Extreme Precipitation Estimates Research Articles

Disentangling Satellite Precipitation Estimate Errors of Heavy Rainfall at the Daily and Sub-Daily Scales in the Western Mediterranean

In the last decade, substantial improvements have been achieved in quantitative satellite precipitation estimates, which are essential for a wide range of applications. In this study, we evaluated the performance of Integrated Multi-satellitE Retrievals for GPM (IMERG V06B) at the sub-daily and daily scales. Ten years of half-hourly precipitation records aggregated at different sub-daily periods were evaluated over a region in the Western Mediterranean. The analysis at the half-hourly scale examined the contribution of passive microwave (PMW) and infrared (IR) sources in IMERG estimates, as well as the relationship between various microphysical cloud properties using Cloud Microphysics (CMIC–NWC SAF) data. The results show the following: (1) a marked tendency to underestimate precipitation compared to rain gauges which increases with rainfall intensity and temporal resolution, (2) a weaker negative bias for retrievals with PMW data, (3) an increased bias when filling PMW gaps by including IR information, and (4) an improved performance in the presence of precipitating ice clouds compared to warm and mixed-phase clouds. This work contributes to the understanding of the factors affecting satellite estimates of extreme precipitation. Their relationship with the microphysical characteristics of clouds generates added value for further downstream applications and users’ decision making.

The Multi‐Scale Interactions of Atmospheric Phenomenon in Mean and Extreme Precipitation

AbstractClimate change increases the frequency and intensity of extreme precipitation, which in combination with rising population enhances exposure to major floods. An improved understanding of the atmospheric processes that cause extreme precipitation events would help to advance predictions and projections of such events. To date, such analyses have typically been performed rather unsystematically and over limited areas (e.g., the U.S.) which has resulted in contradictory findings. Here we present the Multi‐Object Analysis of Atmospheric Phenomenon algorithm that uses a set of 12 common atmospheric variables to identify and track tropical and extra‐tropical cyclones, cut‐off lows, frontal zones, anticyclones, atmospheric rivers (ARs), jets, mesoscale convective systems (MCSs), and equatorial waves. We apply the algorithm to global historical data between 2001–2020 and associate phenomena with hourly and daily satellite‐derived extreme precipitation estimates in major climate regions. We find that MCSs produce the vast majority of extreme precipitation in the tropics and some mid‐latitude land regions, while extreme precipitation in mid and high‐latitude ocean and coastal regions are dominated by cyclones and ARs. Importantly, most extreme precipitation events are associated with phenomena interacting across scales that intensify precipitation. These interactions are a function of the intensity (i.e., rarity) of extreme events. The presented methodology and results could have wide‐ranging applications including training of machine learning methods, Lagrangian‐based evaluation of climate models, and process‐based understanding of extreme precipitation in a changing climate.

A Novel Response Priority Framework for an Urban Coastal Catchment Using Global Weather Forecasts‐Based Improved Flood Risk Estimates

AbstractA novel flood risk forecasting‐based response priority framework is proposed in this study, which will facilitate efficient decision‐making ahead of an extreme precipitation event. This study is demonstrated over a highly flood‐prone and geomorphologically diverse coastal catchment in Mumbai city, the financial capital of India, and is subjected to high spatio‐temporal variability of precipitation. A regional precipitation forecast model is developed, where a quantile regression (QR)‐based statistical approach is applied to the global weather forecasts for improvement in finer resolution extreme precipitation estimates. A QR is performed between the observed synoptic‐scale predictors obtained from ERA‐Interim Reanalysis data set and the observed subdaily precipitation within the study area. Subsequently, a set of reliably well‐simulated forecasted predictors obtained from the ensemble members of the Global Ensemble Forecast System are used in the established relationship to obtain extreme precipitation forecasts for higher quantile levels. The forecasted precipitation data serves as an essential input to a comprehensive hydrodynamic flood modeling framework. The flood inundation maps corresponding to different quantiles of forecasted precipitation are quantified to identify the flood hotspots. The set of flood inundations maps developed for different quantiles are utilized to demarcate the areas based on critical response time, that is, the time required by each cell within the study area to be inundated to a certain threshold depth, and the response priority levels for each cell are determined. This information is combined with socioeconomic vulnerability (SEV), a critical component of flood risk, to derive conjugated SEV‐based response priority maps.

