Urban Hydrological Models Research Articles

Modeling Three-Dimensional Exfiltration Rates from Permeable Street Stormwater Inlets as One-Dimensional Water Flux in Urban Hydrological Models

Climate change has increased the intensity and frequency of weather systems, increasing the risk of inundation in urban areas. To mitigate these risks, not only rivers but also entire catchments need to be managed, and the use of infiltration and retention units needs to be expanded. The ability to evaluate the effects of promoting infiltration and retention in catchments using distributed hydrological models, clarify the three-dimensional behavior of exfiltration from catchments into natural base soils, and parameterize this flow as a one-dimensional hypothetical water flux is essential. Using VGFlow2D (Forum8) and field observations, numerical analyses were conducted to parametrize the flux and assess the features of q/Ks values, representing the volume of three-dimensional water exfiltration from stormwater inlet bases into natural soils relative to the saturated hydraulic conductivity (Ks) of the soils. The findings were integrated into the hydrological model Infoworks ICM (Innovyze) by adding a single parameter, the “exfiltration loss rate”, to each inlet without increasing computational demands. The obtained q/Ks values were compared to previously reported values, and variations were evaluated using infiltration theory.

Isotope-based source assessment of water flowing from storm sewer systems to a receiving river during dry weather periods

Urban stormwater management systems, particularly storm sewers, are critical for managing runoff in urban areas. These systems are designed to function during wet weather events; however, field-based observations of these systems suggest that they may also be active flow pathways in dry weather conditions, ultimately contributing to streamflow. Unlike dry weather flow in wastewater systems, storm sewer dry weather flow has not been thoroughly explored. This research used stable isotopes of oxygen and hydrogen in water to examine the sources of dry weather flow from storm sewers in a highly urban catchment. A stable isotope mixing model was applied at the outfalls of two stormwater catchments and the receiving Black Creek, located in Toronto, Canada. Findings suggest that during dry periods, storm sewers receive non-stormwater inputs from tap water, wastewater, and groundwater, along with some precipitation, and that these sources may constitute up to 19 % of Black Creek's flow at the watershed scale. Seasonal patterns in flow and water sources were observed for the Black Creek and outfalls. At one outfall, dry weather flow was predominantly from the water distribution system (i.e., tap water and/or wastewater) throughout spring, summer, and fall. In contrast, at the second outfall, groundwater dominated in spring and summer, and groundwater and water distribution were equally proportioned in fall. Black Creek baseflow comprises a dynamic mix of water sources that at times are similar to the sources observed at the stormwater outfalls. Considering these findings, future work should incorporate strategic sampling of additional outfalls, and multiple years of data collection to explore inter-annual variability in these processes and focus on replicating a similar study in other urban watersheds with different climates and/or water infrastructure design. The study findings highlight that our understanding of dry weather flow from storm sewers is relatively limited, emphasizing the need for further exploration of this phenomenon to inform urban hydrological modelling, water quality studies, and urban water management.

Comprehensive evaluation of satellite-based precipitation products at hourly scale in Beijing

With the increase of short-duration rainstorms, accurate precipitation data with higher temporal resolution, such as hours or even minutes, has been required for urban hydrological modeling and forecasting. As a potential data source, hourly or half-hour satellite-based precipitation products (SPPs) perform differently in different metropolitan regions, so evaluation is necessary before their application. Based on the observations of 20 meteorological stations from 2015 to 2018 over a typical metropolitan region, Beijing, a comprehensive evaluation of four widely used SPPs at hourly scale was conducted. Results showed that IMERG and GSMaP had the best performance, followed by CMORPH, while PERSIANN performed worst at hourly scale; it was suggested to use data series composed of wet hours for evaluation at hourly scale with more consistent spatial distribution characteristics and reliable results; IMERG, GSMaP and CMORPH performed better during 9:00–12:00 for accuracy and 5:00–9:00 and 20:00–21:00 for detection capability while PERSIANN didn't show obvious diurnal variation. In addition, the short-duration extreme precipitation in Beijing was defined as an event with an hourly precipitation >20 mm. Overall, hourly or half-hour SPPs still need a lot of progress and our study would provide some significant references for developers and potential users.

