FEWS-Waterways For Economically And Efficiently Navigating On Inland Waterways.

This paper describes the use of Delft-FEWS as part of a tool for navigating on Inland Waterways economically and efficiently. Delft-FEWS, as developed by Deltares, is an operational real time forecasting system which links data and models in real time. FEWS-Waterways forecasts water depth, , flow velocity, air clearance based on measured and forecasted hydrological and metrological data and current state of the waterway system. This feature of Delft-FEWS is used in an economy planner giving advice to ship masters with respect to: maximal cargo volume, minimum fuel consumption and the optimal ship speed in order to arrive in time at the destination, e.g. reliable Expected Time of Arrival

KKF-Model Platform Coupling : summary report KKF01b

Nederland bereidt zich voor op een sneller stijgende zeespiegel en een veranderend klimaat. Hiervoor is het Deltaprogramma gestart. Dit deltaprogramma voorziet een serie beslissingen die grote gevolgen zullen hebben voor het beheer van het water in Nederland. Om deze beslissingen zorgvuldig te nemen is informatie nodig over hoe het klimaat en de stijgende zeespiegel dit waterbeheer zullen beïnvloeden. De modellen die de gevolgen van klimaatverandering berekenen zullen daarom met dezelfde klimaat forcering en gekoppeld aan elkaar moeten worden gebruikt. In dit onderzoek is gekeken naar het linken van hydrologische en hydrodynamische modellen – en daaraan gekoppelde modellen die de ontwikkelingen in natuur en landgebruik modelleren -- die het gebied van de Alpen tot en met de Noordzee inclusief Nederland beschrijven

Towards a decision support system for flood management in a river basin

A platform for flood forecasting (FEWS-LIMA) in the Portuguese river Lima basin was implemented applying Delft-FEWS software. This platform integrates SOBEK Sacramento hydrological model, SOBEK rivers hydrodynamic models (working together in predicting river hydrodynamics behaviour), and a comprehensive hydrological database. The calibration of these models was achieved using historical river flow data of different rainfall events for two different periods: after the dams construction and before its construction. Models predictions use rainfall time series as input data obtained from Numerical Weather Prediction models. The performance of forecasting platform was verified in real rainfall events, using a backcasting approach for four flood events occurred in the years 2006, 2010, and 2011 in order to demonstrate the accuracy of the modelled processes. In addition, a forecasting event was also considered in order to show the applicability of this methodology in future situations. It was verified, in this case study, that the obtained results have a high correlation to the actually measured typical flood hydraulic parameters

Development of an Operational Storm Surge Forecasting System for the Gulf of Thailand

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Hydrological modelling using convective scale rainfall modelling – phase 3

