Journal of Hydrology X最新文献_第2页

Understanding the organizing scales of winter flood hydroclimatology and the associated drivers over the coterminous United States 了解临近美国冬季洪水水文气候学的组织尺度及其相关驱动因素

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Hydrology X

Pub Date : 2025-03-04 DOI: 10.1016/j.hydroa.2025.100200

Jeongwoo Hwang , Carl J. Schreck III , Anantha Aiyyer , Arumugam Sankarasubramanian

Floods occur everywhere and in every season. Yet, most studies have focused only on annual maximum floods (AMFs), their climatology, and the associated impacts. Given that monthly/seasonal floods also cause significant damage and disruptions to daily life, this study may be the first to explore winter flood hydroclimatology, a predominantly a non-AMF season, and its associated large-scale climate drivers over the Coterminous US (CONUS). Using a mixed-effects model, we find that the influence of various hydroclimate predictors on winter floods is largely consistent within subregions. Antecedent land-surface conditions are crucial for winter floods in inland areas, while the Pacific sea surface temperatures (SSTs) significantly affects coastal watersheds. The Atlantic SSTs impact winter floods in the south and northeast, while atmospheric conditions influence the Midwest and California. Additional analysis reveals that damage from winter floods is more widespread compared to AMFs across the nation, affecting the entire eastern seaboard, Southwest US, and over the Great Lakes region. Thus, a comprehensive understanding of floods across all seasons (non-AMFs) is critical for developing effective mitigation measures, as it provides information on impacts and required compensation for smaller return period floods.

洪水在任何地方、任何季节都会发生。然而，大多数研究只关注年最大洪水（AMFs）、它们的气候学和相关影响。考虑到月度/季节性洪水也会对日常生活造成重大破坏和干扰，本研究可能是第一个探索冬季洪水水文气候学（主要是非amf季节）及其相关的大尺度气候驱动因素的研究。使用混合效应模型，我们发现各种水文气候预测因子对冬季洪水的影响在次区域内基本一致。前期陆面条件对内陆地区冬季洪水至关重要，而太平洋海面温度（SSTs）对沿海流域有显著影响。大西洋海温影响南部和东北部的冬季洪水，而大气条件影响中西部和加利福尼亚。进一步的分析显示，与AMFs相比，冬季洪水造成的破坏在全国范围内更为广泛，影响了整个东部沿海地区、美国西南部和五大湖地区。因此，全面了解所有季节的洪水（非汛期）对于制定有效的缓解措施至关重要，因为它提供了有关较小回复期洪水的影响和所需补偿的信息。

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引用次数: 0

Association of climate variability modes with concurrent droughts and heatwaves in India 印度气候变率模式与同期干旱和热浪的关联

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Hydrology X

Pub Date : 2025-01-01 DOI: 10.1016/j.hydroa.2024.100196

Ruhhee Tabbussum , Rajarshi Das Bhowmik , Pradeep Mujumdar

The natural variability in the occurrence of concurrent extremes of droughts and heatwaves is frequently attributed to climate change and anthropogenic causes, disregarding its association with large-scale global teleconnections. This study explores this association by demonstrating how concurrent droughts and heatwaves (CDHW) in India are temporally and spatially connected to multiple global teleconnections (referred to as climate variability modes). Composite and wavelet coherence analyses are implemented for the univariate evaluation of droughts and heatwaves—measured using the standardized precipitation index (SPI) and the standardized heat index (SHI), respectively—in relation to the climate variability modes. Furthermore, an attribution table framework is employed to examine the extremal dependence of concurrent heatwaves and droughts in India on the climate variability modes during 1951–2018. The results exhibit a higher probability of CDHW events when they are preceded by a large-scale global teleconnection. Overall, the insights drawn from this study suggest the possibility of relying on the climate variability modes to issue season-ahead forecasts of CDHW.

