bluuebunny/crossref_metadata_2025 huggingface dataset:
Physical climate impacts in overshoot scenarios
Authors: Sillmann Jana , Pfleiderer Peter , Schleussner Carl-Friedrich
Published: February 2023

With ongoing greenhouse gas emissions it becomes increasingly unlikely that the global mean temperature (GMT) can be stabilized at 1.5°C without considerable negative emissions. As a result, most emission scenarios that would allow to reach 1.5°C GMT at the end of the century are overshoot scenarios: In these scenarios GMT warms until net-zero emissions are reached and slowly starts to cool afterwards. Here we want to have a closer look at the local climate responses after peak warming to get a first idea of potential consequences of overshoots.The analysis is mainly based on the overshoot scenarios SSP119 and the SSP534-over from the “Coupled Model Intercomparison Project (Phase 6)”. We identify regions in which precipitation or temperature has an asymmetric response to GMT changes around peak warming. In some regions, and especially for temperature related variables, the asymmetries could result from lagged responses in the climate system. However, there are also a number of dynamic mechanisms that could influence local climate signals after peak warming and there are only few regions where analyzed earth system models (ESM) agree on the sign of change.In many regions, the projected trends in precipitation or temperature after peak warming are in the range of trends that can be found in control runs without anthropogenic forcings. Here, single model initial-condition large ensembles (SMILEs) are necessary to estimate the forced response in overshoot scenarios. For a comprehensive understanding of the mechanisms explaining these non-linear responses to GMT changes around peak warming, more large ensemble simulations of idealized overshoot scenarios for different ESMs would be required.
An automated earthquake detection algorithm by combining pair-input deep learning and migration location methods
Authors: Mohammadigheymasi Hamzeh , Tavakolizadeh Nasrin , Shi Peidong , Xiao Zhuowei , Mousavi S. Mostafa , Fernandes Rui
Published: February 2023

Modern seismology can benefit from a rapid and reliable earthquake catalog preparation process. In recent years Deep Learning (DL)-based methods attracted seismologists’ attention to keep up with the constantly increasing seismic data for maximally processing and locating the recorded events. This study focuses on deploying an optimized workflow that integrates DL and waveform migration algorithms to achieve a comprehensive automated phase detection and earthquake location workflow. The goal is to deeply scan the seismic datasets for Pand S- phases, associate and locate the detected events, and improve the performance of DL algorithms in processing out-of-distribution and low signal-to-noise ratio data. The workflow consists of six steps, including the preparation of one-minute data segments by employing the framework of ObsPy, deep investigation of the recorded data for P- and S- phases by a low threshold EQTransformer (EQT), and a pair-input Siamese EQTransformer (S-EQT), phase association by Rapid Earthquake Association and Location (REAL) method, applying MIgration Location (MIL) to accurately locate the outputs of REAL, and calculating the local magnitude of the located earthquakes. Eighteen months of the Ghana Digital Seismic Network (GHDSN) dataset (2012-2014), is processed by this integrated and automatic workflow, and a catalog of 461 earthquakes is acquired. Although S-EQT and EQT, with the respective number of 758 and 423 earthquakes, show a figurative superiority in the number of detected events, they are scattered with inaccurate hypo-central depth. Conversely, the compiled catalog show high accordance with the previously interpreted seismogenic sources by Mohammadigheymasi et al. (2023), and a new seismogenic source is also delineated. This workflow significantly enhanced the seismic catalog compilation process and lowered the computational costs while increasing the accuracy of phase detection, association, and location processes. This work was supported by the European Union and the Instituto Dom Luiz(IDL) Project under Grant UIDB/50019/2020, and it uses computational resources provided by C4G (Collaboratory for Geosciences) (Ref. PINFRA/22151/2016). P. S. is supported by the DEEP project (http://deepgeothermal.org) funded through the ERANET CofundGEOTHERMICA (Project No. 200320-4001) from the European Commission. The DEEP project benefits from an exploration subsidy of the Swiss federal office of energy for the EGSgeothermal project in Haute-Sorne, canton of Jura (contract number MF-021-GEO-ERK), which is gratefully acknowledged.
Improved hydrologic conditioning of the TanDEM-X dataset for HydroSHEDS v2
Authors: Warmedinger Leena , Huber Martin , Walper Carolin , Anand Mira , Lehner Bernhard , Thieme Michele , Roth Achim
Published: February 2023