This project explores hydrological model concepts and associated computational methods that make best use of the latest Met Office technology in high resolution and probabilistic rainfall forecasting. Regional case studies in the South West and the Midlands were used to evaluate the hydrological models, and these were subsequently extended to include a nationwide test of the G2G (Grid-to-Grid) distributed hydrological model. The potential for operational use of ensemble rainfall forecast products such as MOGREPS (Met Office Global and Regional Ensemble Prediction System), STEPS (Short-Term Ensemble Prediction System) and NWP (Numerical Weather Prediction) were also investigated. Two test cases were used in the project: the Boscastle flood of August 2004 in South West Region and the June/July 2007 floods in the Midlands. For these studies, existing or newly calibrated lumped hydrological models were used as benchmarks against which to assess the potential value of a distributed hydrological modelling approach to flood forecasting. For the Boscastle study, a split sample method was used where distinct calibration and verification periods were identified. For the Midlands test case (which was modelled as part of the nationwide study), paired benchmark catchments were identified, one of each pair being treated as gauged and the other as ungauged. The hydrological modelling included two lumped rainfall-runoff models of the type used operationally - the PDM (Probability Distributed Model) and MCRM (Midlands Catchment Runoff Model) – together with two distributed hydrological models: the physics-based REW (Representative Elementary Watershed) model (Boscastle test case only) and the physical-conceptual G2G model. For the Boscastle test case, model performance ranged from good to excellent for catchments across the Tamar and Camel river basins. The lumped PDM model performed best, followed by the G2G model and then the REW model. For both the distributed models, the performance for ungauged sites was similar to the performance for gauged sites indicating the potential of these models to forecast floods at ungauged river locations. When used in combination with different resolution (12, four and one km) NWP model rainfall forecasts, hydrological models performed best using the higher resolution forecasts, with the greatest performance moving from 12 to four km. When driven with a pseudo-ensemble of high resolution NWP rainfall forecasts (produced by random position displacements within a defined radius) the distributed model was better able to capture differences between the ensemble members. The generated hydrographs showed a spread in size and shape that sensibly reflected the changing position of the storm pattern over the catchments assessed. The test case over the Midlands considered rural and urban catchments of low relief in the Avon and Tame river basins respectively, providing a more challenging modelling problem than the higher relief Tamar and Camel catchments of the Boscastle test case. The G2G model was assessed with reference to the summer 2007 floods, using the lumped MCRM as a benchmark model reflecting operational practice in the Midlands. Whilst the site-specific lumped models, as expected, proved hard to improve, the G2G model performed well across a range of catchment types. However, problems arose where the natural flow regime was affected by water imports/exports in urban catchments. Floods in summer 2007 were examined in detail using ensemble rainfall forecasts from NWP and STEPS. Their use for flood warning is illustrated in flood risk maps showing the probability of exceedance of flows of a given return period, either as a spatial time series as the flood propagates through the river system or at a given time over a forecast horizon of given length. The sensitivity of the G2G model to the spatio-temporal structure of storms makes it particularly suitable for ensemble rainfall forecasts for probabilistic flood forecasting of convective-scale events. The success of the G2G model in the Boscastle test case resulted in a project extension to consider a nationwide study of the G2G model across England and Wales. Performance proved to be mixed, with R2 efficiency averaging 0.56 over a two-year period encompassing the summer 2007 floods. Model calibration and assessment was affected by problems with rainfall data obtained from the operational National Flood Forecasting System (NFFS) archive and by unaccounted for catchment abstractions and returns. Assessment using benchmark pairs of gauged/ungauged catchments indicates that the G2G model gives comparable performance for both, confirming its utility for forecasting at ungauged catchments. The G2G model offers a practical approach to nationwide flood forecasting that complements more detailed regional flood forecasting systems. It is able to represent a wide range of hydrological behaviours through its link with terrain and soil properties. The distributed model forecasts, however, are best used alongside, and not instead of, those from lumped catchment models in typical rainfall conditions. The possibilities for using MOGREPS and STEPS ensemble rainfall forecast products were investigated within the current NFFS configurations for North East and Thames regions. Evaluation included configuration issues, data volumes, run times and options for displaying probabilistic forecasts within NFFS. A nationwide calibration of the G2G model was also tested in an operational NFFS environment and a trial system has been running since summer 2009. Although available, ensemble rainfall forecasts from MOGREPS were not extensive enough to fully verify its performance. Nevertheless, the use of MOGREPS in current Environment Agency regional forecasting can provide better information to the forecaster than deterministic forecasts alone. In addition, with careful configuration in NFFS, MOGREPS can be used in existing systems without a significant increase in system load. Configuration of STEPS ensemble rainfall forecasts for use as hydrological model input was demonstrated within the NFFS environment, and required relatively little effort to implement. No verification of the actual performance was possible

Flood and landslide warning based on rainfall thresholds and soil moisture indexes: the HEWS (Hydrohazards Early Warning System) for Sicily

