bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the EC-EARTH3-VEG climate model in reproducing the evolution of maximum and minimum air temperatures in Brazil
Authors: De Amorim Formigoni Fernanda , De Abreu Heil Catarina , Da Costa Mendes Reis Juliana , Esposte Coutinho Priscila , Sancho Lívia , Cataldi Marcio
Published: March 2024

According to the Sixth Report of the Intergovernmental Panel on Climate Change (IPCC), published in 2023, between 2011 and 2020, global surface temperatures increased by 1.1°C compared to the period 1850-1900. This upward trend in temperature is related to changes in the observed climate patterns, which will potentially lead to a greater incidence of extreme weather events and ecosystem changes, as well as impacting the health of human populations. In Brazil, this scenario could also result in biodiversity losses, reduced agricultural productivity and changes in the availability of water resources, with consequences for the country's economy and energy security. Considering this context, this study aims to evaluate the ability of the EC-Earth3-Veg model, part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), to reproduce the evolution of maximum and minimum air temperatures at 2 meters in Brazil during the historical period, at intervals between 1961 and 2014, comparing it with ERA5 reanalysis data. The CMIP6 data was interpolated to the ERA5 grid to carry out the desired analysis. Based on this, it was observed that EC-Earth3-Veg was able to reproduce the historical climatology for Brazil but showed climatological differences when compared to ERA5 in the four periods observed. It is common among the periods analyzed that the further north of the country is warmer, with maximum temperatures in summer and autumn. In winter and spring, the same happens in the eastern part of the North, the Center-West, and the northern part of the Southeast of Brazil. In the fall, the Northeast and Midwest show cooler highs. The period in which the model's results were closest to ERA5 was from 1961 to 1990, especially for minimum temperatures in summer and winter. Even so, in the fall and spring of this period, the model showed warming in relation to the minimums in the South, and, in all the quarterly cut-outs, it showed cooling in the minimums near the Northeast. In general, certain regional and seasonal patterns were observed in the results, which may indicate a limitation of the model in terms of horizontal resolution in considering a more characteristic atmosphere for Brazil. In the southern region, for example, the maximum and minimum temperatures in the model showed warming. This may indicate that the model is limited in its horizontal resolution and does not consider an atmosphere that is more characteristic of Brazil. Therefore, to improve the model's performance in simulating the climate in Brazil, it is necessary to correct the bias and use EC-Earth3-Veg in conjunction with other models to reduce systematic errors. This study aims to replicate this work for other models.
Human-induced climate change has decreased wheat production in northern Kazakhstan
Authors: Romanovska Paula , Undorf Sabine , Schauberger Bernhard , Duisenbekova Aigerim , Gornott Christoph
Published: March 2024

Northern Kazakhstan is a major exporter of wheat, contributing to food security in Central Asia and beyond, but wheat yields fluctuate and low-producing years occur frequently. The most severe low-producing year in this century was in 2010, leading to severe consequences for the food security of wheat importing countries. To date, the extent to which human-induced climate change contributes to this is unclear.
In this session, we present the first attribution study for wheat production in northern Kazakhstan, which is at the same time one of the very first climate impact attribution studies for the agricultural sector in general. We quantify the impact of human-induced climate change on the average wheat production as well as economic revenues and on the likelihood of a low-production year like 2010. For this, we use bias-adjusted counterfactual and factual climate model data from two large ensembles of latest-generation climate models as input to a statistical subnational yield model. The climate data and the yield model were shown to be fit for purpose as the factual climate simulations represent the observations, the out-of-sample validation of the yield model performs reasonably well with a mean R2 of 0.54, and the results are robust under the performed sensitivity tests.
Our results show that human-induced climate change, and explicitly increases in daily-minimum temperatures and extreme heat, have had a critical impact on wheat production, decreasing yields between 2000 and 2019 by around 6.2 to 8.2% (uncertainty range of two climate models), increasing the likelihood of the 2010 low-production event by 2.1 to 3 times, and leading to economic losses of 119 to 158 million USD. The latest IPCC report assessed that climate change has today mixed positive and negative impacts on wheat production in Central Asia, but the results are stated with low confidence as studies are sparse in this region. This climate impact attribution study addresses this gap, finding clear indications for a negative influence of climate change, especially via temperature increases, on wheat production in northern Kazakhstan.
Geochemistry of marine black shale of Cambrian Qiongzhusi formation, Yangtze plate, southwestern China: implications for provenance and paleoweathering
Authors: Fang Zexin , Guo Ling
Published: March 2024

