HS3.2 | Integrated Approaches for Resilient Water Systems: Spatio-Temporal Analysis and Uncertainty Management
Integrated Approaches for Resilient Water Systems: Spatio-Temporal Analysis and Uncertainty Management
Convener: Yunqing Xuan | Co-conveners: Gerald A Corzo P, Vitali Diaz, Peter van Thienen, Georgia PapacharalampousECSECS, Paul Muñoz
| Tue, 16 Apr, 16:15–18:00 (CEST)
Room 2.31
Posters on site
| Attendance Tue, 16 Apr, 10:45–12:30 (CEST) | Display Tue, 16 Apr, 08:30–12:30
Hall A
Posters virtual
| Attendance Tue, 16 Apr, 14:00–15:45 (CEST) | Display Tue, 16 Apr, 08:30–18:00
vHall A
Orals |
Tue, 16:15
Tue, 10:45
Tue, 14:00
The management of water systems is subject to a multitude of challenges on different spatial and temporal scales, ranging from hydrological extremes to uncertainties in planning and decision-making. Both aspects relate to the probability distributions of relevant variables in time and space, exploring the full range of the distributions and the extremes. This integrated session recognizes the interconnected nature of these challenges, and seeks to merge insights from two distinct yet interrelated domains: spatio-temporal analysis and uncertainty management in water networks. Spatio-temporal analysis can be applied to enhance prediction and management of hydrological extremes, in particular floods, droughts, and compound hazards. Hydrological challenges often manifest in spatial, temporal, or spatio-temporal dimensions. Leveraging technological advancements such as remote sensing, the first block of this session explores the integration of diverse data sources into hydrological models and analyses. Statistical methods and Machine Learning (ML) are pivotal, addressing challenges posed by data scarcity and the dynamic nature of hydrological events. This emphasis extends to spatio-temporal analyses, vital for refined risk assessment and early management strategies in the face of increasing hydrological variability.

The deterministic paradigm has traditionally underpinned hydraulic modeling and planning of drinking water, wastewater and urban drainage networks. While methods like calibration and scenario approaches address some uncertainties, an evolving understanding of uncertainties demands a more comprehensive approach. This second block of this session focuses on the treatment of uncertainty in planning, modeling, and decision-making for water networks, encompassing drinking water, wastewater, and urban drainage.

This integrated session provides a platform for interdisciplinary approaches, aiming at hydrologists, statisticians, and water system experts. Combining spatio-temporal analysis and uncertainty management brings together complementary methodologies and applications for resilient water systems in both urban and rural contexts.

Orals: Tue, 16 Apr | Room 2.31

Chairpersons: Peter van Thienen, Vitali Diaz, Gerald A Corzo P
On-site presentation
Maria C. Cunha

Water distribution networks (WDNs) are essential systems that supply water for the most basic normal activities of our society. Their design is a complex problem because WDNs must function properly, permanently responding to consumer needs and taking into account many different technical and social issues.

In recent decades, they have been extensively studied considering only a single demand setting. Therefore, the pipe sizes obtained using this approach are unreliable to address a wide spectrum of situations that WDNs face during their service life. Water demand is one of the most important sources of uncertainty for the design of WDNs.

In fact, water demand is affected by different types of uncertainty (Walker et al., 2003). It can be the result of users' behavioural variability on a daily and seasonal scale (which can be classified as 'statistical uncertainty’) and can also be related to "social variability" due to the unpredictable nature of social, economic, and cultural dynamics (a more challenging level of uncertainty, 'scenario uncertainty').  'Statistical uncertainty’ can be addressed by extracting information from available data and making assumptions about the statistical parameters of water demand distribution (Magini et al., 2019). 'Scenario uncertainty’ may be handled through various approaches for creating plausible future demand changes i.e., likely assumptions about the future hypotheses on demand change that can concern the number of users, socio-economic situation, changes in technology, tariffs, cultural dynamics and users' behaviour (Cunha. 2023).

