Flood Warning, Forecasting and Emergency Response

Sene, Kevin

doi:10.1007/978-3-540-77853-0

Cited by 75 publications

(42 citation statements)

References 1 publication

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“…Additionally, temperature observations play an important role in real-time operational forecasting systems, especially in regions with significant snow melt (e.g. Verbunt et al, 2007;Sene, 2008).…”

Section: O Rakovec Et Al: State Updating Of a Distributed Hydrologimentioning

confidence: 99%

State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy

Rakovec

Weerts

Hazenberg

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model. The Ensemble Kalman filter (EnKF) is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property).Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km 2 ), a relatively quickly responding catchment in the Belgian Ardennes. We assess the impact on the forecasted discharge of (1) various sets of the spatially distributed discharge gauges and (2) the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty.

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Section: O Rakovec Et Al: State Updating Of a Distributed Hydrologimentioning

confidence: 99%

State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy

Rakovec

Weerts

Hazenberg

et al. 2012

Hydrol. Earth Syst. Sci.

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“…[2] Flood is one of the most destructive and frequent natural disasters threatening people's lives and properties [Sene, 2008]. Accurate forecasting of river flood with a sufficient lead time plays a significant role in timely flood warning and emergency responses.…”

Section: Introductionmentioning

confidence: 99%

“…However, there is scope to reduce error in current flood forecasts. The error arises due to uncertainties in initial conditions, precipitation inputs, and the representation of the flooding process itself [Sene, 2008]. Data assimilation can provide an effective way to reduce the uncertainty in streamflow forecasts by incorporating observations into forecasting models [Kostov and Jackson, 1993;Reichle, 2008].…”

Section: Introductionmentioning

confidence: 99%

Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags

Ryu

Western

et al. 2013

Water Resources Research

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[1] General filtering approaches in hydrologic data assimilation, such as the ensemble Kalman filter (EnKF), are based on the assumption that uncertainty of the current background prediction can be reduced by correcting errors in the state variables at the same time step. However, this assumption may not be valid when assimilating stream discharge into hydrological models to correct soil moisture storage due to the time lag between the soil moisture and the discharge. In this paper, we explore the utility of an ensemble Kalman smoother (EnKS) for addressing this time-lag issue. The EnKF and the EnKS are compared for two different updating schemes with the probability distributed model (PDM) via synthetic experiments: (i) updating soil moisture only and (ii) updating soil moisture and routing states simultaneously. The results show that the EnKS is superior to the EnKF when only soil moisture is updated, while the EnKS and the EnKF exhibit similar results when both soil moisture and routing storages are updated. This suggests that the EnKS can better improve the streamflow forecasting for models that do not adopt storage-based routing schemes (e.g., unit-hydrograph-based routing). For models with dynamic routing stores, errors in soil moisture are transferred to the routing stores, which can be corrected effectively by real-time filters. The EnKS-based soil moisture updating scheme is also tested with the GR4H model, for which unit-hydrograph-based routing is used. The result confirms that the EnKS is superior to the EnKF in improving both soil moisture and streamflow forecasting.

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“…Perubahan penggunaan lahan menjadi kawasan terbangun dapat meningkatkan risiko bencana banjir disebabkan oleh berubahnya karakteristik run off, dan jalur drainase bagi air (Cahyadi et al, 2011;Cahyadi dkk., 2012). Kondisi ini diperparah dengan terjadinya perubahan iklim di mana beberapa tempat mengalami perubahan frekuensi curah hujan (Sene, 2008;Dibyosaputro dkk., 2016).…”

Section: Pendahuluanunclassified

Kerentanan Masyarakat Perkotaan terhadap Bahaya Banjir di Kelurahan Legok, Kecamatan Telanipura, Kota Jambi

Arif

Giyarsih

Mardiatna

2017

MGI

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Penelitian ini bertujuan untuk memetakan bencana banjir serta menganalisis kerentanan fisik dan sosial ekonomi masyarakat perkotaan terhadap bencana banjir di Kelurahan Legok, Kecamatan Telanipura, Kota Jambi. Penelitian ini menggunakan metode deskriptif kuantitatif. Data yang digunakan dalam penelitian ini terdiri atas data primer dan data sekunder. Data primer merupakan data yang diperoleh melalui observasi lapangan dan wawancara terstruktur dengan panduan kuesioner, sedangkan data sekunder merupakan data yang diperoleh dari dokumen-dokumen melalui instansi terkait yang berkompeten. Wawancara dilakukan terhadap 90 rumah tangga yang diambil dengan metode simple random sampling dari 476 populasi. Pengolahan dan analisis data dilakukan secara deskriptif kuantitatif dan dilengkapi dengan analisis spasial. Hasil penelitian menunjukkan bahwa berdasarkan hasil interpolasi kedalaman banjir tahun 2004 dapat diketahui bahwa sebagian besar wilayah penelitian terkena dampak banjir dengan kedalaman maksimum mencapai 3 meter di bagian timur wilayah penelitian. Fenomena ini disebabkan oleh beberapa faktor seperti kondisi topografi yang lebih rendah dibandingkan wilayah lainnya, terjadi penyempitan sungai dan kotornya saluran sungai sehingga menghambat air yang mengalir ke outlet utama. Penelitian ini juga menemukan bahwa tingkat kerentanan fisik tinggi ditemukan sebanyak 35 bangunan, kerentanan fisik sedang sebanyak 46 bangunan, dan kerentanan rendah sebanyak 9 bangunan. Tingkat kerentanan sedang hingga tinggi mendominasi wilayah penelitian karena jenis bangunan berupa bangunan non tembok. Temuan penelitian yang lain adalah kerentanan sosial ekonomi lebih tinggi ditemukan pada elemen tingkat pendidikan dengan sebagian besar tingkat berpendidikan rendah yaitu sekolah dasar (SD) (44,4%) dan elemen penduduk rentan karena usia lanjut dan anak-anak sebanyak 30,4%. (i.e., elementary school, 44.4%) and vulnerable groups (i.e., elderly and children, 30.4%). Kata-kata kunci :

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Flood Warning, Forecasting and Emergency Response

Cited by 75 publications

References 1 publication

State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy

State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy

Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags

Kerentanan Masyarakat Perkotaan terhadap Bahaya Banjir di Kelurahan Legok, Kecamatan Telanipura, Kota Jambi

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