同时发生的极端干旱和热浪的自然变率通常被归因于气候变化和人为原因，而忽略了其与大规模全球遥相关的关系。本研究通过展示印度同时发生的干旱和热浪（CDHW）如何在时间和空间上与多个全球遥相关（称为气候变率模式）相关联，探讨了这种关联。利用标准化降水指数（SPI）和标准化热指数（SHI）分别对干旱和热浪的单变量评估与气候变率模式的关系进行了复合和小波相干分析。此外，采用归因表框架分析了1951-2018年印度同期热浪和干旱对气候变率模态的极端依赖性。结果表明，在大规模全球遥相关之前发生CDHW事件的概率更高。总体而言，本研究得出的见解表明，依靠气候变率模式发布CDHW季前预报的可能性。

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引用次数: 0

Climatology of extreme precipitation spells induced by cloudburst-like events during the Indian Summer Monsoon 印度夏季风期间由类似云暴事件引起的极端降水的气候学

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Hydrology X

Pub Date : 2025-01-01 DOI: 10.1016/j.hydroa.2024.100197

Akash Singh Raghuvanshi , Ricardo M. Trigo , Ankit Agarwal

This study enhances existing understanding of extreme precipitation spells induced by cloudburst-like (EPS-CBL) events in India, emphasizing climatology and geographical distribution often overlooked by traditional observations. EPS-CBL is defined as continuous rainfall exceeding 200 mm/day and intermittent extreme rates above 30 mm/hour or the 99.9th percentile threshold, differing from definitions proposed by the IMD and other studies. Our findings reveal significant biases in various precipitation products compared to IMD data. CMORPH consistently outperforms other datasets by capturing more extreme events and showing significant rising trends in regions influenced by orographic effects, such as the Himalayan foothills and the Western Ghats. Although IMERG aligns well with IMD overall, it exhibits variability in extreme events, while IMDAA tends to underestimate these extremes, especially in complex terrains. Analysis of EPS-CBL trends from 2000 to 2022 highlights regional differences across datasets. Both CMORPH and IMERG show an increase in EPS-CBL events in the hilly region, while IMDAA indicates a decline. Understanding EPS-CBL climatology provides valuable insights for modeling studies exploring the underlying mechanisms of these events.

这项研究加强了对印度由类云暴（EPS-CBL）事件引起的极端降水的现有认识，强调了传统观测经常忽略的气候和地理分布。EPS-CBL被定义为连续降雨量超过200毫米/天，间歇性极端率超过30毫米/小时或99.9百分位阈值，与IMD和其他研究提出的定义不同。我们的研究结果表明，与IMD数据相比，各种降水产品存在显著偏差。CMORPH通过捕获更多极端事件，并在受地形影响的地区（如喜马拉雅山麓和西高止山脉）显示出显著的上升趋势，始终优于其他数据集。尽管IMERG总体上与IMD保持一致，但它在极端事件中表现出可变性，而IMDAA往往低估了这些极端事件，特别是在复杂地形中。对2000年至2022年EPS-CBL趋势的分析突出了各数据集之间的区域差异。CMORPH和IMERG均显示丘陵地区EPS-CBL事件增加，而IMDAA显示EPS-CBL事件减少。了解EPS-CBL气候学为探索这些事件的潜在机制的建模研究提供了有价值的见解。

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引用次数: 0

Practical application of time-lapse camera imagery to develop water-level data for three hydrologic monitoring sites in Wisconsin during water year 2020 延时相机图像在威斯康星州三个水文监测点在2020水年期间开发水位数据的实际应用

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Hydrology X

Pub Date : 2025-01-01 DOI: 10.1016/j.hydroa.2024.100199

Keegan E. Johnson, Paul C. Reneau, Matthew J. Komiskey

Using camera imagery to measure water level (camera-stage) is a well-researched area of study. Previous camera-stage studies have shown promising results when implementing this technology with tight constraints on test conditions. However, there is a need for a more comprehensive evaluation of the extensibility of camera-stage to practical applications. Therefore, the aim of this study was to test a camera-stage method under a wide variety of test conditions to better understand the successes and challenges of using this technology in real-world scenarios. In this study, this approach was tested during Water Year 2020 at three existing U.S. Geological Study (USGS) stream gaging stations in south central Wisconsin that had existing USGS water-level instrumentation. The specific reference objects tested were white pipes and a concrete wall. Since successful application of camera-stage relies on use of suitable images, all captured images in this study were visually inspected to determine suitability for application of camera-stage. Camera-stage measurements were then computed only on images deemed suitable and the results were compared with ground-truth stage values to determine the accuracy. For the purposes of this study, camera-stage values within ±0.10 ft of the actual stage were considered acceptable. One major challenge highlighted was the potential difficulty in obtaining suitable imagery, with the proportion of suitable images varying greatly between the four trials from 38 % to 92 %. The results from applying camera-stage to suitable images were encouraging though, with 79 % to 99 % of evaluated camera-stage values qualifying as acceptable among the four test trials.