Robust optimization models may embrace scenarios with different levels of demand uncertainty. It is essential to generate demand scenarios to develop robust WDNs, in multi-objective environments, including issues such as their reliability and the level of service they provide (Cunha et al., 2023). Thorough comparisons of the robustness of WDNs sized either using deterministic approaches or considering different levels of uncertainty are vital to understand the benefits of these last ones.

This presentation will provide an overview of the key issues and challenges in defining robust WDNs to cope with multiple states of the world.



Cunha, M. C. (2023). Water and Environmental Systems Management Under Uncertainty: From Scenario Construction to Robust Solutions and Adaptation. Water Resources Management, 1–15

Cunha, M. C., Magini, R., & Marques, J. (2023).  Multi-objective optimization models for the design of water distribution networks by exploring scenario-based approaches. Water Resources Research, 59, e2023WR034867. https://doi. org/10.1029/2023WR034867

Magini, R., Boniforti, M. A., & Guercio, R. (2019). Generating scenarios of cross-correlated demands for modelling water distribution networks. Water (Switzerland), 11(3), 493.

Walker, W. E., Harremoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support. Integrated Assessment, 4(1), 5–17.

Acknowledgments: The author thanks the Portuguese public agency “Fundação para a Ciência e a Tecnologia” (FCT) the support of national funds under the project UIDB/ 00285/2020. 

How to cite: Cunha, M. C.: Key issues and challenges for  managing water distribution networks under uncertainty , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21604, https://doi.org/10.5194/egusphere-egu24-21604, 2024.

On-site presentation
Janneke Remmers, Ryan Teuling, and Lieke Melsen

The usage of hydrological models is diverse and omnipresent. For practical purposes, these models are applied to, for example, flood forecasting, water allocation, and climate change impacts. Numerous methods exist to execute any modelling study. Choosing a method creates a narrative behind each model result, which implies that models are not neutral. So, how do modellers make these decisions? What motivates them to choose a certain method? We conducted fourteen semi-structured interviews between September and December 2021 with nine modellers from six different water authorities and five modellers from four different consultancy companies in the Netherlands. The interviewees are hydrodynamic modellers executing decision-support modelling. The interviews were all recorded and transcribed. We executed an inductive content analysis on the transcriptions. We will discuss how the interviewees motivate the decisions they have made in the modelling process, exploring the non-neutrality of the modelling process. With these insights, we aim to contribute to a discussion on how models, despite their unavoidable non-neutrality, can be robust and dependable to support decision making. Understanding the social aspects behind the modelling process is necessary to create a more complete picture of all the uncertainties involved in modelling, which should include sharing and reflecting on the narrative behind the modelling results.

How to cite: Remmers, J., Teuling, R., and Melsen, L.: A modeller’s compass: how modellers navigate dozens of decisions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18665, https://doi.org/10.5194/egusphere-egu24-18665, 2024.

On-site presentation
Mohammed N. Assaf, Nicolo Salis, Enrico Creaco, Lorenzo Tamellini, Manenti Sauro, and Sara Todeschini

Hydrological models are crucial in various engineering applications, including streamflow forecasting and flood risk estimation. Tools like the Stormwater Management Model (SWMM) are indispensable for efficacious water resource management. Calibrating these models is a necessary step to minimize parameter uncertainties and ensure accurate representation of a catchment area's hydrological response. However, the calibration process often faces challenges due to the need for extensive parameter adjustments. Sensitivity analysis (SA) is employed to mitigate these challenges by identifying and focusing on the most influential parameters, thereby streamlining the calibration process. In this work, the Morris method was applied to identify the sensitive parameters in the SWMM model, which were subsequently considered in the optimization process using Genetic Algorithms (GA). The results of the sensitivity analysis highly depend on the model output targets, such as total runoff volume and peak flow rate.

The traditional approach of dividing data into calibration and evaluation subsets is a fundamental practice in model development. Nevertheless, the impact of data allocation on model evaluation performance has not received sufficient attention in the literature. This study investigates the influence of calibration data selection on model performance, utilizing high-resolution experimental rainfall-runoff data from the urban catchment of Cascina Scala in Pavia, Italy. Four criteria—rainfall depth, mean intensity, hydrograph's center of mass, and maximum rainfall depth over five minutes—were employed to select the calibration set. From a total of 24 events, the four criteria were employed to select 8 events from 16 for calibration, while the remaining 8 events were designated for validation. The findings underscore that the selection of the calibration dataset substantially influences the optimally calibrated parameters, subsequently altering model performance.

