Hasil untuk "River protective works. Regulation. Flood control"

Yaoshen Fan, Guangzhou Wang, Shentang Dou et al.

Sediment coarsening in submerged deltas is commonly attributed to seabed erosion because of insufficient sediment input. The Yellow River subaqueous delta (YRSD) has exhibited distinct coarsening patterns following both accretion and erosion events. To investigate these contrasting mechanisms, grain size distributions, elevation changes, and bottom shear stress patterns were analyzed across the delta from 1992 to 2022. The results revealed distinct sedimentary patterns among the abandoned YRSD, active YRSD, southern Laizhou Bay, and adjacent Bohai Sea, with average median grain size (D50) increases of 17, 17, 6, and 0 μm, respectively. Sediment coarsening occurred primarily from 1992 to 2000, when the river mouth position was artificially altered and fluvial sediment grain size increased from 16 to 29 μm. From 1992 to 2015, the active YRSD experienced accretion at a rate of 7.8 mm/yr. Moreover, the abandoned YRSD and southern Laizhou Bay experienced significant erosion. The erosion rates were −5.1 and −1.0 mm/yr, respectively. This led to the identification of two mechanisms of sediment coarsening: erosion-driven coarsening in sediment-deficient areas and accretion-driven coarsening where the input sediment grain size increased. Although marine processes did not intensify during this period, the bottom shear stress distribution changed substantially due to morphological evolution, with correlation coefficients between grain size and shear stress showing increasing trends in littoral zones. This strengthening relationship, coupled with the declining fluvial sediment load, demonstrates the YRSD transition from river-dominated to wave-dominated processes, providing important insight into delta evolution under changing sediment regimes. The insights gained can guide Yellow River Delta management through targeted strategies and provide essential evidence for predicting delta evolution.

Fulvio D. Lopane, Richard J. Dawson

ABSTRACT Infrastructure systems provide crucial services to human settlements. Extreme weather events, especially flooding, can disrupt these vital services. Temporary and demountable flood protections (TDFPs) are increasingly used to protect infrastructure assets and provide resilience. Budget constraints mean that TDFP are typically deployed to multiple sites from a single warehouse. Identifying optimal locations to maximise coverage and minimise costs is a complex spatial problem not yet tackled in the literature. To address this, a Spatial Resource Allocation Optimisation (SRAO) framework, using a genetic algorithm (GA), has been developed. The SRAO framework is applied to a case study in the Humber Estuary (UK) where 133 strategic infrastructure assets serve over 400,000 people in the floodplain. Eight scenarios assess how cost, TDFP availability, transport and asset prioritisation for protection influence warehouse size and sites. The SRAO identifies optimal strategies that, relative to other strategies, reduce annual costs by 40%–50% and deployment times by 60%–70%. Furthermore, 8 ‘hotspot’ sites appear in over 60% of optimal solutions; these can be considered robust to model uncertainties and scenario assumptions, providing decision‐makers with locations performing well under varied conditions. The methodology benefits local authorities, infrastructure operators and emergency management agencies, reducing costs and improving resilience for communities.

Tushar Khankhoje, Samrat Boro, Parthasarathi Choudhury

Continuous water and sediment flow monitoring across river cross sections is essential for the management of flood- and sediment-related problems in watersheds. The sediment rating curve (SRC) estimates missing or uncertain sediment flow via its corresponding water discharge. Generally, a power form of relationship correlates the two quantities. The log-transformed water discharge and sediment discharge data were used to depict the SRCs developed in the present study. SRC parameter estimation via least squares regression using at-site dataset pairs can be found in the literature. However, the availability of reliable datasets at the site limits model applicability. This method does not describe the SRC on the basis of the continuity aspects of river system flow characteristics. Therefore, the current study proposes integrated SRC estimation models (Model 2 and Model 3) using modified Muskingum equations abiding by the spatial and temporal continuity of the entire river system state. These models are derived from streamflow storage balance criteria and ensure flow continuity norms. Moreover, Model 3 considers an inverse power form of the relationship depicting the water flow characteristics that govern the sediment transport phenomena through the river system. Standalone models for SRC parameter estimation (Model 1) were also developed for comparison among all three models via the root mean square error (RMSE), NRMSE (normalized root mean square error) and coefficient of determination (R2). The Mahanadi River system within Chhattisgarh state, India comprises five sections at tributaries, and the main channel was considered for the study. The improved NRMSE by Model 2 (7.53%) and Model 3 (7.14%) at Rajim and Model 3 (3.44%) at Bamnidhi in comparison to Model 1 at Rajim (9.19%) and Bamnidhi (4.80%) encouraged the application of integrated models for SRC estimation in river systems. Moreover, Model 3 outperformed Model 2 in some cases where the sediment transport process may be governed by water flow characteristics.

