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Relationship between flood i q and rainfall i P intensities for an example catchment ("Melk", catchment area 95.2 km 2 ). Dashed black lines indicate constant durations d, dotted red lines indicate constant return periods T.
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The aim of this paper is to explore how rainfall mechanisms and catchment characteristics shape the relationship between rainfall and flood probabilities. We propose a new approach of comparing intensity-duration-frequency statistics of maximum annual rainfall with those of maximum annual streamflow in order to infer the catchment behavior for runo...
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... severe wind gusts and tornados in these regions (Fig. A1, Dotzek et al. (2009) and Merz and Blöschl (2003)), while short-rain and long-rain floods from orographic rainfall are most frequent along the Alpine ridge (Merz & Blöschl, 2003), aligned with longer wet spells and shorter dry spells along the Alpine ridge compared to the lowlands (Fig. A2). Throughout Austria, floods are more frequent in the summer than in the winter (Merz & Blöschl, ...
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... the above assumption and by combining Equations (2) and (3), we can now plot, for each catchment, the rainfall-flood probability relationship i q (i P , T, d) (Fig. 2), where constant duration d and constant return period T are represented by dashed black and dotted red lines, ...
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... d = const., Equation (7) has a constant slope λqd η P λPd η q (as reflected by the dashed black lines in Fig. 2). From Equation (7), the elasticity ε 1 of streamflow relative to changes of rainfall assuming d = const. is obtained ...
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... indicates that ε 1 is only a function of the return period and the location parameters. For CV P =CV q , ε 1 = 1. On the other hand, expressing the i q (i P , T, d) relationship in terms of T = const. (red dotted lines in Fig. 2) gives: . 6. Cumulative distribution functions of rainfall i P (upper row) and specific streamflow i q (bottom row) extremes with a duration of 24hrs in the five rainfall regions. Colored lines represent the median CDFs in each region, numbers in the plot area refer to the 5% and 99% quantiles (italic) of the median. Dashed and dotted ...
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... from these catchment values (ordinary kriging). In these regions catchment concentration times tend to be short as indicated by high η q (Fig. 7c), and storm durations tend to be short indicated by high η P (as high values η P indicate fast decrease of rainfall intensity with duration), also indicated by the short average wet spell duration in Fig. A2b). Generally, the closer ε 2 is to unity, the more similar is the duration of flood triggering rainstorms and the concentration time of the catchment (Equation (11)). On the other hand, the elasticities ε 2 in the Alpine regions (e.g. Northern orographic region) are ...
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... Elevation, slope, precipitation, and flow accumulation were numerical factors while LULC and soil maps are descriptive factors. According to Breinl et al. (2021), the relationship between rainfall and flood probabilities is shaped by precipitation mechanisms and watershed features. The precipitation data used in the study is a historical monthly 2000 climate data, which has a 30 s (~ 1 km 2 ) spatial resolution, and it was resampled to 30 m to match with spatial resolution of the other factors. ...
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... Rainfall depths tend to be largest in the north-west of the region, mainly as a result of orographic effects. At the northern fringe of the Alps in the north-west of Austria, rainfall associated with a return period of 100 years is of the order of 150 mm (Breinl et al., 2021). In the basins at the northern fringe of the high Alps, long duration rainfall driven floods are particularly common. ...
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... For instance, the 1989 floods in SW Mallorca reached peaks over 700 m 3 /s, with a highest value of 1054 m 3 /s in the area of Campos [60]. Again, these values have to be considered in the knowledge that rainfall intensities and its spatial distribution over catchments play an important role in the flood generation processes and such information is not available for the vast majority of Mallorca's events [61,62]. ...