Assessment of the Urban Extreme Precipitation by Satellite Estimates over Mainland China

The accurate estimation of urban extreme precipitation is essential for urban design and risk management, which is hard for developing countries, due to the fast urbanization and sparse rain gauges. Satellite precipitation products (SPPs) have emerged as a promising solution. Not only near real-time SPPs can provide critical information for decision making, but post-processed SPPs can also offer essential information for climate change adaption, risk management strategy development, and related fields. However, their ability in urban extreme precipitation estimation has not been examined in detail. This study presents a comprehensive evaluation of four recent SPPs that are post-processed, including IMERG, GSMaP_Gauge, MSWEP, and CMFD, for their ability to capture urban extreme precipitation in mainland China at the national, city, and inner-city scales. The performance of the four SPPs was assessed using daily observations from the 821 urban gauges from 2001 to 2018. The assessment includes: (1) the extreme precipitation estimates from the four SPPs in the total urbanized areas of mainland China were evaluated using correlation coefficients (CC), absolute deviation (AD), relative deviation (RB), and five extreme precipitation indices; (2) The extreme precipitation estimates over 21 Chinese major cities were assessed with the two most important extreme indices, namely the 99th percentile of daily precipitation on wet days (R99) and total precipitation when daily precipitation exceeding R99 (R99TOT); and (3) Bivariate Moran’s I (BMI) was adopted to assess the inner-city spatial correlation of R99 and R99TOT between SPPs and gauge observations in four major cities with most gauges. The results indicate that MSWEP has the highest CC of 0.79 and the lowest AD of 1.61 mm at the national scale. However, it tends to underestimate urban precipitation, with an RB of −8.5%. GSMaP_Gauge and IMERG performed better in estimating extreme values, with close extreme indices with gauge observations. According to the 21 major cities, GSMaP_Gauge also shows high accuracy in estimating R99 and R99TOT values, with the best RB and AD in these cities, while CMFD and MSWEP exhibit the highest CC values for R99 and R99TOT, respectively, indicating a strong correlation between their estimates and those obtained from gauge observations. At the inner-city scale, MSWEP shows advantages in monitoring the spatial distribution of urban extreme precipitation in most of cities. The study firstly provided the multiscale assessment of urban extreme precipitation by SPPs over mainland China, which is useful for their applications.

Assessment of the Uncertainty Associated with Statistical Modeling of Precipitation Extremes for Hydrologic Engineering Applications in Amman, Jordan

Estimates of extreme precipitation are commonly associated with different sources of uncertainty. One of the primary sources of uncertainty in the statistical modeling of precipitation extremes comes from extreme data series (i.e., sampling uncertainty). Therefore, this research aimed to quantify the sampling uncertainty in terms of confidence intervals. In addition, this article examined how the data record length affects predicted extreme precipitation estimates and data set statistics. A nonparametric bootstrap resample was utilized to quantify the precipitation quantile sampling distribution at a particular non exceedance probability. This sampling distribution can provide a point estimation of the precipitation quantile and the confidence interval at a particular non exceedance probability. It has been shown that the different types of probability distributions fit the extreme precipitation data series of various weather stations. Therefore, the uncertainty analysis should be conducted using the best-fit probability distribution for extreme precipitation data series rather than a predefined single probability distribution for all stations based on modern extreme value theory. According to the 95% confidence intervals, precipitation quantiles are subject to significant uncertainty and the band of the uncertainty intervals increases with the return period. These uncertainty bounds need to be integrated into any frequency analysis from historical data. The average, standard deviation, skewness and kurtosis are highly affected by the data record length. Thus, a longer record length is desirable to decrease the sampling uncertainty and, therefore, decrease the error in the predicted quantile values. Moreover, the results suggest that a series of at least 40 years of data records is needed to obtain reasonably accurate estimates of the distribution parameters and the precipitation quantiles for 100 years return periods and higher. Using only 20 to 25 years of data to obtain estimates of the higher return period quantile is risky, since it created high sampling variability relative to the full data length.

