Evaluation of the Adaptability of Six Sets of Precipitation Data in Inner Mongolia

Based on daily precipitation data of 119 weather stations over Inner Mongolia in 2018,the adaptability of six sets of precipitation data( GLDAS2.1,ITPCAS,CLDAS2.0,CMPAS2.0 ERA5 and CMPAS2.1) in Inner Mongolia was evaluated and analyzed by using Pearson correlation coefficient( R),mean deviation( Bias) and root mean square error( RMSE).The results indicate that the six sets of precipitation data could well reflect the spatial and temporal variation of precipitation over Inner Mongolia.On a ten-day scale,the mean deviation of CMPAS2.1 had a smaller variation,which slightly underestimated precipitation;the mean deviation of CLDAS2.0 had a smaller variation in the eastern region;the mean deviation of both ITPCAS and CMPAS2.0 was more stable in the central region than in the eastern and western regions;the mean deviation of GLDAS2.1 and ERA5 had a relatively larger variation,and ERA5 overestimated precipitation to a certain extent;the root mean square error of CMPAS2.1 had the smallest variation,whereas that of ERA5 was relatively larger.The monthly scale was similar to the ten-day scale.The correlation coefficient of the six sets of precipitation data in the central region was better than that in the east and west,and the mean deviation and root mean square error were relatively larger in areas with more complex mountain topography.According to statistical indicators,CMPAS2.1 performed better in Inner Mongolia than other five sets of data.

Evaluation and Hydrological Application of CMADS Reanalysis Precipitation Data against Four Satellite Precipitation Products in the Upper Huaihe River Basin, China

Satellite-and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool(SWAT) model(CMADS)reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs;i.e., Tropical Rainfall Measuring Mission(TRMM) Multisatellite Precipitation Analysis 3B42 Version 7(TMPA 3B42V7), Climate Prediction Center(CPC) morphing technique satellite–gauge blended product(CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data(CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record(PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang(XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that:(1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient(CC) and rootmean-square error(RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias(BIAS;-22.72%);(2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation;(3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash–Sutcliffe efficiency(NSE) values(0.85 and 0.75 for calibration period and validation period, respectively);and(4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in datascarce regions with similar climates and topography in the Global Precipitation Measurement(GPM) era.

Evaluating the Influence of Multisource Typhoon Precipitation Data on Multiscale Urban Pluvial Flood Modeling

Based on station precipitation observations,radar quantitative precipitation estimates(QPE), and radar fusion data during Typhoon Fitow(2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China,as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows:(1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%.(2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details.(3)One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result-all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems.

Analysis on the Variation of Rainfall Data from Guilin Weather Station during 1957-2007

[Objective] The research aimed to study the variation of rainfall data from Guilin Weather Station during 1957-2007.[Method] Based on the daily rainfall data in Guilin during 1957-2007,the trend,period and mutation of precipitation in Guilin in 51 years were analyzed by using the trend analysis,wavelet analysis and Mann-Kendall non-parameter statistics test method.[Result] The rainfall in Guilin in 51 years presented the rising trend.The rainfall variation was same in the first,second and third quarters of most years,except in the individual year.The rainfall in the fourth quarter had the decrease trend,and the variation was obvious in each year.It illustrated that the rainfall variation in winter was very unstable and had the decrease trend in recent years.But as a whole,the variation of total rainfall in Guilin wasn't obvious and had the rise trend.It illustrated that the climate variation in Guilin in 51 years wasn't obvious.The wavelet analysis showed that the rainfall variation in Guilin had 15-year big period and the small period of 2-3 years.Mann-Kendall non-parameter statistics test showed that the mutation situation of total rainfall in Guilin in 51 years wasn't obvious.But the mutation situations in the second and third quarters were more.The variation in recent 10 years was the most obvious.Maybe it was affected by the global climate variation.[Conclusion] The research provided the theory basis for analyzing the climate variation in Guilin.