Kate Baker, Sarah Ward, Lucinda Guy et al.

ABSTRACT Shifting away from traditional deficit‐based approaches like the ‘Decide‐Announce‐Defend’ model toward more inclusive and equitable community engagement presents both challenges and opportunities. Flood authorities often lack awareness or understanding of how to use effective active listening tools and methods to meaningfully engage and include flood‐affected communities. In this paper, we present two innovative methods rooted in the Asset‐Based Community Development framework, which emphasise listening and dialogue, and have been shown to build relationships and address power imbalances. Community Radio Practice (CRP) was used as a landing exercise to learn about the communities, and Walk in My Wellies (WiMW) was an initiative where Flood and Coastal Erosion Risk Management (FCRM) professionals were paired with community leaders. Talking, listening and communicating in the ‘right’ way was discussed at length in the WiMW programme, and the CRP approach led professional stakeholders such as local authorities to shift their preconceptions, and understand the importance of their relationships with the community. These innovative talking initiatives created ‘safe’ spaces to understand different perspectives; FCRM professionals and community members listened to each other with openness, honesty and respect, identifying barriers and sharing ideas to increase effective community engagement. CRP created lightbulb moments for FCRM professionals revealing the value of engaging with communities and power asymmetries began to shift, as did awareness of additional engagement approaches. Both CRP and WiMW highlighted the need for appropriate training, as enhancing FCRM professionals' understanding of community engagement is critical to reducing barriers and supporting a more equitable distribution of power and agency.

Paras Sharma, Swastika Sharma

Flash floods are the most destructive natural hazard in Himachal Pradesh (HP), India, causing over 400 fatalities and $1.2 billion in losses in the 2023 monsoon season alone. Existing risk maps treat every pixel independently, ignoring the basic fact that flooding upstream raises risk downstream. We address this with a Graph Neural Network (GraphSAGE) trained on a watershed connectivity graph (460 sub-watersheds, 1,700 directed edges), built from a six-year Sentinel-1 SAR flood inventory (2018-2023, 3,000 events) and 12 environmental variables at 30 m resolution. Four pixel-based ML models (RF, XGBoost, LightGBM, stacking ensemble) serve as baselines. All models are evaluated with leave-one-basin-out spatial cross-validation to avoid the 5-15% AUC inflation of random splits. Conformal prediction produces the first HP susceptibility maps with statistically guaranteed 90% coverage intervals. The GNN achieved AUC = 0.978 +/- 0.017, outperforming the best baseline (AUC = 0.881) and the published HP benchmark (AUC = 0.88). The +0.097 gain confirms that river connectivity carries predictive signal that pixel-based models miss. High-susceptibility zones overlap 1,457 km of highways (including 217 km of the Manali-Leh corridor), 2,759 bridges, and 4 major hydroelectric installations. Conformal intervals achieved 82.9% empirical coverage on the held-out 2023 test set; lower coverage in high-risk zones (45-59%) points to SAR label noise as a target for future work.

Ali Pourzangbar, Peter Oberle, Andreas Kron et al.

ABSTRACT This article provides an analysis of the utilization of Machine Learning (ML) models in Flood Susceptibility Mapping (FSM), based on selected publications from the past decade (2013–2023). Recognizing the challenge that some stages of ML modeling inherently rely on experience or trial‐and‐error approaches, this work aims at establishing a clear roadmap for the deployment of ML‐based FSM frameworks. The critical aspects of ML‐based FSM are identified, including data considerations, the model's development procedure, and employed algorithms. A comparative analysis of different ML models, alongside their practical applications, is made. Findings suggest that despite existing limitations, ML methods, when carefully designed and implemented, can be successfully utilized to determine areas at risk of flooding. We show that the effectiveness of ML‐based FSM models is significantly influenced by data preprocessing, feature engineering, and the development of the model using the most impactful parameters, as well as the selection of the appropriate model type and configuration. Additionally, we introduce a structured roadmap for ML‐based FSM, identification of overlooked conditioning factors, comparative model analysis, and integration of practical considerations, all aimed at enhancing modeling quality and effectiveness. This comprehensive analysis thereby serves as a critical resource for professionals in the field of FSM.