Enhancing Urban Flood Forecasting: Integrating Weather Forecasts and Hydrological Models

Precipitation data in urban hydrological models are derived from an ideal stormwater model, which has some uncertainties and limited prediction times. Therefore, to reliably forecast urban flooding, prolong prediction time periods, and better support associated research in urban flood forecasting, a combination of weather forecasts and urban hydrology is necessary. By applying comprehensive cloud microphysical schemes in the Weather Research and Forecasting (WRF) model to the predecessor torrential rainfall associated with Typhoon Khanun (2017), this study evaluated different configurations of atmospheric-hydrological simulations based on the WRF model and InfoWorks ICM. Results showed that the microphysics scheme could significantly affect spatial and temporal distributions of the simulated torrential rainfall. Generally, the combination of WRF and NSSL schemes produced better performance. Applying the NSSL scheme to the WRF model and combining it with the InfoWorks ICM system can reproduce torrential rainfall and urban flood formations.

Estimating rainfall intensity based on surveillance audio and deep-learning

Rainfall data with high spatial and temporal resolutions are essential for urban hydrological modeling. Ubiquitous surveillance cameras can continuously record rainfall events through video and audio, so they have been recognized as potential rain gauges to supplement professional rainfall observation networks. Since video-based rainfall estimation methods can be affected by variable backgrounds and lighting conditions, audio-based approaches could be a supplement without suffering from these conditions. However, most audio-based approaches focus on rainfall-level classification rather than rainfall intensity estimation. Here, we introduce a dataset named Surveillance Audio Rainfall Intensity Dataset (SARID) and a deep learning model for estimating rainfall intensity. First, we created the dataset through audio of six real-world rainfall events. This dataset's audio recordings are segmented into 12,066 pieces and annotated with rainfall intensity and environmental information, such as underlying surfaces, temperature, humidity, and wind. Then, we developed a deep learning-based baseline using Mel-Frequency Cepstral Coefficients (MFCC) and Transformer architecture to estimate rainfall intensity from surveillance audio. Validated from ground truth data, our baseline achieves a root mean absolute error of 0.88 mm h-1 and a coefficient of correlation of 0.765. Our findings demonstrate the potential of surveillance audio-based models as practical and effective tools for rainfall observation systems, initiating a new chapter in rainfall intensity estimation. It offers a novel data source for high-resolution hydrological sensing and contributes to the broader landscape of urban sensing, emergency response, and resilience.

Parameter Regionalization With Donor Catchment Clustering Improves Urban Flood Modeling in Ungauged Urban Catchments

AbstractThe lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression‐based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least‐squares regression is identified as optimal regression‐based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression‐based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results.

Intelligent control of combined sewer systems using PySWMM—A Python wrapper for EPA’s Stormwater Management Model

Wastewater utilities face competing priorities as they work to protect human health and water quality, and to maintain infrastructure in their communities. Budgetary constraints can be especially pronounced among small to medium-sized utilities. Utilities are increasingly turning to so-called intelligent water approaches as a cost-effective alternative to upgrading aging infrastructure. Intelligent water encompasses automated control and real-time decision support technologies and can be applied at scale to large and small utilities alike accommodating differences in needs, capabilities, and funds. Intelligent water upgrades can be designed to optimize existing conveyance, storage, and treatment during storms to help mitigate flooding and combined sewer overflows. The most promising real-time control algorithms coordinate control of upstream and downstream assets and are designed using urban hydrologic and hydraulic modeling software. The capabilities of legacy software, however, can sometimes inhibit the creation of sophisticated control algorithms. In this paper, we present PySWMM — an open-source Python wrapper developed for the EPA Storm Water Management Model (SWMM). PySWMM enables runtime interactions with the SWMM computational engine to flexibly read, modify system parameters, and control digital infrastructure during a simulation. Crucially, it allows modelers to easily combine SWMM with the rich set of scientific computing, big data, and machine learning modules found in the Python ecosystem. We highlight two real-world intelligent water case studies utilizing PySWMM in the cities of Cincinnati and Columbus, Ohio where it has helped to eliminate tens of millions of gallons of combined sewer overflows annually.

