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

Xingming Zhao, Weidong Tian, R. Jiang et al.

The complex biological systems consist of distinct molecules that exert their functions by interacting with each other, which makes it a big challenge to understand how the cellular machinery works. Recently, the accumulation of a large amount of multiscale omics data, such as next-generation sequencing data and protein interaction data, provides opportunity to investigate the functions of molecules from a systematic perspective. On the other hand, the analysis of these huge datasets demands efficient and robust computational methods. In this special issue, we reported the recent progress made in developing new computational methodologies to analyze the genomics data, construct gene networks, and identify disease genes. Understanding the Functions of Molecules in the Postgenomic Age. In recent years, the advance of next-generation sequencing (NGS) technology makes it more easier for researchers to access and analyze genetics data and has influential effects on the biomedical research community. However, compared with sequencing, computational analysis of the flooding sequencing data with appropriate tools is becoming a more important task when interpreting the data. In their review paper, M. P. Dolled-Filhart et al. described the pipeline for bioinformatics analysis of the NGS data, starting from alignment to variant calling as well as filtering and annotation. In each step, they discussed the tools or software that should be used as well as their advantages and caveats. This survey of the bioinformatics analysis of NGS data can help researchers to choose appropriate tools when dealing with the sequencing data. Along with the sequencing technology, lines of evidence show that a lot of noncoding RNAs (ncRNAs) play important roles in various biological processes. Unlike the protein-coding genes that are well studied, the functions of most ncRNAs are not clear. Therefore, it is highly desirable to develop computational methods to predict the functions of the ncRNAs. H. Ma et al. conducted a survey about the computational approaches developed to predict and annotate the long noncoding RNAs (lncRNAs), which can help researchers to learn the progress in this filed and future directions in which bioinformaticists should work while annotating lncRNAs. While annotating the functions of molecules, standard and controlled vocabularies are required. Hence, the ontologies that are represented as abstract description systems of knowledge are becoming more and more popular recently. At the same time, it is becoming a difficult task to calculate the semantic similarity between ontology terms quantitatively. M. Gan et al. introduced popular methods in quantitating the semantic similarity between ontology terms and their software implementations. Furthermore, they classified these methods into distinct categories and discussed their advantages and shortcomings, which can help researchers to select appropriate tools and methods when working on ontologies. Gene expression profiles can describe the molecular mechanisms that underlie certain phenotypes. However, while analyzing the gene expression data, it is inappropriate to treat genes independently considering genes interact with each other within the cell. O. Frings et al. proposed a network-based approach to analyze the gene expression data and applied it to investigate the development of sex-specific chicken gonad and brain tissues. By combining the chicken network and the gene expression data, they identified some sex-biased characteristics, for example, same sex-biased genes tend to be tightly connected in the network, and provided new insights into the molecular underpinnings of sex-biased genes. Construction and Analysis of Gene Networks. Construction of gene regulatory networks (GRNs) is a crucial step in systems biology, where gene expression data is widely explored to infer the GRNs. However, the high dimensionality and notorious noise of the gene expression data makes it a nontrivial task to infer the GRNs. N. You et al. presented a new Laplace error penalty (LEP) model to calculate the partial correlation coefficients between genes and construct the GRNs. Compared with the popular least absolute shrinkage and selection operator (LASSO) and smoothly clipped absolute deviation (SCAD) approaches, the LEP method reached the highest precision. Except for gene expression data, integration of different data sources may improve the accuracy of inferred GRNs. H. Chen et al. surveyed the strategies to integrate distinct data sources and their effectiveness and recommended how to choose an appropriate strategy while integrating distinct data sources. N. Nakajima et al. proposed a novel network completion approach, DPLSQ, to infer gene networks. Benchmarking on artificial datasets, their proposed DPLSQ outperforms popular ARACNE and GeneNet with the highest accuracy. By investigating a 2-gene network, A. V. Spirov et al. found that gene cooption can affect the robustness of GRNs, and the findings provide new insights into the evolvability and robustness of GRNs. Network modules are found to be functional blocks of gene networks, the identification of which is becoming a hot research topic. By taking the hierarchical modular structure into account, S. Zhang presented a new stochastic block model to detect the hierarchical modules. Applied to the real yeast gene coexpression network, the proposed method can efficiently detect the hierarchical modular structures that are consistent with biological functions. Recently, it is found that a particular type of ncRNAs, microRNAs, plays important roles in gene regulation by working together with transcription factors. W. Mu et al. proposed a new local genetic algorithm to predict condition-specific regulatory modules that consist of microRNAs, transcription factors, and their commonly regulated genes, and these modules provide useful insights into the regulatory mechanisms underlying gene expression. Computational Approaches to Hunting Disease-Associated Genes. The identification of genetic variants that are responsible for human diseases is critical for understanding the development of diseases and designing new effective drugs. Thanks to the genome-wide association studies (GWASs), some genetic variants that drive diseases have been identified, among which single nucleotide polymorphisms (SNPs) and nonsynonymous single nucleotide polymorphisms (nsSNPs) are receiving more and more attention. In this issue, J. Wu and R. Jiang reviewed the databases that collect nsSNPs and summarized popular computational methods that identify deleterious nsSNPs. In addition, they introduced machine learning models that are useful in predicting deleterious nsSNPs. Beyond SNP-based association analysis, gene-based association analysis is receiving increasing attention. X. Guo et al. comprehensively compared these two approaches on the data from the study of addiction and found that these two approaches complement with each other and can get better results when used together. The differentially expressed genes identified from microarray data are generally regarded as candidate disease genes. However, the number of differentially expressed genes may reach hundreds or even thousands, thereby making it difficult to identify the potential disease genes. In this issue, L. Li et al. proposed a new hybrid approach to predict disease genes based on estimation of distribution algorithm and support vector machine. Benchmarking on B-cell lymphoma and colon cancer datasets, their method outperforms two other popular approaches and identify some new candidate genes for future validation.