Marine black shale in the Lower Cambrian Qiongzhusi (QZS) Formation in the southwestern Yangtze Plate, SW China, is the primary focus for shale gas development. The formation of organic-rich shale is significantly influenced by the paleoenvironment. Through the analysis of major, trace and rare earth elements (REE), the authors examined the element composition, paleoweathering and provenance of Qiongzhusi black shale (QZS shale). The results revealed that the main components of the Qiongzhusi Formation sample are SiO2, Al2O3 and total Fe2O3 (TFe2O3), with the average values of 64.08 wt%, 15.00 wt% and 5.39 wt%, respectively. Redox-sensitive elements, such as V, Cr, Ni, Zn and U, are richer in QZS shale contrasted with the upper continental crust (UCC). The total concentration of REE (∑REE) of QZS shale is 174.58 ppm on average, which is higher than that of UCC (average 146.37 ppm) and the North American Shale Composite (NASC) (average 173.21 ppm). The ratios of w(SiO2)/w(Al2O3) and w(Al2O3)/w(TiO2), the Zr-TiO2 diagram, the Th/Sc vs Zr/Sc plot, the discriminant function of F1 vs F2 and F3 vs F4, as well as the discrimination diagram of ∑REE vs La/Yb indicated that the main provenances of QZS shale are sedimentary and felsic igneous rocks. The values of a chemical weathering index, the chemical index of alteration (CIA), of the Lower QZS Formation (Stage 1) range from 51.84 to 64.33, indicating a low degree of chemical weathering and a cold and dry climate. The CIA values of the Upper QZS Formation (Stage 2) range from 66.58 to 82.42, suggesting a medium degree of chemical weathering, lilely in a humid climate.
Constantly nitrogen and phosphorus input will increase methane and carbon dioxide emissions from global lakes
Authors: wu fan , Gao Yang , Jia Junjie , Wen Xuefa , Yu Guirui
Published: March 2024

Due to the high nitrogen (N) and phosphorus (P) input and the weak self-cleaning ability of waterbodies, lakes are more prone to be eutrophication. The continuous input of N and P in enriched lakes regulates the carbon (C) cycle process, which affects the production of greenhouse gases such as CO2 and CH4. Therefore, we assessed the water CO2 and CH4 emission fluxes and their response to ongoing N and P inputs based on data from 707 globally distributed lakes. We found that CO2 and CH4 emission fluxes were higher in the tropics than in the temperate zone, with Antarctica acting as a methane sink. The emission fluxes of CH4 and CO2 increased with the increase in N and P concentration, and the effect of total phosphorus (TP) on the emission fluxes of CO2 and CH4 was the highest. When the TP load is increased by 3 times, the CO2 emission reaches 443.99 mg m-2 d-1, and the CH4 emission reaches 205.47 mg m-2 d-1, which is 1.48 and 3.85 times of the normal condition respectively. If the TP load is reduced by 3 times, it reduces 597.83 mg m-2 d-1 C emissions. This study shows that lake C emissions are highly dependent on continuous N and P input, which provides a scientific reference for the C cycle process of eutrophic lakes, and provides an important basis for the study of lake response to climate.
A potential earliest trilobite-brachiopod fossil assemblage from the Sichuan Basin, South China
Authors: Luo Jinzhou , Wang Tong , Pan Xiaoqiang
Published: March 2024

Parabadiella is considered to be the earliest representative of trilobites from southern China, and was first recovered from the middle and upper part of the Guojiaba Formation in the Liangshan area of Hanzhong, southern Shaanxi Province, where the oldest trilobites in China and various fossils associated with them were also produced. Previous studies have suggested that the earliest brachiopods coexisted with the oldest trilobites, i.e., the Parabadiella-Eoobolus assemblage in the XYB section of the Zhenba area in southern Shaanxi Province is considered the earliest trilobite-brachiopod assemblage in southern China. The occurrence of both brachiopods and trilobites in the JS103 well extends the first occurrence of this assemblage in the Cambrian stratigraphy to the central part of the Yangtze Platform. The JS103 is a shale gas exploration well targeting the Lower Cambrian Jiulaodong Formation in the southern part of the Sichuan Basin, which is about 350 m thick in the well, and is in a paralleling unconformable contact with the underlying Terreneuvian Maidiping Formation and a conformably contact with the overlying Yuxiansi Formation. This well confirms that the Jiulaodong Formation has been continuously deposited and is divided into 2 sections and 11 layers, which belong to 3 sedimentary gyres, among which the lower 1-9 layers (3310-3592 m) with a total length of 282 m are fully coring layers, and the uppermost fossil layer is a siliceous clastic rock with a thickness of nearly 10 m. The fossil layer is a siliceous clastic rock with a thickness of nearly 10 m, and it has been found that two different trilobites have been discovered here. Two different trilobite range zones are found here, including the lower Parabadiella zone and the upper Wutingaspis-Eoredlichia zone. In addition, conventional studies consider the lowermost stratigraphic part of the Guojiaba Formation to be a unfossiliferous zone, with a thickness ranging from 200 to 250 metres. It is worth pointing out that fossilised shells of Linguliform brachiopod were found nearly 200 m below the zone of the earliest trilobite, Parabadiella, in the Well JS 103. The earliest brachiopod fossils were previously thought to be co-occurred with or slightly later than the earliest trilobites, e.g., Liangshan Mountain, Hanzhong, and Xiaoyangba, Zhenba, Shaanxi, etc. The present discovery of brachiopods may be the lowest known brachiopod fossil horizon in South China. Thus, the discovery of the brachiopod-trilobite assemblage provides new insights into the biostratigraphy of Sichuan and the surrounding area, and the biostratigraphy of the siliciclastic clastics along the western margin of the Yangtze Platform also contrasts with great potential.
Monitoring agricultural drought in the Mediterranean region using a high-resolution (1-km) standardized evaporation deficit index
Authors: Yu. Petrova Irina , G. Miralles Diego , M. Vicente-Serrano Sergio , Massari Christian
Published: March 2024