The research presented herein studies three episodes of flooding that affected the ephemeral basin of the Sant Jordi stream in northwestern Mallorca. These events are considered common since they do not reach the proportions in terms of the flow rates of other cases that have occurred in Mallorca, but they are nevertheless important due to the impact they have on human activity and also due to the morphological changes caused in the basin itself. On the one hand, the development of the field work to characterize and calculate the peak flows is presented, and on the other hand, the geomorphic changes caused by the water and the materials carried away are explained. The results allow us to identify a type of Mediterranean flood, which happens on a regular basis, but which does not stand out for its flows or for its major socioeconomic impacts but still has an effect on the natural and anthropic environment. This information can be valuable for local and regional authorities as well as for the public to avoid risk situations and prevent impacts on public and private property caused by future events.
... The detection of rainfall plays a vital role in preventing floods, as it enables the identification of suitable thresholds that are liable to cause flood damage [1], understanding of the relationship between rainfall and flood probabilities [2], identify spatiotemporal and fluvial-pluvial sources of flooding [3], and evaluate the impact of climate change on flood and extreme precipitation events [4]. Additionally, rapid onset flooding, commonly known as flash floods, can rise within a brief duration of time, varying from a few minutes to a few hours, triggered by intense rainfall, a sudden release of water, or a failure of a dam or levee [5]. ...
Rainfall is crucial for flood prevention and comprehending the correlation between rainfall and flooding. Cavite province in the Philippines is vulnerable to flooding caused by heavy rainfall and climate change impacts. Early detection of flooding through early warning systems can prevent excessive damage loss and potentially save lives. It can also provide major savings in terms of monetary benefit and increased interagency coordination for rapid decision-making. Machine learning is an important tool for predicting rainfall which can be used to predict rainfall in the province. The objective of this study is to conduct a comparative analysis of various models for predicting daily rainfall, using relevant atmospheric features such as maximum, minimum, and mean temperature, relative humidity, wind speed, wind direction, cloud cover, pressure, and evaporation. The study seeks to identify the most effective model for accurately predicting rainfall in the Cavite Province to benefit the local community. Among the five machine learning models evaluated, the Gaussian Process Regression model demonstrated the highest accuracy in predicting daily rainfall. The findings of this study can be leveraged to mitigate the damage caused by flooding in the Cavite Province and serve as a useful reference for similar studies in other regions prone to flooding.
... Quantifying severe weather events is of particular interest to actuaries, since events such as flooding account for a large part of global economic losses (Boudreault et al., 2020). An increase in extreme rainfall can lead to a possibly greater increase in river discharge (Breinl et al., 2021). Therefore, one would gain from obtaining reliable rainfall projections to assess flood risks. ...
... Through a combination of hydrological and hydraulic models such as Hydrotel (Fortin et al., 2001), HEC-RAS (Brunner, 2016) or the Hillslope Link Model (Demir & Krajewski, 2013), one can produce discharge flood projections under different rainfall scenarios. Breinl et al. (2021) used elasticity to illustrate the relationship between extreme precipitation and flooding, where depending on ground dampness, an increase in precipitation will have an at least equivalent increase in river discharge, leading to increased flood severity. Supposing that the reduction in ARF will mitigate the impact of an increase in quantiles due to more localised rainfall, such that for example we have an approximately 7% and 19% increase under, respectively, the Cooke and BMA-EM scenarios, the relationship between discharge and rainfall would clearly imply a greater risk of increased flood losses in the latter case. ...
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... In more recent years, the QDF model has been used to characterize flood events of different duration in Algeria (Renima et al., 2018), to inform development of a depth-duration-frequency relationship used to assess risk of rainfall-driven floods in Poland Markiewicz (2021) and as a comparison point to IDF models when assessing catchment behavior for runoff extremes in Austria (Breinl et al., 2021). As noted in Breinl et al. (2021), the relationship quantified by the QDF model is an analogue to the relationship quantified in IDF modeling for precipitation extremes: in the hypothetical situation where all rainfall becomes runoff and the time of concentration is instantaneous, the QDF and IDF models have identical relationships. ...
