resulting in a closer approximation to reality in flood estimation at the downstream section. The model has been validated in the district between Fengshuba and Longchuan, exhibiting a certainty coefficient of 97% and a prediction qualification rate of nearly 90%. In comparison with the conventional Maskingen evolution method, the calculated outcomes provide enhanced support for flood control safety, enabling precise hourly control of downstream flood processes and upstream reservoir outflow processes.

How to cite: Li, N., Tan, C., Zhao, B., Huang, J., and Qin, Y.: Research on Data Mining-Based Precision Flood Control Scheduling Strategy for Reservoirs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1313, https://doi.org/10.5194/egusphere-egu24-1313, 2024.

In recent years, heavy rainfall and flash flood events have occurred worldwide, leading to wide damage on technical and social infrastructure. Due to climate change, it can be assumed that these water extreme events will increase in future. A water-sensitive urban development is one strategy to address these flash floods and to minimize their consequences. For this purpose, emergency drainage routes are required in order to divert the water masses through urban areas with as little damage as possible. The research project “Urban Flood Resilience – Smart Tools” (FloReST), funded by the German Federal Ministry of Education and Research (BMBF), focuses on the assessment of emergency drainage routes and flow paths with the aim to increase the resilience of infrastructures against flash floods within the context of a water-sensitive urban development.

In this study, both load-independent and load-dependent grid-based analyses for flow path identification were conducted on digital terrain models (DTM) of varying spatial resolutions. The objective is to assess the impact of spatial resolution on modelling results and derive the potential vulnerability of infrastructure to flash floods. To achieve this, freely available geospatial data generated through airborne laser scanning, as well as additional geospatial data collected through terrestrial surveying, are utilized.

Identifying emergency drainage routes requires information on flow paths, water depths, and potential flooding extents. Both one-dimensional analysis and two-dimensional hydrodynamic modelling are typically based on digital terrain models with a resolution of 1 m x 1 m (DTM1). However, for precise planning of emergency drainage routes, the DTM1 is inadequate due to its limited spatial resolution.

In our study area in the Ahr Valley (Germany), various flow path analyses were conducted on DTMs with different spatial resolutions. Analyses based on state-of-the-art methods using the DTM1 showed that the calculated flow paths align with the actual flow paths in rural areas but significantly deviate in urban areas. Local, runoff-relevant structures, such as curbs and smaller walls, were either not covered or inadequately represented with this resolution. However, these structures can have a significant impact on flow paths and flood vulnerability in urban areas.

To simulate water movement more accurately the DTM was refined. Higher-resolution terrain models are generated by processing raw data from freely available geospatial sources and used for 2D hydrodynamic modelling. This approach, allows to identify more detailed flow paths and water depths especially in urban areas. Depending on local conditions, additional surveying may be necessary to capture all runoff-relevant structures. In a further step, a combined DTM is created using both terrestrial surveying and freely available geospatial data generated through airborne laser scanning. Flow path analyses based on this combined DTM enable a detailed assessment of urban infrastructure vulnerability to flash floods as well as a high-resolution planning of measures. 

How to cite: Stratmann, G., Kirschbauer, P. Dr.-Ing. L., and Hörter, L.: Grid-based 2D hydrodynamic modelling for heavy rainfall prevention: Impact of geospatial resolution and the assessment of urban infrastructure vulnerability to flash floods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1950, https://doi.org/10.5194/egusphere-egu24-1950, 2024.

A watershed with insufficient streamflow data faces challenges in mitigating flood damages through infrastructure. Many small/middle-size watersheds adopt data from nearby watersheds with sufficient measurements, based on the similarity of watershed characteristics and weather conditions. However, not many areas have optimal conditions to utilize data from nearby sources. To generate streamflow, we employ a regional weather model (the Weather Research and Forecasting model or WRF) and a rainfall-runoff model known as Génie Rural à 4 paramètres Horaires (GR4H). The WRF model generated rainfall data for past years base on the globally simulated data (Final (FNL) data from NCEP) with possible physical atmospheric conditions. The optimally conditioned GR4H produced streamflow data using rainfall data from WRF. All produced streamflow data is statistically tested for the applicability as basic data for background information to decrease the flood damages.

