Peak Rainfall Intensity Research Articles

Nonlinear storage–discharge dynamics of forested headwater catchment: A hysteresis index approach

AbstractThe relationship between catchment storage and discharge is nonlinear. The dynamicity of this relationship is dependent on the distance of the storage measurement from the stream, the depth of the soil moisture (SM) measurement, antecedent SM storage, and precipitation characteristics. Understanding the relative influence of these factors is critical for interpreting runoff generation processes and predicting discharge. In this study, we used a hysteresis index approach and analysed the nonlinear dynamics of catchment storage–discharge relationship across points, hillslope and catchment scales, and their controlling factors. A small headwater forested catchment located in the southern part of the Sierra Nevada region, California, was selected as a case study. In this Mediterranean catchment, the anticlockwise class IV hysteresis loop, indicating an earlier discharge peak than SM, was observed as a prevalent hysteresis class across all the scales, irrespective of seasons (i.e., dry vs. wet) and years (i.e., normal vs. drought). A few clockwise hysteresis loops were observed at the shallow depths (10 and 30 cm) of upslope and lower slope topographic positions. Further, we found a shorter lag time between SM peak to discharge peak at 60 and 90 cm soil depths during the wet season, and during the drought period. The shorter lag at the deeper depth was due to the presence of subsurface flow during high antecedent SM storage conditions and preferential flow through the soil pores during the drought periods. The variability in hysteresis at catchment and hillslope scales was controlled by peak rainfall intensity and antecedent SM storage. However, rainfall characteristics (intensity and depth) were major governing factors for most of the point scale locations. Overall, the current study highlighted the role of SM sensor's location in characterizing storage–discharge behaviour.

Modeling of some hydrodynamics characteristics of Orashi River in the Southern Region of Nigeria

The dynamic nature of rivers and its effect on the inhabitants and its surroundings is of fundamental concern to Hydro Scientists, Coastal Engineers, and Surveyors. These changes have remained unpredicted and hence the focus of this study. The aim of this study is to develop a geo-model of the dynamics of Orashi River in Ogba/ Egbema/ Ndoni Local Government Area of Rivers State. The objectives are to determine the river dynamics parameters, to develop a functional multiple regression model of Orashi River dynamics and to determine the percentage contributions of each of the independent variables. This study was carried out using classical survey methods such as river current measurement, river cross-sectional area determination, river water discharge rate determination, bathymetric operation, runoff water computation, water gauge reading and conventional ground surveying. A multiple regression analysis was carried out using data obtained in 2013, 2015 and 2017 used as the independent variables while the river level changes is used as the dependent variable. The result of this study show changes in the river current and river discharge. The data analysis show the contribution of the independent variable to the dynamics of Orashi River for 2013 to be 24.83%, 30.63%, 20.24%, 0.33% and 24.97% for river current, runoff peak discharge, rainfall intensity, temperature and humidity respectively. The analysis was also carried out in 2015 & 2017and different results were obtained. A functional model of the dynamics of the changes in Orashi River was developed for 2013, 2015 and 2017 which can be used in monitoring the changes in Orashi River.

Evaluating the soil hydraulic functions of vegetation restoration in a subtropical hilly catchment: Insights from continuous soil moisture monitoring