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... Available QDF models usually assume that only the index flood changes with duration, with the growth curve assumed constant across durations (e.g. Cunderlik and Ouarda, 2006;Breinl et al., 2021). Here the index flood is the median annual maximum flood. ...
... Typically, daily predictions are more complex than monthly or annual predictions. In general, precipitation and stream discharge are highly correlated (Breinl et al., 2021;Rohith et al., 2021). The minimal reduction in model performance (Table 1) for the case without discharge (Q) as input (Case 4) indicates precipitation as a good surrogate for discharge in modeling continuous daily stream nitrate concentration. ...
High-frequency stream nitrate concentration provides critical insights into nutrient dynamics and can help to improve the effectiveness of management decisions to maintain a sustainable ecosystem. However, nitrate monitoring is conventionally conducted through lab analysis using in situ water samples and is typically at coarse temporal resolution. In the last decade, many agencies started collecting high-frequency (5-60 min intervals) nitrate data using optical sensors. The hypothesis of the study is that the data-driven models can learn the trend and temporal variability in nitrate concentration from high-frequency sensor-based nitrate data in the region and generate continuous nitrate data for unavailable data periods and data-limited locations. A Long Short-Term Memory (LSTM) model-based framework was developed to estimate continuous daily stream nitrate for dozens of gauge locations in Iowa, USA. The promising results supported the hypothesis; the LSTM model demonstrated median test-period Nash-Sutcliffe efficiency (NSE) = 0.75 and RMSE = 1.53 mg/L for estimating continuous daily nitrate concentration in 42 sites, which are unprecedented performance levels. Twenty-one sites (50 % of all sites) and thirty-four sites (76 % of all sites) demonstrated NSE >0.75 and 0.50, respectively. The average nitrate concentration of neighboring sites was identified as a crucial determinant of continuous daily nitrate concentration. Seasonal model performance evaluation showed that the model performed effectively in the summer and fall seasons. About 26 sites showed correlations >0.60 between estimated nitrate concentration and discharge. The concentration-discharge (c-Q) relationship analysis showed that the study watersheds had four dominant nitrate transport patterns from landscapes to streams with increasing discharge, including the flushing pattern being the most dominant one. Stream nitrate estimation impedes due to data inadequacy. The modeling framework can be used to generate temporally continuous nitrate at nitrate data-limited regions with a nearby sensor-based nitrate gauge. Watershed planners and policymakers could utilize the continuous nitrate data to gain more information on the regional nitrate status and design conservation practices accordingly.
... In more recent years, the QDF model has been used to characterize flood events of different duration in Algeria (Renima et al., 2018), to inform development of a depth-duration-frequency relationship used to assess risk of rainfall-driven floods in Poland Markiewicz (2021) and as a comparison point to IDF models when assessing catchment behavior for runoff extremes in Austria (Breinl et al., 2021). As noted in Breinl et al. (2021), the relationship quantified by the QDF model is an analogue to the relationship quantified in IDF modeling for precipitation extremes: in the hypothetical situation where all rainfall becomes runoff and the time of concentration is instantaneous, the QDF and IDF models have identical relationships. ...
... In more recent years, the QDF model has been used to characterize flood events of different duration in Algeria (Renima et al., 2018), to inform development of a depth-duration-frequency relationship used to assess risk of rainfall-driven floods in Poland Markiewicz (2021) and as a comparison point to IDF models when assessing catchment behavior for runoff extremes in Austria (Breinl et al., 2021). As noted in Breinl et al. (2021), the relationship quantified by the QDF model is an analogue to the relationship quantified in IDF modeling for precipitation extremes: in the hypothetical situation where all rainfall becomes runoff and the time of concentration is instantaneous, the QDF and IDF models have identical relationships. ...
... Available QDF models usually assume that only the index flood changes with duration, with the growth curve assumed constant across durations (e.g. Cunderlik and Ouarda, 2006;Breinl et al., 2021). Here the index flood is the median annual maximum flood. ...