How to cite: Jung, Y., Shin, M. J., and Jeon, S. J.: Enhancing Flood Resilience through Integrated Models in a Streamflow-Scarce Watershed, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4950, https://doi.org/10.5194/egusphere-egu24-4950, 2024.

Past studies on weather and climate extremes have focused on individual extremes. These studies cannot effectively track/model compound extreme events. The major objective of this study is to evaluate the changing risk of compound precipitation and temperature extreme events based on historical observed period (1964–2014) and the future period (2045-2054 and 2085-2094). We explored four different compound extreme event impacts of temperature and precipitation (dry-warm, dry-cold, wet-cold, and wet-warm) at the United States Department of Energy Office of Environmental Management (DOE-EM) sites. 25% and 75% quantile thresholds were used to define extreme climate conditions. The empirical approach for the analysis of compound extremes was conducted by counting the number of concurrent occurrences of multiple extremes during the same month (year). The empirical probability density function of compound events was constructed using nonparametric kernel density estimators to compare the seasonal distribution of four different compound event modes. Our results show slightly increasing trends in both Wet-Cold Mode and Wet-Warm Mode, and slightly decreasing trends in Dry-Cold Mode. Results from our study provide better understanding of the impact of climate extremes on mission-critical assets at EM cleanup sites.

How to cite: Dhakal, N.: Evaluation of compound precipitation and temperature extremes in a changing climate , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14045, https://doi.org/10.5194/egusphere-egu24-14045, 2024.

Water demand is influenced by a number of factors with temperature and precipitation being among the key elements. Especially during longer dry and heat periods, water demand changes due to changes in consumption patterns, filling of swimming pools and increased garden irrigation. 

Therefore, a reliable water demand forecast is very important for Austrian water utilities in order to be able to react to increasing water demand peaks. In a previous study (Stelzl A.; Fuchs-Hanusch D.), long-term forecasting models were developed using climate indices. The developed modeling approach achieved satisfactory results in terms of prediction accuracy. However, it was found that the effect of dry and hot periods could not yet be modeled with sufficient accuracy. For this reason, this study attempts to improve the modeling approach by adding the Standardised Precipitation Evapotranspiration Index (SPEI) as an additional parameter into the model. In addition, the new work targets short-term water demand forecasts to provide water utilities with a basis for taking timely action to cope with peak water demand or inform customers about necessary water saving measures. Current short-term forecasts of the meteorological situation (e.g. SPEI) are provided by Land Steiermark (Land Steiermark, 2024). The water demand forecasting model developed in this study can be applied to these short-term forecasts.

In a first step, the relationship between SPEI, climate indices and water demand was determined. The SPEI and the climate indices are calculated from historical weather records for the selected study sites. During the model building process, a stepwise forward variable selection process is carried out to determine the significant parameters. The SPEI was found to be a significant parameter for water demand forecasting. The model building process and evaluation is still ongoing. It is expected that the use of the SPEI will improve the accuracy of peak water demand forecasting model. The final results will be available at the conference.

References:

Stelzl, A.; Fuchs-Hanusch, D. Forecasting Urban Peak Water Demand Based on Climate Indices and Demographic Trends. Water 2024, 16, 127. https://doi.org/10.3390/w16010127

Land Steiermark, A 14 (2024) Dürreindex - Wasserversrogung. [online] https://www.wasserwirtschaft.steiermark.at/cms/beitrag/12903795/173854972. (Accessed:  10. January 2024)

How to cite: Stelzl, A. and Fuchs-Hanusch, D.: Short-term water demand forecast considering SPEI and climate indices, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19471, https://doi.org/10.5194/egusphere-egu24-19471, 2024.

Continual improvements in the quality, coverage, and accessibility of global geospatial datasets now mean that it is feasible to derive hydraulically plausible urban drainage networks and simulation models of these networks in cities worldwide. Privacy concerns, coupled with the cost and uncertainties in developing traditional network models, have fuelled the demand for such synthetic alternatives. We present SWMManywhere, which can create a hydraulically plausible Storm Water Management Model (SWMM) simulation model for a city using only the boundary coordinates of the target area. The datasets used in SWMManywhere are global, although their quality varies from country to country. We assess the utility of a SWMManywhere model by comparing pluvial flooding, in-pipe flows, and drainage network outflows in known networks. A previously unexplored difficulty with the use of synthetic network generation in urban environments is delineating the network’s boundaries when there are multiple and competing plausible outfalls, which is typical of most large cities. By using a sensitivity analysis approach, we explore how changing parameters associated with the network topology and boundaries can alter simulations. Assessing the uncertainty in our method helps to understand whether synthetically generated network models can produce meaningful simulations in their presumed most common use case: a dense urban environment where little is known about the network’s boundaries or outfalls.  