The global trend of vegetation “greening” in the context of ecological restoration necessitates an urgent assessment of ecosystem services. As essential components of ecosystem services, the hydraulic functions of soil in infiltrating and retaining water following vegetation restoration remain unclear, especially in subtropical mountainous and hilly areas with complex topographies. From 2018 to 2021, soil moisture data collected at five-minute intervals were monitored for three restoration strategies in a hilly catchment of China’s Three Gorges Reservoir area. The restoration strategies included planted forest (PF) and natural restoration (naturally regenerated forest, NF; deforested pasture, DP). The soil moisture response to rainfall under these strategies was evaluated using several metrics, including the time difference between peak rainfall intensity and peak soil moisture response (Tp2p), cumulative infiltration, and occurrence frequency of preferential flow (PRF). The results showed that the average soil moisture content (SMC) of PF was significantly (p < 0.05) lower than that of NF and DP in both dry and wet seasons, regardless of upslope or downslope location. However, the topographic position affected the difference in average SMC between DP and NF. At the downslope location, the average SMC of DP (0.39 cm3 cm−3) was significantly higher than that of NF (0.33 cm3 cm−3). Conversely, at the upslope location, the average SMC of DP (0.27 cm3 cm−3) was lower than that of NF (0.30 cm3 cm−3). These findings suggested that PF had a lower amount of soil water storage than NF and DP, which was supported by the lowest cumulative infiltration in PF during storm events. The response of soil moisture to storms in PF (Tp2p = 3.1 h) was slower than that in NF (Tp2p = 1.9 h) and DP (Tp2p = 2.5 h). This was consistent with the lower occurrence frequency of preferential flow in PF (PRF = 19.2 %) than NF (PRF = 39.2 %) and DP (PRF = 32.9 %). Therefore, longer response time and less preferential flow indicated that the PF had a relatively poor soil moisture responsiveness to storm events. Accordingly, this study highlights the insufficiency of afforestation in soil water infiltration and retention compared to natural restoration, meriting consideration when assessing soil hydraulic functions in vegetation restoration areas.

Early warning analysis of mountain flood disaster based on Copula function risk combination

Mountain torrent disaster prevention is the focus of flood control and disaster reduction in China. Critical rainfall is an important indicator to determine the success or failure of mountain torrent disaster early warning. In this paper, the M-Copula function is introduced, the multi-dimensional joint distribution of critical rainfall is constructed, and the joint distribution of rainfall and peak rainfall intensity is analyzed. Taking A village in Xinxian County as an example. The critical rainfall of the combined probability is calculated, and the critical rainfall of the flash flood disaster water level, the pre-shift warning and the sharp-shift warning is warned and analyzed. The results show that the flood peak modulus calculated by Yishangfan group is 8.89, which has certain rules for the flood peak modulus of rivers in hilly areas. The larger the basin area is, the smaller the flood peak modulus is, the smaller the area is, and the larger the flood peak modulus is. The calculation result of the design flow of 533 m3/s is reasonable. It is reasonable and reliable to select the M-Copula function as the connection function to fit the joint distribution of rainfall and peak rainfall intensity, which can provide theoretical support for flash flood disaster warning in other regions.

Inter-event and intra-event dynamics of microplastic emissions in an urban river during rainfall episodes

Urban rivers represent the major conduits for land-sourced microplastics in the global oceans, yet the real-time dynamics of their emissions in rivers during rainfall (and runoff) events are poorly understood. Herein, we report the results of high-frequency sampling of microplastic particles (MPs) and fibers (MPFs) in the surface water of an urban river in Japan over the course of three rainfall events (i.e., light, moderate, and heavy rainfalls). The event mean concentrations (EMCs) of MPs amounted to 35,000 items/m3, 929,000 items/m3, and 331,000 items/m3; and the corresponding total loads were 0.5 kg, 19.8 kg, and 35.0 kg for light, moderate and heavy rainfalls, respectively. The inter-event total loads of MPs correlate well with the total rainfall, while the concentrations were linked with the number of antecedent dry days. The dynamic trends show that <2000 μm MPs displayed first flush effects during light to moderate rainfall events (>50% mass discharged with the initial 20–40% of flow). Small-sized MPs (10–40 μm) mobilized rapidly at lower rainfall intensities, whereas MPs over 2000 μm discharged immediately after the peak rainfall intensity. Moreover, <70 μm MPs depicted a surge following heavy rainfall events due to turbulent flow conditions reverting the deposited MPs into suspension. Overall, the three events increased the loads by 4–110 folds, and EMCs by 10–350 folds compared to the concentrations during dry weather while portraying a significant impact on 300–1000 μm MPs. The dynamics of MPs were correlated with those of suspended solids in river water, and the characteristics were comparable to the same of road dust sampled in Japan. Although the dynamic trends between MPs and MPFs in river water were comparable, MPFs were relatively less impacted by rain, likely due to the intervention of separate sewer systems in the study area.

