Establishment of a Flood Nomograph in Coastal Areas Considering Sea-Level Rise

Climate change caused by global warming is raising the average sea level. The rise in sea level leads to an increase in river water levels within the affected range, which increases the possibility of flooding in water due to erosion of outfall to the coast and rivers. Therefore, it is necessary to recognize in advance the risk of occurrence of domestic flooding, which is aggravated by the effect of rising sea levels, and to construct new boundary conditions for predicting urban flooding accordingly. In this study, Flood Nomograph for two research areas was selected in consideration of the regional characteristics of coastal areas and the scenario of sea level rise. As a result of the analysis, as the sea level rose, the amount of flood critical rainfall decreased numerically. It is believed that this study can be used as a necessary basis for improving flood forecast and warning data considering sea level rise in coastal cities in the future.

How Stakeholders Adopt and Share Flood Forecast Information: A Survey of Mississippi River Outlook Users

Guided by the literature in diffusion of innovations, the technology acceptance model, and risk information sharing, this paper reports the results of a survey distributed to National Weather Service (NWS)-Memphis Weather Forecast Office (WFO) stakeholders who receive the Mississippi River Outlook product and its embedded 28-day experimental forecast. The survey examined perceptual factors that likely influence participants’ adoption of flood forecast information provided in the Outlook, and assessed Outlook recipients’ forecast-sharing behaviors and perceptions. Results revealed that the first responders perceived the Outlook product to be more useful than experts, while experts experienced less social influence to use it than first responders or the public. Although participants were generally favorable toward and intended to use the Outlook in the future, experts were significantly less likely to do so and hold a favorable attitude. The majority of participants reported sharing the Outlook with an average of 11 people, and were most likely to share either the entire Outlook verbatim or specific, verbatim sections. Implications of the Outlook’s perceived characteristics and participants’ Outlook-sharing behaviors are discussed.

Development of Short-term Flood Forecast Using ARIMA

The aim of this study is to use the Box-Jenkins method to build a flood forecast model by analysing real-time flood parameters for Pengkalan Rama, Melaka river, hereafter known as Sungai Melaka. The time series was tested for stationarity using the Augmented Dickey-Fuller (ADF) and differencing method to render a non-stationary time series stationary from 1 July 2020 at 12:00am to 30th July 2020. A utocorrelation (ACF) and partial autocorrelation (PACF) functions was measured and observed using visual observation to identify the suitable model for water level time series. The parameter Akaike Information Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to find the best ARIMA model (BIC). ARIMA (2, 1, 3) was the best ARIMA model for the Pengkalan Rama, with an AIC of 5653.7004 and a BIC of 5695.209. The ARIMA (2, 1, 3) model was used to produce a lead forecast of up to 7 hours for the time series. The model's accuracy was tested by comparing the original and forecast sequences by using Pearson r and R squared. The ARIMA model appears to be adequate for Sungai Melaka, according to the findings of this study. Finally, the ARIMA model provides an appropriate short-term water level forecast with a lead forecast of up to 7 hours. As a result, the ARIMA model is undeniably ideal for river flooding.

Flood forecast skill for Early Action: Results and Learnings from the development of the Early-Action Protocol for Floods in Uganda

<p>Global flood forecasting systems are helpful in complementing local resources and in-country data to support humanitarians and trigger early action before an impactful flood occurs. Freely available global flood forecast information from the European Commission’s Global Flood Awareness System (GloFAS, a Copernicus EMS service) is being used by the Uganda Red Cross Society (URCS) alongside in-country knowledge to develop appropriate triggers for early actions for flood preparedness, within the Forecast-based Financing (FbF) initiative. To scale up the first FbF pilot to a national level, in 2020 URCS collaborated with several partners including the Red Cross Red Crescent Climate Centre (RCCC), the Uganda’s Ministry of Water and Environment, through the Directorate of Water Resources Management (DWRM), the Uganda National Meteorological Authority (UNMA), the 510 Global team and the University of Reading, through the UK-supported project Forecasts for Anticipatory Humanitarian Action (FATHUM). The new Early Action Protocol (EAP) for floods, submitted to the IFRC’s validation committee in September 2020, is now under review.</p><p>One of the aims of an EAP is to set the triggers for early action, based on forecast skill information, alongside providing a local risk analysis, and describing the early actions, operational procedures, and responsibilities. Working alongside our partners and practitioners in Uganda, we developed a methodology to tailor flood forecast skill analysis to EAP development, that could be potentially useful for humanitarians in other Countries and forecasters engaging with them. The key aim of the analysis is to identify skilful lead times and appropriate triggers for early action based on available operational forecasts, considering action parameters, such as an Action Lifetime of 30 days, and focusing on relevant flood thresholds and skill scores. We analysed the skill of probabilistic flood forecasts from the operational GloFAS (v2.1) system across Uganda against river flow observations and reanalysis data. One of the challenges was to combine operational needs with statistical robustness requirements, using relevant flood thresholds for action. Here we present the results from the analysis carried out for Uganda and the verification workflow, that we plan to make openly available to all practitioners and scientists working on the implementation of forecast-based actions.</p>

Assessment of GloFAS ensemble flood forecast for the Brahmaputra basin: skilful lead-times to predict monsoon floods for early action in Bangladesh

<p>Flood is a frequent natural hazard in the Brahmaputra basin in Bangladesh during the South Asian summer monsoon between June to September. When will flooding start during monsoon and how long it will last are two important questions that forecasters need to answer. Predicting flood timing and duration with a sufficient lead-time is challenging for forecasters due to strong intraseasonal variation of floods within a monsoon.</p><p>The GloFAS forecasting system is run by ECMWF as part of the Copernicus Emergency Management Service and provides operational extended-range ensemble flood forecast with 30 days lead-time for the major river basins in the world. In this study, we evaluated GloFAS reforecasts for the Brahmaputra basin in Bangladesh for the period 1997–2019 at different lead-times against observed stream gauge and ECMWF ERA5 reanalysis river discharge data. We used various probabilistic forecast verification metrics, such as Relative Operating Characteristic (ROC), False Alarm Ratio (FAR), and Probability of Detection (POD), to study how forecast skill varies over different lead-times. We also assessed the skilful lead-times of the GloFAS forecast to predict flood timing and duration during the monsoon. These scores were calculated considering relevant flood threshold levels and action-based parameters, such as Action Lifetime, based on user needs in Bangladesh. The GloFAS forecast case study for the recent 2020 monsoon floods in the Brahmaputra basin shows that the onset of flood events was successfully predicted with a lead-time of 15 days. These forecasts were disseminated among the different stakeholders, including humanitarian agencies, flood and disaster management organisations, to inform forecast-based actions, such as evacuation of vulnerable people to safer places ahead of flood events. Our study demonstrates that GloFAS ability to predict monsoon floods in terms of timing and duration can improve national flood forecasting capabilities providing sufficient lead-time for early actions in Bangladesh. The study will help forecasters as well as users to understand forecast skill and associated uncertainty in probabilistic forecasts to predict flood events in Bangladesh.</p><p> </p><p> </p><p> </p>