How to cite: Assaf, M. N., Salis, N., Creaco, E., Tamellini, L., Sauro, M., and Todeschini, S.: Optimizing hydrological modeling on real urban catchment: impact of calibration data selection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12604, https://doi.org/10.5194/egusphere-egu24-12604, 2024.

On-site presentation
Emmanouil A Varouchakis, Andreas Pavlides, and Dionissios T Hristopulos

Environmental mining and exploration present a challenge for spatial analysis due to small sample sizes and data clustering near mining sites. Additionally, the properties of the covariance function may vary across different mines within the same region, necessitating adaptable geostatistical techniques. A novel approach to analyzing mining data spatial dependence introduces the use of Gaussian Anamorphosis, employing the recently proposed Kernel Cumulative Density (KCDE) method. This technique is particularly effective for data sets that exhibit non-Gaussian distributions, such as the typically asymmetrically distributed natural resources data. Gaussian Anamorphosis through KCDE enables the transformation of skewed probability density functions (PDFs) into the normal distribution. KCDE converts the original data distribution into a continuous cumulative density function (CDF), smoothing out the discontinuities inherent in the traditional staircase CDF estimation approach.
We extend our analysis by conducting Kriging interpolation on the transformed data. Since the transformed data distribution closely approximates the normal distribution, it is possible to use the Kriging variance to reliably estimate prediction intervals before the results are inversely transformed back to their original scale for practical interpretation.
To explore the variability of our results, we implemented Monte Carlo simulations based on the transformed data. The simulations provide insights into the potential outcomes and their variabilities, which were then inversely transformed back to their original scale for practical interpretation.
The findings of this study underscore the effectiveness of Gaussian Anamorphosis using KCDE transformation in dealing with non-Gaussian data distributions in geostatistical analyses. The approach enhances the reliability of spatial predictions and offers robust confidence intervals. Our research demonstrates the potential of combining advanced transformation techniques with geostatistical models to address complex spatial dependencies of natural resources data.

The research project is implemented in the framework of H.F.R.I call “Basic research Financing (Horizontal support of all Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union – NextGenerationEU (H.F.R.I. Project Number: 16537).

How to cite: Varouchakis, E. A., Pavlides, A., and Hristopulos, D. T.: A Gaussian anamorphosis model for asymmetrically distributed data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10654, https://doi.org/10.5194/egusphere-egu24-10654, 2024.

Virtual presentation
María José Merizalde, Gerald Corzo, Paul Muñoz, Pablo Guzmán, Esteban Samaniego, and Rolando Célleri

In recent years, frequent climate extreme events have significantly impacted various sectors, especially critical ones like hydropower generation. In Latin America and the Caribbean, hydropower constitutes a pivotal element, contributing 45% to the electricity supply. Among the countries in the region, Ecuador heavily relies on hydropower generation (80%). However, since October 2023, Ecuador has faced unprecedented challenges marked by significant deficits in energy production, not witnessed in the last few decades. This crisis, attributed to severe drought events in the Amazon region, directly impacts one of Ecuador's most crucial hydropower systems—the Paute system. In addition to the crisis, suboptimal reservoir management practices exacerbate these impacts due to the lack of provision for extreme events. The resultant energy deficits are currently causing extensive power outages throughout the country, highlighting the urgency of addressing the issues in reservoir management.

In this research, we introduce an innovative approach to enhance reservoir management efficiency. This approach involves integrating hydrometeorological in-situ and satellite-based data to develop forecasting models for reservoir water levels. We use Ecuador’s largest reservoir, the Mazar reservoir belonging to the Paute system, as a case study. The modeling will employ advanced machine learning (ML) techniques, such as the proven-effective Long-Short Term Memory (LSTM), with the aim of identifying key influencers that significantly impact reservoir level forecasting. Furthermore, we will complement the modeling with the Shapley Additive Explanation method to enhance interpretability, providing insights into hydrological processes. This is intended not only to deepen our understanding of the relationship between hydrometeorological variables and reservoir water levels but also to enrich the input space for our reservoir level forecasting models, contributing to a more accurate and comprehensive predictive framework.