A web-based urban hydrology model for municipal scale applications

Extensive data and computational requirements limit the application of existing urban hydrology models at municipal scales. Community-enabled Lifecycle Analysis of Stormwater Infrastructure Costs (CLASIC) is a web-based deployment of the SWMM model with decoupled hydrologic and hydraulic components to enable hydrologic assessment at the municipal and larger scales. This study comprehensively evaluates the performance validity of CLASIC for characterization of hydrologic responses against SWMM and observed data. Furthermore, global sensitivity analysis is used to explore the significance of hydrologic and hydraulic model parameters across spatial and temporal scales. CLASIC reliably represents the urban hydrological processes and accurately quantifies stream discharge at the municipal scale and temporal scales greater than the catchment's time of concentration. Notably, the computational requirements of CLASIC are substantially lower than those of SWMM as the catchment drainage area increases. The application of CLASIC for flood assessment may be conducted with careful examination of the estimated peak discharge at sub-daily timescales.

Evaluation of the number of events’ influence on model performance and uncertainty in urban data-scarce areas based on behavioral parameter ranking method

Rainfall-runoff data in drainage systems in urban areas is the essential input variable for urban hydrological modeling. Obtaining high quality and sufficient size of urban flood events is always difficult due to the inconvenient underground observation and the untimely capture of the rapid rising and recession stages of urban runoff. It largely constrains the efficiency of model calibration and brings large uncertainty in urban rainfall-runoff simulation. Therefore, the evaluation of the number of events’ influence on model performance and uncertainty is of great significance, which can provide useful information to help the decision makers select sufficient useful observation data for model calibration with relatively fewer events. In this study, we constructed a comprehensive method of behavioral parameter ranking of multi-events (BPROME) based on coupling the Generalized Likelihood Uncertainty Estimation (GLUE) algorithm and the Storm Water Management Model (SWMM). The results in the two small demonstration areas (named Case #A and Case #B) in Shenzhen city of China proved the good performance of the BPROME in uncertainty assessment in urban areas. We found the model uncertainty (average bandwidth (B) and average deviation amplitude (D)) and model performance (containing Ratio (CR) and the maximum value of behavioral samples’ NSE (NSEmax)) became stable at a certain number of events in both two case areas. The B and D decrease and the CR and NSEmax increase as the number of event increases. In particular, the model performance and uncertainty reach their appropriate state concerning the limited observations at a certain range of numbers of events (both three to five in our two case areas). Our results demonstrate the potential influence of the numbers of events for the urban rainfall-runoff modeling calibration which can balance the model efficiency and data collection cost (the number of input rainfall-runoff data). The findings could help decision-makers seek a trade-off between data investment and acceptable model performance.

Application of Radar-Based Precipitation Data Improves the Effectiveness of Urban Inundation Forecasting