HydroSHEDS is a well-established database containing global hydrographic information. Although being widely used, the SRTM-based version 1 of HydroSHEDS has important limitations, in particular in areas above 60° N latitude. The coverage of this region is of low quality because no underpinning SRTM elevation data were available. As most hydrological models require topographic information and hydrographic data in terms of stream networks or catchment boundaries, the increased availability of accurate remote sensing data promotes the development of a second and refined version of the HydroSHEDS database. For this reason, HydroSHEDS v2 is currently created in collaboration between the German Aerospace Center (DLR), McGill University, Confluvio Consulting and the World Wildlife Fund. Foundation of HydroSHEDS v2 is the digital elevation model (DEM) of the TanDEM-X mission (TerraSAR-X add-on for Digital Elevation Measurement). This 0.4 arc-second resolution DEM with global coverage of land surfaces was created in partnership between DLR and Airbus Defence and Space. Enhanced pre-processing techniques are applied to preserve details of the high-resolution DEM in its hydrologically conditioned version. These pre-processing steps include an infill of invalid and unreliable elevation values, an automatic coastline delineation refined with manual corrections, an AI-based water detection algorithm, and a modification of elevation data in urban and vegetated areas for improved evaluation of the flow of water. Additionally, experiences and preliminary results from processing the water body mask at global scale are outlined. The hydrologically pre-conditioned DEM and the water body mask derived from the TanDEM-X dataset are in the subsequent steps further processed with refined hydrological optimization and correction algorithms to derive flow direction and flow accumulation maps. These gridded datasets are the core products of HydroSHEDS v2 and will be complemented with secondary information on river networks, lake shorelines, catchment boundaries, and their hydro-environmental attributes in vector format. The main release of HydroSHEDS v2 is scheduled for 2023 under a free license.
Deep learning approximations of a CFD model for operational wind and turbulence forecasting
Authors: Loe Margrethe Kvale , Bremnes John Bjørnar
Published: February 2023

The Norwegian Meteorological Institute has for many years applied a CFD model to downscale operational NWP forecasts to 100-200m spatial resolution for wind and turbulence forecasting for about 20 Norwegian airports. Due to high computational costs, however, the CFD model can only be run twice per day, each time producing a 12-hour forecast. An approximate approach requiring far less compute resources using deep learning has therefore been developed. In this, the relation between relevant NWP forecast variables at grids of 2.5 km spatial resolution and wind and turbulence from the CFD model has been approximated using neural networks with basic convolutional and dense layers. The deep learning models have been trained on approximately two year of the data separately for each airport. The results show that the models are to a large extent able to capture the characteristics of their corresponding CDF simulations, and the method is in due time intended to fully replace the current operational solution. 
Deep learning to support ocean data quality control
Authors: Chouai Mohamed , Simon Reimers Felix , Mieruch-Schnülle Sebastian
Published: February 2023

In this study, which is part of the M-VRE [https://mosaic-vre.org/about] project, we aim to improve a quality control (QC) system on arctic ocean temperature profile data using deep learning. For the training, validation, and evaluation of our algorithms, we are using the UDASH dataset [https://essd.copernicus.org/articles/10/1119/2018/]. In the classical QC setting, the ocean expert or "operator", applies a series of thresholding (classical) algorithms to identify, i.e. flag, erroneous data. In the next step, the operator visually inspects every data profile, where suspicious samples have been identified. The goal of this time-consuming visual QC is to find "false positives", i.e. flagged data that is actually good, because every sample/profile has not only a scientific value but also a monetary one. Finally, the operator turns all "false positive" data back to good. The crucial point here is that although these samples/profiles are above certain thresholds they are considered good by the ocean expert. These human expert decisions are extremely difficult, if not impossible, to map by classical algorithms. However, deep-learning neural networks have the potential to learn complex human behavior. Therefore, we have trained a deep learning system to "learn" exactly the expert behavior of finding "false positives" (identified by the classic thresholds), which can be turned back to good accordingly. The first results are promising. In a fully automated setting, deep learning improves the results and fewer data are flagged. In a subsequent visual QC setting, deep learning relieves the expert with a distinct workload reduction and gives the option to clearly increase the quality of the data. Our long-term goal is to develop an arctic quality control system as a series of web services and Jupyter notebooks to apply automated and visual QC online, efficient, consistent, reproducible, and interactively.
Automatic Picking of Teleseismic SH- and SV-Phases using an Autoregressive and Singal Analytic Approach
Authors: Stampa Johannes , Meier Thomas
Published: February 2023

In the recent decade, the amount of available seismological broadband data has increased steeply. Picking later arriving phases such as S-phases is difficult, and the number of manual picks available for these phases  is low compared to first arriving P-Phases. Moreover, data sets of manual picks can also be problematic, since phase arrival picks are sensitive to the parameters of the filtering, which are often unknown, and also depend on the individual picking behavior of the analysts. However, accurate arrival times, especially for these phases, could be used to improve the accuracy of velocity models obtained from seismic tomography. This necessitates the adoption of automatic techniques for determining teleseismic phase arrival times consistently over a large data set.In this work, a robust automatic picking algorithm based on multi-component, multiple-sample forward autoregressive prediction is examined with regards to its accuracy and applicability to massive data sets. The phase is identified using the Akaike criterion and the onset time is found evaluating discontinuities in the instantaneous period of the signal. This signal analytic approach is tested using synthetic waveforms as well as real data in conjunction with manual picks obtained from the reviewed ISC-catalog.Picking errors are estimated by comparing the automatic picks with manual picks, automatic picks at the neighboring stations as well as statistical methods. The quality evaluations suggest potential of using these automatically determined phase arrival times for SH and SV travel time tomography.
Plastic transport and retention around river bifurcations
Authors: Thi Khoa , van Emmerik Tim , Hoitink Ton , Vermeulen Bart , Pham Quy Nhan
Published: February 2023