The results of the innovative approach will be then used to develop a methodological framework named ML-Driven Reservoir Management with Integrated Extreme Events Forecasting for the Mazar reservoir, aimed at enhancing reservoir management efficiency during extreme events. The expected results include the identification of crucial hydrometeorological variables for Mazar level forecasting, with models capable of predicting reservoir levels at 15-day to monthly intervals based on dominant variables. This will provide a tangible demonstration of its application to improve management in future extreme event scenarios. Beyond optimizing reservoir management for enhanced hydropower generation efficiency, this approach aims to mitigate adverse impacts on Ecuador's developing sectors, fostering sustainability. By addressing inefficiencies in reservoir management, our study contributes to a more resilient and sustainable hydropower sector in Ecuador.

How to cite: Merizalde, M. J., Corzo, G., Muñoz, P., Guzmán, P., Samaniego, E., and Célleri, R.: Enhancing Reservoir Management for Sustainable Hydropower Generation: A Machine Learning-Driven Approach in Response to Increasing Extreme Events in Ecuador, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13490, https://doi.org/10.5194/egusphere-egu24-13490, 2024.

On-site presentation
Marcela Antunes Meira and Yunqing Xuan

Hydroclimatic extremes, such as droughts, floods, and extreme rainfall have been increasing worldwide leading to severe impacts on society and ecosystems. For that reason, hydrological modelling research has advanced to improve flood and rainfall prediction and control.  This estimation has been traditionally carried out using physical and process-based hydroclimatic models, however, they have limitations due to their physical-based nature. They often require a large amount of different hydro-geomorphological monitoring datasets, as well as in-depth knowledge and expertise regarding hydrological parameters, which must be correctly selected, calibrated, and further interpreted to ensure the reliability of the model. In recent years, data-driven hydrological modelling, such as Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) methods have demonstrated a great deal of promise for enhancing the forecasting of hydroclimatic extremes. In data-driven modelling, the models use a generalized relationship between input and output disregarding the physical mechanism behind the process, built based on historical data. ML methods have some advantages over physical-based models, such as not requiring an understanding of internal specific mechanisms, which can be highly complex to reproduce, as well as having a higher calculation efficiency which may provide a quicker response to extreme events of high-intensity and short duration such as urban flash floods. Although there have been significant advances from the scientific community toward understanding and testing different ML and AI models for various hydrological applications, there are still limitations in their applications. A huge challenge that remains in ML modelling for future extreme floods, is its ‘black-box’ nature where the interactions among various components are unknown, which hinders its further use in supporting important decision-making. Along with that, other challenges in the current hydroclimatic modelling approaches presented by the hydrological community are data availability and assimilation, uncertainty analysis, and model generalisation. Some studies have addressed these issues, showing satisfactory results, especially for hybrid models between ML and traditional process-based approaches and ensembles of multiple methods. However, in light of so many new methodologies and algorithms, we must address their benefits and drawbacks, through an interdisciplinary effort. Understanding the best way to select appropriate methodologies for different settings of data availability, climate variability, and uncertainty, generating rapid and interpretable responses to urgent hydrologic hazards.

How to cite: Antunes Meira, M. and Xuan, Y.: Machine Learning Modelling for Future Hydroclimatic Extremes Under Climate Change: A Review, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20800, https://doi.org/10.5194/egusphere-egu24-20800, 2024.

