Watershed Flood Research Articles

An Assessment of the Integrated Multi-Criteria and New Models Efficiency in Watershed Flood Mapping

An Assessment of the Integrated Multi-Criteria and New Models Efficiency in Watershed Flood Mapping

Remote Sensing-Supported Flood Forecasting of Urbanized Watersheds—A Case Study in Southern China

Urbanization has significant impacts on watershed hydrology, but previous studies have been confirmatory and not comprehensive; in particular, few studies have addressed the impact of urbanization on flooding in highly urbanized watersheds. In this study, this effect is studied in Chebei Creek, a highly urbanized watershed in the Pearl River Delta, southern China. Landsat satellite images acquired in 2015 were used to estimate land use and cover changes using the Decision Tree (DT) C4.5 classification algorithm, while the Liuxihe model, a physically based distributed hydrological model (PBDHM), is employed to simulate watershed flooding and hydrological processes. For areas with high degrees of urbanization, the duration of the flood peak is only 1 h, and the flood water level shows steep rises and falls. These characteristics increase the difficulty of flood modeling and forecasting in urbanized areas. At present, hydrological research in urbanized watersheds generally focuses on the quantitative simulation of runoff from urban areas to the watershed, flood flows, peak flood flow, and runoff depth. Few studies have involved real-time flood forecasting in urbanized watersheds. To achieve real-time flood forecasting in urbanized watersheds, PBDHMs and refined underlying surface data based on remote sensing technology are necessary. The Liuxihe model is a PBDHM that can meet the accuracy requirements of inflow flood forecasting for reservoir flood control operations. The accuracies of the two flood forecasting methods used in this study were 83.95% and 97.06%, showing the excellent performance of the Liuxihe model for the real-time flood forecasting of urbanized rivers such as the Chebei Creek watershed.

Quantitative assessment of the impact of precipitation and vegetation variation on flooding under discrete and continuous rainstorm conditions

Precipitation and vegetation are two critical factors affecting floods in watersheds. A quantitative evaluation of their contribution to flooding is of great scientific significance for vegetation construction and water resource management. Thus, this study took the Pengchongjian small watershed, located in Southeastern China, as the research object. Based on geographic and hydrometeorological data from 1983 to 2017, the characteristics of rainstorm and flood in the watershed were analyzed. Meanwhile, the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) model was used to quantitatively calculate precipitation and vegetation variation contribution rates to the total flood volume and peak flow under discrete and continuous rainstorm conditions. The results indicated that the rainstorm volume and total flood volume showed a non-significant upward trend in the two periods of 1983–2003 and 2004–2017, and the variation coefficient of total flood volume was higher than that of rainstorm volume. At the same time, the periodic variation characteristics of rainstorm volume and total flood volume in 1983–2017 were basically consistent. The HEC-HMS model offered good simulation of discrete and continuous rainstorm floods, and the simulation accuracy met our requirements. Under the condition of discrete rainstorm, the average contribution rates of precipitation and vegetation variation to the total flood volume were 58.9 % and 41.1 %, and to the peak flow were 54.2 % and 45.8 %, respectively. In the case of continuous rainstorm, when the contribution rate of vegetation variation was positive, the average contribution rates of precipitation and vegetation variation to the total flood volume were 72.2 % and 27.8 %, and to the peak flow were 84.9 % and 15.1 %, respectively. When the contribution rate of vegetation variation was negative, the average contribution rates of precipitation and vegetation variation to the total flood volume were 125.0 % and −25.0 %, and to the peak flow were 132.1 % and −32.1 %, respectively. These results could provide a basis for the comprehensive management of soil and water loss, water resource planning, vegetation construction, and hydrological benefit evaluation.