Erika R. Meléndez‐Landaverde, Daniel Sempere‐Torres

Abstract The recent extreme rainfall events in Spain such as the storm Gloria have highlighted the gaps in emergency communication, particularly the disconnect between the available impact‐based early warning systems (IBEWSs) and the steps communities take during emergencies. This paper presents a community‐centred framework named ‘site‐specific early warning system’ (SS‐EWS) to co‐design and co‐evaluate with communities an IBEWS for vulnerable locations within high‐risk areas. The components of the framework guide communities in identifying and evaluating local impacts; establishing impact and advisory tables; deriving impact‐based rainfall thresholds and warning levels; and configuring the SS‐EWS with radar‐based nowcasting and numerical weather prediction (NWP) models. A first implementation and evaluation of the SS‐EWS have been done for a public school, two ford crossings and the city of Terrassa, Spain. The SS‐EWS shows promising results in triggering location‐based or site‐specific warnings compatible with the reported impacts and proposing actions to reduce the local risk. Furthermore, the combination of NWP and radar‐based nowcasting improved the capacity of the SS‐EWS to monitor the evolution of the precipitation and capture highly intense rainfall. The SS‐EWS can be a straightforward and cost‐efficient complement for regional EWS to increase the preparedness of communities.

Jorge Mongil-Manso, Carmen Patino-Alonso, José Nespereira-Jato et al.

The land use and vegetation type of a reservoir's catchment substantially affect the hydrological processes of soil infiltration and runoff. They also act as drivers or constraints for erosive processes. All the previous processes influence the amount of water and sediment that reach the reservoir and affect its functioning. This study is mainly aimed to improve the knowledge of these processes in southeastern Spain by means of experimental rainfall simulation and multivariate statistical analysis. The results show that the mean infiltration rate is 1.06 times higher in forests than in shrublands and 1.07 times higher than in olive crops (280.52, 265.02, and 262.08 mm/h, respectively), with mean surface runoff consequently 1.57 times lower in forests than in shrublands and 2.41 times lower than in olive crops (20.81, 32.58, and 50.24 mm/h). Likewise, the sediment concentration in the runs is 5.48 times higher in olive groves (518.43 g/L) than in forests (94.61 g/L) and 2.94 times higher than in shrublands (176.48 g/L). Soil properties and parent material might have a more important effect on the studied variables than the different vegetation types. Furthermore, root systems and the use of tillage on crops could favor infiltration, which would tend to equalize the values of the variables analyzed; but this needs to be demonstrated in future research. The results obtained are of interest for vegetation cover and soils management in reservoirs’ catchments in Mediterranean areas. Furthermore, the current research provides an opportunity to study more specifically the origin of the sediment that reaches the reservoirs, beyond sheet and rill erosion.

