A flood-crest forecast prototype for river floods using only in-stream measurements

Muste, Marian; Kim, Dongsu; Kim, Kyungdong

doi:10.1038/s43247-022-00402-z

Cited by 16 publications

(11 citation statements)

References 37 publications

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“…Essential features of hysteresis behavior during flood wave propagation Temporal variation of hydraulic variables (Fig. 2a The information assembled in Figures 2a-2d and Table 1 expose several important practical considerations, with most of them related to the limited capability of the widely-used steady rating (Q0) to document hysteresis (Henderson, 1966;Fenton, 2001;Rátky, 2000;Muste et al, 2022aMuste et al, , 2022b): a) the peaks of the main hydraulic variables are not simultaneous, rather they are systematically occurring in the following order: Smax, Vmax, Qmax, and Hmax. b) the H-Q relationship appears as a loop rather than a one-to-one relationship described by the rating developed with steady-state assumption; c) the actual flow discharge (Q) is not symmetrically positioned with respect to the uniform and steady rating (Q0).…”

Section: Characterization Of Hysteretic Behaviormentioning

confidence: 99%

Decoding the Hysteretic Behavior of Hydraulic Variables in Lowland Rivers with Multivariate Monitoring Approaches

Muste,

Kim,

Kim

et al. 2024

Preprint

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This paper demonstrates that the multivariate monitoring methods are capable to underpin the systematic investigation of the hysteretic behavior occurring during gradually-varied flows. For this purpose, we present simultaneous measurements of stage, index velocity, and free-surface slope acquired continuously with high-frequency sampling instruments deployed at several river gauging sites exposed to a range of storm magnitudes. The experimental evidence reveals intrinsic features of unsteady open-channel flow mechanics that are hinted by pertinent governing equations but rarely substantiated with in-situ measurements. The illustrations are intentionally made for fluvial waves propagating at sites located in lowland areas where the relationships among flow variables are most likely displaying hysteretic loops and phasing in the hydraulic variable progression. The set of presented measurements highlights that: a) the hysteretic behavior is apparent in both time-independent and time-dependent graphical representations of any two of the hydraulic variables; b) the severity of the hysteresis is commensurate with the geomorphic, hydraulic, and hydrological characteristics of the measurement site; and c) there is a pressing need for changing the flow paradigms currently used in tracking flow variables during gradually-varied flows. Also discussed are research needs associated with flow hysteresis for advancing the understanding of the mechanisms underlying the movement and storage of water in the lowland river environments as well as for increasing the accuracy of streamflow monitoring, modeling, and forecasting.

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Section: Characterization Of Hysteretic Behaviormentioning

confidence: 99%

Decoding the Hysteretic Behavior of Hydraulic Variables in Lowland Rivers with Multivariate Monitoring Approaches

Muste,

Kim,

Kim

et al. 2024

Preprint

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“…Some studies have shown that the lack of or limited snow accumulation in winters plays an important role in the following period of streamflow (Dierauer et al, 2018) and its predictability during drought periods (Modi et al, 2022), and groundwater recharge and its monitoring (Sit et al, 2021). Understanding snow accumulation is an important factor for flood forecasting (Krajewski et al, 2021;Muste et al, 2022) and mapping to support mitigation (Alabbad et al, 2022) and agricultural planning decisions (Yildirim and Demir, 2022). Earlier snow disappearance has increased the possibility of wildfire and tree mortality (Hall et al, 2015;Gergel et al, 2017;Bales et al, 2018), negatively impacted agricultural activities (Moursi et al, 2017;Qin et al, 2020;, reduced water transit time (Segura, 2021), and affected the community as well (Musselman et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Is Snow Drought a Messenger for the Upcoming Severe Drought Period? A Case Study in the Upper Mississippi River Basin

Yeșilköy¹,

Baydaroğlu²,

Demir³

2023

Preprint

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The exacerbation of floods and the extension of droughts, attributed to climate change and other human-induced factors, are posing a substantial risk to communities by causing water scarcity and insecurity. The significance of safeguarding water resources and managing them is increasingly gaining prominence. Snow is an efficient source of water for recharging groundwater compared to rainfall. This is attributed to its gradual melting process and capacity to infiltrate the soil, thereby providing sustenance to the groundwater. Thus, snow drought can be considered a major contributing factor to the issue of water scarcity. The objective of this study was to investigate the evolution of snow drought over the period spanning from 1980 to 2022, as well as its impact on agricultural drought across the Upper Mississippi River Basin (UMRB). This research employed the AgERA5 reanalysis gridded data at surface level with a spatial resolution of 0.1°, obtained from the European Center for Medium-Range Weather Forecasts (ECMWF), to assess the snow drought. An analysis is conducted for comparison between the spatial estimations of snow drought in the UMBR and two other drought indicators, namely the evaporative demand drought index (EDDI) and water deficit amounts. The effects of the El Niño and La Niña phenomena on the UMRB as well as the results of the summer drought conditions were reviewed. The results point to two important findings. The former is that the snow-drought-affected zones show an increasing trend from the past to the present in the UMRB. The latter is that severe snow droughts in the winter of a water year trigger severe agricultural droughts in the summer months of the same water year. It is seen that monitoring snow droughts is as essential as following rainfall regimes in the planning of water resources, agricultural production, and irrigation methods.

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“…These factors, along with climate changes, has further substantial the frequent occurrence of flood in Malaysia 7 . A simple and low-cost tool for monitoring flood occurrence is streamflow time series monitoring, an effective indicator of trends and changes in the hydro-climatic system 8 , 9 .…”

Section: Introductionmentioning

confidence: 99%

Streamflow classification by employing various machine learning models for peninsular Malaysia

AlDahoul,

Momo,

Chong

et al. 2023

Sci Rep

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Due to excessive streamflow (SF), Peninsular Malaysia has historically experienced floods and droughts. Forecasting streamflow to mitigate municipal and environmental damage is therefore crucial. Streamflow prediction has been extensively demonstrated in the literature to estimate the continuous values of streamflow level. Prediction of continuous values of streamflow is not necessary in several applications and at the same time it is very challenging task because of uncertainty. A streamflow category prediction is more advantageous for addressing the uncertainty in numerical point forecasting, considering that its predictions are linked to a propensity to belong to the pre-defined classes. Here, we formulate streamflow prediction as a time series classification with discrete ranges of values, each representing a class to classify streamflow into five or ten, respectively, using machine learning approaches in various rivers in Malaysia. The findings reveal that several models, specifically LSTM, outperform others in predicting the following n-time steps of streamflow because LSTM is able to learn the mapping between streamflow time series of 2 or 3 days ahead more than support vector machine (SVM) and gradient boosting (GB). LSTM produces higher F1 score in various rivers (by 5% in Johor, 2% in Kelantan and Melaka and Selangor, 4% in Perlis) in 2 days ahead scenario. Furthermore, the ensemble stacking of the SVM and GB achieves high performance in terms of F1 score and quadratic weighted kappa. Ensemble stacking gives 3% higher F1 score in Perak river compared to SVM and gradient boosting.

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A flood-crest forecast prototype for river floods using only in-stream measurements

Cited by 16 publications

References 37 publications

Decoding the Hysteretic Behavior of Hydraulic Variables in Lowland Rivers with Multivariate Monitoring Approaches

Decoding the Hysteretic Behavior of Hydraulic Variables in Lowland Rivers with Multivariate Monitoring Approaches

Is Snow Drought a Messenger for the Upcoming Severe Drought Period? A Case Study in the Upper Mississippi River Basin

Streamflow classification by employing various machine learning models for peninsular Malaysia

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