The transport of plastic in rivers is affected by a wide variety of factors, such as river discharge, wind drag, and tides. These dynamic processes include the travelling time or distance, retention time or location, and remobilization rate of plastic items, which can be quantified by using GPS-based trackers. However, these properties are still unknown in some specific locations, including river bifurcations where the changes in river discharge, flow velocity, and river morphology are significant. Here, we demonstrate the behaviour of plastic transport in a river bifurcation area not influenced by tides during flood season in the Red River system of Vietnam. While all trackers retained somewhere in the river after hours or days, we found that after 10.5 kilometers downstream of the bifurcation, 9 out of 10 trackers followed the main channel retained in the same approximately 6-kilometer-long river segment. Meanwhile, 50% of the 6 trackers that left the main channel to enter the tributary also retained in the same 2.5-kilometer-long river segment 9 kilometers downstream of the bifurcation. These findings are linked to the concept of rivers as plastic reservoirs, as none of the trackers that stranded on the riverbanks for several days was remobilized. Furthermore, the retention of trackers in the same area after leaving the bifurcation clearly indicates shared driving factors on plastic transport, which are likely the river discharge, wind direction or velocity, and river morphology. Our results underscore the need for future research on delineating exact accumulation zones of the plastics in riverbanks considering the effects of wind, river discharge, and river morphology.
Machine-learning enhanced forecast of tropical cyclone rainfall for anticipatory humanitarian action
Authors: Ficchì Andrea , Ascenso Guido , Giuliani Matteo , Scoccimarro Enrico , Magnusson Linus , Emerton Rebecca , Stephens Elisabeth , Castelletti Andrea
Published: February 2023

Tropical Cyclones (TCs) have the potential to cause extreme rainfall and storm surge, which in turn can lead to riverine and coastal flooding with huge damage to property and loss of lives.The use of precipitation forecasts in the context of decision-making and anticipatory action is currently hampered by the limited skill of numerical weather prediction models in forecasting the characteristics of such extreme rainfall events (especially their severity and location) with a sufficiently long lead time.In this study, we present a post-processing scheme for precipitation forecasts based on a popular deep-learning algorithm (U-Net). We design our Machine Learning (ML) model to reduce the local biases of precipitation forecasts from TCs and adjust the spatial distribution of extreme rainfall. For this, we use a composite loss function to train the model, based on the combination of the Mean Absolute Error (MAE) and the Fractions Skill Score (FSS). We first demonstrate the potential of our ML-based approach working on ERA5 reanalysis data and subsequently apply it to the ensemble mean of ECMWF sub-seasonal forecasts with a lead time up to 10-days. As for the ensemble spread, we investigate possible post-processing adjustments based on the improvement of the spread-error relationship and of action-relevant scores of interest for humanitarian agencies, namely False Alarm Ratios (FAR) and Hit Rates (HR). We train and validate the model on a historical dataset of global TC precipitation events, using ECMWF re-forecasts over 20 years and a multi-source observational dataset (MSWEP) as reference. The results are evaluated with a multi-criteria approach including MAE, FSS, FAR, and HR, to assess the capacity of improving the predicted severity and spatial patterns of TC precipitation, as well as their potential for triggering anticipatory actions. Finally, we discuss how the outputs of our model can be used and further improved to support humanitarian actions aimed at saving lives in vulnerable communities in Mozambique.
Synthetic aperture radar burst overlapped interferometry for the analysis of large ground instabilities: Experiments in volcanic regions.
Authors: Pepe Antonio , Barone Andrea , Mastro Pietro , Tizzani Pietro , Castaldo Raffaele
Published: February 2023