Droughts, with their far-reaching and detrimental effects across multiple domains, remain critical climate events that demand better monitoring and early warning capabilities. Regions that are highly dependent on water supply for agriculture, such as the Mediterranean, are vitally dependent on timely monitoring of drought conditions. The crop losses in the region as a consequence of drought events continue to rise. Simultaneously, climate models agree regarding the exacerbation of drought following global warming in the region. Therefore, better understanding and monitoring of drought occurrence is imperative to mitigate drought adverse effects and improve water resource management in the region and beyond.Operational drought monitoring, whether based on models or observed data, commonly employs a set of drought indices designed to assess anomalies in land or atmosphere dryness. However, these indices are typically available at relatively coarse spatio-temporal scales, rendering them unsuitable for evaluating the local drought impacts that are relevant to agriculture and ecosystems. This limitation does not facilitate decision-making by local authorities and farmers and impedes the straightforward development of on-site adaptation strategies.In this study, we undertake the assessment and validation of an evaporation-based drought index, the Standardized Evaporation Deficit Index (SEDI: Kim&Rhee 2016, GRL), at an unprecedentedly high resolution (1 km, daily) over the Mediterranean domain. The index is constructed using data of potential and actual evaporation derived using GLEAM (Miralles et al. 2011, HESS), as part of the ESA 4DMED-Hydrology project. Unlike most other drought indices, SEDI is directly related to plant water stress, given the significance of the evaporation deficit for plant hydraulic and physiological processes. Such approach offers the potential to provide early-warning information on ecological and agricultural plant water stress at local scales. Our study of the relationship between SEDI and vegetation stress over seven years (2015–2021) and across 28 Mediterranean river basins, sheds light on critical factors that cause differential stress in crops and natural ecosystems under drought conditions. We also explore the role of irrigation in the SEDI–vegetation stress relationship using 1 km irrigation volumes obtained during the 4DMED-Hydrology project. In the future, the framework will be extended globally, with the subsequent aim to provide valuable information for optimizing irrigation timing in major irrigated breadbasket regions. 
Frequent marine heatwaves hidden below the surface of the global ocean
Authors: Sun Di , Li Furong , Jing Zhao , Hu Shijian , Zhang Bohai
Published: March 2024

Marine heatwaves are extreme warm water events that can cause devastating impacts on ecosystems and have complex socio-economic ramifcations. Surface signals and drivers of marine heatwaves have been extensively investigated based on satellite observations, whereas their vertical structure in the global ocean remains unclear. In this study, we identify marine heatwave events in the epipelagic zone (0–200 m) using a four-dimensional spatio-temporal framework based on three ocean reanalysis datasets. We find that only about half of the marine heatwave events have continuous surface signals during their life cycles and around one-third always reside in the subsurface ocean without any imprint on sea surface temperature. The annual number of these subsurface marine heatwave events shows a signifcant increase in response to subsurface mean-state warming during the past three decades. Our findings reveal the limitation of identifying marine heatwaves solely based on the sea surface temperature and underscore the necessity of subsurface observations for monitoring marine heatwaves.
A novel framework for turbidity source analysis of the urban lakeside river network
Authors: Yan Renhua , Gao Junfeng
Published: March 2024

