Comprehensive applicability evaluation of four precipitation products at multiple spatiotemporal scales in Northwest China

Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relief.Currently,the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated.In this study,precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products(the fifth generation of European Centre for Medium-Range Weather Forecasts(ECMWF)atmospheric reanalysis of the global climate(ERA5),Global Precipitation Climatology Centre(GPCC),Climatic Research Unit gridded Time Series Version 4.07(CRU TS v4.07,hereafter CRU),and Tropical Rainfall Measuring Mission(TRMM))based on the following statistical indicators:correlation coefficient,root mean square error(RMSE),relative bias(RB),mean absolute error(MAE),probability of detection(POD),false alarm ratio(FAR),and equitable threat score(ETS).The results showed that precipitation in Northwest China was generally high in the east and low in the west,and exhibited an increasing trend from 1979 to 2020.Compared with the station observations,ERA5 showed a larger spatial distribution difference than the other products.The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity.The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations.The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation.At the monthly scale,GPCC performed well but underestimated precipitation in the Tarim Basin(RB=-4.11%),while ERA5 and TRMM exhibited poor accuracy in high-altitude regions.ERA5 had a large bias(RB≥120.00%)in winter months and a strong dispersion(RMSE≥35.00 mm)in summer months.TRMM showed a relatively low correlation with station observations in winter months(correlation coefficients≤0.70).The capture performance analysis showed that ERA5,GPCC,and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased.ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased.GPCC had the lowest FAR values.TRMM was statistically ineffective for predicting the occurrence of daily precipitation events.The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.

Evaluation of high-resolution satellite precipitation products with surface rain gauge observations from Laohahe Basin in northern China

Three high-resolution satellite precipitation products, the Tropical Rainfall Measuring Mission （TRMM） standard precipitation products 3B42V6 and 3B42RT and the Climate Precipitation Center＇s （CPC） morphing technique precipitation product （CMORPH）, were evaluated against surface rain gauge observations from the Laohahe Basin in northern China. Widely used statistical validation indices and categorical statistics were adopted. The evaluations were performed at multiple time scales, ranging from daily to yearly, for the years from 2003 to 2008. The results show that all three satellite precipitation products perform very well in detecting the occurrence of precipitation events, but there are some different biases in the amount of precipitation. 3B42V6, which has a bias of 21%, fits best with the surface rain gauge observations at both daily and monthly scales, while the biases of 3B42RT and CMORPH, with values of 81% and 67%, respectively, are much higher than a normal receivable threshold. The quality of the satellite precipitation products also shows monthly and yearly variation： 3B42RT has a large positive bias in the cold season from September to April, while CMORPH has a large positive bias in the warm season from May to August, and they all attained their best values in 2006 （with 10%, 50%, and -5% biases for 3B42V6, 3B42RT, and CMORPH, respectively）. Our evaluation shows that, for the Laohahe Basin, 3B42V6 has the best correspondence with the surface observations, and CMORPH performs much better than 3B42RT. The large errors of 3B42RT and CMORPH remind us of the need for new improvements to satellite precipitation retrieval algorithms or feasible bias adjusting methods.

Evaluation of CMPAS precipitation products over Sichuan,China

High-quality and high-resolution precipitation data are the basis for mesoscale numerical weather forecasting,model verification,and hydrological monitoring,which play an important role in meteorological and hydrological disaster prevention and mitigation.In this study,high-density rain gauge data are used to evaluate the fusion accuracy of the China Meteorological Administration Multisource Precipitation Analysis System(CMPAS),and four CMPAS products with different spatial and temporal resolution and different data sources are compared,to derive the applicability of CMPAS.Results show that all the CMPAS products show high accuracy in the Sichuan Basin,followed by Panxi Area and the western Sichuan Plateau.The errors of the four products all rise with the increase in precipitation.CMPAS overestimates precipitation in summer and autumn and underestimates it in spring and winter.Overall,the applicability of these fused data in the Sichuan Basin is quite good.Due to the lack of observations and the influence of the terrain and meteorological conditions,the evaluation of CMPAS in the plateau area needs further analysis.

Assessing the Applicability of Multi-Source Precipitation Products over the Chinese Mainland and Its Seven Regions

Satellite-based and reanalysis precipitation products provide valuable information for various applications.However,their performance varies widely across regions due to different data sources and production processes.This paper evaluated the daily performance of four precipitation products(MSWEP,ERA5,PERSIANN,and TRMM)for seven regions of the Chinese mainland,using observations from 2462 ground stations across the country as a benchmark.We used four statistical and four classification indicators to describe their spatial and temporal accuracy,and capability to detect precipitation events while analyzing their applicability.The results show that according to the precipitation char-acteristics and accuracy of different types of precipitation products over the Chinese mainland,MSWEP was the most suitable product over the Chinese mainland,having the lowest root mean square error and mean absolute error,along with the highest coefficient of determination.It was followed by TRMM and ERA5,whereas PERSIANN lagged behind in terms of performance.In terms of different regions,MSWEP still performed well,especially in North China and East China.The accuracy of the four precipitation products was relatively low in the summer months,and they all overestimated in the northwest region.In other months,MSWEP and TRMM were better than PERSIANN and ERA5.The four precipitation products had good detection performance over the Chinese mainland,with probability of detection above 0.5.However,with the increase of precipitation threshold,the detection capability of the four products decreased,and MSWEP and ERA5 had good detection capability for moderate rain.TRMM’s detection capability for heavy rain and rainstorms was better than that of the other three products,and PERSIANN’s detection capability for moderate rain,heavy rain and rainstorms was relatively poor,with a large deviation.

