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Pluvial flooding in urban areas is characterized by a gradually varying inundation process caused by surcharge of the sewer manholes. Therefore urban flood models need to simulate the interaction between the sewer network and the overland flow in order to accurately predict the flood inundation extents. In this work we present a methodology for linking 2D overland flow models with the storm sewer model SWMM 5. SWMM 5 is a well-known free open-source code originally developed in 1971. The latest major release saw its structure re-written in C ++ allowing it to be compiled as a command line executable or through a series of calls made to function inside a dynamic link library (DLL). The methodology developed herein is written inside the same DLL in C + +, and is able to simulate the bi-directional interaction between both models during simulation. Validation is done in a real case study with an existing urban flood coupled model. The novelty herein is that the new methodology can be added to SWMM without the need for editing SWMM's original code. Furthermore, it is directly applicable to other coupled overland flow models aiming to use SWMM 5 as the sewer network model.

There is still little experience on the effect of the Manning roughness coefficient in coupled hydrological-hydraulic distributed models based on the solution of the Shallow Water Equations (SWE), where the Manning coefficient affects not only channel flow on the basin hydrographic network but also rainfall-runoff processes on the hillslopes. In this kind of model, roughness takes the role of the concentration time in classic conceptual or aggregated modelling methods, as is the case of the unit hydrograph method. Three different approaches were used to adjust the Manning roughness coefficient in order to fit the results with other methodologies or field observations—by comparing the resulting time of concentration with classic formulas, by comparing the runoff hydrographs obtained with aggregated models, and by comparing the runoff water volumes with observations. A wide dispersion of the roughness coefficients was observed to be generally much higher than the common values used in open channel flow hydraulics.

In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system’s accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations.

Grass coverage and its spatial distribution patterns have crucial influences on erosion. The laboratory scouring experiments were conducted to research the influence of grass cover on runoff, erosion rates, and overland flow hydraulic characteristics in the plots with differing grass coverage rates (30, 50, 70, and 90 %), grass distribution patterns (where US, MS, and DS stand for the grass laid on up-slope, middle-slope and down-slope, respectively) and with a bare soil plot (CK) at a slope gradient of 20. The results illustrate that the grassplots had a 2.06–10.94 % runoff reduction and 28.57–75.4 % sediment decreases, respectively, as compared with CK plot. There was no significant difference in the runoff rate among the three grass distribution patterns for the same grass coverage, while DS had the lowest sediment yield rate and greatest sediment yield reduction in comparison with US and MS. The sediment yield rates were found to have a significantly negative exponential relationship with the grass coverage ( p  < 0.01), while the sediment concentration had a significantly negative linear relationship with the grass coverage ( p  < 0.01). The overland flow velocity ( V ) increased with increasing inflow discharges and deceased with increasing grass cover, and it was negatively correlated with the grass coverage following a linear trend ( p  < 0.01). The mean Froude number ( Fr ) holds to a similar variation law with the changes in the V . There was no significant relationship found to exist between the grass coverage and Reynolds number ( Re ). The average Darcy–Weisbach resistance coefficient ( f ) of the whole slope for grass plots was 2.2–25.6 times of that for CK plot, and f was found to be an exponent correlated with the coverage rate ( p  < 0.01). In addition, f was negatively correlated with the erosion rate following a power function ( p  < 0.01); however V , Fr , and Re were positively correlated with the erosion rate ( p  < 0.01). The sediment yield rate itself was a function of the runoff rate for each treatment, and their relationships could be well described by the linear equation ( p  < 0.01). These results indicate that both grass coverage rates and distribution patterns have significant effects on hydrological characteristics of overland flow.

Although playing an important role in shaping the environment, the mechanisms responsible for runoff initiation and yield in arid and semiarid regions are not yet fully explored. With infiltration-excess overland flow, known also as Hortonian overland flow (HOF) taking place in these areas, the uppermost surface ‘skin’ plays a cardinal role in runoff initiation and yield. Over large areas, this skin is composed of biocrusts, a variety of autotrophs (principally cyanobacteria, green algae, lichens, mosses) accompanied by heterotrophs (such as fungi, bacteria, archaea), which may largely dictate the infiltration capability of the surface. With most biocrust organisms being capable of excreting extracellular polymeric substances (EPS or exopolymers), and growing evidence pointing to the capability of certain EPS to partially seal the surface, EPS may play a cardinal role in hindering infiltration and triggering HOF. Yet, despite this logic thread, great controversy still exists regarding the main mechanisms responsible for runoff generation (runoff initiation and yield). Elucidation of the possible role played by EPS in runoff generation is the focus of the current review.

The stability and efficiency of a rainfall–runoff model are of concern for establishing a flood early warning system. To tackle any problems associated with the numerical instability or computational cost of conducting a real-time runoff prediction, the neural network (NN) method has emerged as an alternative to calculate the overland-flow depths in a watershed. Therefore, instead of developing a new algorithm of machine learning to improve the predicted accuracy, this study focuses on thoroughly exploring the influence of input data that are highly related to the flow responses in space, and then establishing a procedure to process all the input data for the NN training. The novelty of this study is as follows: (1) To improve the overall accuracy of the 2D flood prediction, geomorphological factors, such as the hydrologic length (L), the flow accumulation value (FAV), and the bed slope (S) at the location of each element extracted from the topographic dataset were considered together and were classified into multiple zones for separate trainings. (2) An optimal length of the effective rainfall condition (To) was proposed by conducting a correlation analysis to determine the most informative precipitation data. In this study, the outcomes of four types of NN models were examined and compared with one another. The results show that the simplest structure of the NN methods could achieve satisfactory predictions of flow depth, as long as the approaches of data preprocessing and model training proposed in this study were implemented.