Abstract. The main focus of the paper is to present a flood and landslide early warning system, named HEWS (Hydrohazards Early Warning System), specifically developed for the Civil Protection Department of Sicily, based on the combined use of rainfall thresholds, soil moisture modelling and quantitative precipitation forecast (QPF). The warning system is referred to 9 different Alert Zones in which Sicily has been divided into and based on a threshold system of three different increasing critical levels: ordinary, moderate and high. In this system, for early flood warning, a Soil Moisture Accounting (SMA) model provides daily soil moisture conditions, which allow to select a specific set of three rainfall thresholds, one for each critical level considered, to be used for issue the alert bulletin. Wetness indexes, representative of the soil moisture conditions of a catchment, are calculated using a simple, spatially-lumped rainfall–streamflow model, based on the SCS-CN method, and on the unit hydrograph approach, that require daily observed and/or predicted rainfall, and temperature data as input. For the calibration of this model daily continuous time series of rainfall, streamflow and air temperature data are used. An event based lumped rainfall–runoff model has been, instead, used for the derivation of the rainfall thresholds for each catchment in Sicily characterised by an area larger than 50 km2. In particular, a Kinematic Instantaneous Unit Hydrograph based lumped rainfall–runoff model with the SCS-CN routine for net rainfall was developed for this purpose. For rainfall-induced shallow landslide warning, empirical rainfall thresholds provided by Gariano et al. (2015) have been included in the system. They were derived on an empirical basis starting from a catalogue of 265 shallow landslides in Sicily in the period 2002–2012. Finally, Delft-FEWS operational forecasting platform has been applied to link input data, SMA model and rainfall threshold models to produce warning on a daily basis for the entire region

NCR-days 2008 : 10 years NCR: November 20-21

De verschillende subthema’s van de NCR-dagen 2008, (i) Stroomgebied en Overstromingsrisico management (ii) Hydrologie en (iii) Geomorfodynamica en Morfologie, dekken een groot gedeelte van het hedendaagse onderzoek dat in Nederland op rivierkundig gebied wordt uitgevoerd

Developing surface water flood forecasting capabilities in Scotland: an operational pilot for the 2014 Commonwealth Games in Glasgow

Existing surface water flood forecasting methods in Scotland are based on indicative depth‐duration rainfall thresholds with limited understanding of the likelihood of inundation or associated impacts. Innovative risk‐based solutions are urgently needed to advance surface water forecasting capabilities for improved flood resilience in urban centres. A new model‐based solution was developed for Glasgow, linking 24‐h ensemble rainfall predictions from the Met Office Global and Regional Ensemble Prediction System for the UK (MOGREPS‐UK) with static flood risk maps through the Grid‐to‐Grid hydrological model. This new forecasting capability was used operationally by the Scottish Flood Forecasting Service during the 2014 Commonwealth Games to provide bespoke surface water flooding guidance to responders. The operational trial demonstrated the benefits of being able to provide targeted information on real‐time surface water flood risk. It also identified the high staff resource requirement to support the service due to the greater uncertainty in surface water flood forecasting compared to established fluvial and coastal methods

A DSS for operational management of wastewaters under uncertain conditions

Wastewater treatment facilities of the Ave River basin (located in NW Portugal) are especially vulnerable to infiltration since they present considerable extensions of sewers installed in streams and rivers and collect wastewaters from longstanding sewer networks of five municipalities. The operational management of this complex system involves decision variables such as the selection of the treatment plant where collected wastewater will be treated, with implications for pumped volumes and consequent energy consumption. Aiming to reduce these inflows and increase the management performance of TRATAVE, the company responsible for operating the system, a monitoring network that includes the entire drainage network and treatment facilities operated by the company was designed and implemented. Several flow measurement devices were installed at strategic locations within the sewer network and integrated with a SCADA system responsible for its operation. All measured data was organized in databases. This monitoring platform will support the implementation of a decision support system (DSS) based on a hydrological model of the basin, a hydrodynamic model of the river network and the sewer network. The DSS is being implemented using the Delft-FEWS platform, integrating monitoring data and models. The DSS conceptual framework and the first results of the estimated infiltration volumes are presentedinfo:eu-repo/semantics/publishedVersio