Improving the model robustness of flood hazard mapping based on hyperparameter optimization of random forest

Traditional machine learning algorithms face challenges in assessing flood susceptibility reliably due to their low robustness and the inherent 'black-box' nature. This paper utilizes five hyperparameter optimization algoirthms (HPO), namely grid search (GS), random search (RS), gauss process (GP), tree-structured parzen estimator (TPE) and simulated annealing (SA), to tune the traditional random forest’s (RF) hyperparameters to improve the robustness of flood hazard mapping (FHM) models at Ningxiang City Hunan Province, China. Additionally, SHapley Additive exPlanations (SHAP) method were used to interpret the decision-mechanisms of these flood hazard models. This study considers 19 pluvial flood influencing factors and 2064 flood locations to create a geospatial database. The performance of each hybrid model was evaluated by area under the receiver operating characteristic (ROC) curve (AUC) and several validation methods. The results demonstrate that the developed hybrid models demonstrated good performance, with RF-TPE achieving the highest AUC (0.9660), followed by RF-GP (0.9648), RF-SA (0.9624), RF-GS (0.9612), RF-RS (0.9600), and RF (0.9539). The RF-TPE model exhibits superior robustness than other models, and the FHM constructed using it is more reliable. HPO is an effective approach to improve the predictive accuracy and robustness of FHM models. When considering limited computational resources, Bayesian optimization (TPE) should be prioritized for optimizing FHM models, followed by metaheuristic algorithms and model-free algorithms. Moreover, the study revealed that distance from river, peak rainfall intensity, continuous rainfall, antecedent effective rainfall, and terrain relief, are the most significant for pluvial FHM modeling in this region.

Thinking inside the box: Investigating peak storm response in a simplified outdoor slope setup

Hillslopes are the fundamental building blocks of catchments, however, fully disentangling their hydrological response remains an outstanding challenge. In natural settings, hillslope hydrology is challenged by the interplay of several factors, such as, heterogeneous morphology, vegetation, and complex soils. Herein, we attempt to clarify the mechanisms that control the rainfall-runoff relationship by investigating the response of an artificial outdoor slope setup with uniform geometry, soil properties, topography, and in the absence of vegetation located in a Mediterranean climate. Based on the analysis of more than 100 storms across six consecutive hydrological years, our results demonstrate that either pre-event soil water content or rainfall conditions alone are not sufficient to explain the slope response. Seasonality played a major role through soil moisture conditions, and pre-event soil moisture, peak rainfall intensity and rainfall duration influenced the slope runoff response. Observations suggest that in fall and winter subsurface flow was the prevalent runoff mechanism, while infiltration excess overland flow mostly occurred in the dry season. Our findings show that continuous measurement of runoff could be beneficial to understand the peak rainfall-peak runoff relationship.

Landslides triggered by an extraordinary rainfall event in Central Italy on September 15, 2022

Timely and systematic collection of landslide information after a triggering event is pivotal for the definition of landslide trends in response to climate change. On September 15, 2022, a large part of central Italy, particularly Marche and Umbria regions, was struck by an anomalous rainfall event that showed characteristics of a persistent convective system. An extraordinary cumulated rainfall of 419 mm was recorded by a rain gauge in the area in only 9 h. The rainfall triggered 1687 landslides in the area affected by the peak rainfall intensity and caused widespread flash floods and floods in the central and lower parts of the catchments. In this work, we describe the characteristics of the landslides identified during a field survey started immediately after the event. Most of the mass movements are shallow, and many are rapid (i.e., debris flows, earth flows) and widely affecting the road network. Landslide area spans from a few tens of square meters to 105 m2, with a median value of 87 m2. Field evidence revealed diffuse residual risk conditions, being a large proportion of landslides located in the immediate vicinity of infrastructures. Besides reporting the spatial distribution of landslides triggered by an extreme rainfall event, the data collected on landslides can be used to make comparisons with the distribution of landslides in the past, validation of landslide susceptibility models, and definition of the general interaction between landslides and structures/infrastructures.