Comprehensive Analysis of PERSIANN Products in Studying the Precipitation Variations over Luzon

This study evaluated the capability of satellite precipitation estimates from five products derived from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-CCS-CDR, and PDIR-Now) to represent precipitation characteristics over Luzon. The analyses focused on monthly and daily timescales from 2003–2015 and adopted surface observations from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) platform as the evaluation base. Among the five satellite precipitation products (SPPs), PERSIANN-CDR was observed to possess a better ability to qualitatively and quantitatively estimate spatiotemporal variations of precipitation over Luzon for the majority of the examined features with the exception of the extreme precipitation events, for which PERSIANN-CCS-CDR is superior to the other SPPs. These results highlight the usefulness of the addition of the cloud patch approach to PERSIANN-CDR to produce PERSIANN-CCS-CDR to depict the characteristics of extreme precipitation events over Luzon. A similar advantage of adopting the cloud patch approach in producing extreme precipitation estimates was also revealed from the comparison of PERSIANN, PERSIANN-CCS, and PDIR-Now. Our analyses also highlighted that all PERSIANN-series exhibit improved skills in regard to detecting precipitation characteristics over west Luzon compared to that over east Luzon. To overcome this weakness, we suggest that an adjustment in the cloud patch approach (e.g., using different cloud temperature thresholds or different brightness temperature and precipitation rate relationships) over east Luzon may be helpful.

Improving daily precipitation estimation in the data scarce area by merging rain gauge and TRMM data with a transfer learning framework

Merging the satellite and gauge precipitation has been proofed as an efficient approach to improve the accuracy of quantitative precipitation estimation. However, for areas with few and unevenly distributed rain gauges, accurate precipitation estimation still remains challenging. To address this problem, this paper proposes a framework to improve precipitation estimation for the data scarce area based on transfer learning. Taking the Qinghai-Tibet Plateau as a representative case study, we used two transfer learning methods (fine-tuning, domain-adversarial neural network (DANN)) to transfer precipitation fusion model from the source domain to the target domain. Results indicate that in comparison with the original TRMM data, the root mean square error (RMSE) and mean absolute error (MAE) of the merged precipitation in the Qinghai-Tibet Plateau during 2001–2005 are reduced by 27.6 % and 22.5 % by using the fine-tuning method, and reduced by 29.4 % and 21.5 % by using the DANN method, respectively. Meanwhile, the correlation coefficient (CC) is increased from 0.54 (TRMM data-rain gauge data) to 0.65 (merged data-rain gauge data). The performances of the proposed methods vary spatially, with CC decreased from southeast (0.80) to northwest (<0.40) of the study area. The DANN method performed well on different precipitation intensities, while Swish loss function can help DANN achieve better results on extreme precipitation estimation, with RMSE and MAE reduced by 2.5 % and 4.5 % respectively. The performances of the proposed methods are affected by various factors such as source domain selection and the length of study period. Findings imply that transfer learning provides new insights and new methods to improve precipitation estimation for the data scarce area, which would benefit regional water-related disaster defense and water resources management.