Diwen Huang, Yong You, Hao Sun et al.

Self-cleaning is a crucial feature of slit dams, which not only enhances upstream and downstream hydraulic connections but also automatically restores a dam's debris flow storage capacity. In this work, a series of specially designed flume tests are performed to simulate the self-cleaning process. The flow rate, relative opening, bed-slope angle, and number of openings are considered. The erosion process, topographic characteristics, and relative erosion depth are analyzed to gain insight into the self-cleaning details. The current results reveal that when the boulders jamming the openings are removed, erosion occurs in three stages (downcutting, headward erosion, and lateral erosion). Conversely, when the blockage remains stable, only surface armoring occurs. Furthermore, after the self-cleaning process reaches a quasiequilibrium state, the topographic features are summarized, and the differences in the maximum erosion depth at the opening are analyzed for different experimental conditions. This paper proposes a critical criterion (F) for the self-cleaning of slit dams on the basis of dimensional analysis. The critical criterion takes into account the interactions of three parameters (Froude number, relative opening, and opening rate) and can be conveniently applied to existing slit dams. When F is less than 0.25, the opening remains blocked, and only surface armoring occurs; when F is between 0.25 and 0.38, the blockage may be removed; and when F is greater than 0.38, self-cleaning leads to massive erosion, and the blockage is removed. Therefore, the proposed critical criterion can help design the opening dimensions of a slit dam, restoring its storage capacity. Finally, the positive effect of self-cleaning on restoring the storage capacity of slit dams is discussed.

Alexandra Seawell, Hayley J. Fowler, Stephen Blenkinsop et al.

ABSTRACT The temporal distribution of rainfall is a key driver of flood response. Yet, flood estimation methods are frequently based on symmetrical design profiles. Recent research using sub‐hourly rainfall data from Great Britain indicates that a significant proportion of observed rainfall events are non‐symmetrical. This paper investigates how different rainfall profiles affect river flow hydrographs for a set of small, flash‐flooding catchments. Results show that rainfall profiles affect observed hydrograph peak flow and timing. Most importantly, back‐loaded rainfall profiles lead to higher peak flows than symmetrical or front‐loaded profiles. These observations are compared to current design practice, using the Revitalised Flood Hydrograph (ReFH2.3) model to simulate flows from different rainfall profiles. Simulated events reproduce the observed response of peak magnitude but differ for peak time. A comparison of modelled flows with catchment descriptors indicates that steep, low permeability, wet catchments are most sensitive to rainfall profile shape. These are also the most vulnerable catchments to flash flooding. We recommend that different rainfall profile shapes should be considered for flood risk assessments in rapid response catchments, particularly since global warming is increasing the number of intense, short‐duration downpours.

Mohammadreza Maddahi, Robert Michael Boes, Ismail Albayrak

Sediment bypass tunnels (SBTs) divert sediment-laden flows from river systems around reservoirs to the tailwater reach and thus help prevent reservoir sedimentation. However, their bypassing efficiency largely depends on reservoir operation, particularly for type-B SBTs with an intake located within the reservoir. The present study aims to investigate the effect of reservoir operation conditions on the bypass efficiency of a type-B SBT at the case study Solis Reservoir in Switzerland. Four annual measurement campaigns were conducted in the reservoir between 2018 and 2021. Flow velocities were measured, and bathymetry was mapped using an acoustic Doppler current profiler at high spatial resolution along the elongated and narrow reservoir. In- and outflow sediment volumes were measured using turbidimeters and Swiss plate geophone systems and estimated by using state-of-the-art sediment transport equations, respectively. Two floods with one-year and five-year return periods, respectively, in 2019 and a one-year return period flood in 2020 were captured. The results show that the average sediment bypass efficiency, i.e. the ratio of outflowing to inflowing sediment volumes, increased from 17% to 88% by operating SBT. The results highlight that the SBT bypass efficiency is highly dependent on the reservoir water level. For high efficiencies above 170%, an optimal value of the reservoir drawdown level is around 813 m asl. Bypass efficiencies up to 250% indicate that the type-B SBT does not only stop sedimentation but can also help regain active storage volume of the reservoir if operated under optimal conditions in terms of reservoir water level. Without SBT operation, ca. 205,000 m3 of net sediment deposition volume would have resulted in an aggradation of 1 m on average from 2018 to 2021. The findings of this study contribute to improved SBT and reservoir operation regimes in terms of reducing the sedimentation rates and prolonging the reservoir lifetimes.