Debris flow occurrence under changing climate and wildfire regimes: A southern California perspective

In southern California, wildfire incidents are increasing in frequency and magnitude. There is a great deal of research predicting the impact of climate change on large scale fire regimes. These impacts, now the new normal, include increases in future fire incidents and burn severities. Yet predictive studies on how ecosystems respond to changing fire regimes are lacking. Identifying trends in post-fire erosion patterns, such as post-fire debris flows, is critical for increasing human resiliency and adaptation to changing weather patterns and eventual long-term climate change. The relative historical abundance of post-fire debris flows in southern California presents this concern: will worsening wildfire regimes result in more post-fire debris flows? Our research addresses this concern by utilizing a descriptive approach to analyze post-fire debris flows in southern California from 2001 to 2018 to elucidate possible trends. We found that 84 % of debris flows originate from watersheds with soils burnt at moderate soil burn severity. We found that debris flow occurrence is more reliant on the percent coverage of a soil burn severity classification than its areal extent. Further, larger fires also tended to produce more debris flows and most (73 %) debris flow watersheds occurred in predominantly bedrock areas. Our research suggests that the number of post-fire runoff-generated debris flows in the region will increase if fires continue to increase in size, occur on slopes of at least 20°, burn south-facing watersheds with bedrock of igneous or sedimentary origin to at least a moderate severity, and peak rainfall intensity return intervals do not change. These findings highlight the interconnectivity between natural erosive processes, wildfires, humans, and climate change.

Design Hyetograph for Short-Duration Rainstorm in Jiangsu

The rainstorm intensity formula and the design of rainstorm hyetographs are important aspects in drainage design standards. Against the backdrop of climate change, in most cities in Jiangsu Province of China, significant trends of increasing intensities of heavy rainfall are apparent. The parameters of the rainstorm intensity formula are no longer applicable in the current context of significantly stronger rainstorms. To adapt to this change, we first contrasted the fitting accuracy of the Gumbel distribution, the exponential distribution, and the Pearson III distribution for the rainstorm intensity formula in Jiangsu. It was found that the Gumbel distribution has the smallest relative mean square error in most cities, proving that it provides the best estimation of rainstorm intensity formula parameters. Therefore, the rainstorm intensity formula parameters for 13 cities was revised using the Gumbel distribution based on the rainfall data from 1991 to 2020. Then, the precipitation with a 100-year return period was calculated using the revised formula. Moreover, to compensate for the lack of storm hyetographs that have been designed for Jiangsu, we designed short-duration rainstorm hyetographs for 13 cities using the Chicago hyetograph method and the Pilgrim and Cordery (PC) method. The results show that most of the short-duration rainstorms lasted between 45 and 120 min and were dominated by single-peaked patterns, with the peak position typically occurring in the first half of the rainfall cycle. The peak coefficient distribution of short-duration rainstorms shows that short-duration rainstorms in the south reached their peak rainfall intensity earlier than those in the north. On this basis, using the Chicago method and PC method, short-duration storm hyetographs were designed, which could be used in the design of drainage systems to provide support in effectively reducing urban flood threats. By comparing the hyetographs with real short-duration rainstorm patterns, it was found that the precipitation process designed using the PC method was most similar to the actual precipitation process. However, the PC method was found to be highly dependent on local precipitation data, whereas only the rain peak coefficient is required to design the Chicago rainstorm hyetograph. Therefore, we primarily recommend hyetographs designed using the PC method for Jiangsu Province’s 13 major cities, while we recommend the Chicago hyetograph for the surrounding areas of the 13 cities that have no meteorological stations or lack data.

An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost

The machine-learning “black box” models, which lack interpretability, have limited application in landslide susceptibility mapping. To interpret the black-box models, some interpretable machine learning algorithms have been proposed recently. Among them is SHaply Additive ExPlanation (SHAP), which has attracted much attention because of its ease of operation and comprehensiveness. In this study, a novel interpretable model based on SHAP and XGBoost is proposed to interpret landslides susceptibility evaluation at global and local levels. The established evaluation model provided 0.75 accuracy and 0.83 AUC value for the test sets. The global interpretation shows that the peak rainfall intensity and elevation are the dominant factors that influence the occurrence of landslides in the study area. The combination of local interpretation and field investigations can provide a comprehensive framework for evaluating designated landslides, and it can also be used as a reference for preventing and managing the hazards of landslides.