Quantifying turbidity source is essential but complex for precise environmental rehabilitation of worldwide urban lakeside river networks impaired by turbidity. A new turbidity source analysis framework including in-situ sampling, vessel monitoring, remote sensing, and MIKE 11-based simulation was proposed, where the critical processes of flow reversal from turbid lake, navigation, and urban non-point source in urban lakeside river networks were explicitly considered. The case study results showed that turbidity problem was primarily led by suspended inorganic matter (58%), followed by suspended organic matter (19%) and other matter. The Tiaoxi River and busy shipping contributed most to annual total suspended solids (TSS) load of target river, with the same percentage of 34%. The turbid Lake Taihu provided 31% of total TSS load, focusing on autumn and winter months. Tiaoxi River load was highly concentrated on the storm events, which accounted for 71.4% of annual Tiaoxi River source. Coronavirus disease (COVID-19) lockdown and Spring Festival holiday has a significant effect on the change of shipping and Tiaoxi River sources, through limiting the waterway transportation and the market demand for building materials. River estuary’s vegetation interception belt, shipping restriction and rerouting and integrated catchment management were advised to mitigate the urban river network turbidity.
Resolving weather fronts increases the large-scale circulation response to Gulf Stream SST anomalies in variable-resolution CESM2 simulations
Authors: Jnglin Wills Robert , Herrington Adam , Simpson Isla , Battisti David
Published: March 2024

Canonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea-surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the horizontal resolution of modern GCMs, ranging from roughly 300 km to 25 km, is too coarse to fully resolve mesoscale atmospheric processes such as weather fronts. Here, we investigate the large-scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14-km regional grid refinement over the North Atlantic, and compare it to the response in simulations with 28-km regional refinement and uniform 111-km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.8-standard-deviation anomaly in the seasonal-mean NAO for 2°C SST anomalies. The lower-resolution simulations show a weaker response with a different spatial structure. The enhanced large-scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient-eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger North Atlantic jet that varies less in latitude, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher-resolution models and that regional refinement in key regions offers a potential pathway to improve multi-year regional climate predictions based on midlatitude SSTs.
Reproducing the magnetospheric response to southward turnings in MHD simulations
Authors: Samsonov Andrey , Milan Stephen , Sun Tianran , Buzulukova Natalia , Varela Jacobo , Forsyth Colin
Published: March 2024

State-of-the-art numerical models have been developed to reproduce magnetospheric dynamics in response to solar wind variations. However, we do not understand how accurate the predictions of the models would be in different solar wind and magnetospheric conditions. In this study, we consider the two relatively simple cases with southward interplanetary magnetic field turnings which have been simulated by several MHD models (SWMF, LFM, PPMLR-MHD, PLUTO). We compare numerical results with observations in terms of global magnetospheric characteristics such as the polar cap open flux and the indices of magnetospheric activity. Our purpose is to understand why some models can make better predictions. To answer this question we also compare the results of the same MHD model with different numerical resolutions and ionospheric conductances and show that both resolution and conductance are important for accurate predictions. By comparing simulations with observations, we can figure out the optimal parameters in the models which should be used in the future.
When will the Antarctic ice shelves not be viable anymore?
Authors: Burgard Clara , Jourdain Nicolas C. , Kittel Christoph , Mosbeux Cyrille , Caillet Justine , Mathiot Pierre
Published: March 2024

The Antarctic contribution to sea-level rise in the coming centuries remains very uncertain, due to the possible triggering of instabilities such as the Marine Ice Sheet Instability (MISI) and Marine Ice Cliff Instability (MICI). These instabilities are mainly linked to the evolution of the floating ice shelves, which usually buttress the ice flow from the ice-sheet to the ocean. However, these are currently thinning. Better understanding the evolution of ice shelves in the next decades to centuries is therefore important and crucial to better anticipate the evolution of sea-level rise.In this study, we investigate the viability of ice shelves for a number of climate models and scenarios. This is estimated from the emulation of the surface and basal mass balance of MAR and NEMO respectively, and from high-end dynamical ice flows obtained through Elmer/Ice. We then use a Bayesian calibration to give weight to members closer to observations. We find that large uncertainties remain, mainly because of the uncertainty in basal melt, and that viability limits vary largely depending on the ice-shelf location.
Spatial coherences of flood-generating processes in Europe and their impact on flood statistics
Authors: Fischer Svenja , Schumann Andreas
Published: March 2024

Flood events in Europe are caused by different generating mechanisms that lead to events with different peaks, volumes and hydrographs. Understanding such mechanisms is crucial not only for deterministic or stochastic modelling of floods, but also for practical purposes such as hydrological planning and design estimation. In this study, driving mechanisms of floods are analysed and the associated catchment and atmospheric attributes controlling these flood types are identified through a classification and regression tree approach. In addition, the role of flood types in flood statistics is analysed using type-based flood statistics. It is shown which flood types dominate the more frequent floods and which flood types are most frequently associated with extreme floods. Ordinary and extraordinary floods are identified by a Likelihood-Ratio test and tested for a significant difference in the frequency distribution of flood types. Our results show that the flood types vary regionally in Europe. In the Alpine region, heavy rainfall floods are responsible for the most extreme flood events, while in the northern parts of Europe flood events caused by snowmelt lead to the largest peaks. This is reflected in the flood statistics in the type-specific distributions, which have a different tail heaviness. These findings provide information to identify the most crucial circumstances in which floods become extreme and on the flood event itself.
Enhancing member-by-member post-processing with neural networks
Authors: Lerch Sebastian , Freytag Jakob , Muschinski Thomas , Allen Sam
Published: March 2024