Using radar to estimate and forecast precipitation as input for hydrological models has become increasingly popular in recent years because of its superior spatial and temporal simulation compared with using rain gauge data. This study used radar-based quantitative precipitation estimation (QPE) to select the optimal parameter set for the MIKE URBAN hydrological model and radar-based quantitative precipitation forecasting (QPF) to simulate inundation in Nam Dinh city, Vietnam. The results show the following: (1) radar has the potential to improve the modeling and provide the data needed for real-time smart control if proper bias adjustment is obtained and the risk of underestimated flows after heavy rain is minimized, and (2) the MIKE URBAN model used to calculate two simulation scenarios with rain gauge data and QPE data showed effectiveness in combining the application of radar-based precipitation for the forecasting and warning of urban floods in Nam Dinh city. The results in Scenario 2 with rainfall forecast data from radar provide better simulation results. The average relative error in Scenario 2 is 9%, while the average relative error in Scenario 1 is 15%. Using the grid radar-based precipitation forecasting as input data for the MIKE URBAN model significantly reduces the error between the observed water depth and the simulated results compared with the case using an input rain gauge measured at Nam Dinh station (the difference in inundation level of Scenario 2 using radar-based precipitation is 0.005 m, and it is 0.03 m in Scenario 1). The results obtained using the QPE and QPF radar as input for the MIKE URBAN model will be the basis for establishing an operational forecasting system for the Northern Delta and Midland Regional Hydro-Meteorological Center, Viet Nam Meteorological and Hydrological Administration.

Urban hydrological model (UHM) developed for an urban flash flood simulation and analysis of the flood intensity sensitivity to urbanization

An urban hydrological model (UHM) forced by radar-observed rainfalls was developed for producing rainstorm-related urban inundation maps for obtaining flash flood forecasts. The parameter sets of the land use and land cover (LULC) and urban drainage capacity derive from satellite multispectral images and high spatial resolution GIS datasets, relating to the urban hydrology and hydraulic properties. The hydrodynamics model was based on the simplified shallow water equation (SWE) neglecting the convective acceleration and pressure terms in the St. Venant equation. The intense convective rainstorm that induced heavy flash floods on 21 Jul. 2012 and the large-scale stratiform precipitation that occurred from 19 Jul. to 21 Jul. 2016 in Beijing were deliberately selected to perform flood-simulation and model-validation case studies. The simulation of severe flooding scenarios in the 2012 event was fairly reproduced and verified using media reports. In the case studies, the sensitivity tests verified that the flood intensity (1) would be enhanced slightly on pure impervious surfaces, (2) would be reduced significantly on pure pervious surfaces, and (3) would increase by 30%–60% without a drainage system. No false alarm could be perceived in the simulation of the 2016 event in a prolonging stratiform precipitation. The case studies confirmed the perspectives of using the hydrological model fed by QPEs/QPFs for flash flood forecasting and concluded that the hourly precipitation surpassing 30 mm/h would be indicative in impending flash floods in Beijing metropolis.

Impact and analysis of urban water system connectivity project on regional water environment based on Storm Water Management Model (SWMM)

SWMM stands as a dynamic rainfall-runoff urban hydrological model, crafted by the U.S. Environmental Protection Agency. It adeptly replicates urban rainfall production and ground runoff, and the Pressure-State-Response (PSR) theory artfully reflects human-environment interactions. Urban Water System Connectivity (UWSC) fortifies connections among regional rivers, transforming them into an intricate, dense network. To study the improvement of the regional water environment by the UWSC project, this study takes the UWSC project in Runan County, Zhumadian, China as the research object, constructs a hydrodynamic and water quality model based on SWMM, and simulates it under different rainfall situations. At the same time, the ecological service value theory and PSR theory are used to establish an ecological effect index system to analyze the ecological effect before and after the UWSC project. This paper evaluates the urban water system connectivity project from three aspects: hydrodynamic, water quality, and ecological effects. Results underscore that the post-UWSC era witnesses decreased river flood flows in varied design rainfalls, accompanied by delayed flood peaks. Under P = 10a and P = 20a rainfalls, peak flow reductions of 6.6% and 4.96% respectively, manifest with peak time lags of 0.17h. Flow rate adjustments align with flow behavior, exhibiting decreased peak flow rates accompanied by temporal lags. Initial river flow increases by about 30%, amplifying river water body exchange capacity. Following the UWSC implementation, the study area's five primary rivers display TSS reductions ranging from 46% to 86.94%, COD from 36.98% to 92.43%, TN from 45.9% to 92.96%, TP from 35.71% to 84.78%, and NH3–N from 38.52% to 92.19%. Simulated point source pollution, under comparable initial conditions, indicates pollutant decay rates escalating from 3.2%–20%–35.71%–92.43%. Ecological impact ascends from 6.33 × 108 RMB to 9.65 × 108 RMB, reflecting a 52.5% escalation. The UWSC initiative enriches urban river flood resilience and water quality, emblematic of urban water ecological civilization progress.

