Evaluation of the Impact of Rainfall Inputs on Urban Rainfall Models: A Systematic Review

Over the past several decades, urban flooding and other water-related disasters have become increasingly prominent and serious. Although the urban rain flood model’s benefits for urban flood simulation have been extensively documented, the impact of rainfall input to model simulation accuracy remains unclear. This systematic review aims to provide structured research on how rain inputs impact urban rain flood model’s simulation accuracy. The selected 48 peer-reviewed journal articles published between 2015 and 2019 on the Web of Science™ database were analyzed by key factors, including rainfall input type, calibration times and verification times. The results from meta-analysis reveal that when a traditional rain measurement was used as the rainfall input, model simulation accuracy was higher, i.e., the Nash–Sutcliffe efficiency coefficient (NSE) of traditional technology for rain measurement was higher than the 0.18 for the new technology rain measurement with respect to flow simulation. In addition, the single-field sub-flood calibration model was better than the multi-field sub-flood calibration model. NSE was higher than 0.14. The precision was better for the verification period; NSE of the calibration value showed a 0.07 higher verification value on average in flow simulation. These findings have certain significance for the development of future urban rain flood models and propose the development direction of the future urban rain flood model. Finally, in view of the rainfall input problem of the urban storm flood model, we propose the future development direction of the urban storm flood model.

Rain Measurement Simulation Using the Hall Effect Flow-Meter

The sensor analysis or flow-meter measuring instrument has been successfully carried out on the signal output to see the stability or accuracy of a measurement. The flowmeter measurement value was analyzed using a rainfall simulator. The rainfall intensity value will then be predicted using the Kalman filter. Kalman filters can predict various data or output signals so that the measurement results can be more stable and accurate. This research methodology consists of several stages, namely the stages of literature study, designing research tools and components, designing systems, making or assembling tools, testing all components, programs and testing the flow-meter output signal record. The flowmeter is controlled by the Arduino Nano microcontroller. Tests were carried out in this study ten times, with a time span of 60 seconds for each experiment. The increase in water flow was detected by the flow-meter which was then captured by the hercules application and the data was then copied to Ms. Excel. After the rainfall intensity value is obtained, the value will be estimated using the Kalman filter. The estimation results will show the stability and accuracy value of the flow-meter.

Refinements to Data Acquired by 2-Dimensional Video Disdrometers

The 2-Dimensional Video Disdrometer (2DVD) is a commonly used tool for exploring rain microphysics and for validating remotely sensed rain retrievals. Recent work has revealed a persistent anomaly in 2DVD data. Early investigations of this anomaly concluded that the resulting errors in rain measurement were modest, but the methods used to flag anomalous data were not optimized, and related considerations associated with the sample sensing area were not fully investigated. Here, we (i) refine the anomaly-detecting algorithm for increased sensitivity and reliability and (ii) develop a related algorithm for refining the estimate of sample sensing area for all detected drops, including those not directly impacted by the anomaly. Using these algorithms, we explore the corrected data to measure any resulting changes to estimates of bulk rainfall statistics from two separate 2DVDs deployed in South Carolina combining for approximately 10 total years of instrumental uptime. Analysis of this data set consisting of over 200 million drops shows that the error induced in estimated total rain accumulations using the manufacturer-reported area is larger than the error due to considerations related to the anomaly. The algorithms presented here imply that approximately 4.2% of detected drops are spurious and the mean reported effective sample area for drops believed to be correctly detected is overestimated by ~8.5%. Simultaneously accounting for all of these effects suggests that the total accumulated rainfall in the data record is approximately 1.1% larger than the raw data record suggests.

Multidimensional evaluation of the TRMM 3B43V7 satellite-based precipitation product in mainland China from 1998–2016

This study evaluates the applicability of the Tropical Rain Measurement Mission (TRMM) 3B43V7 product for use throughout mainland China. Four statistical metrics were used based on the observations made by rain gauges; these metrics were the correlation coefficient (R), the relative bias (RB), the root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE), and they were chosen to evaluate the performance of the 3B43V7 product at temporal and spatial scales. The results revealed that 3B43V7 performed satisfactorily on all timescales (R > 0.9 and NSE > 0.86); however, it overestimated the results when compared with the rain gauge observations in certain circumstances (RB = 9.7%). Monthly estimates from 3B43V7 were in agreement with rain gauge observations. 3B43V7 can effectively capture the seasonal patterns of precipitation characteristics over mainland China. However, 3B43V7 tends to register a greater overestimation of precipitation in the winter (RB = 14%) than in other seasons while showing greater consistency with the observations made by rain gauges during dry periods. The 3B43V7 product performs well in the eastern part of mainland China, while its performance is poor in the western part of mainland China. In terms of altitude, 3B43V7 performs satisfactorily in areas with moderate to low altitudes (when altitude < 3,500 m, R > 0.9, NSE > 0.8 and RB < 10.2%) but RB values increase with altitude. Overall, 3B43V7 had a favorable performance throughout mainland China.

Design of Turbine-Based Rain Measurement System

Rain measurement systems have significantly developed. In this work, a novel modification to commonly used rain measurement systems is developed. It comprises a turbine with an infrared sensor to detect its rotation. The design of the rainfall collecting funnel refers to that of general use. The prototype of the design has been characterized with an artificial rainfall, namely continuously flowing water kept at a certain debit. For characterization purpose, the water debit is gradually changed to simulate variations in rainfalls. Important results based on the characterization are threshold value of 0.01 mm/min and average deviation of 1.36%, suggesting that this tool is able to detect even a weak rainfall.

Validation of the New Algorithm for Rain Rate Retrieval from AMSR2 Data Using TMI Rain Rate Product

A new algorithm is derived for rain rate (RR) estimation from Advanced Microwave Sounding Radiometer 2 (AMSR2) measurements taken at 6.9, 7.3, and 10.65 GHz. The algorithm is based on the numerical simulation of brightness temperatures (TB) for AMSR2 lower frequency channels, using a simplified radiation transfer model. Simultaneous meteorological and hydrological observations, supplemented with modeled values of cloud liquid water content and rain rate values, are used for the calculation of an ensemble of AMSR2TBs and RRs. Ice clouds are not taken into account. AMSR2 brightness temperature differences at C- and X-band channels are then used as inputs to train a neural network (NN) function for RR retrieval. Validation is performed against Tropical Rain Measurement Mission (TRMM) Microwave Instrument (TMI) RR products. For colocated AMSR2-TMI measurements, obtained within 10 min intervals, errors are about 1 mm/h. The new algorithm is applicable for RR estimation up to 20 mm/h. ForRR<2 mm/h the retrieval error is 0.3 mm/h. ForRR>10 mm/h the algorithm significantly underestimates TMI RR.