N. Al-Kharousi, A.R. Kacimov, A. Al-Maktoumi et al.

Sidr (Christ-thorn) phreatopytic-xerophitic wild trees growing in the reservoir area of the Al-Khoud groundwater recharge/flood protection dam in Muscat (Oman) were surveyed. Trees’ loci, heights and stem diameters were measured. Soil profiles were described in two pedons (trenches) excavated near two pilot trees. A remarkable increase in infiltration rate was observed when double-ring infiltrometers were placed near the tree stems and at the bottom of the trenches. Sap flow metering conducted on one tree over a period of 9 months resulted an average rate of 12.5 L/d. This value was used to evaluate the reservoir water balance, considering it as the difference between infiltration of ponded post-flash-flood water and transpiration. Sidr trees are recommended as excellent eco-engineers, that can alleviate the negative impact of reservoir bed siltation by enhancing the infiltration-recharge of the underlying aquifer.

Davide Wüthrich, Paul A. Korswagen, Harish Selvam et al.

Abstract The July 2021 flood heavily affected many inhabitants, buildings and critical infrastructure throughout Germany, Belgium and the Netherlands. Specifically, the Ahr Valley (Germany) showcased the destructive power associated with these extreme events. Hence, this region was the focus of a field survey, aiming at describing the flood‐induced damage to buildings and assessing the possible underlying processes that led to structural failures. The field assessment revealed a close connection between building failures and (1) local flow depths and velocities, (2) building location, (3) distance from the riverbank and (4) construction type. Although it is difficult to identify the exact causes that induced failures, the detailed assessment revealed that damages mainly originated from local scour and hydraulic loads, often unevenly distributed around buildings. Importantly, many buildings were significantly affected by (large) floating debris impacts and damming, both responsible for additional loads, highlighting their importance in flood‐resistant building design. Furthermore, data showed that buildings near the riverbanks and in the upstream part of villages were more severely damaged. Altogether, data provide a better understanding of the flood processes that lead to building failures, fostering future research towards the development of safer protection measures and more effective flood risk management strategies.

Rafael Silva Araújo, Miho Ohara, Mamoru Miyamoto et al.

ABSTRACTPeople's vulnerability to disasters depends largely on their social and physical aspects, such as economic disadvantages and mobility constraints related to age. Those characteristics will influence how individuals experience the disaster and recover. Thus, assessing the vulnerable population's location and exposure to hazards such as floods is important for designing disaster risk reduction policies. This study conducts such an analysis considering five disadvantage dimensions: age, gender, race, socioeconomic status, and housing spatially distributed in cell grids, which were compiled into a disadvantage index (DI). The DI is further overlayed with the population density (DI*pop.dens.). From the derived DI*pop.dens. map, priority areas for flood management budget allocation can be extracted. The methodology is applied to the Itapocu River Basin (IRB), in southern Brazil, as a case study and compared with the flood inundation area estimated by a hydrological simulation. The places that could be regarded as priority areas for future public policy were classified into high, medium, and low‐priority areas, considering higher exposure of the disadvantaged population, higher flood depth, and higher flood frequency. In the IRB, there are priority areas near the main urban areas. Thus, flood control budgets are suggested to be allocated there to protect the vulnerable population.

Mohamad Hakam Shams Eddin, Yikui Zhang, Stefan Kollet et al.

Recent deep learning approaches for river discharge forecasting have improved the accuracy and efficiency in flood forecasting, enabling more reliable early warning systems for risk management. Nevertheless, existing deep learning approaches in hydrology remain largely confined to local-scale applications and do not leverage the inherent spatial connections of bodies of water. Thus, there is a strong need for new deep learning methodologies that are capable of modeling spatio-temporal relations to improve river discharge and flood forecasting for scientific and operational applications. To address this, we present RiverMamba, a novel deep learning model that is pretrained with long-term reanalysis data and that can forecast global river discharge and floods on a $0.05^\circ$ grid up to $7$ days lead time, which is of high relevance in early warning. To achieve this, RiverMamba leverages efficient Mamba blocks that enable the model to capture spatio-temporal relations in very large river networks and enhance its forecast capability for longer lead times. The forecast blocks integrate ECMWF HRES meteorological forecasts, while accounting for their inaccuracies through spatio-temporal modeling. Our analysis demonstrates that RiverMamba provides reliable predictions of river discharge across various flood return periods, including extreme floods, and lead times, surpassing both AI- and physics-based models. The source code and datasets are publicly available at the project page https://hakamshams.github.io/RiverMamba.