Virtual presentation
Karel Aldrin Sanchez Hernandez, Gerald Augusto Corzo Perez, German Ricardo Santos Granados, Juan Manuel Gacharna Gonzalez, Carlos Alfredo Tami Riveros, Guillermo Hernandez Torres, Gustavo Herran, Diego Gutierrez, and Fabio Rubiano

The atmospheric dynamics of the Amazon, critical for global environmental stability, have faced increasing influence from numerous El Niño and La Niña events in recent decades. While reanalysis data has incorporated these events through models and measurements, the intricate mechanics of spatial and temporal water vapor transport remain unclear. In this study, we present a preliminary analysis of these dynamics, utilizing over twenty years of ERA5 monthly data over atmospheric layer. Our investigation was constructed on two primary scales, each offering unique insights. The first scale aims to replicate and validate the system's seasonality concerning the Intertropical Convergence Zone (ITCZ), those patterns allow evaluating some patterns and its effects in land hydrological process observed along the basin integrating specific methods and models to clarify how the seasonality was replicated and validated. On the second scale, we delve into smaller hydrological sub-units of the Amazon, identifying their contribution to water recycling, and net fluxes across the basin boundaries. We provide an innovative estimation of transport paths using a 4 cardinal directional approach (brubaker box scheme modified) that makes possible identificate, analyse and understanding, water sources and sinks and its relevance in the normal hydrological production and synergically systems 

The findings indicate the system's relatively stable dynamics in terms of water vapor sources and altitudinal variation across atmospheric layers. Our methodology introduces a novel framework for calculating comprehensive trajectories of water vapor transport from a hybrid lagrangian-eulerian approach, significantly enhancing our understanding of the Amazon's hydrological cycle from an atmospheric perspective. 

To provide more precision, we specify that the stability observed in the system pertains to water vapor sources and altitudinal variation. These stable dynamics contribute valuable insights into the intricate water vapor transport mechanisms in the Amazon. Additionally, we highlight the implications of our findings for future research in understanding how to create alternatives to mitigate some impacts of El Niño and La Niña events on the Amazon's atmospheric dynamics. 

How to cite: Sanchez Hernandez, K. A., Corzo Perez, G. A., Santos Granados, G. R., Gacharna Gonzalez, J. M., Tami Riveros, C. A., Hernandez Torres, G., Herran, G., Gutierrez, D., and Rubiano, F.: Understanding the Amazon's Atmospheric Hydrology: Insights from ERA5 Data and Directional Transport Analysis , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20683, https://doi.org/10.5194/egusphere-egu24-20683, 2024.

On-site presentation
Keyu Gong, Zhang Wen, Qinghua Li, and Qi Zhu

In the realm of scientific water supply and the vigilant monitoring of advancing marine geohazards, the importance of sustainable aquifer management in coastal cities cannot be overstated. Investigating aquifer heterogeneity and the characteristics of coastal groundwater fluctuations serves as an effective approach to unveil the dynamic nature of aquifers. In this study, we simulated discrete geological variables using SISIM and T-PROGS, leveraging data from 8629 sample points across 111 boreholes in Beihai city, southern China. The results demonstrate heightened accuracy in depicting both lateral sediment distribution driven by river dynamics and vertical processes governing hydraulic conductivity coefficients within the aquifer. Our analysis underscores effectiveness of SISIM in reducing initial data requirements without necessitating Gaussian transformation, ensuring broad applicability, while T-PROGS proves suitable in environments characterized by prevalent lateral accumulation. This study employed the fractal method and wavelet analysis to investigate coastal zone groundwater fluctuations. Daily groundwater fluctuations displayed a distinct periodic variation, indicating a biased stochastic traveling pattern and potential short-term predictability. Notably, time-frequency characteristics exhibited a strong correlation with tidal fluctuations at smaller scales (12-24 hours). Additionally, the study provides initial modeling insights into the impact of heterogeneity on groundwater fluctuations.

How to cite: Gong, K., Wen, Z., Li, Q., and Zhu, Q.: Geostatistical Stochastic Simulation of Hydraulic Conductivity and  Groundwater Dynamics Interpretation in an Alluvial-Marine Sedimentary System: A Case Study in Beihai City, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14513, https://doi.org/10.5194/egusphere-egu24-14513, 2024.