Performance Evaluation of ERA5 Extreme Precipitation in the Yangtze River Delta, China

Accurate extreme precipitation information is crucial for disaster risk management, social and economic development security, and climate change research. Taking the Yangtze River Delta (YRD), China, a high-impact area of extreme precipitation, as an example, this study evaluates the spatiotemporal performance of extreme precipitation in the latest fifth-generation reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (i.e., ECMWF ERA5) for 1961–2018 based on surface observational precipitation data. The results showed that the 90th-percentile threshold of extreme precipitation extracted from ERA5 data with a daily precipitation amount &gt;1 mm is closer to the actual observations. The ERA5 data can effectively capture the spatiotemporal patterns of the observed extreme precipitation in the YRD. The ERA5 data can successfully represent the seasonal cycle and interannual variability of daily, daytime, and nighttime extreme precipitation. However, the daytime (nighttime) extreme precipitation frequencies and amounts tend to be overestimated (underestimated) for the period 1961–2000, whereas they were significantly underestimated for the period 2000–2018. The trend estimation of seasonal and annual extreme precipitation in ERA5 needs to be improved. The ERA5 data revealed that the extreme precipitation in the YRD was dominated by large-scale precipitation, followed by convective precipitation, but their long-term trends were not clear. This study has conducted a detailed and reliable evaluation of the ERA5 extreme precipitation data. The findings serve as valuable guidance and provide accurate references to extreme climatic variables for data users and algorithm developers.

Evaluation of Performance of Three Satellite-Derived Precipitation Products in Capturing Extreme Precipitation Events over Beijing, China

Extreme precipitation events have a more serious impact on densely populated cities and therefore reliable estimation of extreme precipitation is very important. Satellite-derived precipitation products provide precipitation datasets with high spatiotemporal resolution. For improved applicability to estimating urban extreme precipitation, the performance of such products must be evaluated regionally. This study evaluated three satellite-derived precipitation products, the Integrated Multi-satellite Retrievals for GPM (IMERG_V06), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2), and China Meteorological Forcing Dataset (CMFD), in capturing extreme precipitation using observations acquired at 36 rainfall stations during 2001–2016 in Beijing, China. Results showed that MSWEP had the highest accuracy regarding daily precipitation data, with the highest correlation coefficient and the lowest absolute deviation between MSWEP and the rainfall station observations. CMFD demonstrated the best ability for correct detection of daily precipitation events, while MSWEP maintained the lowest rate of detecting non-rainy days as rainy days. MSWEP performed better in estimating precipitation amount and the number of precipitation days when daily precipitation was &lt;50 mm; CMFD performed better when daily precipitation was &gt;50 mm. All three products underestimated extreme precipitation. The Structural Similarity Index, which is a map comparison technique, was used to compare the similarities between the three products and rainfall station observations of two extreme rainstorms: “7.21” in 2012 and “7.20” in 2016. MSWEP and CMFD showed higher levels of similarity in terms of spatial–temporal structure. Overall, despite systematic underestimation, MSWEP performed better than IMERG and CMFD in estimating extreme precipitation in Beijing.

Evaluation of IMERG, TMPA, ERA5, and CPC precipitation products over mainland China: Spatiotemporal patterns and extremes

A comprehensive assessment of representative satellite-retrieved (Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA)), reanalysis-based (fifth generation of atmospheric reanalysis by the European Centre for Medium Range Weather Forecasts (ERA5)), and gauge-estimated (Climate Prediction Center (CPC)) precipitation products was conducted using the data from 807 meteorological stations across mainland China from 2001 to 2017. Error statistical metrics, precipitation distribution functions, and extreme precipitation indices were used to evaluate the quality of the four precipitation products in terms of multi-timescale accuracy and extreme precipitation estimation. When the timescale increased from daily to seasonal scales, the accuracy of the four precipitation products first increased and then decreased, and all products performed best on the monthly timescale. Their accuracy ranking in descending order was CPC, IMERG, TMPA, and ERA5 on the daily timescale and IMERG, CPC, TMPA, and ERA5 on the monthly and seasonal timescales. IMERG was generally superior to its predecessor TMPA on the three timescales. ERA5 exhibited large statistical errors. CPC provided stable estimated values. For extreme precipitation estimation, the quality of IMERG was relatively consistent with that of TMPA in terms of precipitation distribution and extreme metrics, and IMERG exhibited a significant advantage in estimating moderate and heavy precipitation. In contrast, ERA5 and CPC exhibited poor performance with large systematic underestimation biases. The findings of this study provide insight into the performance of the latest IMERG product compared with the widely used TMPA, ERA5, and CPC datasets, and points to possible directions for improvement of multi-source precipitation data fusion algorithms in order to better serve hydrological applications.