Lorenzo Zino, Alessandro Casu, Alessandro Rizzo

We propose an epidemic model for the spread of vector-borne diseases. The model, which is built extending the classical susceptible-infected-susceptible model, accounts for two populations -- humans and vectors -- and for cross-contagion between the two species, whereby humans become infected upon interaction with carrier vectors, and vectors become carriers after interaction with infected humans. We formulate the model as a system of ordinary differential equations and leverage monotone systems theory to rigorously characterize the epidemic dynamics. Specifically, we characterize the global asymptotic behavior of the disease, determining conditions for quick eradication of the disease (i.e., for which all trajectories converge to a disease-free equilibrium), or convergence to a (unique) endemic equilibrium. Then, we incorporate two control actions: namely, vector control and incentives to adopt protection measures. Using the derived mathematical tools, we assess the impact of these two control actions and determine the optimal control policy.

Giannis Delimpaltadakis, Pol Mestres, Jorge Cortés et al.

Recently, there has been a surge of research on a class of methods called feedback optimization. These are methods to steer the state of a control system to an equilibrium that arises as the solution of an optimization problem. Despite the growing literature on the topic, the important problem of enforcing state constraints at all times remains unaddressed. In this work, we present the first feedback-optimization method that enforces state constraints. The method combines a class of dynamics called safe gradient flows with high-order control barrier functions. We provide a number of results on our proposed controller, including well-posedness guarantees, anytime constraint-satisfaction guarantees, equivalence between the closed-loop's equilibria and the optimization problem's critical points, and local asymptotic stability of optima.

Robin Strässer, Manuel Schaller, Julian Berberich et al.

We derive novel deterministic bounds on the approximation error of data-based bilinear surrogate models for unknown nonlinear systems. The surrogate models are constructed using kernel-based extended dynamic mode decomposition to approximate the Koopman operator in a reproducing kernel Hilbert space. Unlike previous methods that require restrictive assumptions on the invariance of the dictionary, our approach leverages kernel-based dictionaries that allow us to control the projection error via pointwise error bounds, overcoming a significant limitation of existing theoretical guarantees. The derived state- and input-dependent error bounds allow for direct integration into Koopman-based robust controller designs with closed-loop guarantees for the unknown nonlinear system. Numerical examples illustrate the effectiveness of the proposed framework.

L. Limantara, Duki Malindo, P. Juwono et al.

Purpose: The primary purposes of this present study are to show and compare 3 (three) different types of spillways, namely normal, differential, and labyrinth, by means of passing exceptional flood events with acceptable safety and margin for the dam and spillway while maintaining a hydraulic safety point of view.   Theoretical reference: Ciawi dry dam is a homogenous type of dam with an inclined wet core built in the Ciliwung River, Jakarta, Indonesia. Its purpose is to retain 50 years of floods and cut the peak of floods as part of the Jakarta flood control system. It has been functioning since the year 2022 with another dry dam in the same River basin called the Sukamahi dry dam.   Method: The flood control capacity of the dry dam with a spillway and gateless bottom outlets designed at the riverbed level is investigated with an analytical approach using a theoretical formula. Numerous design discharges were verified and compared for specific scenarios engaging the operation of bottom outlets reviewed based on flood control and the safety of the dam. In addition, a spillways type comparison is made for a scenario with one gate opening and one gate closing of the bottom outlet using PMF design discharge to confirm the satisfaction of dry dam outlet capacity.   Result and Conclusion: The best design choice that can be recommended for the worst scenario is the Labyrinth spillway with a width of 155m and an angle of 18o while the spillway span width is kept to the same existing normal spillway width. This proposed design improvement option with labyrinth spillway for the PMF flood discharge still has a freeboard of 1.17 m allowance to the crest of the dam in the closed gate condition, 1.88 m of freeboard in 1 gate opened condition, and 2.55 m of freeboard while both gates are opened.   Implication of research: This present study evaluates the performance of a dry dam design outlet in flood control by assessing the reservoir capacity to compare the existing built normal spillway with the other types of spillways, namely differential and labyrinth. It will recommend an alternative type of spillway for the Ciawi dry dam or another dam to face global climate change and a future challenge.   Originality/value: The comprehensive investigation of the limitations and benefits of comparing 3 different types of spillways: normal, differential and labyrinth, with a combination scenario of 2 gates opening and closing in the embankment type of dry dam. This proposed type of spillway could be used for upgrading works and even for a new dam.