Augustine Kiptum, Emmah Mwangi, George Otieno et al.

Abstract Kenya and the wider East African region suffer from significant flood risk, as illustrated by major losses of lives, livelihoods and assets in the most recent years. This is likely to increase in future as exposure rises and rainfall intensifies under climate change. Accordingly, flood risk management is a priority action area in Kenya's national climate change adaptation planning. Here, we outline the opportunities and challenges to improve end‐to‐end flood early warning systems, considering the scientific, technical and institutional/governance dimensions. We demonstrate improvements in rainfall forecasts, river flow, inundation and baseline flood risk information. Notably, East Africa is a ‘sweetspot’ for rainfall predictability at sub‐seasonal to seasonal timescales for extending forecast lead times beyond a few days and for ensemble flood forecasting. Further, we demonstrate coupled ensemble flow forecasting, new flood inundation simulation, vulnerability and exposure data to support Impact based Forecasting (IbF). We illustrate these advances in the case of fluvial and urban flooding and reflect on the potential for improved flood preparedness action. However, we note that, unlike for drought, there remains no national flood risk management framework in Kenya and there is need to enhance institutional capacities and arrangements to take full advantage of these scientific advances.

Guoshuai Zhang, Qi Chen, Yisen Wang et al.

ABSTRACT Under the operation of the large reservoir, the variation law of water level in the fluctuating backwater area is complex, which causes river protection engineering to lack a theoretical basis. The changing trend of daily water level in the fluctuating backwater area of the Three Gorges Reservoir (Cuntan hydrological station) was calculated, based on the relationship between daily discharge and water level, and the flow duration curve method. From 2002 to 2021, the daily water level processes had a distinct plateau stage after the flood season since 2008. The water level processes were composed of two parts, including the natural period (2002–2008) and the response period (2009–2021). The average daily discharge increased from 10214.93 m3/s to 10893.38 m3/s, and the average water level increased from 163.87 m to 169.03 m since 2008. The coefficient parameter of the relationship between daily discharge and water level decreased from 0.041 to 0.026, which indicates that the effect of daily discharge variation on the water level change was weakened. The maximum flood discharge and water depth increased by 29.82% and 27.21%, respectively, which led to a higher flood risk in the fluctuating backwater area. In this study, we proposed a novel approach to test trend change in the relationship between daily discharge and water level, which can be generalized to rivers influenced by human activities. Combining the trend test method and flow duration curve method, the characteristic daily discharge and water level can be calculated to guide engineering projects.

Anna Katya Opel, Elizabeth Chisolm Matthews

ABSTRACT The current practice of flood loss prediction presents limitations in accurately predicting building flood losses at multiple scales. While whole‐building estimates can more accurately predict high‐level losses (i.e., large groups of buildings), a significant analysis error is revealed with small‐scale (i.e., individual, or small groups of buildings) investigation. This research presents a more robust, data driven, small‐scale, flood damage estimation approach for residential buildings. The approach is based on component‐level, depth–damage curves derived from experimental analysis. Structures with standard residential construction materials typical to the south‐eastern United States were built and incrementally flooded for short durations. The materials were assessed to determine the level of damage inflicted. This experimentally derived damage data were then translated into a set of flood depth–damage functions (DDFs). The DDFs were tailored for analysis at smaller scales and incorporated the ability to apply damage uncertainty in damage analysis. To demonstrate the applicability of the experimentally derived DDFs to damage estimation at smaller scales, the functions are applied to a hypothetical building design typical of the south‐eastern United States.

Carlo Giudicianni, Hossein Aghaee, Luca Ventura et al.