This work presents an overview of some applications of synthetic aperture radar (SAR) interferometry technology for the detection and analysis of large ground displacements occurring in volcanic areas, with the aim to retrieve the three-dimensional (3-D) ground displacement field (up-down, east-west, north-south). Specifically, the work summarizes and investigates the potential of Bursted Overlapped Interferometry (BOI) that properly combined can allow the retrieval, at different scales of resolution and accuracies, of the north-south components of the ground deformations, which are usually not available considering conventional SAR interferometry techniques. In this context, the almost global coverage and the weekly revisit times of the European Copernicus Sentinel-1 SAR sensors permit nowadays to perform extensive analyses with the aim to assess the accuracy of the BOI techniques. More recently, Spectral Diversity (SD) methods have been exploited for the fine co-registration of SAR data acquired with the Terrain Observation with Progressive Scans (TOPS) mode. In this case, considering that TOPS acquires images in a burst mode, there is an overlap region between consecutive bursts where the Doppler frequency variations is large enough to allow estimating and compensating for, with great accuracy, potential bursts co-registration errors. Additionally, and more importantly, in the case of non-stationary scenarios, it allows detecting the ground displacements occurring along the azimuthal directions (almost aligned along north-south) with centimeter accuracy. This is done by computing the difference between the right and left interferograms, i.e., the burst overlapped interferogram, and relating it to the ongoing deformation signals.This work aims to apply the BOI technique in selected volcanic and seismic areas to evaluate the impact of this novel technology for the analysis of quantifying, over small, covered regions, the accumulated ground displacements in volcanic areas. In such regions, the interest is on quantifying the accuracy of integrated BOI systems for the retrieval of 3-D displacements. To this aim, we selected as a test site the Galapagos Island and we analyze with BOI the north-south ground displacements. At the next EGU symposium, the results of the BOI analyses will be presented, thus also providing comparative analyses with the results obtained from the use of potential field method applied on the ground displacements in volcanic areas. More specifically, by adopting this technique, we are able to estimate independently the north-south components of the ground displacement by exploiting the harmonic properties of the elasticity field.
Extremely hot East Asia and flooding western South Asia in the summer of 2022 tied to reversed flow over Tibetan Plateau
Authors: He Chao , Zhou Tianjun , Zhang Lixia , Chen Xiaolong , Zhang Wenxia
Published: February 2023

In the summer (July and August) of 2022, unprecedented heat wave occurred along the Yangtze River Valley (YRV) over East Asia while unprecedented flood occurred over western South Asia (WSA), which are located on the eastern and western sides of Tibetan Plateau (TP). By analyzing the interannual variability based on observational and reanalysis data, we show evidences that these two extreme events are mutually connected, and the anomalous zonal flow over subtropical Tibetan Plateau (TP) explains a major fraction the extreme events occurred in 2022. In summer, there is a warm center in the atmosphere over TP, and the isentropic surfaces incline eastward (westward) with altitude on the eastern (western) side of the warm center over TP. As adiabatic flow move along isentropic surfaces, anomalous easterly (westerly) flow generates anomalous descent (ascent) on the eastern side of TP and anomalous ascent (descent) on the western side of TP via isentropic gliding. The anomalous easterly flow is extremely strong to reverse the climatological westerly flow over subtropical TP in 1994, 2006, 2013 and 2022. The easterly flow in 2022 is the strongest since 1979, and it generates unprecedented descent (ascent) anomaly on the eastern (western) side of TP, leading to extreme heat wave over YRV and extreme flood over WSA in 2022. The anomalously strong easterly flow over subtropical TP in 2022 is dominated by atmospheric internal variability related to mid-latitude wave train, while the cold sea surface temperature anomaly over the tropical Indian Ocean increases the probability of a reversed zonal flow over TP by reducing the meridional gradient of tropospheric temperature.
Assessing Risk and Resilience in Urban Low-Income Neighbourhoods
Authors: Taylor Faith
Published: February 2023

This presentation aims to set the scene for the session and promote debate about how we as a scientific community can better address the issue of risk in low-income neighbourhoods (also referred to as ‘slums’ and ‘informal settlements’).Low- and middle-income countries are experiencing rapid urbanisation, often with limited capacity to manage the rapid growth and associated accumulation of risk. One billion people currently live in low-income neighbourhoods. For many cities in the Global South, this means that large proportions (up to 60%) of the urban population live in very small proportions of the urban land which are not formally planned/monitored and are often exposed to a range of single and multi-hazards. This creates challenges in compiling records of hazards, their impacts and feedbacks as many methods are not of sufficient resolution to record high-frequency low-magnitude hazard events that chronically affect these settlements and erode capacity to cope. Additionally, although low-income neighbourhoods often appear as one homogeneous unit on maps, this masks the fact that these areas often act as urban centres within their own right, with high levels of heterogeneity in terms of infrastructure, land use and demographics. This creates challenges in understanding exposure and vulnerability. Overall, these issues mean that our ability to understand single and multi-hazards in low-income neighbourhoods is limited, and resultantly we may miss large proportions of the population in city-wide risk assessments.Based on my work on a number of projects in low-income neighbourhoods, I will discuss some ways of addressing these challenges and their associated strengths and weaknesses. This includes: (a) Whatsapp focus groups to map social networks of resilience, (b) low-cost smartphone GPS mapping to document the bottom-up response to shocks and stresses within low-income neighbourhoods, (c) exploration of novel ‘big’ and 'medium' data sources. These techniques are participatory and designed to support both residents and city-level stakeholders in better understanding the complexities of risk in slums. The results of this work document that residents of low-income neighbourhoods are not passive but often the first or only responders in dealing with risk in these areas.
Quantification of Carbon Uptake in Urban Roadside Ecosystem by Measuring Carbon Exchange from the Leaf, Stem, and Root of the Shrub
Authors: Jeong Minseop , Yoo Gayoung
Published: February 2023