Range Dependence of Polarimetric Radar Estimates for Extreme Flood-Producing Rainfall in Urban Watersheds

Abstract We examine polarimetric rainfall estimates of extreme rainfall through intercomparisons of radar rainfall estimates with rainfall observations from a dense network of rain gauges in Kansas City. The setting provides unique capabilities for examining range dependence in polarimetric rainfall estimates due to the overlapping coverage of the Kansas City, Missouri, and Topeka, Kansas, WSR-88D radars. We focus on polarimetric measurements of specific differential phase shift, KDP, for estimating extreme rainfall. Gauge–radar intercomparisons from the “close-range” Kansas City radar and from the “far-range” Topeka radar show that KDP can provide major improvements in estimating extreme rainfall, but the advantages of KDP rainfall estimates diminish with range. Storm-to-storm variability of multiplicative bias remains an important issue for polarimetric rainfall estimates; variability in bias is comparable at both close and far range from the radar. “Conditional bias,” in which peak radar rainfall estimates are lower than rain gauge observations, is a systematic feature of polarimetric rainfall estimates, but is more severe at far range. The Kansas City region has experienced record flooding in urban watersheds since the polarimetric upgrade of the Kansas City and Topeka radars in 2012. Polarimetric rainfall estimates from the far-range Topeka radar provide useful quantitative information on basin-average rainfall, but the ability to resolve spatial variation of the most extreme rain rates diminishes significantly with range from the radar.

Pattern of spatio-temporal variability of extreme precipitation and flood-waterlogging process in Hanjiang River basin

Based on the uniform distributed rainfall data in the Hanjiang River basin, the characteristics of spatio-temporal variability of extreme precipitation and flood-waterlogging process are diagnosed in terms of index definition and mathematical statistics. The study found that: (1) no significant trends of extreme rainfall for the entire basin were tested, but there was a jump variation in 1990s from abundance to dryness, accompanied by 20-year main periodic oscillation. Since 2000, the frequency of extremely heavy rain has increased significantly. (2) there is a spatial distribution pattern of lower in the northeast and higher in the southwest, and decreasing from upstream to downstream in the annual means of extreme precipitation. High-value centers are concentrated in the southwestern mountainous areas and the plains in the downstream, which are prone to form the distribution pattern of Central-North Radiation Type and Southeast-Northwest Type. (3) basin usually suffers 5–6 waterlogging processes per year, most of which were light or moderate grade, lasting 2–4 days. Since 2000, the flood-waterlogging process has shown evolution characteristics in increasing of the intensity, frequency, duration, and fluctuation, having strong continuity in the future. (4) affecting by climate and topography, spatial distribution of flood-waterlogging is uneven. Downstream and southwest of upstream are regularly the high-value centers, which are easy to form three kinds of distribution patterns, namely Northeast Radiation Type, Middle-lower Radiation Type and Middle-upper Radiation Type. Since the 21st century, the Hanzhong area and the watershed below Xiangyang both often encountered flood-waterlogging processes of high-intensity, long-duration, and high-frequency, which easy to induce flood disasters. Spatio-temporal variability of extreme precipitation and flood-waterlogging is a direct result of the rational development and utilization of water resources. We expect the results here to provide a deep insight into flood-waterlogging and serve as a reference for the watershed flood disaster response, rain-flood utilization, and optimal allocation of water resources.

Temporal downscaling of daily precipitation to 10 min data for assessment of climate change impact on floods in small-size watersheds applied to Jinju, South Korea

Temporal downscaling of daily precipitation to 10 min data for assessment of climate change impact on floods in small-size watersheds applied to Jinju, South Korea

Predictive multi-watershed flood monitoring using deep learning on integrated physical and social sensors data

This paper presents a deep learning model based on the integration of physical and social sensors data for predictive watershed flood monitoring. The data from flood sensors and 3-1-1 reports data are mapped and fused through a multivariate time series approach. This data format is able to increase the data availability (partially due to sparsely installed physical sensors and fewer reported flood incidents in less urbanized areas) and capture both spatial and temporal interactions between different watersheds and historical events. We use Harris County, TX as the study site and obtained seven historical flood events data for training, validating, and testing the flood prediction model. The model predicts the flood probability of each watershed in the next 24 hours. By comparing the flood prediction performance of three different datasets (i.e., flood sensor only, 3-1-1 reports only, and integrated dataset), we conclude that the physical-social data integrated approach can better predict the flood with an accuracy of 0.825, area under the receiver operating characteristics curve (AURC) of 0.902, area under the precision-recall curve (AUPRC) of 0.883, area under the F-measure curve (AUFC) of 0.762, and Max. F-measure of 0.788.