利用相机图像测量水位（相机级）是一个研究得很好的研究领域。在严格限制测试条件的情况下，先前的相机级研究已经显示出有希望的结果。但是，需要对摄像机平台在实际应用中的可扩展性进行更全面的评价。因此，本研究的目的是在各种测试条件下测试相机级方法，以更好地了解在实际场景中使用该技术的成功和挑战。在本研究中，该方法在2020年水年期间在威斯康星州中南部的三个现有的美国地质调查局（USGS）溪流测量站进行了测试，这些测量站拥有现有的USGS水位仪器。测试的具体参考对象是白色管道和混凝土墙。由于camera-stage的成功应用依赖于使用合适的图像，因此本研究中所有捕获的图像都进行了视觉检查，以确定camera-stage应用的适用性。然后，仅对认为合适的图像计算相机级测量值，并将结果与地面真实级值进行比较，以确定精度。为了本研究的目的，相机级值在实际级的±0.10英尺内被认为是可以接受的。一个突出的主要挑战是获得合适图像的潜在困难，在四次试验中，合适图像的比例相差很大，从38%到92%。然而，将相机级应用于合适的图像的结果是令人鼓舞的，在四个测试试验中，79%到99%的评估相机级值符合可接受的要求。

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引用次数: 0

AutoVL: Automated streamflow separation for changing catchments and climate impact analysis AutoVL：自动溪流分离变化集水区和气候影响分析

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Hydrology X

Pub Date : 2025-01-01 DOI: 10.1016/j.hydroa.2024.100195

Vincent Lyne

The separation of streamflow into fastflow and slowflow components has been historically ambiguous, with existing separation methods like the Lyne-Hollick (LH) algorithm facing challenges due to subjective parameter choices. Here, we address this issue by developing the AutoVL algorithm which objectively and automatically partitions streamflow for no parameter input. AutoVL uses iterative statistical models, including a Signal Reconstructor for fastflow and an autoregressive moving-average (ARMA) model for slowflow, to estimate key hydrologic parameters. The algorithm couples the two models to iteratively estimate these parameters and to accurately separate streamflow. When applied to the Harvey River, Dingo Road station data, AutoVL identified significant seasonal and long-term variations in hydrologic parameters, reflecting the possible influence of climate change altering the temporal dynamics of catchment responses. The algorithm highlighted strongly coupled changes in infiltration and decay rates from altered streamflow patterns, offering a clearer understanding of streamflow responses to climate change. This performance suggests that AutoVL provides a more reliable, objective, efficient, and standard method for streamflow separation compared to previous approaches, enabling more accurate and confident hydrological modeling. By providing objective, dynamic insights into catchment behavior, AutoVL offers a promising tool for climate change studies and streamflow analysis.

从历史上看，将水流分为快流和慢流的方法一直很模糊，现有的分离方法，如Lyne-Hollick （LH）算法，由于主观参数的选择而面临挑战。在这里，我们通过开发AutoVL算法来解决这个问题，该算法可以客观地自动划分无参数输入的流。AutoVL使用迭代统计模型，包括快速流量的信号重构器和慢流量的自回归移动平均（ARMA）模型，来估计关键的水文参数。该算法将两种模型结合起来，迭代估计这些参数，并精确分离水流。当AutoVL应用于Harvey河、Dingo路站数据时，发现水文参数存在显著的季节性和长期变化，反映了气候变化改变流域响应时间动态的可能影响。该算法强调了由改变的水流模式引起的入渗率和衰减率的强烈耦合变化，从而更清楚地了解了水流对气候变化的响应。这一性能表明，与之前的方法相比，AutoVL提供了一种更可靠、客观、高效和标准的溪流分离方法，可以实现更准确、更自信的水文建模。通过对流域行为提供客观、动态的洞察，AutoVL为气候变化研究和溪流分析提供了一个很有前途的工具。