An urban hydrological model for flood simulation in piedmont cities: Case study of Jinan City, China

Urbanization has great potential to adversely affect the local hydrological cycle, as well as result in severe ecological and environmental problems. Piedmont cities have a special topography and complicated overland flow processes, thus creating challenges for the simulation of urban flooding/waterlogging processes. The TVGM-USWM model, a new distributed urban hydrological model that considers the flow routing in urban drainage systems and complicated overland flow processes, was developed in this study. We applied this model to the Huangtaiqiao drainage basin of Jinan City, China and set up two simulation scenarios. The spatial heterogeneity of the proportion of directly connected impervious area (DCIA) and disconnected impervious area (DIA) in sub-catchments was considered in scenario B, but not in scenario A. We conducted sensitivity analysis on model parameters by using the Sobol method. Results showed that: (1) model parameters have a certain degree of uncertainty, and the parameter characterizing the proportion of the DCIA and DIA showed a relatively high sensitivity under rainstorms of different levels and different accuracy assessment standards; (2) The simulation of scenario A was poor (with a mean Nash coefficient of 0.59), which ignored the different flow paths followed by runoff from DCIA and DIA; and (3) Considering the spatial differences in the overland flow process, the prediction accuracy of scenario B significantly increased (with a mean Nash coefficient of 0.82). This indicated the importance of the disconnected impervious area and the lateral water exchange across the impervious-pervious interface on the flood simulation in a piedmont city. Compared to most urban hydrological models, the TVGM-USWM model can represent complex rainfall-runoff mechanisms in urban areas with varied terrain. The findings of this study can provide scientific decision-making for the planning and construction of a sponge city in Jinan City.

Aerial characterization of surface depressions in urban watersheds

Digital elevation models (DEM) are one of the most fundamental inputs for hydrological modeling. It has been a common practice to remove all surface depressions in a DEM as they are assumed to be data errors. The emerging technology of unmanned aircraft systems (UAS) provides an opportunity to re-examine this assumption at the hyperspatial resolution. This study was the first attempt to characterize small surface depressions in urban environments using UAS imagery. Using an urban area in south Texas as the study site, UAS flights were conducted to yield hybrid DEMs at the resolution of 8–14 cm, coupled with comprehensive ground truth collection. Surface depressions identified from the UAS DEMs were first corrected based on the vertical accuracy of DEMs and then validated through field surveys, with comparisons to two existing LiDAR DEMs (1-m and 10-m). The hydrological impacts of different DEM-derived estimates of catchment depression storage were examined using the Curve Number method across different design storms. Results show that the UAS DEMs outperformed the LiDAR DEMs in describing the microtopographic control of urban overland flow and associated hydrological connectivity across built and natural features. The 8-cm UAS DEM revealed 926% more depression storage than the 10-m LiDAR DEM. This demonstrates a compelling correlation between increasing DEM resolution and enhanced quantification of depression volume. Consequently, the increased depression storage reduced surface runoff by 41% under a two-year design storm and 13% under a 200-year design storm. The results suggest a strong relationship between the DEM resolution and the derived depression estimates, aligning with the fractal nature of watershed systems. Also, the results indicate that the centimeter-level UAS DEMs were not immune from problems. They could yield fake depressions caused by factors such as vegetation, temporary street objects, and underground sewer pipes. The findings of this study suggest the need to quantify the relationships between DEM resolution and associated hydrological attributes and develop new digital drainage analysis algorithms that could effectively incorporate UAS data into urban hydrological modeling.