One of the ways to increase green areas that are shrinking due to urbanization is to create urban roadside greenery. Among the various ecosystem services of roadside greenery, carbon uptake plays a significant role in reducing CO2, the main factor of climate change. Multi-layered planting can enhance carbon uptake, which is focused on as an effective method. Hence, the roadside ecosystem consists of trees, understory shrubs, and soil. Although shrubs are as crucial as trees because of the large number of populations per unit area, only a few studies were focused on shrubs. Therefore, considering shrub carbon uptake is necessary for estimating the accurate carbon exchange on the roadside ecosystem.This study focused on the roadside greenery composed of a tree, shrubs, and soil in the unit 1m x 8m area. The experiment was conducted in Suwon city, the Republic of Korea. The selected tree and shrub are Zelkova serrata and Euonymus japonicus, the most common species in Suwon. Net Ecosystem Exchange(NEE) was calculated by the equation [NEE = NPPtree + NPPshrub + Rheterotroph]. NPPtree was estimated through the allometric equation. NPPshrub and Rheterotroph were calculated through measurements. To calculate NPPshrub, two experiments were conducted. One was field measurement using the closed chamber with LI-820, and another was greenhouse incubation and harvesting. In the field measurement, the closed chamber measured the real-time change of CO2 concentration including leaf photosynthesis and stem respiration, and the results showed the aboveground NPPshrub. Also, environmental factors such as air temperature, PAR (photosynthetically active radiation), and leaf area were collected. In the greenhouse experiment, the results showed the accurate NPPshrub without considering field conditions. With those two results, the equation for calculating field shrub NPP was developed considering field conditions and root respiration. However, the closed chamber has a problem with installation, management, and stability, so the leaf chamber would be more adaptable for field measurement than the closed chamber. For accurate measurement of field shrub NPP, this study also did an experiment using Vaseline to block the stomata to calculate the proportion of stem respiration in the aboveground NPPshrub. The stem respiration can be measured by comparing the CO2 concentration change before and after pasting Vaseline on the shrub leaves in the closed chamber. Soil respiration(Rs) was measured by EGM-5 in the field and used the equation [Rs = Rroot + Rheterotroph].The results of these experiments accurately estimated NPPshrub and Rheterotroph, and the NEE of the 1m x 8m roadside greenery section could be quantified as 5.23 kg C/yr. This amount could mitigate 1.09% of annual vehicle carbon emissions in Suwon city if roadside greenery is applied on all roadsides in Suwon.
A climate perturbation at the Middle –Late Jurassic Transition? Evaluating the isotopic evidence
Authors: Price Gregory , Heszler Bernát , Tansley Charlton Lauren-Marie , Cox Jade
Published: February 2023

The Jurassic greenhouse is punctuated by short cooling intervals with at times postulated polar ice-sheet development. For example, oxygen isotope records of belemnite rostra and fish teeth from the Russian Platform, eastern France and western Switzerland have been interpreted to reveal a prominent decrease in seawater temperature during the Late Callovian–Early Oxfordian. This is in part the basis for a proposed an ice age at the Middle-Late Jurassic Transition. In contrast relatively constant oxygen isotope records and therefore seawater temperatures and carbon isotope values characterized by significant scatter but showing more positive values during the middle and late Callovian have been reported from elsewhere. The aim of this research has been to determine a stable isotope stratigraphy (from belemnites and oysters) principally from the Callovian-Oxfordian interval (from southern England) and integrate these data with existing data to assess the pattern of carbon and oxygen isotopic change.  Our marine macrofossil record reveals isotopic patterns that are generally comparable with other European basins. Carbon isotopic trends are consistent with bulk carbonate carbon isotope records displaying systematic fluctuations, the largest of which (Middle Callovian, Calloviense/Jason Zones to Early Oxfordian, Mariae Zone) corresponds to previously identified phases of environmental perturbation. Such a trend may have resulted from enhanced burial and preservation of organic matter, leaving the seawater more positive in terms of carbon. Cooling post-dates this positive carbon isotope excursion. Inferred cooling, derived from our oxygen isotope data from southern England, occurs within the Late Callovian and Oxfordian (Athleta to Mariae zones). Enhanced carbon burial and atmospheric carbon dioxide draw down may have induced cooling. In this study the analysis of a single region (southern England) allows some constraints on potential variable that may influence isotope records. 
Al, Si diffusion in bridgmanite to estimate the Earth's lower mantle rheology
Authors: Czekay Laura , Miyajima Nobuyoshi , Frost Daniel
Published: February 2023