... These methods mainly focused on at-site estimates centered on the multifractality of flood quantiles (Pandey et al., 1998;Sauquet et al., 2008) and streamflow and rainfall dynamics (Tessier et al., 1996). In addition, a large body of flood scaling literature explores evaluating the flood frequency curves, intensity-duration-frequency curves (Breinl et al., 2021), flood elasticity (Sankarasubramanian et al., 2001), Fourier power spectrum (Telesca et al., 2012), regional flood frequency analysis using index flood method (Ishak et al., 2011;Stephens et al., 2015), and varying quantile regression techniques (Gupta & Waymire, 1990;Telesca et al., 2012;Tessier et al., 1996;Zaman et al., 2013). For example, the flood frequency analysis utilized by the United State Geological Survey (USGS) fits a log-Pearson Type III (LP3) probability distribution to annual flood peaks observed at individual streamgage in a region (Benson, 1962;US-Water-Resources-Council, 1982). ...
Accurate flood risk assessment requires a comprehensive understanding of flood sensitivity to regional drivers and climate factors. This paper presents the scaling of floods (duration, peak, volume) with geomorphologic characteristics of the basin (i.e., drainage area, slope, elevation) and precipitation patterns (rainfall accumulation, variability). Long‐term daily streamflow observations over the 20th and early 21st centuries from Hydro‐Climatic Data Network streamgages across the conterminous United States are used to create a flood event database based on their flood stage information. Antecedent daily rainfall accumulation and variability corresponding to these floods are computed using Global Historical Climatology Network daily data set. Two Bayesian scaling models are developed, and the spatial organization of scaling exponents is investigated. The baseline model quantifies the scaling of floods to geomorphologic characteristics. The dynamic model quantifies the scaling of floods to antecedent precipitation distribution which is further conditioned on geomorphologic characteristics. Results show that small and low‐elevation basins have a stronger response to antecedent rainfall distribution in amplifying flood peaks, while high‐elevation steeper basins have a lower response for flood duration and volume. The dynamic models demonstrate that there are significant variations in the flood scaling rates, with the largest rates up to 40% and 4.5% for flood duration, 64% and 44% for peak, and 98% and 40% for volume found across the Northeast, Coastal Southeast, and Northwest with intensifying rainfall accumulation and variability, respectively. This study advances flood predictions by better informing the flood attributes in the context of dynamical land‐atmosphere perturbations.
... lokasinya yang berada didekat pantai tersebut. Namun, saluran drainase sudah tidak mampu menampung air saat hujan dengan intensitas tinggi dan pasang surut air laut yang terjadi secara beriringan [4] [5]. ...
Drainage is a basic facility that must be provided to meet community needs, so it’s very important in urban spatial planning. The benefit of building drainage is to prevent stagnant water so that it does not interfere with community activities. The condition of the drainage channels on Jalan Marina isn’t maintained because the dimensions of the channels are small and sedimentation occurs, causing silting of the channels. This causes floods that always occur every year. Re-dimension of the drainage channel was carried out because the initial dimensions could not accommodate the planned flood discharge. Some of data the basic for determining the re-dimensional drainage channels are rainfall data, sea tide data, and initial drainage channel dimension data. From the calculation results, the value of planned flood discharge is 172.634 mᶟ/s. So, the dimensions of the drainage channel are re-planned because the channel discharge value is smaller than the planned flood discharge value. The results of this study are that the planned flood discharge is greater than the drainage channel water discharge so that the initial channel dimensions cannot accommodate water. The results of re-planning the dimensions of the drainage channel based on the planned flood discharge value are h of 8.5 meters, b of 9.8 meters, and W of 2 meters. Re-dimensional planning of the drainage channel is fulfilled because Qs<Qp (172.634 mᶟ/s <227.103 mᶟ/s). This research is expected to provide dimensions of drainage channels that are in accordance with field conditions so that no more flooding occurs.