Using post-processing methods to correct systematic errors of ensemble forecasts has become standard practice in research and operations. During recent years, a new focal point of research interest has been the use of modern machine learning methods to allow for more flexible post-processing methods that incorporate additional input predictors. In particular, neural network (NN) models have been shown superior predictive performance in various case studies [1-3].
In contrast, the member-by-member (MBM) post-processing approach [4] adjusts each ensemble member individually using a relatively simple statistical model. This has the advantage that the post-processed ensemble forecasts are not only calibrated, but physically consistent over time, space and different weather variables. Therefore, multivariate dependencies are preserved even if MBM is applied separately for each component. The drawback is that MBM has no straightforward way of incorporating additional input variables (beyond ensemble predictions of the target variable) and therefore typically fails to perform as well as NN-based post-processing approaches [3].
To address this shortcoming, we propose a novel NN-enhanced MBM post-processing approach (“MBM-NN”), which combines the basic idea of MBM with a neural network for incorporating additional predictors to leverage advantages of both approaches. In case studies on probabilistic wind gust forecasting over Germany and on the EUPPBench dataset [5], we demonstrate that the MBM-NN model achieves significant improvements over the standard MBM approach, and reaches comparable performance to state-of-the-art NN-based post-processing models, while retaining multivariate dependencies.

References
[1] Rasp, S. and Lerch, S. (2018). Neural networks for post-processing ensemble weather forecasts. Monthly Weather Review, 146, 3885-3900
[2] Vannitsem, S., Bremnes, J.B., Demaeyer, J., Evans, G.R., Flowerdew, J., Hemri, S., Lerch, S., Roberts, N., et al. (2021). Statistical Postprocessing for Weather Forecasts - Review, Challenges and Avenues in a Big Data World. Bulletin of the American Meteorological Society, 102, E681-E699
[3] Schulz, B. and Lerch, S. (2022). Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison. Monthly Weather Review, 150, 235-257
[4] Van Schaeybroeck, B. and Vannitsem, S. (2015). Ensemble post‐processing using member-by-member approaches: theoretical aspects. Quarterly Journal of the Royal Meteorological Society 141, 807-818
[5] Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A. Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., et al. (2023). The EUPPBench postprocessing benchmark dataset v1.0. Earth System Science Data, 15, 2635-2653
Towards high-resolution prediction of drought effects on Switzerland’s Beech forests for improved management
Authors: Bloom Colin , Koch Tiziana , Meusburger Katrin , Scherrer Daniel , Walthert Lorenz , Baltensweiler Andri
Published: March 2024

Over the past decade, extreme temperature and drought have resulted in widespread early leaf discoloration in European Beech (Fagus sylvatica) forests across central Europe. Discoloration during the particularly hot and dry summer of 2018 was ultimately associated with increased rates of crown dieback and tree mortality. Given the trend towards hotter and drier growing seasons under a changing climate, there is an increasing demand for site-specific recommendations on drought-resilient forest management practices in Switzerland. Making these recommendations requires a robust understanding of empirical forest disturbance and estimates of future forest health under a range of climatic and management conditions. To that end, using 2018 field observations, manual mapping of forest discoloration in aerial imagery, and multispectral Sentinel-2 imagery, we are developing 10 m/pixel estimates of European Beech discoloration across Switzerland during the 2018 to 2023 foliated periods. To date, we have 1) developed a robust interpolated Sentinel-2 time series from 2018 to 2023 for all of Switzerland, 2) trained a random forest model using 2018 ground control data and several vegetation indices from the Sentinel-2 time series to predict 2018 early leaf discoloration across Switzerland’s European Beech forests with c. 90% accuracy and, 3) used the Chlorophyll Red-Edge Index derived from the Sentinel-2 time series to approximate tree phenology and the length of the foliated period. We estimate that the 2018 foliated period was, on average, 45±19 days shorter for discolored sites as compared to sites without discoloration. Our results generally align well with previous studies of the 2018 drought in Switzerland and additional observational data is being compiled to validate the application of 2018 ground truth data across the foliated periods from 2018 to 2023. In combination with high-resolution soil maps, meteorological data, topographic derivatives, and information on Swiss forest structure, we will use empirical discoloration estimates to train ensemble models of site-specific susceptibility to drought. By artificially varying the meteorological and forest structure variables in these models we will have the unique opportunity to better understand European Beech susceptibility to drought and test the influence of a range of future climate scenarios and forest management strategies on Swiss forest health at a high spatial resolution.
Probabilistic Wind Speed Downscaling for Future Wind Power Assessment
Authors: Effenberger Nina , Pförtner Marvin , Hennig Philipp , Ludwig Nicole
Published: March 2024