On-site presentation
Bentje Brauns, John Bloomfield, David Hannah, Ben Marchant, and Anne van Loon

Groundwater, constituting approximately 65 percent of Europe's drinking water sources, plays a crucial role in sustaining both urban and agricultural needs. Particularly during periods of drought, groundwater abstraction becomes a key resource, alleviating adverse impacts on people's livelihoods. Recent European drought events, for example in 2003 and 2015, exhibited spatial coherence in surface water deficits across European regions, hinting at potential impacts on groundwater levels. However, the unique hydrogeological settings and recharge patterns of groundwater systems, coupled with diverse meteorological influences, can also lead to distinct spatial coherence in groundwater droughts. Despite these complexities, no comprehensive, decadal pan-European analysis of historic groundwater level data has been conducted until now.

To bridge this gap, we conducted a continent-wide assessment of groundwater drought responses, based on over 3000 groundwater level timeseries spanning from 1986 to 2015, and providing the first extensive overview of historic groundwater droughts across Europe. Utilizing the Standardised Groundwater Index (SGI), the spatio-temporal analysis allowed for consistent comparisons of sites across disparate regions. Impulse response functions were used to identify differences in response times of the aquifers and cluster analysis of the standardized hydrographs allowed for the identification of spatially coherent 'type' groundwater hydrographs, characterized by differences in autocorrelation and reflective of continental-scale variations.

Initial findings highlighted variations in groundwater system responses to meteorological drivers, distinctions between fast and slow responding sites and their spatial coherence. For example, differences in response times of the aquifers in Northern Germany produced local differences in the effects of the 2015 drought in this region and droughts in the late 90s showed good spatial coherence across large areas of Europe, but with distinctly smaller impact on groundwater levels in Balkan region.


This analysis, coupled with an examination of driving factors, promises to enhance our understanding of how catchment and local characteristics influence groundwater responses. Additionally, areas particularly vulnerable to groundwater droughts will be identified, thus allowing for improved groundwater management.  

How to cite: Brauns, B., Bloomfield, J., Hannah, D., Marchant, B., and van Loon, A.: Spatio-temporal analysis of drought: A multidecadal study of European groundwater systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17276, https://doi.org/10.5194/egusphere-egu24-17276, 2024.

On-site presentation
Sarai Díaz García and Javier González Pérez

The quality of water supplied through water distribution systems is traditionally assured through sampling of different physical, chemical, and biological parameters at the exit of water treatment works and at different locations within the network. However, sampling is periodic and does not capture the complex processes that take place within the network over time and space. The time of residence of the resource within the network (also called water age) has been used in the past as surrogate indicator for water quality (Machell and Boxall, 2012). As water age increases, water quality parameters tend to worsen (Machell and Boxall, 2013). This is more likely in terminal and meshed areas with low renovation rates (high water age). This work presents a detailed analysis of residence times at a terminal branch. This benchwork branch is inspired in the real topology of water connections at several dead ends in a water supply network in Spain. Stochastic demands are simulated per water connection thanks to a demand model inspired in SIMDEUM (Blokker et al., 2010). Demands are then used to run a hydraulic and water quality model with high resolution. Different metrics are then computed to probabilistically assess water age within the branch. These metrics are useful to identify topologies that are especially problematic.


The authors would like to thank the financial support provided by the Spanish Ministry of Science and Innovation - State Research Agency (Grant PID2019-111506RB-I00 funded by MCIN/AEI/10.13039/ 501100011033; Grant TED2021-131136B-100 funded by MCIN/AEI/10.13039/501100011033) and Junta de Comunidades de Castilla-La Mancha (Grant No. SBPLY/19/180501/000162, funded by Junta de Comunidades de Castilla-La Mancha and ERDF A way of making Europe).


Machell, J. and Boxall, J. (2012) Field studies and modeling exploring mean and maximum water age association to water quality in a drinking water distribution network. Journal of Water Resources Planning and Management, 138(6), 624-638, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000220

Machell, J. and Boxall, J. (2013) Modeling and field work to investigate the relationship between age and quality of tap water. Journal of Water Resources Planning and Management, 140(9), 04014020, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000383

Blokker, E.J.M., Vreeburg, J.H.G. and van Dijk, J.C. (2010) Simulating residential water demand with a stochastic end-use model. Journal of Water Resources Planning and Management, 136(1), 19-26, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000002

How to cite: Díaz García, S. and González Pérez, J.: Probabilistic topology-based analysis of water age at terminal areas in water distribution networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14731, https://doi.org/10.5194/egusphere-egu24-14731, 2024.