ANALISIS RUNTUN WAKTU FUZZI UNTUK PREDIKSI BANJIR SECARA WAKTU NYATA

Kejadian bencana banjir di Indonesia sering menimbulkan banyak korban, baik jiwamaupun materi. Secara umum 34% dari seluruh kejadian bencana yang terjadi di seluruhIndonesia didominasi oleh bencana banjir. Untuk mencegah bertambahnya jumlah korban,maka dari segi pengetahuan dapat dilakukan pendekatan secara struktural dan nonstruktural, salah satu pendekatan non struktural adalah dengan mengembangkan sistemperingatan dini. Tujuan penelitian ini adalah mengimplementasikan metode runtun waktu fuzzi dalam aplikasi yang dapat memprediksi banjir secara waktu nyata dan membangunaplikasi sistem informasi berbasis web untuk memberikan informasi hasil prediksi banjirsecara waktu nyata melalui metode runtun waktu fuzzi. Runtun waktu fuzzi (FTS) merupakan metode prediksi data yang menggunakan prinsip prinsip fuzzi sebagai dasarnya. Sistem prediksi dengan runtun waktu fuzzi menangkap pola dari data yang telah lalu kemudian digunakan untuk memproyeksikan data yang akandatang. Metode ini sering digunakan oleh para peneliti untuk menyelesaikan masalahprediksi. Tahapan yang dilakukan dengan menggunakan runtun waktu fuzzi didasarkanpada deret waktu historis, yaitu : menentukan semesta pembicaraan, pemisahan semestapembicaraan, membangun fuzzi set, fuzzifikasi data history, menentukan fuzzy logicalrelationships (FLR), menentukan fuzzy logical relationships group (FLRG), menghitunghasil prediksi per menit. Dari hasil penelitian ini, dapat disimpulkan bahwa penerapan metode runtun waktu fuzzi dalam prediksi banjir dapat menghasilkan prediksi yang baik, sehingga dapat dipergunakanuntuk acuan memprediksi bencana banjir secara real time pada sebuah ketinggian level airdi suatu tempat. Penerapan metode runtun waktu fuzzi dalam memprediksi bencana banjirsecara waktu nyata diperoleh melalui percentage error yang diukur dengan menggunakanMean Absolute Percentage Error (MAPE) diperoleh error rata-rata sebesar 0,44%, dandiperoleh juga nilai Mean Standart Error (MSE) sebesar 0,67 hal ini artinya membuktikanbahwa prediksi yang dihasilkan dapat mendekati data aktual. Kata-kunci : runtun waktu fuzzi, prediksi, banjir, waktu nyata Existance Flood in Indonesia often cause many casualties, both mental and material. In general 34% of all disaster events that occurred in Indonesia is dominated by the flood disaster. To prevent the increasing number of victims, then in terms of knowledge can be approached structural and non-structural, non-structural one approach is to develop an early warning system. The purpose of this research is to implement a method of fuzzy time series in applications that can predict the Flood in real-time and build a web-based informationsystem application to provide information which results in real-time Flood prediction based on time series methods Fuzzi. Fuzzy time series is a method that uses the fuzzy principles as the basis for predicting the data. Fuzzy Time series prediction system capture patterns in the data that has been and is then used to project the data to come. This method is often used by researchers to solve the prediction. Problem the research steps of using fuzzy time series is based on a time series of historical, i.e. : defining the universe of discourse, splitting the universe of discourse, building fuzzy sets, fuzzification history data, determining the fuzzy logical relationships (FLR), determining the fuzzy logical relationships of group (FLRG), counting predicted results per minute. From results of this research the application of the method of fuzzy time and series in flood predictions can yield good predictions, so it can be used to predict floods reference in real time at a height of water level somewhere. Application of the method of fuzzy time series in predicting floods in real time obtained by percentage error is measured using the Mean Absolute Percentage Error (MAPE) obtained an average error of 0.44%, and also the value obtained Mean Standard Error (MSE) by 0.67 it means proving that the predictions generated can be closer to the actual data. Keywords: fuzzy time series, prediction, flood, real-tim