نازلی زنوزی علمداری, بهروز سبحانی, مهدی اصلاحی et al.

مقدمه تغییرات آب و هوایی با تغییر بارش و دما به چرخه هیدرولوژیک، منابع آب قابل دسترس و تقاضای آب و انرژی اثر می‌‌گذارد. در این راستا، پیش‌بینی تغییرات بارش و دما به‌وسیله مدل‌‌های گزارش ششم تغییر اقلیم به‌دلیل افزایش دقت در برونداد آنها می‌‌تواند کمک شایانی برای برنامه‌‌ریزی و مدیریت منابع آب در دوره آتی باشد. این مدل‌‌ها قادر به مدل‌سازی پارامترهای اقلیمی با استفاده از سناریوهای تأیید شده هیات بین‌الدول تغییر اقلیم (IPCC) برای یک دوره بلندمدت هستند. هم اکنون در سطح جهانی مراکز و مدل‌‌های گوناگونی برای مدل‌‌سازی وضعیت اقلیم دهه‌‌های آینده کره زمین با استفاده از سناریوهای انتشار، ساختار فیزیکی و محاسباتی گوناگونی وجود دارد. شبیه‌سازی‌های حاصل از مدل‌‌های گردش عمومی جو که بخشی از CMIP6 هستند، مبنایی برای بسیاری از نتیجه‌‌گیری‌‌های هیئت بین‌‌الدول در ارتباط با تغییرات اقلیمی آینده است. از این داده‌‌ها به‌صورت مستقیم و یا پس از ریزمقیاس‌‌نمایی برای ارزیابی تغییرات اقلیمی آینده در مقیاس‌‌های محلی و منطقه‌‌ای استفاده می‌‌شود. این پژوهش، سعی در تحلیل و پیش‌بینی روند بارش و دمای کمینه و بیشینه استان آذربایجان شرقی تحت شرایط تغییر اقلیم در دوره 2021 تا 2100 دارد.   مواد و روش‌‌ها این پژوهش برای بررسی و پیش‌بینی بارش و دمای کمینه و بیشینه و تعیین روند آنها با استفاده از مدل‌‌های اقلیمی گزارش ششم CMIP6)) گردش عمومی جو و شبیه‌ساز صحیح اریبی در دوره آتی (2021 تا 2100) در ایستگاه‌های تبریز، اهر، جلفا، مراغه و میانه انجام شده است. برای ارزیابی روند بارش، دما بیشینه و کمینه استان تا پایان قرن 21، از داده‌‌های 12 مدل (ACCESS-CM2، BCC-CSM2-MR، CESM2، CNRM-CM6-1، CanESM5، MIROC6، MRI-EMS2-0، IPSL-CM6A-LR، GISS-E2-1-G، HadGEM3-GC31- LL، NESM3 و NOR-ESM2-MM) از مجموعه مدل‌‌های در دسترس CMIP6 با سه سناریوی (SSP1-2.6، SSP2-4.5 و SSP5-8.5) استفاده شد. برای شناسایی بهترین مدل برای شبیه‌سازی داده‌های بارش و دما دوره آتی (2021 تا 2100) از روش کلینگ-کوپتا استفاده شد و داده‌های تاریخی هر مدل را با داده‌‌های مشاهداتی (2018-1989) ایستگاه‌‌های منتخب مورد ارزیابی قرار گرفت. در ادامه، از برون داد تصحیح اریبی شده مدل‌‌های اقلیمی برای پیش‌نگری داده‌های تحت سناریوهای SSP در دوره آینده استفاده شد. در مرحله آخر، میانگین سری‌‌های زمانی بارش و دمای کمینه و بیشینه دوره آینده در هر سناریو با ترکیب نتایج مدل‌‌ها در دوره پایه (تاریخی) مقایسه شدند تا میزان تغییرات دما کمینه، دمای بیشینه و بارش 80 سال آینده (2021 تا 2100) استان آذربایجان شرقی تعیین شود.   نتایج و بحث در این پژوهش، عملکرد 12 مدل اقلیمی از مجموعه مدل‌های گزارش ششم تغییر اقلیم در بازه تولید داده‌‌های اقلیمی در زمان گذشته (1989 تا 2018) بررسی شد. بر اساس نتایج بررسی عدم قطعیت دو مدل BCC-CSM2-MR و MIROC6 که بهترین شبیه‌سازی‌ را برای بارش و دما داشتند، برای پیش‌بینی  پارامترهای بارش و دمای کمینه و بیشینه با استفاده از تصحیح اریبی برای دوره آینده (2021 تا 2100) تحت سه سناریوی خوش‌بینانه، متوسط و بدبینانه در استان آذربایجان شرقی مورد استفاده قرار گرفت و درنهایت متوسط تغییرات دما بیشینه و کمینه و بارش در افق 2021 تا 2100 به‌صورت نقشه و نمودار ارائه شد. نتایج نشان داد که در تمام سناریوهای انتشار، دمای سالانه افزایش و بارندگی سالانه کاهش پیدا خواهد کرد. دمای میانگین بیشینه سالانه سه سناریوی SSP در ایستگاه‌‌های منتخب (تبریز، مراغه، میانه، جلفا و اهر) به‌ترتیب 2.1، 1.2، 3.4، 5.2 و 1 درجه سلسیوس و دمای کمینه سالانه به‌ترتیب سه، 2.9، 3.3، شش و 1.4 درجه سلسیوس افزایش و بارش به‌طور میانگین در سه سناریوی (SSP1-2.6، SSP2-4.5 و SSP5-8.5) در ایستگاه منتخب به‌ترتیب 3.2، 2.9، 3.1، 3 و 2.4 درصد کاهش خواهد یافت.   نتیجه‌‌گیری نتایج این پژوهش بیانگر این امر است که از بین ۱۲ مدل CMIP6 مورد ارزیابی در این پژوهش، دو مدل بهینه BCC-CSM2-MR و MIROC6 به‌خوبی توانسته‌‌اند، شبیه‌سازی‌ پارامترهای بارش و دما را برای دوره‌‌های آینده شبیه‌سازی کنند و می‌‌توان با صحت بالا از این داده‌‌های شبیه‌‌سازی شده برای آینده‌‌نگری مناسب‌تر از شرایط آب و هوایی در دوره‌‌های آتی استفاده کرد و به کمک آن مدیریت کلان آینده را در زمینه‌‌های بهره‌وری مناسب‌‌تر از منابع و به‌خصوص منابع آبی ارتقاء بخشید.