ABSTRACT This paper proposes a novel methodology for peak flow estimation. This methodology uses single‐event hydrological modeling based on the software HEC‐HMS and curve numbers (CNs) estimated as a function of antecedent and current weather variables and is applied to the river Brembo case study in Northern Italy. By using rainfall, weather, and water discharge data collected over an eleven‐year‐long period, from 2013 to 2023, HEC‐HMS is first used to optimize the CN values at two cross sections in the Brembo basin, in an attempt to reproduce the flood peak in numerous single rain events. Then, regression equations are constructed to express CN as a function of current event rainfall depth and antecedent rainfall depth and temperature, as explicative variables for current soil conditions. The good predictive performance of HEC‐HMS based on CN values estimated through the regression equations (for the peak flow at the two cross sections, a mean absolute percentage error [MAPE] of 0.26 and 0.29, respectively, in calibration, and 0.33 and 0.45, respectively, in validation; and an index of agreement [d] of 0.84 and 0.92, respectively, in calibration, and 0.86 and 0.88, respectively, in validation) makes the modeling tool constructed in the paper efficient and effective for potential early‐warning applications.

Riley C. Hales, E. James Nelson, Michael Souffront et al.

Abstract Most people face some level of water insecurity. Wise water management practices to address water security issues typically require data derived from a combination of observation and model data. This data has historically proven difficult to sustainably supply in many areas of the world. We present the design and development of a global, modeled streamflow data source for the Group on Earth Observation (GEO) Global Water Sustainability (GEOGloWS) implemented at the European Centre for Medium‐Range Weather Forecasts (ECMWF). This GEOGloWS ECMWF Streamflow Service (GEOGloWS Service) is a solution and prototype to sustainably address this need for data. The GEOGloWS Service centralizes computing and human resources to build a global hydrologic model and exposes data and mapping web services that allow users to consume the resulting data to meet their specific needs. The global hydrologic model produces global 15‐day ensemble streamflow forecasts and a historical simulation since January 1979. We present case studies in several countries and research environments which demonstrate the utility of the approach taken by the GEOGloWS Service. The case studies show how the global modeled data are being applied to make informed decisions and advance projects in ways that otherwise would not have been possible.

Hongli Mu, Yifan Zhuo, Yanjuan Wu et al.

Grass, shrubs and tree stems can increase flow depth and resistance and prevent soil erosion, and it is necessary to quantify the relationship between flow depth and hydraulic parameters for high surface roughness of vegetation stem. Therefore, the experimental design included flow depth, velocity and transport capacity, which were measured for different stem covers (bare flume to cover 30%), diameters (2, 10, and 36 mm), and arrangements (bead, tessellation, stagger, random, and stripe) to clarify the relationship between flow depth and the hydraulic radius, Reynolds number Re, Manning coefficient nm, Darcy-Weisbach resistance f and transport capacity Tc. The result shows that flow depth could be effectively predicted by stem cover and stem diameter; the greater the surface roughness was, the more the difference between flow depth and hydraulic radius; and flow depth could not be used as the hydraulic radius to calculate hydraulic parameters for high surface roughness. Re, nm, and f were significantly impacted by flow depth. The linear relationship between flow depth and Re, nm, and f became stronger as stem cover decreased and stem diameter increased, and they were more affected by stem cover than by diameter. The relationship between flow depth and f was less impacted by high surface roughness of vegetation stem. Tc was not significantly impacted by flow depth; the Manning coefficient and Darcy-Weisbach resistance were not appropriate for predicting transport capacity; and the Reynolds number could illustrate the mechanism of sediment transport capacity affected by vegetation stem cover from the perspective of flow resistance.

Qiuyu Zhu, Megan Klaar, Thomas Willis et al.

ABSTRACT While natural flood management (NFM) as a flood mitigation strategy is becoming widely used, there remains a lack of evidence regarding the effectiveness of different NFM scenarios under high flow events. To demonstrate how different types and extents of NFM interventions interact to flood peaks at larger catchment scales, combined scenarios of existing NFM interventions and an ideal maximum woodland scenario were modelled in the Upper Aire, northern England, using a coupled model that integrates Spatially Distributed TOPMODEL (SD‐TOPMODEL) with a 2D hydrodynamic model (Flood Modeller 2D) at an 81.4 km2 catchment. The coupled model exhibited a strong fit with observed data (NSE up to 0.95), effectively capturing flood peaks and peak shapes. Leaky dams were found to be more effective at delaying flood peaks with mean values ranging from 8.6 to 60 min than reducing peak discharge (mean values ranging from 0.53% to 1.84%), though these effects were inversely proportional and influenced by tributary characteristics such as channel gradient. Simulations applying multiple NFM interventions consistently demonstrated positive flood mitigation impacts, including reduced peak discharge up to 2.59% and delayed peaks up to 30 min, while inundation depths reduced by 0.5 m in most areas, with inundation extent reduction at critical points in an urban area. The study demonstrated the utility of the coupled model for evaluating NFM strategies while emphasising the need for further validation and exploration of systematic interventions at larger catchment scales. By providing insights into the interactions between NFM interventions and catchment characteristics, this research contributes to the optimisation of flood risk management strategies and informs future policy development.