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引用次数: 0

Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions 使用深度学习的水文和衰退流模拟：分水岭唯一性和目标函数

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Hydrology X

Pub Date : 2025-01-01 DOI: 10.1016/j.hydroa.2024.100198

Abhinav Gupta , Sean A. McKenna

This study examines streamflow simulations using deep learning (DL) to understand the information extraction capability of global DL models trained on multiple watersheds. The study separately examined the entire streamflow time series and recession flow predictions. It introduces a global–local (GL) modeling strategy, where the global model outputs are fed as input to a locally trained model, with the hypothesis that the local model can leverage watershed-specific information that the global model may miss. The GL models demonstrate enhanced accuracy in recession flow prediction for 20-30% of the watersheds compared to the global and local models. However, considering the entire hydrograph, the GL models often perform worse than the global model. Further, the DL models were trained on two different objective functions. The performance of the global model in a watershed depended strongly upon the objective function used. These results suggest that the performance of global models is affected by watershed uniqueness, suggesting that even a global DL model should be tailored to individual watersheds for optimal performance.

本研究考察了使用深度学习（DL）的流模拟，以了解在多个流域上训练的全局深度学习模型的信息提取能力。该研究分别检查了整个流量时间序列和衰退流量预测。它引入了一种全局-局部（GL）建模策略，其中将全局模型的输出作为输入输入到局部训练的模型中，并假设局部模型可以利用全局模型可能遗漏的流域特定信息。与全局和局部模型相比，GL模型在预测20-30%流域的衰退流量方面显示出更高的准确性。然而，考虑到整个海线，GL模式往往比全球模式表现得更差。此外，深度学习模型在两个不同的目标函数上进行训练。在流域中，全局模型的性能很大程度上取决于所使用的目标函数。这些结果表明，全局模型的性能受到流域独特性的影响，这表明即使是全局DL模型也应该针对单个流域进行定制以获得最佳性能。

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引用次数: 0

Revisiting model complexity: Space-time correction of high dimensional variable sets in climate model simulations 重新审视模型的复杂性：气候模型模拟中高维变量集的时空修正

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Hydrology X

Pub Date : 2024-11-17 DOI: 10.1016/j.hydroa.2024.100193

Cilcia Kusumastuti , Rajeshwar Mehrotra , Ashish Sharma

Multivariate bias correction (BC) models are well-known to correct more statistical attributes in climate model simulations. However, their inherent complexity and excessive parameters can introduce higher uncertainty into future climate simulations. In contrast, univariate BC models, with fewer parameters, are limited to correcting certain attributes. An issue that has not been investigated in-depth is the impact of an increased number of variables in the multivariate BC has on the bias-corrected climate models’ stability. This study compares the performance of a multivariate BC approach, Multivariate Recursive Nested Bias Correction (MRNBC), and a univariate BC approach, Continuous Wavelet-based Bias Correction (CWBC), as the number of variables to be corrected increases, known as the “curse of dimensionality” (CoD). The analysis uses high-resolution climate model outputs for both current and future simulations of sea surface temperature and precipitation in the Niño 3.4 region. Results show both BC models effectively correct current climate biases. As the number of variables increases, CWBC remains robust and produces sensible future simulations, while MRNBC’s complexity leads to deterioration in standard deviations and spatial cross-correlation. CWBC, based on univariate correction, is relatively unaffected by the CoD.

众所周知，多变量偏差校正（BC）模型可以校正气候模型模拟中的更多统计属性。然而，其固有的复杂性和过多的参数会给未来气候模拟带来更高的不确定性。相比之下，单变量 BC 模型参数较少，仅限于修正某些属性。一个尚未深入研究的问题是，多元 BC 中变量数量的增加对偏差校正气候模式稳定性的影响。本研究比较了多变量偏差校正方法--多变量递归嵌套偏差校正（MRNBC）和单变量偏差校正方法--基于连续小波的偏差校正（CWBC）在需要校正的变量数量增加（即 "维度诅咒"（CoD））时的性能。分析使用了高分辨率气候模式输出，对 3.4 尼诺地区当前和未来的海面温度和降水量进行了模拟。结果表明，两种 BC 模式都能有效纠正当前的气候偏差。随着变量数量的增加，CWBC 仍然保持稳健，并产生了合理的未来模拟，而 MRNBC 的复杂性导致标准偏差和空间交叉相关性恶化。基于单变量校正的 CWBC 相对不受 CoD 的影响。