Wind power and other renewable energy sources are essential for the energy supply. However, due to their dependence on both climate and highly local, variable weather conditions, they are less reliable and challenging to forecast.Recent projections of climate models indicate that the mean annual energy density will change in the future [Pryor et al., 2020]. To avoid costly planning mistakes and improve return on investment, predictions of wind conditions with adequate spatial and temporal resolution are thus indispensable, to facilitate efficient planning of renewables. Recent research regarding the temporal resolution of wind speed data shows that inter-daily wind speed variability can be accounted for by instantaneous data of six-hourly resolution [Effenberger et al., 2024]. However, as wind is a very local phenomenon, the spatial resolution of climate and weather data is paramount in wind power forecasting.Simulated climate data generally lacks the spatial resolution needed for highly localized wind power forecasts and needs to be downscaled. The downscaled data is subject to mainly two types of predictive uncertainty that are often ignored, yet non-negligible for decision-making. Firstly, climate projections depend on unknown physical processes, like the evolution of atmospheric CO2 concentration, and are thus inherently uncertain. We account for this uncertainty by ensembling across different climate models and scenarios. The second source of uncertainty, which is the main focus of this work, is that the coarse resolution of the simulated data introduces additional uncertainty, since interpolating wind speeds spatially is non-trivial. By downscaling different wind speed projections using a probabilistic Gaussian process simulation method, we can model the uncertainty that stems from interpolating wind speed data to unseen locations. Leveraging techniques from physics-informed machine learning, e.g. conditioning on partial differential equations [Pförtner et al., 2022], allows for a more realistic model, consistent with the actual dynamics of the atmosphere.The resulting, physics-informed Gaussian process models, provide uncertainty-aware, location-specific wind speed predictions on multi-decadal scales. When planning new turbine locations, these wind speed projections based on climate model data can serve as a proxy for expected future wind power generation.References:Effenberger, N., Ludwig, N., and White, R. H. (2024). Mind the (spectral) gap: how the temporal resolution of wind data affects multi-decadal wind power forecasts. Environmental Research Letters, 19.Pförtner, M., Steinwart, I., Hennig, P., and Wenger, J. (2022). Physics-informed Gaussian process regression generalizes linear PDE solvers. arXiv preprint arXiv:2212.12474.Pryor, S. C., Barthelmie, R. J., Bukovsky, M. S., Leung, L. R., and Sakaguchi, K. (2020). Climate change impacts on wind power generation. Nature Reviews Earth & Environment, 1(12):627–643.  
Data driven assessment of quantitative status of groundwater in the Netherlands
Authors: Zaadnoordijk Willem Jan , Fokker Eldert
Published: March 2024

In the Netherlands, there is no national groundwater monitoring network. The national government has delegated groundwater quantity management to the regional authorities, which perform groundwater head monitoring for this purpose. The regional authorities upload their groundwater head measurements to a national repository that is maintained by TNO Geological Survey of the Netherlands (TNO-GSN).As part of the Geological Survey task,  TNO-GSN makes available information based on these groundwater head measurements through an online Groundwater head viewer (https://www.grondwatertools.nl/gwsinbeeld). Currently, we are working on an extension of this viewer to show the status of the heads for the entire country. The aim is to give a data driven assessment which is independent of physics based distributed three dimensional groundwater modelling. The main reason is that the use of such modelling depends on successful calibration of a national groundwater model and on inclusion of all relevant processes and changes in boundary conditions (land use, pumping, water management, . . .). A second reason is that an independent assessments helps to improve the existing national model (https://nhi.nu) and better understand the changes in the Dutch groundwater system. For the assessment a limited number of monitoring wells are selected, so that the status can be shown qualitatively by colouring the dots. The main challenge is make a representative selection for:Yearly change of groundwater volume.
Drought or wetness.
Regional differences.
The 3 dimensional character of the groundwater system: not only phreatic but also deeper aquifers.
Assessment of large scale trends caused by e.g. climate change, urbanisation.
Effectiveness of large scale governance measures (‘water en bodem sturend’).
Monitoring wells in the national database are selected based on the following criteria:Being used currently;
The longer the measurement timeseries the better;
Preferably multilevel;
Spatial spread and variation in land use;
Covering national variation in precipitation and reference evaporation;
Covering range of response times of transfer-noise models with precipitation and reference evaporation as explaining variables;
Preferably equipped with telemetry and transmitting data to the national database daily.
The current groundwater head for each piezometer can be characterized in various ways, with and without seasonal correction, such as the percentile of all measurements or all measurements in the same month, or a percentile in the regime curve generated with a 30-year simulation of a timeseries model with precipitation and evaporation.The resulting selection will be a useful extension of the trends, vertical head differences, dynamics already available on the Groundwater head viewer for national operational water management, groundwater governance and outreach.
Impact of glacially induced stresses and strains at Canadian candidate sites for nuclear waste disposal
Authors: Steffen Holger , Steffen Rebekka
Published: March 2024