Physical structure and rainfall controls on subsurface hydrological connectivity in hillslope-riparian-stream continuums

The hydrological connectivity of hillslope-riparian-stream (HRS) continuums is crucial for runoff generation and solute transport. The achievement of water resource protection and water quality improvement requires a systematic understanding of the structure and rainfall controls on HRS connectivity. Herein, two HRS continuums with different soil depths and slopes (HRS-1: thin soil depth and steep slope; HRS-2: thick soil depth and gentle slope) were established. We monitored the soil moisture from the surface to the soil-bedrock interface at 15 min intervals from March to June 2021. The HRS connectivity was analyzed based on soil saturation conditions, and partial least squares regression (PLSR) was used to reveal the relationships between rainfall and HRS connectivity. The results showed that the time required to establish hydrological connectivity in HRS-1 was shorter than that in HRS-2, which indicated that the contribution to runoff of the HRS continuum with a thin soil depth and steep slope was dominant during the early stage of rainstorm. As rainfall intensity increased, the required time was shortened exponentially due to the changes in hydrological connectivity patterns. In addition, the higher connectivity strength (i.e., the magnitude of HRS connectivity) was observed in the HRS-2 than that in the HRS-1 during heavy rainfall events. The PLSR analysis showed that rainfall amount, 30 min maximum rainfall intensity, 15 min maximum rainfall intensity, and rainfall duration were important controls affecting connectivity strength. Rainfall amount and peak rainfall intensity exerted more important effects than did antecedent soil moisture on the connectivity strength. Furthermore, there was a clear rainfall threshold for HRS connectivity, from 14.8 mm in HRS-1 to 21.1 mm in HRS-2. The increased soil depth and reduced slope enhanced the rainfall threshold of HRS connectivity. Our results indicate that the physical structure of the HRS continuum exerts a primary control on the rainfall threshold.

Brief communication: Introducing rainfall thresholds for landslide triggering based on artificial neural networks

Abstract. In this communication we show how the use of artificial neural networks (ANNs) can improve the performance of the rainfall thresholds for landslide early warning. Results for Sicily (Italy) show how performance of a traditional rainfall event duration and depth power law threshold, yielding a true skill statistic (TSS) of 0.50, can be improved by ANNs (TSS = 0.59). Then we show how ANNs allow other variables to be easily added, like peak rainfall intensity, with a further performance improvement (TSS = 0.66). This may stimulate more research on the use of this powerful tool for deriving landslide early warning thresholds.

The Determination of Rainfall Threshold Triggering Landslides Using Remote Sensing

Landslide is one of the natural disasters that can cause a lot of loss, both material and fatalities. Banjarnegara Regency is one of Central Java Province regencies where landslides often occur due to the region's topography and high intensity rainfall.. Therefore, it is necessary to determine the threshold of rainfall that can trigger landslides to be used as an early warning for landslides. The rainfall data used for the threshold is daily and hourly rainfall intensity from remote sensing data that provides complete data but relatively rough resolution. So that remote sensing data need to be re-sampled. The remote sensing data used is CMORPH satellite data that has been re-sampled for detailing existing information of rainfall data. The resampling method used is the bilinear method and nearest neighbor by choosing between the two based on the highest correlation. Threshold calculation using Cumulative Threshold (CT) method resulted equation P3 = 7.0354 - 1.0195P15 and Intensity Duration (ID) method resulted equation I = 1.785D-0305. The peak rainfall intensity occurs at the threshold of 97-120 hours before a landslide occur.

A complex network theory based approach to better understand the infiltration-excess runoff generation thresholds