Application of Porous Concrete Infiltration Techniques to Street Stormwater Inlets That Simultaneously Mitigate against Non-Point Heavy Metal Pollution and Stormwater Runoff Reduction in Urban Areas: Catchment-Scale Evaluation of the Potential of Discrete and Small-Scale Techniques

The expansion of pervious areas is an essential and common concept in mitigating non-point pollution runoff in urban areas. In this review, literature related to the expansion of pervious areas is introduced. In addition, the potential application of porous concrete as a medium for constructing the bottom and side walls of street stormwater inlets is investigated. The effectiveness of this medium in reducing (i) the stormwater runoff volume via porous concrete by exfiltrating from the bottom and the wall, and (ii) the heavy metal pollution runoff loads via infiltration through the porous concrete is assessed using data obtained by the author and published in the literature. The urban hydrological model Infoworks ICM (Innovyze) was used to estimate the exfiltration rates through the porous concrete plates set at the bottom and side walls of the street stormwater inlets. The exfiltration rates used in the pre-reported literature varied depending on the methods used. In the present study, sensitivity tests were performed by changing the exfiltration rates. The results of this study indicated that porous concrete used at only the bottom and side walls of the street stormwater inlets is suitable for reducing the runoff volume and removing any heavy metals from stormwater at a catchment scale.

Approximation method for the sewer drainage effect for urban flood modeling in areas without drainage-pipe data

Introduction: The underground drainage-pipe network is one of the vital components of a modern city, and it plays an important role in preventing or mitigating urban flooding. Thus, pipe network data are necessary for simulation of the entire urban rainfall–runoff process. However, pipe network data are sparse or unavailable in most urban areas.Methods: To solve this problem, we developed a novel approximation method that can be used calculate the drainage capacity of the pipe system. This method is named the road-drainage method, and it works under the assumption that the pipe network functions by subtracting the corresponding mass from the water conservation equation only from areas of road. The mass is determined from weir flow formulas together with the road properties and correction for this mass is applied to the rainfall source term.Results: Two test cases were used to compare the performance of the new method with an existing method, under which mass is subtracted from the entire area during the rainfall–runoff process. The results showed that the new method considerably improves the accuracy of simulated peak volume, with an improvement of 2.62%–58.75% compared with the existing method across various scenarios. Moreover, the proposed new method reduces the time shift of the rise and peak of surface inundation by 10–45 min in various scenarios, which reflects a more realistic model of the rainfall–runoff process.Discussion: These results demonstrate that the proposed new method can represent the drainage capability more accurately and is more consistent with reality. The road-drainage method has promising potential for application in urban flood simulation in areas without drainage system data and for the support of large-scale urban hydrologic modeling.

Impact of Urbanization on Regional Rainfall-Runoff Processes: Case Study in Jinan City, China

Rapid urbanization has altered the regional hydrological processes, posing a great challenge to the sustainable development of cities. The TVGM-USWM model, a new urban hydrological model considering the nonlinear rainfall-runoff relationship and the flow routing in an urban drainage system, was developed in this study. We employed this model in the Huangtaiqiao drainage basin of Jinan City, China, and examined the impact of land cover changes due to urbanization on rainfall-runoff processes. Two urbanization scenarios were set up in the TVGM-USWM model during the design rainfall events with different return periods. Results showed that (1) the TVGM-USWM model demonstrated good applicability in the study area, and the RNS values of the flood events are all greater than 0.75 in both calibration and validation periods; (2) the proportion of impervious areas increased from 44.65% in 1990 to 71.00% in 2020, and urbanization played a leading role in the process of land cover change and manifested itself as a circular extensional expansion; and (3) urbanization showed a significant amplifying effect on the design flood processes, particularly for relatively big floods with small frequency, and the impact of urbanization on the time-to-peak of the design flood gradually decreased as the frequency of the design rainfall decreased. The results of this study can provide technical support for flood mitigation and the construction of a sponge city in Jinan City.

Urban Flood Modeling and Risk Assessment with Limited Observation Data: The Beijing Future Science City of China.