Position and Role of Agricultural Land in the New Policy of Integrated Watershed Flood Management of Japan

Position and Role of Agricultural Land in the New Policy of Integrated Watershed Flood Management of Japan

How Extreme Were The Torrential Floods In Smaller Watersheds In Serbia In September 2014

The September torrential floods in 2014 in the Eastern Serbia were a real disaster for local residents in municipalities of Kladovo, Negotin and Majdanpek. Meteorological extreme event caused the hydrological extreme event which led to declaration of the emergency situation in all three municipalities. The combined method of Soil Conservation Service and synthetic unit triangular hydrograph (SCS-SUH) is employed to compute the maximal discharges in small watersheds of Dupljanska reka and Manastiricki potok, in order to assess the extremeness of September 2014 torrential flood events. Since the surface runoff is accompanied by intensive soil erosion on watershed slopes and the maximal discharges by sediment transport in river beds, estimation of mean annual sediment transport is also presented in this work. The September 2014 floods will remain historic given that they took 5 human lives and caused enormous material damage for local municipalities, so presented hydrological analysis should be taken as very important part of flood event documentation along with reports of municipalities’ emergency headquarters.

Utilization and uncertainties of satellite precipitation data in flash flood hydrological analysis in ungauged watersheds

<p>Aim of the study is to examine the potential utilization of satellite precipitation data to estimate the peak discharges of flash floods in ungauged Mediterranean watersheds. Cumulative precipitation heights from local rain gauge and the GPM-IMERG were correlated in a scatter plot. The calculated linear equations were used to adjust the uncalibrated GPM-IMERG precipitation data in Thasos island (Northern Greece), to investigate the mechanisms of the flash floods recorded in November 2019 and to evaluate the significance of satellite precipitation data in hydrological modeling. The uncalibrated GPM-IMERG precipitation failed to explain the flash floods phenomena. The rain gauge data are reliable to accurately predict the peak discharges only in cases, where the rain gauges are within the study area. The strong correlation between ground rainfall data and satellite spatiotemporal precipitation data (R2 > 0.65), provides linear regression equations that, through their extrapolation and appliance to the rest of the flooded area, could adjust and correct the satellite data, optimizing the efficiency and accuracy of flash flood analysis, especially in ungauged watersheds. The proposed methodology could highly contribute to the optimization of flood mitigation measures establishment, flood risk assessment, hydrological and hydraulic simulation of flash flood events in ungauged watersheds.</p>

Spatiotemporal Evaluation of Flood Potential Indices for Watershed Flood Prediction in the Mississippi River Basin, USA

Spatiotemporal Evaluation of Flood Potential Indices for Watershed Flood Prediction in the Mississippi River Basin, USA

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.

Research on the Critical Rainfall of Flash Floods in Small Watersheds Based on the Design of Characteristic Rainfall Patterns

Research on the Critical Rainfall of Flash Floods in Small Watersheds Based on the Design of Characteristic Rainfall Patterns

Responses of Hydrological Processes under Different Shared Socioeconomic Pathway Scenarios in the Huaihe River Basin, China

The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides more scenarios and reliable climate change results for improving the accuracy of future hydrological parameter change analysis. This study uses five CMIP6 global climate models (GCMs) to drive the variable infiltration capacity (VIC) model, and then simulates the hydrological response of the upper and middle Huaihe River Basin (UMHRB) under future shared socioeconomic pathway scenarios (SSPs). The results show that the five-GCM ensemble improves the simulation accuracy compared to a single model. The climate over the UMHRB likely becomes warmer. The general trend of future precipitation is projected to increase, and the increased rates are higher in spring and winter than in summer and autumn. Changes in annual evapotranspiration are basically consistent with precipitation, but seasonal evapotranspiration shows different changes (0–18%). The average annual runoff will increase in a wavelike manner, and the change patterns of runoff follow that of seasonal precipitation. Changes in soil moisture are not obvious, and the annual soil moisture increases slightly. In the intrayear process, soil moisture decreases slightly in autumn. The research results will enhance a more realistic understanding of the future hydrological response of the UMHRB and assist decision-makers in developing watershed flood risk-management measures and water and soil conservation plans.