The diffusion of atoms in minerals at high temperatures and pressures influences Earth’s lower mantle dynamic processes. This study aims to better understand the physical behaviour of Earth’s most abundant mineral with implications for lower mantle viscosity. Previous studies that measured Si-self diffusion coefficients in bridgmanite (Brg) showed a value at 25 ± 1 GPa and 1800 °C of Log10(DSi) = -18 ± 0.5 (based on units of m2/s). Our study revealed a significantly slower diffusion coefficient that may challenge previous calculations of lower mantle viscosity. We investigated Al, Si interdiffusion in Brg experimentally at 24 GPa and 1750 to 2000 °C using a multianvil apparatus using diffusion couples composed of bridgmanites that were pre-synthesised from 0-5 mol.% Al2O3-bearing MgSiO3 enstatite. The Al diffusion profiles were analysed across the diffusion interface in the recovered samples using a scanning transmission electron microscope equipped with an energy-dispersive X-ray spectrometer. The obtained diffusion coefficient for interdiffusion (volume diffusion) at 24 GPa and 1800 °C was Log10(DAl,Si) = -20.1 ± 0.7. The resulting data can be used to estimate deformational strain rates of Brg in the lower mantle from viscosity based on different creep mechanisms.
EDCHM: A c++ based R package for flexible semi-distributed conceptual hydrological modeling
Authors: Lei Kan , Spieler Diana , Schütze Niels
Published: February 2023

Modular hydrological modeling has been around for some time, with early examples such as the Modular Modeling System (MMS) developed in 1996. In 2011,Fenicia et al. introduced the SUPERFLEX modeling framework, refined by Molin et al. (2021) as the Python package SurperflexPy. A framework with an even larger library of processes is the Raven modeling framework introduced by Craig et al. (2020).This work introduces a c++ based R package prioritizing convenience while still offering flexibility for semi-distributed hydrological modelling. The EDCHM framework defines five basic layers: atmosphere, snow pack, land, soil, and ground, with the soil and ground layers able to be further divided into sublayers. Each layer has its own characteristics and state variables such as capacity and water volume. EDCHM defines 12 basic processes, including 10 hydrological and 2 meteorological processes such as evapotranspiration and infiltration. Each process has a single flux output, and it can occur within a single layer or between layers. The input requirements are flexible and depend on the specific method used. A process with a specific method is referred to as a module in EDCHM. EDCHM also includes 34 predefined model structures with fixed connections between processes and layers, ranging from 6 to 15 processes. The key feature of EDCHM is the model builder, which allows users to easily generate the model function just by selecting the process methods, the input data list, and the parameter list with ranges will also be created. This makes it fast and efficient for users to build and calibrate models. EDCHM is implemented in c++ and supports vectorization and parallelization through R-Package Rcpp and furrr. Users can easily build new models with their own ideas or ideas from literature.EDCHM has been tested on 34 east-german catchments, with over 60 models calibrated in lumped form and 6 catchments calibrated with 3 and 5 sub-catchments or more than 50 HRUs. Our results show that EDCHM is highly effective in the application of hydrological modeling, with a key feature being its efficiency. Craig et al. (2020). https://doi.org/10.1016/j.envsoft.2020.104728Fenicia et al. (2011). https://doi.org/10.1029/2010WR010174EDCHM: https://github.com/LuckyKanLei/EDCHM
Synergetic Fuzzy Analytic Hierarchy Process for a realistic assessment of landslide vulnerability
Authors: Ksantini Faten , Sdiri Ali , Aydi Abdelwaheb , Tarquis Ana M
Published: February 2023

Landslide is a common natural geological hazard that causes most damaging effects on natural features worldwide. Around the world, landslides have caused casualties, property damages and other deleterious effects in geological, ecological, environmental and infrastructures. It delineates a significant limitation for the development of urban and industrial planning. Thus, it is fundamentally important to create a landslide susceptibility map that would contribute to a realistic assessment of the threatening natural hazard for a subsequent efficient management and prediction of those potential disasters.In this context, the present study attempted to apply an integrated multicriteria analysis with focus on Analytic Hierarchy Process (AHP) and Fuzzy-AHP (F-AHP) for the assessment of landslide vulnerability in Jabbeus area (Southwestern, Tunisia), which is potential candidate for future excavation as an open pit phosphate mine. The thematic layers and the landslide-causing factors were collected from various geospatial data sources. The main identified causative factors included lithology (LI), slope (S), distance to faults (F), distance to drainage lines (D) and the topographic wetness index (TWI). In addition Synergetic Fuzzy Analytic Hierarchy Process (SF-AHP) was applied as an innovative methodology to quantify the synergetic effect of LI-S and LI-F using fuzzy functions to enhance the interactions of these factors.Finally, Landslide Susceptibility Index (LSI) values were computed according to the weighted linear combination (WLC) based on which zonation maps using the three methods were generated. These zones were classified in four categories, from non vulnerable to highly vulnerable, to landsliding events. The formulated SF-AHP showed significant improvement in vulnerability assessment accuracy compared to other conventional approaches. Keywords: Landslide, assessment, analytical hierarchy process, multi-criteria approach ACKNOWLEDGEMENTSThe first author acknowledges the financial support by the Minister of Higher Educationand Scientific Research,University of Carthage,Tunisia. Project No PID2021-122711NB-C21, from Spanish Ministerio de Ciencia e Innovación, partially funded thiswork.
Assessment of change in drought risk influenced by water management
Authors: Ridolfi Elena , Soncin Laura , Matano Alessia , Russo Fabio , Napolitano Francesco , Di Baldassarre Giuliano , Van Loon Anne
Published: February 2023