The frequency of urban storms has increased, influenced by the climate changing and urbanization, and the process of urban rainfall runoff has also changed, leading to severe urban waterlogging problems. Against this background, the risk of urban waterlogging was analyzed and assessed accurately, using an urban stormwater model as necessary. Most studies have used urban hydrological models to assess flood risk; however, due to limited flow pipeline data, the calibration and the validation of the models are difficult. This study applied the MIKE URBAN model to build a drainage system model in the Beijing Future Science City of China, where the discharge of pipelines was absent. Three methods, of empirical calibration, formula validation, and validation based on field investigation, were used to calibrate and validate the parameters of the model. After the empirical calibration, the relative error range between the simulated value and the measured value was verified by the formula as within 25%. The simulated runoff depth was consistent with a field survey verified by the method of validation based on field investigation, showing the model has good applicability in the study area. Then, the rainfall scenarios of different return periods were designed and simulated. Simulation results showed that, for the 10-year return period, there are overflow pipe sections in northern and southern regions, and the number of overflow pipe sections in the northern region is more than that in the southern region. For the 20-year return period and 50-year return period, the number of overflow pipe sections and nodes in the northern region increased, while for the 100-year return period, the number of overflow nodes both increased. With the increase in the rainfall return period, the pipe network load increased, the points and sections prone to accumulation and waterlogging increased, and the regional waterlogging risk increased. The southern region is prone to waterlogging because the pipeline network density is higher than that in the northern region and the terrain is low-lying. This study provides a reference for the establishment of rainwater drainage models in regions with similar database limitations and provides a technical reference for the calibration and validation of stormwater models that lack rainfall runoff data.

A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)

Abstract. Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax⁡ and Tmin⁡) is particularly valuable and critically needed in the scientific and policy communities but is still not available. In this paper, we developed a global dataset of daily Tmax⁡ and Tmin⁡ at 1 km resolution over land across 50∘ S–79∘ N from 2003 to 2020 through the combined use of ground-station-based Ta measurements and satellite observations (i.e., digital elevation model and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors in our estimates ranged from 1.20 to 2.44 ∘C for Tmax⁡ and 1.69 to 2.39 ∘C for Tmin⁡. We found that the accuracies were affected primarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents a negative relationship between Ta and elevation and a positive relationship between Ta and land surface temperature; it captured spatial and temporal patterns of Ta realistically. This global 1 km gridded daily Tmax⁡ and Tmin⁡ dataset is the first of its kind, and we expect it to be of great value to global studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data have been published by Iowa State University at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022).

X-band polarimetric radar QPE for urban hydrology: The increased contribution of high-resolution rainfall capturing

Increased population is exposed to urban flooding as a result of climate change. Flood early warning can largely reduce the loss of life and asset, while how detailed rainfall information is enough has always been a critical issue in achieving robust flood forecasting. Previous analysis has been limited by lacking high-resolution rainfall observation or a spatially-matched urban hydrological model. This study couples mature modeling technologies with comprehensive hydro-meteorological measurement, that is comprised of X-band polarimetric radar (75 m, 3 min), weather stations, and hydrometric stations, and this study further attempts to understand the rainfall spatial resolution effects and critical resolutions from a multi-scale perspective (e.g. block, district, and catchment scales). The results show that coarser resolutions always lead to underestimation of peak flows, even exceeding 40 % in the case of adopting 10 km rainfall data at the block scale. But such damping effects on discharge simulation can be largely attenuated with increased hydrological scale. In general, spatial resolutions of higher than 500 m, and 10 km are needed to obtain accurate simulation results (i.e. keep the relative bias within 20 %) at district and catchment scales, while at least 300 m for the block scale. In conclusion, refined rainfall forcing contributes to better simulation of peak flows in urban hydrological modeling, but the required rainfall spatial resolution changes with the scale of hydrological application. These findings help to better achieve the balance between rainfall observation cost and flood simulation accuracy.