Deep learning-based multi-source precipitation merging for the Tibetan Plateau

Due to its complex and diverse terrain, precipitation gauges in the Tibetan Plateau(TP) are sparse, making it difficult to obtain reliable precipitation data for environmental studies. Data merging is a method that can integrate precipitation data from multiple sources to generate high-precision precipitation data. However, the more commonly used methods, such as regression and machine learning, do not usually consider the local correlation of precipitation, so that the spatial pattern of precipitation cannot be reproduced, while deep learning methods do incorporate spatial correlation. To explore the ability of using deep learning methods in merging precipitation data for the TP, this study compared three methods: a deep learning method—a convolutional neural network(CNN) algorithm, a machine learning method—an artificial neural network(ANN) algorithm, and a statistical method based on Extended Triple Collocation(ETC) in merging precipitation from multiple sources(gauged, grid,satellite and dynamic downscaling) over the TP, as well as their performance for hydrological simulations. Dynamic downscaling data driven by global reanalysis data centered on the TP were introduced in the merging process to better reflect the spatial variability of precipitation. The results show that:(1) in terms of the meteorological metrics, the merged data perform better than the gauge interpolation data. By using data merging, the error between the raw multi-source and gauged precipitation can be reduced, and the precipitation detection capability can be greatly improved;(2) The merged precipitation data also perform well in the hydrological evaluation. The Xin’anjiang(XAJ) model parameter calibration experiments at the source of the Yangtze River(SYR) and the source of the Yellow River(SHR) were repeated 300 times to remove uncertainty in the model parameter results. The median Kling-Gupta Efficiency Coefficients(KGE) of simulated runoff from the merged data of the ANN, CNN and ETC methods for the SYR and the SHR are 0.859, 0.864, 0.838 and 0.835, 0.835, 0.789, respectively. Except for the ETC merging data at the SHR, the performance of other merged data was improved compared to the simulation results of the gauged precipitation(KGE=0.807 at the SYR, KGE=0.828 at the SHR);and(3) In contrast to the machine learning ANN method and the statistical ETC method, the deep learning method, CNN, consistently showed better performance.

Evaluation of the ECMWF Precipitation Product over Various Regions of Iran

Easy access to accurate and reliable climate data is a crucial concern in hydrological modeling.In this regard,gridded climate data have recently been provided as an alternative to observational data.However,those data should be first evaluated and corrected to guarantee their validity and accuracy.This study offered a new approach to assess the ECMWF gridded precipitation data based on some indicators,including correlation coefficient(CC),normalized root-mean-square error(NRMSE),and absolute error(AE)in daily and monthly intervals(2007-2017)across different climatic and geographical areas of Iran.Besides,an artificial neural network(ANN)model was utilized to correct the ECMWF precipitation product.According to the results,NRMSE was less than 2(in 93%of stations)and 5(in63%of stations)on monthly and daily scales,respectively.Moreover,CC was above 0.6 in 58%and 94%of stations on daily and monthly scales,respectively.The AE values were from-0.5 to 0.5,in 80%(daily scale)and 50%(monthly scale)of stations.Having corrected the ECMWF precipitation product by ANN,the number of stations with NRMSE less than 5 increased from 63%to 74%on the daily timescale,whereas the number of stations with NRMSE less than 2 reached 95%from 93%on the monthly timescale.The results also showed that the number of stations with CC more than 0.6 increased from 58%to 87%on the daily timescale.

Cross-sectional rainfall observation on the central-western Tibetan Plateau in the warm season:System design and preliminary results

The central and western Tibetan Plateau(CWTP)is characterized by harsh environment and strong interactions among the spheres of earth as well as significant changes in climate and water cycles over the past four decades.The lack of precipitation observations is a bottleneck for the study of land surface processes in this region.Over the past six years,we have designed and established two observation transects across the south-north and the west-east in this region to obtain hourly rainfall data during the warm season(May-September).The south-north transect extends from Yadong Valley on the southern slope of the Himalayas to Shuanghu County in the hinterland of the plateau,with a total of 31stations;the west-east transect extends from Shiquanhe in the west to Naqu in the central TP,with a total of 22 stations.The observation dataset has been applied to clarify the spatiotemporal characteristics of precipitation in the CWTP,to evaluate the quality of typical gridded precipitation products,to support the development of regional climate models,and to reveal the processes of summertime lake-air interactions.The observation dataset has been released in the National Tibetan Plateau Data Center.