Continental-scale glaciations lead to deformation, geopotential, rotation, and stress changes of the Earth. Especially glacially induced stress changes in the lithosphere can significantly impact potential nuclear waste repository sites, planned for depths a few hundred meters below ground. Ensuring the long-term stability of these sites warrants analyzing such additional stresses in the crust that might be imposed by future ice sheets during forthcoming glaciations. We focus on North America by investigating the past to quantify such stresses.Utilizing a refined, high-resolution North American ice history spanning 122,000 years from the University of Toronto Glacial Systems Model, alongside various one- and three-dimensional earth structures, we investigate the dynamic nature of glacially induced stresses at Canada’s candidate sites South Bruce and Ignace. Both sites are situated in Ontario but 1000 km apart. Additionally, we examine the corresponding deformation and strain changes.We find that both sites undergo strong variations in glacially induced stresses and strains over a glacial cycle. Especially the horizontal components can change from tensional to compressive within a few thousand years due to fluctuations in ice cover. Surprisingly, despite South Bruce's location farther from the ice sheet center and its temporary position in the forebulge of the ice sheet, stress and strain magnitudes resemble those of Ignace. Moreover, there's potential at South Bruce for altering the direction of pre-existing maximum horizontal stress. Interestingly, the choice of earth structure in the modelling affects strain more than stress.Although the sites are 1000 km apart, our results do not indicate a superior repository site. Instead, they emphasize the need to integrate our findings into the site selection process and barrier integrity assessment. We recommend further studies focusing on a stress analysis with more detailed earth models, considering faults and lineaments near the candidate sites.This research was funded by the Nuclear Waste Management Organization, Canada.
Assessing the risk of climate change to a business
Authors: Pitman Andy , Saribatir Ed , Greenhill Catherine , Green Sam , Pitman Samuel
Published: March 2024

The realisation that climate change threatens economic systems has led investors, standard-setters and regulators to call on businesses to assess their exposure to climate-related risks, and to disclose the financial impact of these in their annual reports and financial statements where material. Indeed, mandatory disclosure requirements have already been implemented in some jurisdictions and are being proposed elsewhere. Mandatory disclosure of physical climate risk by a single business predisposes that the business can reasonably assess this risk. Here, we use the analogy of a spider’s web to examine how changes in the frequency and magnitude of extremes, that break parts of the web, combine to affect the efficiency of a hypothetical business. We demonstrate that the precise location of an extreme event, the precise characteristics of the event, and whether a subsequent event occurs close to or distant from an earlier event strongly influences vulnerability. In short, to estimate the impact of climate change induced extremes on a business requires not merely the general frequency of events, but the precise geolocation of the event mapped on the vulnerabilities of the business. We conclude that mandatory disclosure of future climate risk by a business cannot be other than deeply uncertain and this is not resolvable via foreseeable advances in global or regional climate modelling.
Soil moisture forecasting for dryland fields in Australia
Authors: Amir Qazi Muqeet , Bishop Thomas
Published: March 2024

Australia is frequently susceptible to droughts. Major droughts within the last 50 years such as that of 1982-1983 and the Millennial drought of 1997-2010 severely impacted crop growth across the country. Soil moisture can be in deficit during droughts due to a lack of recharge and high evapotranspiration from the soil. Dryland agriculture is particularly sensitive to droughts as there is no irrigation input into the soil. Soil water availability is a critical constraint to agricultural productivity, so the ability to predict its current and future state accurately is key in informing decisions relating to irrigation, fertiliser use, and yield targets. While soil moisture forecasting has been conducted in literature previously, there is limited understanding of the spatial, seasonal, and meteorological patterns that underlie the forecastability of soil moisture in a particular field. Hence this research aims to understand the spatial, seasonal, and meteorological factors that influence the forecast accuracy of soil moisture in dryland fields in Australia. Across Australia an increasing number of growers have soil moisture probes, which report current and historic soil moisture. The domain of this work is in the CosmOz probe network, consisting of 26 cosmic ray soil moisture probes across Australia, accounting for various geophysical and climatic regions. The probes measure average soil moisture to depths in the soil between 10 to 50 cm. Forecasting soil moisture requires the addition of various modelled/remotely sensed data such as meteorological, vegetation type, and soil property data. Using this data and lagged soil moisture as predictors, soil moisture has been forecasted at the locations of each CosmOz probe. With up to 13 years of training data, machine learning models have been fitted to forecast soil moisture with high accuracy forecasts of up to 30 days. To improve predictions a neural network autoencoder has been employed to engineer features that account for anomalous periods in the predictors.A key outcome of this study is identifying patterns in forecast accuracy and predictor importance with respect to region, soil type, meteorological conditions, and time of year. These patterns create a nationwide perspective of soil moisture forecastability and the potential for forecasting in areas with no soil moisture probe data available. 
Enhancing flash flood simulations through appropriate assimilation of remotely sensed soil moisture
Authors: Liu Yan , Chang Yong , Haag Ingo , Krumm Julia , Sivaprasad Visakh , Aigner Dirk , Vereecken Harry , Hendricks Franssen Harrie-Jan
Published: March 2024