Wasitha Dilshan, Yusuke Hiraga, So Kazama

ABSTRACT Global climate change is increasingly associated with the prevalence of extreme precipitation and large wildfires. The influence of large wildfires on downstream flood discharge is concerning, particularly from a flood risk management perspective, where understanding the impact at a watershed scale is still fairly limited. This study investigates the impacts of wildfires on downstream flood discharge in 30 Californian watersheds. We employed the Soil and Water Assessment Tool (SWAT) to simulate daily discharge over 20 years, achieving robust model performance R2 values of 0.67–0.86 and Nash–Sutcliffe efficiency (NSE) values of 0.65–0.86. The differences between the observed flood discharge volume and the simulated unburned scenario, including model errors (i.e., flood discharge enhancement), during the post‐fire years were assessed. Substantial post‐fire discharge increases, with an average 17.1% increment in 83.3% of watersheds, were found during the first post‐fire year. Statistically significant positive correlations (p < 0.01) were found between the enhancement in discharge volume and the percentage of burned watershed area. We quantified wildfire impacts by adjusting the curve number (CN) in the SWAT model, with CN values increasing by increments ranging from 16.5% to 30% of their original values, depending on burn severity and land use type. A novel relationship between wildfire area burned and CN increment could be described by the equation %CN increment = 0.39 × wildfire area burned % + β, which highlights the proportional increase in CN due to wildfire area burned. The study also showed that incorporating historical wildfire activity significantly raised the probable maximum flood, with discharge volume increases between 3.74% and 25.9%. These wildfire‐induced increases are on par with California's climate change projections (10%–50%), underscoring the need to factor in wildfire effects in flood risk assessments and water management strategies at this type of location.

Connie Susilawati, Bernadetta Devi, Farida Rachmawati et al.

ABSTRACT Rapid urbanisation has resulted in the sealing of pervious surfaces and the construction of houses in flood‐vulnerable areas, aggravating urban flooding. This paper explores the impact of urban flooding on families with vulnerable members, surveying 600 residents in six case study areas in Indonesia. The research study outcomes recommend that the government adopt a community flood resilience framework to develop an inclusive urban flood resilience policy. The study findings confirmed that the elderly, children and these two groups combined experience the worst impacts of urban flooding. Social, economic and environmental factors of these vulnerable population groups can further exacerbate such impacts. Since the diversity and characteristics of vulnerable population groups vary at a location, it is recommended that the community flood resilience policies and programs should be personalised, based on human factors such as types of vulnerable population groups, and contextualised to the social, economic, natural and built infrastructure factors associated with specific vulnerable population groups. This study contributes to innovation management by proposing a novel framework that integrates local vulnerability factors into flood resilience planning. Such an approach aligns with the innovation process of transformation and diffusion, enabling the development of inclusive policies that can adapt to diverse community needs. The framework can serve as a tool for innovation management, promoting equitable innovation by explicitly addressing the challenges faced by specific vulnerable groups.

Thomas L. Ahrens, Ian M. Down, Manoranjan Majji

This paper details a methodology to transcribe an optimal control problem into a nonlinear program for generation of the trajectories that optimize a given functional by approximating only the highest order derivatives of a given system's dynamics. The underlying method uses orthogonal polynomial integral collocation by which successive integrals are taken to approximate all lower order states. Hence, one set of polynomial coefficients can represent an entire coordinate's degree of freedom. Specifically, Chebyshev polynomials of the first and second kind and Legendre polynomials are used over their associated common interpolating grids derived from the bases' roots and extrema. Simple example problems compare different polynomial bases' performance to analytical solutions. The planar circular orbit raising problem is used to verify the method with solutions obtained by other pseudospectral methods in literature. Finally, a rocket landing flip maneuver problem is solved to demonstrate the ability to solve complex problems with multiple states and control variables with constraints. Simulations establish this method's performance, and reveal that the polynomial/node choice for a given problem notably affects the performance.