Robin Strässer, Julian Berberich, Frank Allgöwer

In this paper, we propose a novel controller design approach for unknown nonlinear systems using the Koopman operator. In particular, we use the recently proposed stability- and feedback-oriented extended dynamic mode decomposition (SafEDMD) architecture to generate a data-driven bilinear surrogate model with certified error bounds. Then, by accounting for the obtained error bounds in a controller design based on the bilinear system, one can guarantee closed-loop stability for the true nonlinear system. While existing approaches over-approximate the bilinearity of the surrogate model, thus introducing conservatism and providing only local guarantees, we explicitly account for the bilinearity by using sum-of-squares (SOS) optimization in the controller design. More precisely, we parametrize a rational controller stabilizing the error-affected bilinear surrogate model and, consequently, the underlying nonlinear system. The resulting SOS optimization problem provides explicit data-driven controller design conditions for unknown nonlinear systems based on semidefinite programming. Our approach significantly reduces conservatism by establishing a larger region of attraction and improved data efficiency. The proposed method is evaluated using numerical examples, demonstrating its advantages over existing approaches.

Zixing Wang, Yongkang Huo, Fulvio Forni

We consider the design of a new class of passive iFIR controllers given by the parallel action of an integrator and a finite impulse response filter. iFIRs are more expressive than PID controllers but retain their features and simplicity. The paper provides a model-free data-driven design for passive iFIR controllers based on virtual reference feedback tuning. Passivity is enforced through constrained optimization (three different formulations are discussed). The proposed design does not rely on large datasets or accurate plant models.