Connecting Hydrometeorological Processes to Low‐Probability Floods in the Mountainous Colorado Front Range

AbstractEstimating the probabilities of rare floods in mountainous watersheds is challenging due to the hydrometeorological complexity of seasonally varying snowmelt and soil moisture dynamics, as well as spatiotemporal variability in extreme precipitation. Design storm methods and statistical flood frequency analyses often overlook these complexities and how they shape the probabilities of rare floods. This study presents a process‐based approach that combines gridded precipitation, stochastic storm transposition (SST), and physics‐based distributed rainfall‐runoff modeling to simulate flood peak and volume distributions up to the 10,000‐year recurrence interval and to provide insights into the hydrometeorological drivers of those events. The approach is applied to a small mountainous watershed in the Colorado Front Range in the United States. We show that storm transposition in the Front Range can be justified under existing definitions of regional precipitation homogeneity. The process‐based results show close agreement with a statistically based mixture distribution that considers underlying flood drivers. We further demonstrate that antecedent conditions and snowmelt drive frequent peak discharges and rarer flood volumes, while the upper tail of the flood peak distribution appears to be controlled by heavy rainfall and rain‐on‐snow. In particular, we highlight the important role of early fall extreme rainfall in controlling rare flood peaks (but not volumes), despite only one such event having been observed in recent decades. Notwithstanding issues related to the accuracy of gridded precipitation datasets, these findings highlight the potential of SST and process‐based modeling to help understand the relationships between flood drivers and flood frequencies.

Constructed wetland management in urban catchments for mitigating floods

Wetlands in urban ecosystems provide significant environmental benefits. In the present study, the concept of urban constructed wetland development is studied from the viewpoint of urban planning with dynamic water level orifice setting controller. A two-step modelling procedure is carried out: (1) development of a hybrid model, by coupling a well-established two-dimensional hydrodynamic model (International River Interface Cooperative, iRIC) with a one-dimensional physically-based, distributed-parameter model (Storm Water Management Model, SWMM), to compute and map flood scenarios and to identify the flood-prone areas; and (2) use of SWMM to simulate the water inflow to the proposed constructed wetland, which acts as a cushion for storing excess flood water. The proposed methodology is implemented on the Jahangirpuri drain catchment located in Delhi, India. Results show that the hybrid model is effective, and the simulations are observed to be in good agreement with the recorded data, which assist in detecting the flood-prone areas. Further, an estimation of the impact of the proposed constructed wetland on catchment hydrology indicates an overall reduction of 23% in flooding adjacent to the channel with a significant reduction in backflow as well as water depth in the drain. The flapgate at the outlet of the wetland helps in maintaining the desired water depth in the wetland. The outcomes of this study will assist the hydrologists and administrators in urban stormwater management and planning to mitigate the impact of floods in urban watersheds.

Assessing the Impact of Land Use and Climate Change on Surface Runoff Response Using Gridded Observations and SWAT+

The Anthropocene period is characterised by a general demographic shift from rural communities to urban centres that transform the predominantly wild global landscape into mostly cultivated land and cities. In addition to climate change, there are increased uncertainties in the water balance and these feedbacks cannot be modelled accurately due to scarce or incomplete in situ data. In African catchments with limited current and historical climate data, precise modelling of potential runoff regimes is difficult, but a growing number of model applications indicate that useful simulations are feasible. In this study, we used the new generation of soil and water assessment tool (SWAT) dubbed SWAT+ to assess the viability of using high resolution gridded data as an alternative to station observations to investigate surface runoff response to continuous land use change and future climate change. Simultaneously, under two representative concentration pathways (RCP4.5 and RCP8.5), six regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment Program (CORDEX) and their ensemble were evaluated for model skill and systematic biases and the best performing model was selected. The gridded data predicted streamflow accurately with a Nash–Sutcliffe efficiency greater than 0.89 in both calibration and validation phases. The analysis results show that further conversion of grasslands and forests to agriculture and urban areas doubled the runoff depth between 1984 and 2016. Climate projections predict a decline in March–May rainfall and an increase in the October–December season. Mean temperatures are expected to rise by about 1.3–1.5 °C under RCP4.5 and about 2.6–3.5 °C under RCP8.5 by 2100. Compared to the 2010–2016 period, simulated surface runoff response to climate change showed a decline under RCP4.5 and an increase under RCP8.5. In contrast, the combine effects of land use change and climate change simulated a steady increase in surface runoff under both scenarios. This suggests that the land use influence on the surface runoff response is more significant than that of climate change. The study results highlight the reliability of gridded data as an alternative to instrumental measurements in limited or missing data cases. More weight should be given to improving land management practices to counter the imminent increase in the surface runoff to avoid an increase in non-point source pollution, erosion, and flooding in the urban watersheds.

