Separation of Flood Plain Flow: 1-D Momentum Equation Solver

A floodplain is an area of land close to a stream or river that extends from its banks to the valley walls or high mountains surrounding it. It is frequently inundated during periods of high runoff. Along with the main stream, a large volume of flow passes through this floodplain during flood discharge. The term "floodplain flow" refers to this redundant discharge. When studying the hydro-morphodynamics of a river from bank to bank, it is necessary to understand the flow distribution throughout the main stream and flood plain. The main stream's water level and discharge are critical parameters for estimating river bank erosion. As a result, the floodplain may operate as a sediment sink during periods of high flow. River hydraulic and hydrodynamic processes are intricate. There are numerous river modeling tools and approaches available for evaluating the distribution and generation of river flow over floodplain and main-stream. This study examines such two methods for generating river flow over the flood plain and in the main stream. The approaches involve the use of a one-dimensional momentum equation and a two-dimensional modeling tool (Mike21C) to generate main-stream and flood plain flow. The Kalni-Kushiyara river is chosen as a case study in order to determine its validity.

Global Reach-level 3-hourly River Flood Reanalysis (1980-2019)

Better understanding and quantification of river floods for very local and flashy events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-year period of 1980-2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-year return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) sub-daily modeling. This data record, referred as Global Reach-level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr.

Assessing the Effectiveness of Mitigation Strategies for Flood Risk Reduction in the Segamat River Basin, Malaysia

Flooding is a frequent, naturally recurring phenomenon worldwide that can become disastrous if not addressed accordingly. This paper aims to evaluate the impacts of land use change and climate change on flooding in the Segamat River Basin, Johor, Malaysia, with 1D–2D hydrodynamic river modeling, using InfoWorks Integrated Catchment Modeling (ICM). The study involved the development of flood maps for four different scenarios: (1) future land use in 2030; (2) the impacts of climate change; (3) three mitigation strategies comprising detention ponds, rainwater harvesting systems (RWHSs), and permeable pavers; and (4) a combination of these three mitigation strategies. The obtained results show increases in the flood peaks under both the land use change and climate change scenarios. With the anticipated increase in development activities within the vicinity up to 2030, the overall impact of urbanization on the extent of flooding would be rather moderate, as the upper and middle parts of the basin would still be dominated by forests and agricultural activities (approximately 81.13%). In contrast, the potential flood-inundated area is expected to increase from 12.25% to 16.64% under storms of 10-, 50-, 100-, and 1000-year average recurrence intervals (ARI). Interestingly, the simulation results suggest that only the detention pond mitigation strategy has a considerable impact on reducing floods, while the other two mitigation strategies have less flood reduction advantages for this agricultural-based rural basin located in a tropical region.

Distributed-Framework Basin Modeling System: Ⅲ. Hydraulic Modeling System

A distributed-framework basin modeling system (DFBMS) was developed to simulate the runoff generation and movement on a basin scale. This study is part of a series of papers on DFBMS that focuses on the hydraulic calculation methods in runoff concentration on underlying surfaces and flow movement in river networks and lakes. This paper introduces the distributed-framework river modeling system (DF-RMS) that is a professional modeling system for hydraulic modeling. The DF-RMS contains different hydrological feature units (HFUs) to simulate the runoff movement through a system of rivers, storage units, lakes, and hydraulic structures. The river network simulations were categorized into different types, including one-dimensional river branch, dendritic river network, loop river network, and intersecting river network. The DF-RMS was applied to the middle and downstream portions of the Huai River Plain in China using different HFUs for river networks and lakes. The simulation results showed great consistency with the observed data, which proves that DF-RMS is a reliable system to simulate the flow movement in river networks and lakes.

A COMPARATIVE ASSESSMENT OF DIFFERENT LOSS METHODS AVAILABLE IN MIKE HYDRO RIVER-UHM

Hydrologic modelling studies usually involve data series with a large temporal scale, especially in Romania, focusing on a long-term impact analysis. Nevertheless, event-based runoff models are essential tools for short-term purposes such as flash flood forecasting. Suitable methods or models must be considered in order to ensure the validity of such research based on parameter calibration to a particular area. Therefore, a comparative analysis of methods must be conducted first, in order to determine the optimal ones that can be used for future data prediction. The aim of the present study is to apply and validate the MIKE HYDRO River modeling system - the UHM module, through a comparative analysis of the SCS, Generalized SCS and Proportional Loss methods available, to a small-sized mountainous watershed, where no research has been conducted in this field. To this end, three spring rainfall events were chosen, but with different antecedent moisture conditions, in order to examine how well the chosen methods can reproduce the available observations in such circumstances. The SCS method yielded the highest quality performance, but the Proportional Loss method has also proven effective under these conditions.

Underlying Fundamentals of Kalman Filtering for River Network Modeling

The grand challenge of producing hydrometeorological estimates every time and everywhere has motivated the fusion of sparse observations with dense numerical models, with a particular interest on discharge in river modeling. Ensemble methods are largely preferred as they enable the estimation of error properties, but at the expense of computational load and generally with underestimations. These imperfect stochastic estimates motivate the use of correction methods, that is, error localization and inflation, although the physical justifications for their optimality are limited. The purpose of this study is to use one of the simplest forms of data assimilation when applied to river modeling and reveal the underlying mechanisms impacting its performance. Our framework based on assimilating daily averaged in situ discharge measurements to correct daily averaged runoff was tested over a 4-yr case study of two rivers in Texas. Results show that under optimal conditions of inflation and localization, discharge simulations are consistently improved such that the mean values of Nash–Sutcliffe efficiency are enhanced from −11.32 to 0.55 at observed gauges and from −12.24 to −1.10 at validation gauges. Yet, parameters controlling the inflation and the localization have a large impact on the performance. Further investigations of these sensitivities showed that optimal inflation occurs when compensating exactly for discrepancies in the magnitude of errors while optimal localization matches the distance traveled during one assimilation window. These results may be applicable to more advanced data assimilation methods as well as for larger applications motivated by upcoming river-observing satellite missions, such as NASA’s Surface Water and Ocean Topography mission.