Spatial and temporal variabilities of rainstorms over China under climate change

Rainstorms are one of the extreme rainfall events that cause serious disasters,such as urban flooding and mountain torrents.Traditional studies have used rain gauge observations to analyze rainstorm events,but relevant information is usually missing in gauge-sparse areas.Satellite-derived precipitation datasets serve as excellent supplements or substitutes for the gauge observations.By developing a grid-based rainstorm-identification tool,we used the Tropical Rainfall Measurement Mission(TRMM)Multi-satellite Precipitation Analysis(TMPA)time series product to reveal the spatial and temporal variabilities of rainstorms over China during 1998–2017.Significant patterns of both increasing and decreasing rainstorm occurrences were detected,with no spatially uniform trend being observed across the whole country.There was an increase in the area being affected by rainstorms during the 20-year period,with rainstorm centers shifting along the southwest–northeast direction.Rainstorm occurrence was found to be correlated with local total precipitation.By comparing rainstorm occurrence with climate variables such as the El Ni?o-Southern Oscillation and Pacific Decadal Oscillation,we also found that climate change was likely to be the primary reason for rainstorm occurrence in China.This study complements previous studies that used gauge observations by providing a better understanding of the spatiotemporal dynamics of China’s rainstorms.

Rainfall erosivity estimation over the Tibetan plateau based on high spatial-temporal resolution rainfall records

The Tibetan Plateau(TP)in China has been experiencing severe water erosion because of climate warming.The rapid development of weather station network provides an opportunity to improve our understanding of rainfall erosivity in the TP.In this study,1-min precipitation data obtained from 1226 weather stations during 2018–2019 were used to estimate rainfall erosivity,and subsequently the spatial-temporal patterns of rainfall erosivity in the TP were identified.The mean annual erosive rainfall was 295 mm,which accounted for 53%of the annual rainfall.An average of 14 erosive events occurred yearly per weather station,with the erosive events in the wet season being more likely to extend beyond midnight.In these cases,the precipitation amounts of the erosive events were found to be higher than those of the daily precipitations,which may result in implicit bias as the daily precipitation data were used for estimating the rainfall erosivity.The mean annual rainfall erosivity in the TP was 528 MJ mm·ha^(-1)·h^(-1),with a broader range of 0–3402 MJ mm·ha^(-1)·h^(-1),indicating a significant spatial variability.Regions with the highest mean annual rainfall erosivity were located in the forest zones,followed by steppe and desert zones.Finally,the precipitation phase records obtained from 140 weather stations showed that snowfall events slightly impacted the accuracy of rainfall erosivity calculation,but attention should be paid to the erosion process of snowmelt in the inner part of the TP.These results can be used as the reference data for soil erosion prediction in normal precipitation years.

Estimating Ground Snow Load Based on Ground Snow Depth and Climatological Elements for Snow Hazard Assessment in Northeastern China

Extreme snow loads can collapse roofs.This load is calculated based on the ground snow load(that is,the snow water equivalent on the ground).However,snow water equivalent(SWE) measurements are unavailable for most sites,while the ground snow depth is frequently measured and recorded.A new simple practical algorithm was proposed in this study to evaluate the SWE by utilizing ground snow depth,precipitation data,wind speed,and air temperature.For the evaluation,the precipitation was clas sified as snowfall or rainfall according to the air temperature,the snowfall or rainfall was then corrected for measurement error that is mainly caused by wind-induced undercatch,and the effect of snow water loss was considered.The developed algorithm was applied and validated using data from57 meteorological stations located in the northeastern region of China.The annual maximum SWE obtained based on the proposed algorithm was compared with that obtained from the actual SWE measurements.The return period values of the annual maximum ground snow load were estimated and compared to those obtained according to the procedure suggested by the Chinese structural design code.The comparison indicated that the use of the proposed algorithm leads to a good estimated SWE or ground snow load.Its use allowed the estimation of the ground snow load for sites without SWE measurement and facilitated snow hazard mapping.