Understanding hillslope-scale surface runoff generation processes are challenging due to the non-linear interactions among the different hydro-meteorological variables. Often the complex connection between different hydrologic variables is little understood (i.e., the spatial–temporal connection among the same variable and between two different variables). Therefore, in this paper, we present a network theory-based approach to understand the complex connections of hydrological processes. The article mainly focuses on identifying runoff thresholds in grassed (GA) and agro-forested (AgF) hillslopes and on testing of suitability of network theory for explaining the hillslope hydrological processes. After analyzing 22 rainfall events of AgF and 37 events from GA hillslope, our findings highlight that the runoff generation is associated with the peak rainfall intensity, initial soil moisture conditions and rainfall duration. The runoff thresholds of GA hillslope are higher than the AgF hillslope due to its surface resistance and presence of extremely low soil hydraulic conductivity (<6 mm/h) at the upslope location. Further, from network analysis, we found that the network's topology was highly dependent on event characteristics and soil hydraulic conductivity. The strong network connectivity was observed between runoff and soil moisture nodes during high runoff generating events as the runoff and infiltration take place simultaneously. However, during the low runoff generating events, a weak connection was observed between runoff and soil moisture nodes as most rainwater infiltrates over the hillslope. The outcomes of this study indicate the usefulness of network theory in hillslope-scale runoff process understanding to identify the significant connections.

Impacts of Climate Change on Urban Drainage Systems by Future Short-Duration Design Rainstorms

The adverse impacts of climate change and urbanization are converging to challenge the waterlogging control measures established in the Dong Hao Chong (DHC) Basin. Based on representative concentration pathway (RCP) scenarios, the future (2030–2050) waterlogging was assessed for the DHC basin and combined with future design rainfall. The delta change factors were projected using the regional climate model, RegCM4.6, and the annual maximum one-day rainstorm was modified to develop the annual maximum value method. By combining the delta change and annual maximum value methods, a future short-duration design rainstorm formula is developed in this study. The Chicago hyetograph shapes indicated that the peak rainfall intensity and amount both increase in the five return periods with two RCP scenarios. The InfoWorks ICM urban flood model is used to simulate the hydrological response. The results show that climate change will exacerbate urban waterlogging in DHC Basin. The maximum inundation volume and number of inundation nodes were expected to increase in the five return periods under the RCP4.5 and RCP8.5 scenarios, respectively. The submerged area is increasing due to climate change. This study highlights the link between climate change and urban drainage systems, and suggests that the effect of climate change in extreme rainfall should be considered in urban waterlogging management and drainage system design.

Modelling the effect of rainfall patterns on the runoff control performance of permeable pavements.

With the implementation of low impact development (LID) in urban areas, it is necessary to quantify the actual effectiveness of LID facilities. In this study, a coupled hydrology-hydrodynamic numerical model was utilized to investigate the runoff control effectiveness of permeable pavements in the city centre of Shijiazhuang, China. Two groups of designed rainfall events with the same duration but different rainfall amounts and peak rainfall intensity locations were presented, and the effectiveness of permeable pavement was demonstrated by the reduction in the total runoff volume, water depth, and inundated area. The results indicate that the rainfall amount is the main factor affecting the runoff control of permeable pavements, and their effectiveness decreases with increasing rainfall amounts and peak intensity coefficients. Moreover, permeable pavements are more effective in reducing the residential waterlogging area, and the proportion of the inundated area above a depth of 0.2 m is considerably diminished. This study reveals the response of the runoff control of permeable pavements to different rainfall patterns, which is essential for supporting the design and practical operation of permeable pavements.

Modelling the hydrological responses of green roofs under different substrate designs and rainfall characteristics using a simple water balance model

In spite of many well-known benefits of green roofs, their widespread adoption as source control measures in urban stormwater management requires the use of adequate modelling tools. The purpose of this study was to develop a simple water balance model for green roof runoff simulation based on conceptual hydrological processes and integrated into green roof designs. The model was calibrated and validated with a pilot experimental dataset that included various green roof configurations. The validated model was applied to simulate the hydrological responses of green roofs under different substrate designs and rainfall characteristics. Results showed that the model Nash-Suttcliffe efficiency (NSE) values under calibration and validation period ranged from 0.933 to 0.982, and the volume errors of relative percentage difference (RPD) varied from −1.45% to 5.35%, which indicated the simulated runoff processes of green roofs were satisfactorily accurate. The sensitivity analysis of parameters (e.g., increased parameters by 50%) showed the most sensitive parameter for simulating green roof runoff was the initial substrate water content, which changed by −74.5% for runoff volume and 58.5% for time to runoff. For a 50% increase in the saturated substrate hydraulic conductivity, it increased runoff volume by 60.2%; as well as the substrate depth increased runoff volume by 56.5% and changed time to runoff by −46.3%. The simulated hydrograph indicated that decreasing the saturated substrate hydraulic conductivity distinctly delayed time to runoff and increased runoff retention. Similarly, the runoff rate declined and runoff generation time was delayed with an increase in the substrate depths. Runoff retention percentages of the green roofs declined as rainfall depths increased. With rainfall intensity decreasing, mean time to runoff of the green roofs significantly increased from 30.0 min to 113.3 min. Runoff detention effects of green roofs were distinctly enhanced with the peak rainfall intensity delayed. This study provided a simple modelling tool for simulating hydrological responses of green roofs under a wide set of substrate characteristics and rainfall conditions in order to guide green roof design.