Vegetation filter strips (VFSs) have always been an important measure to control agricultural soil erosion, especially in mountainous and hilly areas with more sloping farmland. To investigate the mechanism of the sediment-trapping process by VFSs, a series of tests were conducted with four gradients of flow rate, 7.5–45 L min−1 m−1, and two different sediment concentrations of 40 and 120 g L−1. The whole process of overland flow was monitored, and sediment and particle size samples from the inflow and outflow were collected and measured. The results showed that the changes in sediment concentration did not significantly affect the corresponding coefficients in the power function relationship between overland flow rate and velocity. Using the Reynolds number alone cannot effectively indicate the flow pattern of overland flow on vegetation hillslopes. The peak particle size and linear function were effective in describing the relationship between sediment particle composition and delivery rate during the sediment-trapping process by VFSs. During the sediment-trapping process, the sediment-trapping capacity of VFSs continued to decrease. The increase in sediment discharge was accompanied by a higher proportion of coarse particles. Under the same flow rate conditions, when the sediment concentration was higher, the coarse particles and their proportion also increased faster. Therefore, using only a certain particle size threshold to distinguish suspended and transported sediment may lead to inaccurate estimation of the sediment-trapping performance of VFSs. This study deepened the understanding of the mechanism of water–sediment processes on vegetation hillslopes and promoted the widespread and efficient application of VFSs management technology.

Rainfall is the main driver of hydrological processes in dryland environments and characterising the rainfall variability and processes of runoff generation are critical for understanding ecosystem function of catchments. Using remote sensing and in situ data sets, we assess the spatial and temporal variability of the rainfall, rainfall–runoff response, and effects on runoff coefficients of antecedent soil moisture and ground cover at different spatial scales. This analysis was undertaken in the Upper Burdekin catchment, northeast Australia, which is a major contributor of sediment and nutrients to the Great Barrier Reef. The high temporal and spatial variability of rainfall are found to exert significant controls on runoff generation processes. Rainfall amount and intensity are the primary runoff controls, and runoff coefficients for wet antecedent conditions were higher than for dry conditions. The majority of runoff occurred via surface runoff generation mechanisms, with subsurface runoff likely contributing little runoff due to the intense nature of rainfall events. MODIS monthly ground cover data showed better results in distinguishing effects of ground cover on runoff that Landsat-derived seasonal ground cover data. We conclude that in the range of moderate to large catchments (193–36,260 km2) runoff generation processes are sensitive to both antecedent soil moisture and ground cover. A higher runoff–ground cover correlation in drier months with sparse ground cover highlighted the critical role of cover at the onset of the wet season (driest period) and how runoff generation is more sensitive to cover in drier months than in wetter months. The monthly water balance analysis indicates that runoff generation in wetter months (January and February) is partially influenced by saturation overland flow, most likely confined to saturated soils in riparian corridors, swales, and areas of shallow soil. By March and continuing through October, the soil “bucket” progressively empties by evapotranspiration, and Hortonian overland flow becomes the dominant, if not exclusive, flow generation process. The results of this study can be used to better understand the rainfall–runoff relationships in dryland environments and subsequent exposure of coral reef ecosystems in Australia and elsewhere to terrestrial runoff.

Subsurface flow and storage dynamics at hillslope scale are difficult to ascertain, often in part due to a lack of sufficient high-resolution measurements and an incomplete understanding of boundary conditions, soil properties, and other environmental aspects. A continuous and extreme rainfall experiment on an artificial hillslope at Biosphere 2's Landscape Evolution Observatory (LEO) resulted in saturation excess overland flow and gully erosion in the convergent hillslope area. An array of 496 soil moisture sensors revealed a two-step saturation process. First, the downward movement of the wetting front brought soils to a relatively constant but still unsaturated moisture content. Second, soils were brought to saturated conditions from below in response to rising water tables. Convergent areas responded faster than upslope areas, due to contributions from lateral subsurface flow driven by the topography of the bottom boundary, which is comparable to impermeable bedrock in natural environments. This led to the formation of a groundwater ridge in the convergent area, triggering saturation excess runoff generation. This unique experiment demonstrates, at very high spatial and temporal resolution, the role of convergence on subsurface storage and flow dynamics. The results bring into question the representation of saturation excess overland flow in conceptual rainfall-runoff models and land-surface models, since flow is gravity-driven in many of these models and upper layers cannot become saturated from below. The results also provide a baseline to study the role of the co-evolution of ecological and hydrological processes in determining landscape water dynamics during future experiments in LEO.

Flooding in urban area affects the lives of people and could cause huge damages. In this study, a model is proposed for urban flood management with the aim of reducing the total costs. For this purpose, a hybrid model has been developed using SWMM and a quasi-two-dimensional model based on the cellular automata capable of considering surface flow infiltration. Based on the hybrid model outputs, stormwater management measure scenarios are proposed. In the next step, a damage estimation model has been developed using depth-damage curves. The amount of damage has been estimated for the scenarios in different rainfall return periods to obtain the damage and cost- probability functions. The conditional values at risk are estimated based on these functions which are the basis of decision making about the stormwater management scenarios. The proposed model is examined in an urban catchment located in Tehran, Iran. In this study, five scenarios have been proposed on the basis of different stormwater management measures. It has been found that the scenario of using permeable pavements results in the lowest risk. The proposed model enables the decision makers to choose the best scenario for the stormwater management with the minimum cost taking into account the risk associated with each scenario.