Optimal Estimation MSG-SEVIRI Clear-Sky Total Column Water Vapour Retrieval Using the Split Window Difference

A new algorithm for the retrieval of day-time total column water vapour (TCWV) from measurements of a MSG-SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager) instrument is presented. The retrieval is based on a forward operator, at the core of which lies Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV). This forward model relates TCWV and surface temperature to brightness temperatures in the split window at 11 and 12µm with the use of a first guess for temperature and humidity profiles from the ERA5 reanalysis. The forward model is then embedded in a full Optimal Estimation (OE) method, which yields pixel by pixel uncertainty estimates and performance indicators. The algorithm is applicable to any instrument which features the split window configuration, given a first guess for atmospheric conditions (i.e., from NWP) and an estimate of surface emissivity at 11 µm. The algorithm was developed within the framework of RealPEP (Near-Realtime Quantitative Precipitation Estimation and Prediction) in which the advancement of the estimation and nowcasting of extreme precipitation and flooding in Germany are studied. Thus, processing and validation has been limited to the German domain. Three independent ground-based TCWV observation data sets were used as reference, i.e., AERONET (Aerosol Robotic Network), GNSS Germany (Global Navigation Satellite System) and measurements from two MWR (Microwave Radiometer) sites. The validation concludes with good agreement, with absolute biases between 0.11 and 2.85 kg/m2, root mean square deviations (rmsds) between 1.63 and 3.24 kg/m2 and Pearson correlation coefficients ranging from 0.96 to 0.98. The retrievals uncertainty estimates were evaluated against AERONET. The comparison suggests that, in sum, uncertainties are estimated well, while still some error sources seem to be over- and underestimated. In limited case studies it could be shown that SEVIRI TCWV is capable to both display large scale variabilities in water vapour fields and reproduce the daily course of water vapour exposed by ground-based observations.

Comparison between observed and remotely sensed attributes to include in the region-of-influence approach of extreme precipitation estimation: a case study in the Yangtze River basin, China

ABSTRACT The application of remotely sensed (RS) data at ungauged locations is well recognized in hydrological studies; however, its suitability for use as a descriptor in the region-of-influence (ROI) approach is hardly assessed. This study compares two types of at-site attributes, observed and RS, to include in the ROI approach for the estimation of extreme precipitation, in particular at ungauged locations in China. The performance of the method against the fixed-group-based regional approach is also examined. The results, which are based on data for the Yangtze River basin, showed that the ROI scheme that used physical proximity and elevation combined produced the lowest error and performed better than that containing RS data. The scheme also outperformed the fixed regional models in terms of error. Overall, although the application of RS data is intuitively attractive, its inclusion is unable to outperform the observed site descriptors for the study region.

Establishing a Range of Extreme Precipitation Estimates in California for Planning in the Face of Climate Change

AbstractFor California water resource planning in the face of climate change, hydrological and water distribution models require inputs of high spatial– and temporal–resolution temperature and prec...

Evaluation of Extreme Precipitation Based on Three Long-Term Gridded Products over the Qinghai-Tibet Plateau

Accurate estimates of extreme precipitation events play an important role in climate change studies and natural disaster risk assessments. This study aimed to evaluate the capability of the China Meteorological Forcing Dataset (CMFD), Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), and Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) to detect the spatiotemporal patterns of extreme precipitation events over the Qinghai-Tibet Plateau (QTP) in China, from 1981 to 2014. Compared to the gauge-based precipitation dataset obtained from 101 stations across the region, 12 indices of extreme precipitation were employed and classified into three categories: fixed threshold, station-related threshold, and non-threshold indices. Correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), and Kling–Gupta efficiency (KGE), were used to assess the accuracy of extreme precipitation estimation; indices including probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were adopted to evaluate the ability of gridded products’ to detect rain occurrences. The results indicated that all three gridded datasets showed acceptable representation of the extreme precipitation events over the QTP. CMFD and APHRODITE tended to slightly underestimate extreme precipitation indices (except for consecutive wet days), whereas CHIRPS overestimated most indices. Overall, CMFD outperformed the other datasets for capturing the spatiotemporal pattern of most extreme precipitation indices over the QTP. Although CHIRPS had lower levels of accuracy, the generated data had a higher spatial resolution, and with correction, it may be considered for small-scale studies in future research.