How to cite: Dobson, B., Jovanovic, T., and Chegini, T.: Development and sensitivity analysis of a tool to generate synthetic urban drainage models globally, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2901, https://doi.org/10.5194/egusphere-egu24-2901, 2024.

In a world with accelerating climate change, rapid population increase and urbanization, urban water systems are under a growing stress. Precise short- and medium-term water demand forecasting are needed to optimize water supply operations. While machine learning methods are commonly used for this task, most studies rely on point predictions which lack a robust characterization of prediction errors. This undermines decision making under uncertainty and related applications. In this work, we employ real data to demonstrate the advantages of probabilistic water demand forecasting up to a week ahead. In particular, we explore the benefits of conformal predictions, a set of novel techniques providing distribution-free prediction intervals. Conformal predictions are model agnostic and may guarantee the validity of the prediction intervals under some assumptions. We apply the conformal prediction framework on several ML models, including tree-based methods, deep neural network models and classical time series analysis. We compare these conformalized approaches against traditional probabilistic methods such as quantile regression and Monte-Carlo dropout.

How to cite: Wewer, C. and Taormina, R.: Conformal Prediction Intervals For Water Demand Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8166, https://doi.org/10.5194/egusphere-egu24-8166, 2024.

Irrigation plays a crucial role in bolstering crop productivity and ameliorating the adverse impacts of drought. Despite its significance, existing studies have not extensively incorporated irrigation into agricultural drought indicators. In this study, we introduce a novel agricultural drought index, the Standardized Irrigation Water Deficit Index (SIWDI) that is quantified using meteorological, phenological, and runoff inputs. To test its robustness, we calculated the irrigation water deficit for three major crops across various time scales in the Yangtze River Basin over the past 23 years. Our analysis reveals that the irrigation water deficit in this region follows a norminvgauss probability distribution. Drawing a mathematical parallel to the Standardized Precipitation Evapotranspiration Index (SPEI), the SIWDI is compared to the SPEI across the Yangtze River Basin. Results underscore the SIWDI’s notable advantage in evaluating drought conditions in agriculturally concentrated regions, alleviating the impact of non-growing season droughts by incorporating crop growth processes and spatial distribution. This innovative index provides monitoring outcomes closely aligned with actual conditions, empowering farmers to respond more effectively to the looming threat of drought.

How to cite: Wang, S., Zhang, Y., and Tian, J.: Enhancing Agricultural Drought Assessment through the Standardized Irrigation Water Deficit Index, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9589, https://doi.org/10.5194/egusphere-egu24-9589, 2024.

The impact of climate change and extreme weather events, such as heatwaves and heavy rainfall, poses a severe environmental crisis, affecting both natural and socioeconomic systems, including governments and businesses. Responding to this, the Task Force on Climate-related Financial Disclosures (TCFD) emphasizes the need for organizations to quantitatively announce their physical risks and opportunities under climate change, highlighting proactive management of their risks.

With its seasonal concentrated rainfall and topographical influences, East Asia faces escalating vulnerabilities to droughts and floods. Collaborative disaster response efforts at governmental and corporate levels are crucial. This study focused on the data availability in South Korea and China's southeastern region to develop flood risk models to support reporting by the TCFD.

For South Korea, a model was developed by using time-series flood traces from 2006 to 2018 as training data, incorporating monthly maximum consecutive 5-day precipitation, topography, soil, and land cover maps into a random forest model. In China, a model was developed by combining monthly maximum consecutive 5-day precipitation and topographic information. Results highlight flood risks, particularly in South Korea's low-lying agricultural areas and southeastern China's lowland and coastal regions. Both countries experience increased flood risk under SSP1-2.6 and SSP5-8.5 scenarios from 2030s to 2050s, corresponding to rising future maximum rainfall.

Given the tendency for floods to persist in previously affected areas, disaster preparedness through predictive measures becomes imperative, shifting the focus from post-disaster recovery to proactive disaster prevention. Additionally, guidelines for government and corporate-level utilization of available data and establishing action priorities in the event of a disaster are necessary.