Disaster risks are the results of complex spatiotemporal interactions between risk components, impacts and societal response. In the context of the water supply chain, human processes have altered hydrological processes through the construction of reservoirs to dispose of the water resources with higher predictability and productivity. While reservoirs may attenuate flood events and help to bridge through period of water scarcity, they may also aggravate drought events in terms of duration and severity. The long-term effect associated to the presence of reservoirs is an over-reliance on the water supply hypothesized as constant and abundant as provided by the hydraulic structure, which in turn increases the vulnerability and the economic damage in case of a drought event occurrence. In addition, socio-economic and political changes should not be underestimated, as the social, economic, and political context can influence the impact and response to extreme events. Indeed, it has been observed that during long periods of drought, variations in exposure and in vulnerability have occurred, due to social dynamics taking place as a result of the extreme event, e.g. the natural migration of the population to water sources.Here we aim at understanding the changes in risk components and the resulting impacts of structural measures, such as reservoirs, and also of nature based solutions in relation to consecutive extreme events. Different case studies around the world are considered to untangle the complexity of the dynamic relationship between human and hydrological processes.
The persistence of errors: How evaluating models over data partitions relates to a global evaluation
Authors: Klotz Daniel , Gauch Martin , Nearing Grey , Hochreiter Sepp , Kratzert Frederik
Published: February 2023

Skillful today, inept tomorrow. Today's hydrological models have pronounced and complex error dynamics (e.g., small, highly correlated errors for low flows and large, random errors for high flows). Modellers generally accept that simple, variance based evaluation criteria — like the Nash-Sutcliffe Efficiency (NSE) — are not fully able to capture these intricacies. The (implied) consequences of this are however seldom discussed.This contribution examines how evaluating the model over two data partitions (above and below a chosen threshold) relates to a global model evaluation of both partitions combined (i.e., the usual way of computing the NSE). For our experiments we manipulate dummy simulations with gradient descent to approximate specific NSE values for each partition individually. Specifically, we set the NSE for runoff values that fall below the threshold, and vary the NSE of the simulations above the threshold as well as the threshold itself. This enables us to study how the global NSE relates to the partition NSEs and the threshold. Intuitively, one would wish that the global NSE somehow reflects the performance on the partitions in a comprehensible manner. We do however show that this relation is not trivial.Our results also show that subdividing the data and evaluating over the resulting partitions yields different information regarding model deficiencies than an overall evaluation. The downside is that we have less data to estimate the NSE. In the future we can use this for model selection and diagnostic purposes.
Piloting a Water Data Management Ecosystem to Enable an Efficient and Resilient Decision Support System for the IJsselmeer
Authors: Seshan Siddharth , Ebbelaar Dave , Ebbelaar Joris , de Vos Eric , Torello Mollie , Zuurbier Koen , Vamvakeridou-Lyroudia Lydia
Published: February 2023

The water sector faces great challenges and stresses on the major water system due to climate change and increasing population. As a result, water utilities are increasingly undergoing a digital transformation, to achieve more resilient and sustainable water services while implementing more data-driven decisions. To tackle challenges such as cybersecurity, data ownership and poor quality of data, the European Commission proposes the creation of Data Spaces, as part of the European strategy for Data. Within the Horizon Europe project called WATERVERSE, a holistic approach is being developed to drive the development of data spaces for water utilities. The project involves the development of a Water Data Management Ecosystem (WDME) to enhance the adoption of data management practices that are affordable, accessible, secure, fair and easy to use, while improving the usability of data. In this work, the piloting of a WDME for the Netherlands case study will be presented. The lake IJsselmeer, is used by the water company PWN as a crucial source of drinking water supply for almost 2 million customers in the North-West region of the Netherlands. However, due to population growth, sea level rise, and climate change, the lake IJsselmeer faces extreme variability in water quality in the future. Furthermore, the lake IJsselmeer is at the end of the Rhine Delta, and therefore faces varying water quality challenges from upstream users and stakeholders and saltwater intrusion from the Wadden Sea. Therefore, the development of a digital twin for the lake IJsselmeer is needed to predict chloride (Cl-) and other important water quality parameters for operational (daily basis) and strategic (coming decades) decision making. Such a digital twin requires various data as input from heterogeneous sources. Therefore, to enable the deployment and efficient use of the digital twin as part of a decision support system, the Cl- source prediction model is being piloted within the WDME. An open-source data exchange system called FIWARE is deployed within the pilot. FIWARE serves as the primary broker to exchange contextual information between the various components. Raw data from various sources such as – PWN’s internal data on water quality, data from the national weather agency, water level data of lake Ijsselmeer from the governmental water management agency, are accessed in real-time and fed into the WDME. The data is then processed and prepared as input to the digital twin, which provides predictions over multiple forecasting horizons. Finally, all relevant data, including the predictions, are relayed to a dashboard.
New investigation of a tropical cyclone: observational and turbulence analysis for the Faraji hurricane
Authors: Ciardullo Giuseppe , Primavera Leonardo , Ferrucci Fabrizio , Lepreti Fabio , Carbone Vincenzo
Published: February 2023