Revision of Frequency Estimates of Extreme Precipitation Based on the Annual Maximum Series in the Jiangsu Province in China

Frequency estimates of extreme precipitation are revised using a regional L-moments method based on the annual maximum series and Chow’s equation at lower return periods for the Jiangsu area in China. First, the study area is divided into five homogeneous regions, and the optimum distribution for each region is determined by an integrative assessment. Second, underestimation of quantiles and the applicability of Chow’s equation are verified. The results show that quantiles are underestimated based on the annual maximum series, and that Chow’s formula is applicable for the study area. Next, two methods are used to correct the underestimation of frequency estimation. A set of rational and reliable frequency estimations is obtained using the regional L-moments method and the two revised methods, which can indirectly provide a robust basis for flood control and water resource management. This study extends previous works by verifying underestimation of the quantiles and the provision of two improved methods for obtaining reliable quantile estimations of extreme precipitation at lower recurrence intervals, especially in solving reliable estimates for a 1-year return period from the integral lower limit of the frequency distribution.

Projection of future extreme precipitation: a robust assessment of downscaled daily precipitation

Statistical and dynamic downscaling approaches are commonly used to downscale large-scale climatic variables from global circulation (GCM) and regional circulation (RCM) model outputs to local precipitation. The performance of these two approaches may differ from each other for daily precipitation projections when applied in the same region. This is examined in this study based on the estimation of extreme precipitation. Daily precipitation series are generated from GCM HadCM3, CGCM3/T47 and RCM HadCM3 models for both historical hindcasts and future projections in accordance with the period from 1971 to 2070. The Waikato catchment of New Zealand is selected as a case study. Deterministic and probabilistic performances of the GCM and RCM simulations are evaluated using root-mean-square-error (RMSE) coefficient, percent bias (PBIAS) coefficient and equitable threat score (ETS). The value of RMSE, PBIAS and ETS is 2.89, − 2.16, 0.171 and 8.72, − 4.01, 0.442 for mean areal and at-site daily precipitation estimations, respectively. The study results reveal that the use of frequency analysis of partial duration series (FA/PDS) is very effective in evaluating the accuracy of downscaled daily precipitation series. Both the statistical and the dynamic downscaling perform well for simulating daily precipitation at station level for a return period equal to or less than 100 years. However, the latter outperforms the former for daily precipitation simulation at catchment level.

Evaluation of TMPA 3B42-V7 Product on Extreme Precipitation Estimates

Availability of precipitation data at high spatial and temporal resolution is crucial for the understanding of precipitation behaviors that are determinant for environmental aspects such as hydrology, ecology, and social aspects like agriculture, food security, or health issues. This study evaluates the performance of 3B42-V7 satellite-based precipitation product on extreme precipitation estimates in China, by using the Fuzzy C-Means algorithm and L-moment-based regional frequency analysis method. The China Gauge-based Daily Precipitation Analysis (CGDPA) product is employed to measure the estimation biases of 3B42-V7. Results show that: (1) for most regions of China, the Generalized Extreme Value and Generalized Normal distributions are preferable for extreme precipitation estimates; (2) the extreme precipitation estimations of 3B42-V7 for different return periods have a high correlation with those of CGDPA, with biases within 25% for a majority of China on extreme precipitation estimates.