How to cite: Park, E., Jo, H.-W., Son, J., Kraxner, F., and Lee, W.-K.: Developing Physical Flood Risk in the face of Climate Change: A Case Study for South Korea and South-Eastern China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11302, https://doi.org/10.5194/egusphere-egu24-11302, 2024.

The escalating challenges posed by rapid urbanization and climate change have intensified the quest for sustainable stormwater management strategies. Permeable pavement practices have emerged as a pivotal solution to effectively control stormwater runoff and address the associated flooding issues. This study delves into the comparative analysis of three prevalent permeable pavement types—permeable asphalts (PA), permeable concretes (PC), and permeable interlocking concrete pavers (PICP)—with the overarching goal of identifying the most efficient solution for alleviating the negative impacts of surface runoff.

In pursuit of this objective, the study conducts simulations for three distinct scenarios, each representing different extreme storm events within a designated catchment area. The evaluation encompasses the performance of PA, PC, and PICP, both with and without the integration of permeable pavements, utilizing the sophisticated Personal Computer Stormwater Management Model (PCSWMM). The selected catchment area is situated in King County, Washington, USA, providing a real-world context for the investigation.

The validation of the PCSWMM model attests to its reliability in predicting peak discharges within the study reach, establishing a robust foundation for subsequent analyses. The outcomes reveal that all three forms of permeable pavement effectively prevent flooding, with PA emerging as the most formidable solution, showcasing a remarkable average reduction of 51.25% in peak flow and 65% in total flow. In contrast, PC demonstrates a slightly more modest improvement, with average reductions of 21.75% in peak flow and 34.25% in overall flow. Furthermore, PICP exhibits the lowest reduction in peak flow (7.0%) and total runoff volume (15.75%). In conclusion, this study offers valuable insights into the comparative effectiveness of permeable pavements in urban stormwater management, emphasizing the critical role of thoughtful pavement selection in sustainable urban planning endeavors.

How to cite: Salem, A., Abduljaleel, Y., and Amen, E. M.: Enhancing Urban Stormwater Resilience: A Comparative Study of Permeable Asphalt, Concrete, and Interlocking Pavers for Sustainable Flood Mitigation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2209, https://doi.org/10.5194/egusphere-egu24-2209, 2024.

Compound extremes characterized by simultaneous or consecutive incidence of multiple
extreme events (i.e. dry and hot extremes), or fusion of extreme events amplifying their
individual impacts, or amalgamation of non-extreme events resulting in an extreme impact
when combined. Our key focus was on understanding compound extremes, particularly the
interaction between prolonged dry spells and intense heat waves. We explored the individual
extremes of drought (dry extreme) and high temperatures (hot extreme) using some essential
indices like SPI, STI, WSDI and CDD. Also to better understand these complex events, we
employed two specialized tool, the Compound Drought and Hot Extreme Index (CDHI) and
one of the Joint extreme index (JEI) i.e. WDS (Warm and Dry Spell) for meteorological
subdivision-17 i.e. west Rajasthan region. Both CDHI and JEIs are used to characterize the
joint occurrence of extreme precipitation and temperature. In this study, we employed
temperature data from the Indian Meteorological Department (IMD), recorded at a resolution
of 1 degree, covering the years 1951 to 2019. Additionally, we gathered monthly
precipitation data, which was observed at a finer resolution of 0.25 degrees, spanning from
1901 to 2019. Moreover, to seamlessly integrate and refine our analyses, we applied 2D
bilinear interpolation, using Euclidean interpolation principles, to align the 0.25-degree
gridded precipitation data with the 1-degree gridded framework of temperature. For instance,
the SPI values of -1.77 and -2.28 for the monsoon seasons of 1987 and 2002, respectively,
suggests that the meteorological drought in 1987 was less severe than in 2002. Conversely,
the STI values indicate that 1987 was hotter than 2002, with STI values of 3.15 and 1.91,
respectively. Consequently, comparing Compound dry and hot extremes based solely on SPI
and STI data proves challenging. Therefore, CDHI served as a valuable metric for comparing
the overall severity of compound drought and hot extremes. A lower CDHI value indicates
more severe compound drought and hot extremes, and vice versa. The CDHI values for the
years 1987 and 2002 were -2.91 and -2.51, respectively, suggesting severe compound drought
and hot extremes in 1987. Also, as we plotted the time series plot for the meteorological
subdivision 17, i.e., west Rajasthan region, it was observed that when the time periods were
tiny (1, 3, or 6 months), the SPI, STI, and CDHI frequently moves above or below zero. But
as the time periods were lengthened or increased (12, 24, 36, or 48 months), the SPI, STI, and
CDHI responded dilatorily to changes in precipitation, i.e., overall periods with positive and
negative values of indices reduced, but the ones which appeared were longer in duration.