In the general framework of the atmosphere, the in-depth study of extreme events of complex nature such as tropical cyclones remains an open problem. Nowadays, it is possible to study and predict the directionality that a cyclone can acquire in a very short term. However, the search for mechanisms and geographical areas of formation and dynamics of such events in different regions needs better constraints on the preliminary nature of the current results. This work focuses on a diagnostic analysis of a tropical cyclone, with the aim of identifying key points that characterize its evolution, from a dynamic and observational point of view. The study is applied to Hurricane Faraji, the most powerful tropical cyclone of the 2021 Indian Ocean season, classified as fifth-category on the Saffir-Simpson intensity scale. The study develops in three main sections, all related to each other. The starting point is a large set of satellite products from both polar and geostationary platforms. From the data acquired by the polar instruments, an accurate study of the evolution of the hurricane is carried out, focused on the extraction of physical information related to the system (temperature and altitude of the associated cloudy system, temperature gradient, pressure in the different regions) at moderate resolutions. From the data acquired by the geostationary instruments, it is possible to obtain very high temporal resolution pictures of the temperature field of the cyclone, from which a study of the turbulent dynamics is carried out. In particular, to investigate the maximum energy content in the different regions of the cyclone, the Proper Orthogonal Decomposition (POD) technique is used to extract the associated spectra of both the spatial and temporal components, studied separately on three different ranges of scales.
Within-patch plant diversity modulates the eco-hydrological source-sink dynamics of dryland landscapes
Authors: Bautista Susana , Nazarova Valeriia , Fuentes David , Rodríguez Francisco
Published: February 2023

Source-sink processes and feedbacks are critical for ecosystem function and dynamics in dryland landscapes. In patchy-vegetation drylands, the runoff generated on bare-soil inter-patches provides resource inputs to downslope patches, enhancing plant growth and increasing patch cover, which in turn controls the size of the bare-soil areas, completing a source-sink feedback loop. Both the efficiency of vegetation patches in capturing and storing runoff-driven resources and the response of the patch vegetation to such resource inputs depend on the functional traits of the species in the patch. We hypothesized that increasing within-patch plant diversity enhances the sink function of the patch, and thus reinforces the strength of the source-sink feedback. To test such hypothesis, we established over 600 vegetation patches on a 0.5 ha bare-soil experimental slope, resulting in six replicated treatments that combined different numbers of species and individuals per patch (up to 8 species and 8 individuals per patch). Based on drone surveys conducted five years after the establishment of the vegetation patches, we estimated the area, height, volume and normalised difference vegetation index (NDVI) of each patch, as well as a variety of metrics that captured the size and shape of the upslope bare-soil inter-patch draining into each patch. We found that increasing the size of the drainage area resulted in a general increase in NDVI and patch growth. For a given patch size (number of plant individuals), increasing within-patch plant diversity increased the overall strength of the positive relationships between patch performance and drainage area. Our results demonstrate that within-patch plant diversity controls eco-hydrological source-sink dynamics in drylands, and highlight the potential of establishing functionally-diverse plant patches for the restoration of degraded drylands.
Dynamic risk modelling of a coupled human-drought system under multi-drought conditions
Authors: Kalthof Maurice , De Bruijn Jens , De Moel Hans , Kreibich Heidi , Aerts Jeroen
Published: February 2023

Drought risk is modified through hazard, vulnerability and exposure, and exacerbated by management shortcomings. A quantitative understanding of the combined effects of these drivers is required to effectively lower risk. Yet, knowledge about the dynamics and effects of risk drivers over time and space and the human-natural feedbacks that steer them is largely lacking. In this study, we propose using GEB, the first large-scale coupled-agent based hydrological model that simulates all individual farmers at field scale, to systemically quantify the relations between dynamic hazard, vulnerability, exposure, management and drought risk over a multi-drought period in the Bhima basin, India. First, we parametrized the coupled hydrological model with meteorological and hydrological data to capture hydrological drought  conditions of different paired drought events. Next, we develop the agent based model part to simulate the drought management behavior of two million farmers and how they respond to drought events. To simulate this behavior, we applied the protection motivation theory, supplemented by theory of planned behavior, to describe farmer agent behavior. The parameters of these theories were parametrized with survey data of Indian farmers fitted to the statistical distribution of the two million Bhima basin farmers. To study the dynamic attribution of the three risk drivers, a Global Sensitivity Analysis of all factors was performed at consecutive time intervals, showing the interaction of drivers before and after each drought event, as well as between the two events. The results are expected to further the understanding of drought risk dynamics and what disaster risk reduction measures can optimally reduce impacts in the long term.