The precondition of a catchment, especially soil wetness that can affect remaining soil water storage capacity and infiltration rate, is crucial for flash flood generations. Remotely sensed (RS) soil moisture (SM) can provide valuable information on soil wetness, but typically for the top 5 cm soil. Many flash flood hydrological models only have few or even a single soil layer. How to appropriately represent wetness of the entire soil by RS SM becomes crucial for enhancing flash flood simulations with data assimilation (DA). In this study, we propose a new approach to use a certain amount of historical RS SM to derive total soil water storage such that we can assimilate it into flash flood simulations. We applied this approach for the Körsch and Adenauer catchments in Germany, where we assimilated RS SM from the Soil Moisture Active Passive (SMAP) Mission into the Large Area Runoff Simulation Model (LARSIM). Our results show that we can build a good relationship between RS SM considering different antecedent and present data and soil storage using random forest regression compared to linear, polynomial and long short-term memory (LSTM) regressions, resulting in R2 of 0.85 and 0.94 for Körsch and Adenauer, respectively. Using our approach to assimilate RS-derived soil storage into flash flood simulations, performance of flash flood event simulations was improved by an increase of ~0.19 in KGE (Kling-Gupta efficiency) for our study sites. Errors in flash flood peak can be reduced up to 15% compared to simulations without assimilating RS SM. The uncertainty of soil wetness over space was reduced as expected. We examined the possibility of transferring our approach to other RS SM products. We also noticed that despite of the enhancement by assimilating RS SM, the simulation of flash flood is still primarily affected by precipitation uncertainty. In general, we provided a feasible way to use RS SM for hydrological models only with a single soil layer. Future studies applying it to more catchments and events can help to better verify the general validity of our proposed approach.
Increasing the availability of Italian daily hydrological measurements with a citizen science approach: the SIREN project
Authors: Mazzoglio Paola , Bertola Miriam , Lombardo Luca , Sacco Chiara , Viglione Alberto , Laio Francesco , Claps Pierluigi
Published: March 2024

In Italy, the National Hydrological Service named “Servizio Idrografico e Mareografico Nazionale” (SIMN) was established to collect hydro-meteorological measurements. This Service was also in charge of publishing the Hydrological Yearbooks, a standardized collection of validated measurements available in printed volumes. The dismantlement of the SIMN, performed about 30 years ago, led to the decentralization of data collection to the regional level. This shift has resulted in challenges related to the availability of comprehensive national-scale datasets since historical hydrological measurements are usually available only in the printed Yearbooks. These volumes have seen limited efforts towards digitization over time, increasing the risk of losing a large (but, so far, little exploited) wealth of information related to the hydrology of the last century.Despite advancements in Optical Character Recognition (OCR) software, machine learning, and artificial intelligence, manual transcription remains the most accurate digitization method in certain conditions, e.g., when the ink is partially damaged or when handwritten corrections are reported. Within the SIREN (Saving Italian hydRological mEasuremeNts) project, a citizen science initiative developed on the Zooniverse platform (https://www.zooniverse.org/projects/siren-project/siren-project), hundreds of volunteers are contributing to digitizing this amount of data. Being an expert is not fundamental for being part of this citizen science project: a tutorial automatically pops out when a volunteer enters the workflow, illustrating all the key characteristics of the Yearbooks and how to interpret them, enriched with a step-by-step description of all the phases of the digitization workflow. To minimize digitization errors, each table is digitized by at least 2 different volunteers, and discrepancies are manually checked and corrected.The time series collected up to now are currently undergoing a detailed quality control procedure to ensure the reliability of the dataset that will be created. The final dataset will be made available on Zenodo in the upcoming months.The SIREN project represents thus a collaborative effort to bridge the historical hydrological data gap, offering valuable insights for both local and national-scale analyses and aiding in the refinement of models predicting current and future hydrological trends.