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引用次数: 0

Effects of model complexity on karst catchment runoff modeling for flood warning systems 模型复杂性对洪水预警系统岩溶集水区径流建模的影响

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Hydrology X

Pub Date : 2024-11-16 DOI: 10.1016/j.hydroa.2024.100194

Paul Knöll , Ferry Schiperski , Antonia Roesrath , Traugott Scheytt

Severe flood events are deemed more frequent in the near future with a changing climate. Headwater catchments, especially when karstified, exhibit a pronounced susceptibility to swift and substantial responses to precipitation events, leading to flooding. In this study, a karstified headwater catchment in SW Germany is investigated, focusing on gaining insights into the key processes controlling its discharge behavior. Intensive fieldwork was conducted and a variety of field data were collected and analyzed to determine the general system behavior during low flow and flood events. Field insights reveal a groundwater borne streamflow generation with a subsurface catchment largely differing from the surface catchment. Episodic and sporadic springs were identified as crucial contributors to stream flow generation.

The study was undertaken to evaluate the viability of simulating streamflow for flood warning using a lumped modeling approach at a sub-daily temporal scale, since lumped models are widely used for karst spring discharge modeling. Based on field data observations, a comparative analysis of different model structures was undertaken, aiming at assessing the required degree of model complexity for representing catchment runoff generation as well as the relevant system features and properties. In order to find an adequate model structure, a total of 21 models with varying degree of complexity were set up and run. Both, subsurface and surface catchment limits were considered. Results show that the hydrograph of the whole catchment can be represented by a rather simple lumped model in the present case under two prerequisites: (1) input needs to represent the groundwater catchment emphasizing the groundwater borne nature of flow and (2) the models need to allow for direct runoff, as the sporadic springs observed in the field contribute significant discharge to streamflow during flood events. It is revealed that it seems valid to start modeling with a relatively simple storage model as long as key processes in the catchment are represented. The general feasibility of such a simple modeling approach in this complex catchment encourages its feasibility in other headwater catchments.

随着气候的变化，在不久的将来，严重的洪水事件会更加频繁。溪流集水区，尤其是岩溶化的溪流集水区，很容易对降水事件做出迅速而强烈的反应，从而导致洪水泛滥。本研究调查了德国西南部的一个岩溶化溪流集水区，重点是了解控制其排放行为的关键过程。研究人员进行了深入的实地考察，收集并分析了各种实地数据，以确定低流量和洪水事件期间的总体系统行为。实地考察结果表明，溪流产生于地下水，地下集水区与地表集水区大不相同。由于岩溶泉水排放模型中广泛使用了叠加模型，该研究旨在评估在亚日时间尺度上使用叠加模型模拟用于洪水预警的可行性。在实地数据观测的基础上，对不同的模型结构进行了比较分析，目的是评估模型的复杂程度，以反映集水区径流的产生以及相关的系统特征和特性。为了找到合适的模型结构，共建立并运行了 21 个复杂程度不同的模型。其中既考虑了地下集水区，也考虑了地表集水区。结果表明，在目前的情况下，整个集水区的水文图可以用一个相当简单的集合模型来表示，但有两个前提条件：（1）输入需要代表地下水集水区，强调水流的地下水性质；（2）模型需要允许直接径流，因为在实地观察到的零星泉水在洪水事件期间会对河水造成很大的排放。研究表明，只要能体现集水区的关键过程，从相对简单的蓄水模型开始建模似乎是可行的。这种简单的建模方法在这一复杂集水区的普遍可行性鼓励了其在其他上游集水区的可行性。

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引用次数: 0

Quantifying the economic value of a national hydrometric network for households 量化国家水文网络对家庭的经济价值