How to cite: Gaur, N., Chavan, S., and Singh, A.: Assessment of Compound Drought and Hot Extreme Conditions Over West Rajasthan, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18117, https://doi.org/10.5194/egusphere-egu24-18117, 2024.

Copula theory has received attention in the field of hydrology. Copula function is used to
derive the multivariate distribution of variables. Using copula have an advantage that
marginal distribution of independent variables can be of any form and the variables can be
correlated. Flood frequency analysis (FFA) help us to quantify the risk associated with flood.
In this study copula theory is used for flood frequency analysis of Krishna River in India.
Four stations (i.e., Kurundwad, Huvinhedigi, K. Agrharam, and Wadenpally) was selected on
Krishna river basin. Peak over threshold method (POT-method) was used to select the
independent events for analysis. Using methodology provided in Flood Estimation Handbook
(FEH), Volume and Duration data is extracted from the selected events. The joint
dependence structure of flood variables is derived, for frequency analysis of Peak Flow (P),
Flood Volume (V), and Flood Duration (D). Best fit marginal distributions of these flood
variables are determined using five parametric (Normal, Exponential, Extreme value,
Lognormal, and Gamma distribution) and one non-parametric (Kernel distribution)
probability distributions. Kolmogorov-Smirnov & Anderson-Darling test was performed to
find out the best fit distribution for flood variables. For modelling of the joint dependence
structure of peak flow-volume (P-V), flood volume-duration (V-D), peak flow-duration (P-
D), five Archimedean family of copulas, namely Independence, Clayton, Frank, Gumbel-
Hougaard, and Ali-Mikhail-Haq Copulas are evaluated. Goodness-of-fit (GOF) test using
Rosenblatt’s probability integral transformation was used to find out the best fitted copula for
bivariate models. Clayton copula has been identified as the best fitted copula for all the
bivariate models considered. Clayton copula function is used to obtain conditional return
periods, Conditional return periods of flood characteristics can be useful for risk based design
of water resource projects.

How to cite: Badoni, A. and Chavan, S.: Bivariate Flood Frequency Analysis of Krishna River using Copula Theory, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18700, https://doi.org/10.5194/egusphere-egu24-18700, 2024.

In recent years, there has been growing concern about deforestation in the Amazon River basin, particularly in relation to its impact on regional water resources. This study performs a spatial and temporal analysis of deforestation variations between 2001 and 2020, using MODIS and Sentinel data. Using supervised classification techniques, we classified land changes into afforestation, deforestation and reforestation and analyzed the transitions between different land uses including forest, pasture, shrubland, crops and urbanization. Our findings show an annual forest loss of about 5,726 square kilometers, much of this deforestation is localized to specific subregions, although the overall size of the watershed suggests a lack of sensitivity.

These results show a significant correlation with variations in evapotranspiration, estimated through a model calibrated with data from the ERA5 reanalysis. This dataset was used to analyze the standardized precipitation and evapotranspiration indexes (SPEI), revealing that, during the last 20 years under study, the region experienced an increase in both the magnitude and intensity of drought compared to the previous 20 years.

In particular, a direct relationship is observed between aggressive agricultural policies in Brazil and Bolivia and increased deforestation rates. In addition, this study serves as the basis for complementary research work assessing the implications of these land use changes on river discharge and estimated groundwater recharge in the Amazon basin.

How to cite: Gacharna Gonzalez, J. M., Corzo Perez, G. A., Santos Granados, G. R., Sanchez Hernandez, K. A., Tami Riveros, C. A., Hernandez Torres, G., Herran, G., Gutierrez, D., and Rubiano, F.: Spatial-temporal analysis of the impact of deforestation on the hydrological variability of the Amazon basin., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20802, https://doi.org/10.5194/egusphere-egu24-20802, 2024.