Mingming Song, Jianyun Zhang, Yanli Liu et al.

Abstract Quantifying flood risk depends on accurate probability estimation, which is challenging due to non‐stationarity and the combined effects of multiple factors in a changing environment. The threat of compound flood risks may spread from coastal areas to inland basins, which have received less attention. In this study, a framework based on time‐varying copulas was introduced for the treatment of compound flood risk and bivariate design in non‐stationary environments. Archimedean copulas were developed to diagnose the non‐stationary trends of flood risk. Return periods, average annual reliabilities, and bivariate designs were estimated. Model uncertainty was analyzed by comparing the results for stationary and non‐stationary conditions. The case study investigated the extreme rainfall and water level series from the Qinhuai River Basin and the Yangtze River in China. The results showed that marginal distributions and correlations are non‐stationary in all bivariate combinations. Ignoring composite effects may lead to inappropriate quantification of flood risk. Excluding non‐stationarity may lead to risk over or underestimation. It showed the limitations of the 1‐day scale and quantified the uncertainty of non‐stationary models. This study provided a flood risk assessment framework in a changing environment and a risk‐based design technique, which is essential for climate change adaptation and water management.

Fei Liu, Chunjiao Ren, Yao Chen

Abstract To examine the attributes, underlying mechanisms, and impacts of rainfall patterns on the vulnerability of pedestrians and vehicles to flood‐induced instability within mountainous urban areas, we introduced an integrated urban flood model that combined the Storm Water Management Model (SWMM) and Finite Volume Coastal Ocean Model (FVCOM). We implemented this model in the Yuelai New Town of Chongqing, China. Our findings indicated that in the case of early peak rainfalls, there was a rapid surge in flood volume during the initial stages of rainfall , while this increase was more gradual when the peak rainfall was delayed. Furthermore, for events with the same return period, flood peaks resulting from later peak rainfalls covered a larger area compared with those from earlier peak rainfalls; however, this effect diminished with increasing return periods. As the return period was extended, the exposed risk area for pedestrians and vehicles expanded. Analysis of instability indices revealed that pedestrians exhibit a lower index compared with vehicles, adults fare better than children, and SUVs outperform sedans. The efficacy of our proposed model framework was demonstrated through its successful application in assessing urban flood risk and evaluating the instability index for pedestrians and vehicles within a mountainous urban setting.

Peter S. Larson, Jamie Steis Thorsby, Xinyu Liu et al.

Abstract Climate change is increasing the frequency and intensity of extreme precipitation events, raising the risk of urban flood disasters. This study uses a crowd‐sourced municipal call database to characterize the spatial distribution of flood risk in Detroit, MI. Call data including dates and addresses were obtained from the City of Detroit Department of Public Works for 2021. Calls were mapped and aggregated to census tract counts and merged with neighborhood‐level data. Associations of predictors with flood calls were tested using spatial regression models. Flooding calls were located throughout the city but were concentrated in specific areas. Multivariate models of census tract level call counts indicated that increased poverty and Black, immigrant, and older residents were positively associated with flood calls, while increased elevation was associated with protective effects. Longer distances from waste water interceptors were associated with higher risk for calls. Crowd‐sourced flood hotline call data can be used for effective spatial flood risk assessment. Though flooding occurs throughout the city of Detroit, infrastructural, neighborhood, and household factors influence flooding extent. Limitations included the self‐reported nature of calls. Future modeling efforts might include input from local stakeholders to improve spatial risk assessment.

Joseph T. D. Lucey, Timu W. Gallien

Abstract Coastal flooding is a growing hazard. Compound event characterization and uncertainty quantification are critical to accurate flood risk assessment. This study presents univariate, conditional, and joint probabilities for observed water levels, precipitation, and waves. Design events for 10‐ and 100‐year marine water level and precipitation events are developed. A total water level formulation explicitly accounting for wave impacts is presented. Uncertainties associated with sampling method, copula selection, data record length, and utilized rainfall gauge are determined. Eight copulas are used to quantify multivariate uncertainty. Generally, copulas present similar results, except the BB5. Sampling method uncertainty was quantified using four sampling types; annual maximum, annual coinciding, wet season monthly maximum, and wet season monthly coinciding sampling. Annual coinciding sampling typically produced the lowest event magnitude estimates. Uncertainty associated with record length was explored by partitioning a 100‐year record into various subsets. Withholding 30 years of observations (i.e., records of less than 70 years) resulted in substantial variability of both the 10‐ and 100‐year return period estimates. Approximately equidistant rainfall gauges led to large event estimate differences, suggesting microclimatology and gauge selection play a key role in characterizing compound events. Generally, event estimate uncertainty was dominated by sampling method and rainfall gauge selection.