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Hydrology X

Pub Date : 2024-11-10 DOI: 10.1016/j.hydroa.2024.100192

Kush Thakar , Neil Macdonald , Karyn Morrissey

This study reports the results of a Choice Experiment to quantify households’ willingness-to-pay for river gauging programmes in Scotland. The hydrometric network is operated and maintained by the Scottish Environment Protection Agency (SEPA), Scotland’s principal environment regulator, a non-department public body of the Scottish Government. Results from mixed logit and latent class modelling show that most households (‘Hydrometric Maximisers’ − around 70 %) have significant, positive willingness-to-pay values for river gauging programmes, but a minority (‘Hydrometric Satisficers’ − around 30 %) do not view this as a major public policy priority. On average, hydrometric data collection delivers non-market benefits worth £84,625,562 to the Scottish economy, with a minimum economic Benefit-to-Cost ratio of 25:1. This is in addition to the infrastructure value and any private returns made by commercial users of the data. The findings demonstrate that traditional approaches to assessing the benefits of hydrometric networks often underestimate their value. The research also highlights the importance of public information campaigns and household engagement initiatives to increase awareness of hydro-meteorological services, and to develop the business case more fully for public investment in environmental observation networks.

本研究报告了一项选择实验的结果，该实验旨在量化家庭对苏格兰河流测量计划的支付意愿。水文测量网络由苏格兰环境保护局 (SEPA) 负责运营和维护，该局是苏格兰的主要环境监管机构，也是苏格兰政府的一个非部门公共机构。混合对数模型和潜类模型的结果表明，大多数家庭（"水文最大化者"--约 70%）对河流测量计划具有显著、积极的支付意愿值，但少数家庭（"水文满意者"--约 30%）并不认为这是一项主要的公共政策优先事项。平均而言，水文数据收集可为苏格兰经济带来价值 84,625,562 英镑的非市场效益，最低经济效益成本比为 25:1。这还不包括基础设施价值和数据商业用户的私人收益。研究结果表明，评估水文测量网络效益的传统方法往往低估了其价值。研究还强调了公共宣传活动和家庭参与活动的重要性，以提高人们对水文气象服务的认识，并为环境观测网络的公共投资提供更充分的商业论证。

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引用次数: 0

Private sensors and crowdsourced rainfall data: Accuracy and potential for modelling pluvial flooding in urban areas of Oslo, Norway 私人传感器和众包降雨数据：挪威奥斯陆城市地区冲积洪水建模的准确性和潜力

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Hydrology X

Pub Date : 2024-10-30 DOI: 10.1016/j.hydroa.2024.100191

Kay Khaing Kyaw , Emma Baietti , Cristian Lussana , Valerio Luzzi , Paolo Mazzoli , Stefano Bagli , Attilio Castellarin

Cloudbursts and extreme rainstorms pose a growing threat to urban areas. Accurate rainfall data is essential for predicting inundations and urban flooding. Private weather stations are becoming increasingly common, and their spatial distribution roughly follows population density. This makes them a valuable source of crowdsourced data for high-resolution rainfall fields in urban areas. We evaluated the performance of private rain gauges in two recent pluvial flood events in Oslo. We also explored the potential use of private rain gauge data in inundation models. Our results indicate that private sensors have excellent rain detection capabilities, but they tend to underestimate the reference value on average by approximately 25%. However, bias-corrected crowdsourced rainfall data can produce significantly more accurate inundation maps than those generated from official rain gauges, if compared with maps resulting from bias-corrected weather radar. Overall, our study highlights the potential of utilizing crowdsourced rainfall data from private sensors for accurately representing pluvial flooding in urban areas. These findings have significant implications for improving flood prediction and mitigation strategies in vulnerable urban settings.

云爆和极端暴雨对城市地区的威胁与日俱增。准确的降雨数据对于预测洪水和城市内涝至关重要。私人气象站越来越普遍，其空间分布与人口密度基本一致。这使它们成为城市地区高分辨率雨量场的宝贵众包数据来源。我们评估了私人雨量计在奥斯陆最近两次冲积洪水事件中的表现。我们还探讨了私人雨量计数据在洪水模型中的潜在用途。我们的结果表明，私人雨量传感器具有出色的雨量检测能力，但它们往往会平均低估参考值约 25%。不过，如果与经过偏差校正的天气雷达生成的地图相比，经过偏差校正的众包雨量数据生成的淹没地图要比官方雨量计生成的地图精确得多。总之，我们的研究强调了利用来自私人传感器的众包降雨量数据准确反映城市地区冲积洪水的潜力。这些发现对改善脆弱城市环境中的洪水预测和减灾策略具有重要意义。

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引用次数: 0