Study of the effects of check dam construction on the Limau Manih river using GIS

The problem of flooding or flash floods in watersheds often occurs, both in large rivers and small rivers. The frequency of occurrence varies greatly depending on the local climate. As a result of flooding, it causes more erosion and collapse of riverbanks and washes away all kinds of materials, such as wood, sand, and stone. Sometimes it also submerges rice fields, villages, and houses downstream of the river. Likewise, in the study location of the Limau Manih river, there has been a flood with a large discharge. It was recorded twice a year, namely in July and September 2012. One of the ways to reduce energy, scour, and avalanches on riverbanks is by building a check dam or weir. A check dam is expected to reduce the energy that arises due to differences in elevation or slope of the river channel. But on the other hand, the weir will raise the water level, so that the puddle becomes wider. Therefore, it is necessary to study the impact of weir construction on the extent of inundation. The study was conducted using ArcGIS to map inundation and HEC-RAS to simulate water levels along the river. Simulations were carried out for several return periods of rainfall events. The results of the study show that the planned flood discharge in the river of Limau Manih watershed is quite large. Meanwhile, from the simulation results, the inundation area is not too significant with the increase in the return period and the planned flood discharge. Although the simulation of the weir without widening the upstream part of the weir shows a significant increase in inundation area. This is most likely due to the weir in the upstream area of the river which is rather steep, so that water flows quickly through the overflow of the weir. However, the water level in the weir is higher than without the weir, as a result, water jumps and erosion occur downstream of the weir. For this reason, it is very necessary to monitor the scour behavior in the weir and the sedimentation rate, because this area has the potential to be eroded.

Multi-source spatial-temporal information fusion for urbanized watershed flood hazard risk assessment

Urbanization is taking place rapidly in developing countries. There are many problems of urbanization, and flood hazard is one of the serious problems. Flooding events and hydrologic process are complex in urbanized watershed. Current methods for urbanized watershed flood hazard risk assessment exist shortage. With the advance of observation technology and geographic information system technology, the multi-source spatial-temporal data have been collected increasingly. The data driven methods have provided a potential solution for the complex urbanized watershed flood hazard risk assessment. The paper has discussed the complexity of urbanized watershed flood hazard. The framework of observation based modeling of urbanized watershed flood hazard risk assessment has been given. Multi-source spatial-temporal information fusion approach for watershed flood hazard risk assessment has been discussed. A case of Hanyang urbanized watershed has been developed based on the multi-source information fusion method of local weighted linear combination.

Application of GA-BP neural network model for small watershed flood forecasting in Chun’an county, China

Flood forecasting for small basins in hilly areas is often plagued by poor performance of hydrological models due to lack of observed data, meanwhile, the traditional Back Propagation (BP) neural network is easy to fall into the local minimum. This paper put forward an approach combined Genetic Algorithms (GA) with BP neural network and established a GA-BP neural network model to promote the flood forecasting. The flood hygrograph of Fenglingang small watershed, in Chun’an county, simulated by GA-BP model indicates that the deviation of runoff volumes is controlled within 10%, the deviation of peak discharge is kept below 20%, and absolute error of time to peak is less than 2h. Additionally, the correlation coefficient of simulation result of GA-BP model for each rainstorm event is above 0.75, which is smaller than that of traditional BP model. Consequently, it is demonstrated that the GA-BP model has a higher simulation precision and can provide reference for local forecasting in the future.