Extreme Events of Precipitation over Complex Terrain Derived from Satellite Data for Climate Applications: An Evaluation of the Southern Slopes of the Pyrenees

Estimating extreme precipitation events over complex terrain is challenging but crucial for evaluating the performance of climate models for the present climate and expected changes of the climate in the future. New satellites operating in the microwave wavelengths have started to open new opportunities for performing such estimation at adequate temporal and spatial scales and within sensible error limits. This paper illustrates the feasibility and limits of estimating precipitation extremes from satellite data for climatological applications. Using a high-resolution gauge database as ground truth, it was found that global precipitation measurement (GPM) constellation data can provide valuable estimates of extreme precipitation over the southern slopes of the Pyrenees, a region comprising several climates and a very diverse terrain (a challenge for satellite precipitation algorithms). Validation using an object-based quality measure showed reasonable performance, suggesting that GPM estimates can be advantageous reference data for climate model evaluation.

Real-time Extreme Rainfall Evaluation System for the Construction Industry Using Deep Convolutional Neural Networks

For the construction industry, timely and reliable information on current and future rain information is vital for enabling forecasters to make accurate and timely forecasts and to allow appropriate construction operations. Because construction works are often delayed during typhoons, a useful scheme for rain forecasts during typhoon periods is highly desirable. This study developed a regional extreme precipitation and construction suspension estimation system (REPCSES) for the construction industry to use when a structure is in the construction stage. The REPCSES has two major functions: a regional extreme precipitation estimation model (comprising Modules 1 and 2) and the construction suspension estimation model (Modules 3 and 4). Module 1 is a regional 1-h-ahead rainfall estimation model, which is used for estimating the hourly rainfall near the construction location. Module 2 is used for estimating the cumulative rainfall within 24 h. Module 3 is designed to plot a hyetograph using the results from Modules 1 and 2. Then, Module 4 determines whether the construction should be suspended according to the plots from Module 3. In addition, this study developed a deep convolutional neural network model for estimating extreme rainfall during a structure under construction, and the experimental area was Nantou County, Taiwan. The collected typhoons (i.e., Soulik, Trami, Kong-Rey, Matmo, Dujuan, and Nesat) affecting the study area occurred from 2013 to 2017. The results indicated that the integrated system could provide accurate estimations of whether work could proceed as well as the number of days that construction should be suspended for.

Comparison of mapping approaches for estimating extreme precipitation of any return period at ungauged locations

Reliable estimation of return period values of extreme precipitation at ungauged locations is considered to be a key exercise in hydrometeorological studies. This study aims to identify an accurate approach in producing spatial maps (i.e. ungauged estimation) of extreme precipitation for any return period within a region. The study compares the following approaches: interpolation of summary of data as represented by L-moments, interpolation of parameters of an extreme value distribution and interpolation of return period quantile value. Several interpolation schemes are considered; however, the aim is to evaluate schemes that employ secondary data. The schemes compared are ordinary kriging, kriging with external drift (KED) and a more traditional, inverse distance weighting. The secondary data namely elevation, satellite based mean annual precipitation (MAP), distance from nearest coast (CD) and geographical coordinates are incorporated in the KED system. Annual maximum 1-day precipitation series at 76 gauging stations from the region of East China have been used to assess the performance. The generalized extreme value (GEV) distribution, appropriate for the study region, with the method of L-moments is used to analyze the frequency of extreme precipitation. It is found that either the approach of interpolating parameters of GEV distribution or L-moments should be the natural choice for estimating design value at any return period. However, in terms of error statistics the approach of interpolating parameters has given a lower RMSE value compared to the approach of interpolating L-moments. The approach of quantile interpolation performed worst and should not be used in practice in interpolating return period values. The KED is recognized as the most appropriate interpolation scheme when a significant covariate is identified. The MAP appears to be a suitable covariate in most cases when interpolating L moments (1st and 2nd L-moment) or GEV parameters (location and scale parameter). There is no spatial dependence identified for L-skewness or shape parameter of GEV distribution and in the future one should concentrate on how a superior spatial model can be identified in this context.