A Comprehensive Web-based System for Flood Inundation Map Generation and Comparative Analysis Based on Height Above Nearest Drainage

It is critical to obtain accurate flood extent predictions in a timely manner in order to reduce flood-related casualties and economic losses. Running a real-time flood inundation mapping model is a critical step in supporting quick flood response decisions. Most inundation systems, on the other hand, are either overly demanding in terms of data and computing power or have limited interaction and customization with various input and model configurations. This paper describes a client-side web-based real-time inundation mapping system based on the Height Above the Nearest Drainage (HAND) model. The system includes tools for hydro-conditioning terrain data, modifying terrain data, custom inundation mapping, online model performance evaluation, and hydro-spatial analyses. Instead of only being able to work on a few preprocessed datasets, the system is ready to run in any region of the world with limited data needs (i.e., elevation). With the system's multi-depth inundation mapping approach, we can use water depth measurements (sensor-based or crowdsourced) or model predictions to generate more accurate and realistic flood inundation maps based on current or future conditions. All of the system's functions can be performed entirely through a client-side web browser, without the need for GIS software or server-side computing. For decision-makers and the general public with limited technical backgrounds, the system provides a one-stop, easy-to-use flood inundation modeling and analysis tool.

Can the re-infiltration process be ignored for flood inundation mapping and prediction during extreme storms?

Coupled Hydrologic & Hydraulic (H&H) models have been widely applied to simulate both discharge and flood inundation due to their complementary advantages, yet the H&H models oftentimes suffer from one-way and weak coupling and particularly disregarded run-on infiltration or re-infiltration. This could compromise the model accuracy, such as under-prediction (over-prediction) of subsurface water contents (surface runoff). In this study, we examine the H&H model performance differences between the scenarios with and without re-infiltration process in extreme events¬ – 100-year design rainfall and 500-year Hurricane Harvey event – from the perspective of flood depth, inundation extent, and timing. Results from both events underline that re-infiltration manifests discernable impacts and non-negligible differences for better predicting flood depth and extents, flood wave timings, and inundation durations. Saturated hydraulic conductivity and antecedent soil moisture are found to be the prime contributors to such differences. For the Hurricane Harvey event, the model performance is verified against stream gauges and high water marks, from which the re-infiltration scheme increases the Nash Sutcliffe Efficiency score by 140% on average and reduces maximum depth differences by 17%. This study highlights that the re-infiltration process should not be disregarded even in extreme flood simulations. Meanwhile, the new version of the H&H model – the Coupled Routing and Excess STorage inundation MApping and Prediction (CREST-iMAP) Version 1.1, which incorporates such two-way coupling and re-infiltration scheme, is released for public access.

Near real time flood inundation mapping using social media data as an information source: a case study of 2015 Chennai flood

During and just after flash flood, data regarding water extent and inundation will not be available as the traditional data collection methods fail during disasters. Rapid water extent map is vital for disaster responders to identify the areas of immediate need. Real time data available in social networking sites like Twitter and Facebook is a valuable source of information for response and recovery, if handled in an efficient way. This study proposes a method for mining social media content for generating water inundation mapping at the time of flood. The case of 2015 Chennai flood was considered as the disaster event and 95 water height points with geographical coordinates were derived from social media content posted during the flood. 72 points were within Chennai and based on these points water extent map was generated for the Chennai city by interpolation. The water depth map generated from social media information was validated using the field data. The root mean square error between the actual water height data and extracted social media data was ± 0.3 m. The challenge in using social media data is to filter the messages that have water depth related information from the ample amount of messages posted in social media during disasters. Keyword based query was developed and framed in MySQL to filter messages that have location and water height mentions. The query was validated with tweets collected during the floods that hit Mumbai city in July 2019. The validation results confirm that the query reduces the volume of tweets for manual evaluation and in future will aid in mapping the water extent in near real time at the time of floods.

Sequential data assimilation for real-time probabilistic flood inundation mapping

Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision-making during the emergency period before an upcoming flood event. Considering the high uncertainties involved in the modeling of a nonlinear and complex flood event, providing a deterministic flood inundation map can be erroneous and misleading for reliable and timely decision-making. The conventional flood hazard maps provided for different return periods cannot also represent the actual dynamics of flooding rivers. Therefore, a real-time modeling framework that forecasts the inundation areas before the onset of an upcoming flood is of paramount importance. Sequential data assimilation (DA) techniques are well known for real-time operation of physical models while accounting for existing uncertainties. In this study, we present a DA hydrodynamic modeling framework where multiple gauge observations are integrated into the LISFLOOD-FP model to improve its performance. This study utilizes the ensemble Kalman filter (EnKF) in a multivariate fashion for dual estimation of model state variables and parameters where the correlations among point source observations are taken into account. First, a synthetic experiment is designed to assess the performance of the proposed approach; then the method is used to simulate the Hurricane Harvey flood in 2017. Our results indicate that the multivariate assimilation of point source observations into hydrodynamic models can improve the accuracy and reliability of probabilistic flood inundation mapping by 5 %–7 %, while it also provides the basis for sequential updating and real-time flood inundation mapping.

Prediksi spasio temporal rob berbasis model LISFLOOD FP di Pesisir Jakarta.

Abstrak Jakarta khususnya daerah pesisir sangat rentan dengan adanya permasalahan lingkungan berupa rob. Pemetaan daerah yang berpotensi terdampak rob sangat diperlukan guna menyusun upaya mitigasi. Pada penelitian ini dilakukan prediksi tinggi muka laut dengan model Delft3D dan digunakan untuk memprediksi daerah tergenang rob menggunakan model LISFLOOD FP pada tanggal 18 – 20 November 2019 di pesisir Jakarta. Hasil penelitian menunjukkan bahwa prediksi tinggi muka laut memiliki akurasi yang baik, dengan koefisien korelasi pada tingkat kuat sebesar 0,93 dan nilai RMSE sebesar 0,13 meter. Sementara itu, prediksi rob model LISFLOOD FP menunjukkan luas maksimum yang terjadi 2 hingga 3 jam setelah fase puncak tinggi muka laut dan menggenangi 8 kecamatan di Jakarta Utara dan Jakarta Barat. Abstract Jakarta region especially the coastal areas are very vulnerable to environmental problems such as coastal inundation. Mapping of areas potentially affected by coastal inundation is needed to arrange mitigation efforts. In this study, sea level prediction was estimated using the Delft3D model and used to predict coastal inundation areas using the LISFLOOD FP model on 18-20 November 2019 on the coast of Jakarta. The results showed that the sea-level prediction model has good accuracy, with a correlation coefficient at a strong level of 0.92 and an RMSE error value of 0.13 meters. Meanwhile, coastal inundation prediction from the LISFLOOD FP model inundated 8 sub-districts in North Jakarta and West Jakarta and showed the maximum area in 2 to 3 hours after the peak phase of sea level.