Yuto Watanabe, Sotaro Fushimi, Kazunori Sakurama

This study addresses a distributed state feedback controller design problem for continuous-time linear time-invariant systems by means of linear matrix inequalities (LMIs). As structural constraints on a control gain result in non-convexity in general, the block-diagonal relaxation of Lyapunov functions has been prevalent despite its conservatism. In this work, we target a class of non-block-diagonal Lyapunov functions with the same sparsity pattern as distributed controllers. By leveraging a block-diagonal factorization of sparse matrices and Finsler's lemma, we first present a nonlinear matrix inequality for stabilizing distributed controllers with such Lyapunov functions, which boils down to a necessary and sufficient condition for such controllers if the sparsity pattern is chordal. As its relaxation, we derive novel LMIs, one of which strictly covers the conventional relaxation, and then provide analogous results for $H_\infty$ control. Lastly, numerical examples underscore the efficacy of our results.

Alessandro Scagliotti

In this paper, we consider ensembles of control-affine systems in $\mathbb{R}^d$, and we study simultaneous optimal control problems related to the worst-case minimization. After proving that such problems admit solutions, denoting with $(Θ^N)_N$ a sequence of compact sets that parametrize the ensembles of systems, we first show that the corresponding minimax optimal control problems are $Γ$-convergent whenever $(Θ^N)_N$ has a limit with respect to the Hausdorff distance. Besides its independent interest, the previous result plays a crucial role for establishing the Pontryagin Maximum Principle (PMP) when the ensemble is parametrized by a set $Θ$ consisting of infinitely many points. Namely, we first approximate $Θ$ by finite and increasing-in-size sets $(Θ^N)_N$ for which the PMP is known, and then we derive the PMP for the $Γ$-limiting problem. The same strategy can be pursued in applications, where we can reduce infinite ensembles to finite ones to compute the minimizers numerically. We bring as a numerical example the Schrödinger equation for a qubit with uncertain resonance frequency.

Jhelum Devendrasinh Rajput, S. Singh

An annual floating-leaved aquatic plant, the water chestnut (Trapa natans L.), is found in temperate and tropical freshwater wetlands, rivers, lakes, ponds, and estuaries. Hydrophytes that produce starch called water chestnut has the potential to serve as a reliable food supply, particularly in flood-prone wasteland areas. It is loaded with minerals and essential nutrients. Water chestnuts are not actually nuts, despite their name. They are aquatic tuber vegetables that may be found in shallow lakes, paddy fields, marshes, and ponds. Water chestnuts are indigenous to numerous islands in the Indian and Pacific oceans, as well as Southeast Asia, Southern China, Taiwan, Australia, and Africa. When the corm, or bulb, acquires a dark brown hue, they are picked. They are a typical ingredient in Asian recipes including stir-fries, chop suey, curries, and salads because of their crisp, white meat, which may be eaten raw or cooked. The huge, nourishing seed of the water chestnut, a native of Eurasia and Africa, has been widely collected since the Neolithic, and it is now grown for food throughout Asia. Water chestnuts have several advantages over other foods, including being particularly nutrient-dense and low in calories. A wonderful source of fibre, water chestnuts may aid in promoting bowel movements, lowering blood cholesterol, neuro-protective, controlling blood sugar levels, and maintaining the health of your gut. In addition, carbohydrates account for the majority of the calories in water chestnuts. Although they are abundant in fibre, potassium, manganese, copper, vitamin B6, and riboflavin, uncooked water chestnuts are 74% water, which means they are often low in calories. Due to its usage in the treatment of gastrointestinal illnesses, genitourinary system disorders, liver, kidney, and spleen disorders, Trapa natans is one of the most significant medicinal plants in Indian Ayurveda.