Posters on site: Tue, 16 Apr, 10:45–12:30 | Hall A

Display time: Tue, 16 Apr 08:30–Tue, 16 Apr 12:30
Chairpersons: Georgia Papacharalampous, Yunqing Xuan, Marcela Antunes Meira
Ningning Li, Chao Tan, Bikui Zhao, Jing Huang, and Yehongping Qin

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.

Gina Stratmann, Prof. Dr.-Ing. Lothar Kirschbauer, and Leonie Hörter

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.

Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, and Anastasios Doulamis

We present the first ensemble learning methods for quantifying predictive uncertainty in satellite precipitation data correction, as well as the large-scale comparison of these methods. Ensemble learning was performed by combining in multiple ways a variety of machine learning algorithms that are particularly suited for the task of interest. Monthly precipitation data from across the contiguous United States supported the comparison, which predominantly relied on skill scores and referred to the ability of the ensemble learning methods in delivering predictive quantiles at many levels. The results allow the ordering from the best to the worst of the ensemble learning methods.

Acknowledgements: The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “3rd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 7368).

How to cite: Papacharalampous, G., Tyralis, H., Doulamis, N., and Doulamis, A.: Quantifying predictive uncertainty in satellite precipitation data correction using ensemble learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2706, https://doi.org/10.5194/egusphere-egu24-2706, 2024.

Hristos Tyralis, Georgia Papacharalampous, Nikolaos Doulamis, and Anastasios Doulamis

Predictive uncertainty estimates for precipitation data acquired through merging satellite and ground-based observations are usually not provided. Here, we present the first benchmark experiments on the use of machine learning algorithms for fulfilling the task of delivering such estimates. These experiments compared six machine learning algorithms (i.e., quantile regression, quantile regression forests, generalized random forests, gradient boosting machines, light gradient boosting machines and quantile regression neural networks) and relied on 15-year-long monthly data that originate from across the contiguous United States. The comparison referred to the ability of the machine learning algorithms in delivering predictive quantiles at various levels. The results allow the ordering from the best to the worst of the machine learning algorithms for the problem of interest.

Acknowledgements: The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “3rd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 7368).

How to cite: Tyralis, H., Papacharalampous, G., Doulamis, N., and Doulamis, A.: Predictive uncertainty estimation in satellite precipitation data correction using machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2707, https://doi.org/10.5194/egusphere-egu24-2707, 2024.

Yong Jung, Mun Ju Shin, and Seong Jae Jeon

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.

Nirajan Dhakal

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.

Anika Stelzl and Daniela Fuchs-Hanusch

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.


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.

Barnaby Dobson, Tijana Jovanovic, and Taher Chegini

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.

Christiaan Wewer and Riccardo Taormina

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.

Shijia Wang, Yongqiang Zhang, and Jing Tian

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.

Eunbeen Park, Hyun-Woo Jo, Jiwon Son, Florian Kraxner, and Woo-Kyun Lee

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.

Posters virtual: Tue, 16 Apr, 14:00–15:45 | vHall A

Display time: Tue, 16 Apr 08:30–Tue, 16 Apr 18:00
Chairpersons: Paul Muñoz, Yunqing Xuan, Gerald A Corzo P
Ali Salem, Yasir Abduljaleel, and Ehab Mohammad Amen

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.

Nishant Gaur, Sagar Chavan, and Amit Singh

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.

Aditya Badoni and Sagar Chavan

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.

Juan Manuel Gacharna Gonzalez, Gerald Augusto Corzo Perez, German Ricardo Santos Granados, Karel Aldrin Sanchez Hernandez, Carlos Alfredo Tami Riveros, Guillermo Hernandez Torres, Gustavo Herran, Diego Gutierrez, and Fabio Rubiano

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.