Is flow control in a space-constrained drainage network effective? A performance assessment for combined sewer overflow reduction

Recurring combined sewer overflows (CSOs) can have a significant impact on the ecological condition of receiving water bodies. There are several structural measures, like adding retention basins and switching to low impact development solutions, that have been proposed to reduce the number of sewage overflows. Besides, several flow control strategies have been discussed in scientific literature that take advantage of the space within urban drainage networks, which is assumed to be adequate, for temporary storage. The adequacy of such storage space, however, is not a universally valid assumption as a large fraction of drainage networks frequently operate close to their design discharge. In this paper, we investigate the efficacy of flow control for a space-constrained drainage network. We employ a low-cost, heuristic real time control strategy with the use of flow control devices (FCDs) and available in-sewer space to reduce the magnitude of CSOs. We consider the performance of the proposed control strategy and discuss the effect of FCD location on CSO reduction. Our results, based on over 300 rainfall-event simulations, show that the flow control strategy using limited sewer capacity is more efficient during relatively small rainfall events, where the CSO is large enough to enable reduction using the chosen control rules. The CSO is reduced, to varying degrees, for around 80% of rainfall events with peak intensity between 10 and 20 mm h−1. For larger rainfall events, the flow control is more unstable in response to abrupt water release during control operation, which seems to be unavoidable because of the water accumulation effect and the transition to pressurized pipe flow in space-constrained networks. We also found that the flow control performance is highly sensitive to the FCD location – as it depends on the interplay of the peak rainfall intensity and the water level condition immediately upstream of the FCD. The efficacy of a location for flow control is determined by the unfilled capacity (i.e., effective in-sewer storage potential) in the pipe upstream of the FCD during the rainfall peak; furthermore, the location also has to be resistant to the water accumulation effect. Using our analysis, we substantiate two anticipated caveats to flow control strategies when the storage space is limited in a drainage network: diminished performance in CSO reduction and the appearance of additional control-related challenges, which are otherwise mitigated in more spacious networks.

Study on the early warning and forecasting of flash floods in small watersheds based on the rainfall pattern of risk probability combination

Flash floods cause great harm to people’s lives and property safety. Rainfall is one of the main causes of flash floods in small watersheds. The uncertainty of rainfall events results in inconsistency between the traditional single rainfall pattern and the actual rainfall process, which poses a great challenge for the early warning and forecasting of flash floods. To carry out the effective flash flood early warning and forecasting, this paper proposes a novel rainfall pattern by coupling total rainfall and peak rainfall intensity based on copula functions, i.e., the rainfall pattern of risk probability combination (RPRPC). On this basis, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) hydrological model is used to simulate the rainfall-runoff process, the trial algorithm is used to calculate the critical rainfall (CR), and the optimistic-general-pessimistic (O–G-P) early warning mode considering the decision maker's risk preference is proposed. The small watershed of Xinxian in Henan province, China, is taken as a case study for calculation. The results show that the RPRPC is feasible and closer to the actual rainfall process than the traditional rainfall pattern, Frank copula function is the best for determining the joint distribution function of total rainfall and peak rainfall intensity, and the HEC-HMS model can be applied to small watersheds in hilly areas. Additionally, both RPRPC and antecedent soil moisture condition (ASMC) have influence on CR, and the variation of RPRPC will change the influence of ASMC on CR. Finally, the effectiveness of O–G-P early warning mode is verified.