Effects of Drought on Net Primary Productivity:Roles of Temperature,Drought Intensity,and Duration

Northeast China has experienced frequent droughts over the past fifteen years. However, the effects of droughts on net primary productivity(NPP) in Northeast China remain unclear. In this paper, the droughts that occurred in Northeast China between 1999 and 2013 were identified using the Standardized Precipitation Evapotranspiration Index(SPEI). The NPP standardized anomaly index(NPP-SAI) was used to evaluate NPP anomalies. The years of 1999, 2000, 2001, and 2007 were further investigated in order to explore the influence of droughts on NPP at different time scales(3, 6, and 12 months). Based on the NPP-SAI of normal areas, we found droughts overall decreased NPP by 112.06 Tg C between 1999 and 2013. Lower temperatures at the beginning of the growing season could cause declines in NPP by shortening the length of the growing season. Mild drought or short-term drought with higher temperatures might increase NPP, and weak intensity droughts intensified the lag effects of droughts on NPP.

Rainfall Estimation using Image Processing and Regression Model on DWR Rainfall Product for Delhi-NCR Region

Observed rainfall is a very essential parameter for the analysis of rainfall,day to day weather forecast and its validation.The observed rainfall data is only available from five observatories of IMD;while no rainfall data is available at various important locations in and around Delhi-NCR.However,the 24-hour rainfall data observed by Doppler Weather Radar(DWR)for entire Delhi and surrounding region(up to 150 km)is readily available in a pictorial form.In this paper,efforts have been made to derive/estimate the rainfall at desired locations using DWR hydrological products.Firstly,the rainfall at desired locations has been estimated from the precipitation accumulation product(PAC)of the DWR using image processing in Python language.After this,a linear regression model using the least square method has been developed in R language.Estimated and observed rainfall data of year 2018(July,August and September)was used to train the model.After this,the model was tested on rainfall data of year 2019(July,August and September)and validated.With the use of linear regression model,the error in mean rainfall estimation reduced by 46.58% and the error in max rainfall estimation reduced by 84.53% for the year 2019.The error in mean rainfall estimation reduced by 81.36% and the error in max rainfall estimation reduced by 33.81%for the year 2018.Thus,the rainfall can be estimated with a fair degree of accuracy at desired locations within the range of the Doppler Weather Radar using the radar rainfall products and the developed linear regression model.

Analysis of Factors Influencing Shut in Pressure Cone in Offshore Strong Bottom Water Reservoir

X oilfield is an offshore strong bottom water reservoir with water cut up to 96% at present, and liquid extraction has become one of the main ways to increase oil production. However, the current liquid production of the oilfield reaches 60,000 m3/d due to the limitation of offshore platform, well trough and equipment, the oilfield is unable to continue liquid extraction. In order to maximize the oil production of the oilfield, it is necessary to study the strategy of shut in and cone pressure. Through numerical simulation, this paper analyzes the influence of different factors, such as crude oil density, viscosity, reservoir thickness, interlayer, permeability and so on, on the drop height of water cone and the effect of precipitation and oil increase after well shut in. At the same time, the weight of each factor is analyzed by combining the actual dynamic data with the fuzzy mathematics method, and the strategy of well shut in and cone pressure is formulated for the offshore strong bottom water reservoir. It provides the basis and guidance for the reasonable use of shut in pressure cone when the reservoir with strong bottom water meets the bottleneck of liquid volume.

Extreme precipitation detection ability of four high-resolution precipitation product datasets in hilly area:a case study in Nepal

Given Nepal's vulnerability to extreme precipitation(EP),it is imperative to conduct a comprehensive analysis to comprehend the historical trends of such events.However,acquiring precise precipitation data for EP remains challenging in mountainous countries like Nepal owing to the scarcity of densely gauged networks.This limitation impedes the dissemination of knowledge pertaining to EP variability events in Nepal.The current research on this topic is deficient for two main reasons:1)there is a lack of studies leveraging recently released high-resolution precipitation products to identify their EP detection capabilities,which further hinders the usability of those products in data-scarce regions like Nepal,and 2)most studies have focused on the characterisation of EP events in Nepal rather than their spatial and temporal variability.To address these issues,this study evaluated the EP detection capabilities of four high-resolution precipitation product datasets(PPDs)across Nepal from 1985 to 2020.These datasets include the ERA5 Land reanalysis data,satellite-based precipitation data(PERSIANN_CCS_CDR and CHIRPS_V2.0)and a merged dataset(TPHiPr).We used various statistical and categorical indices to assess their ability to capture the spatial and temporal variability of EP events.The annual EP events were characterised by 11 indices divided into frequency and intensity categories.The TPHiPr merged dataset offered a robust depiction of monthly precipitation estimates,achieving the highest critical success index,accuracy,probability of detection and a low false alarm ratio for daily precipitation detection of 0.1 mm in Nepal.Conversely,the PERSIANN_CCS_CDR dataset exhibited poor performance.Most PPDs showed increasing trends in EP indices.However,the TPHiPr dataset showcased those trends with fewer errors and stronger correlations for many frequency(R10mm,R20mm and R25mm)and intensity(RX1day,RX5day,PRCPTOT and R99p)indices.The results indicate that TPHiPr outperformed other PPDs in accurately representing the spatial distribution of EP trends in Nepal from 1985 to 2020,particularly noting an exacerbation of EP events mostly in the eastern region of Nepal throughout the study period.While TPHiPr demonstrated superior performance in detecting various EP indices across Nepal,individual products like the ERA5 Land reanalysis dataset showed enhanced performance in the western region of Nepal.Conversely,PERSIANN_CCS_CDR and CHIRPS_V2.0 performed well in the eastern region compared to other PPDs.

Refined Evaluation of Satellite Precipitation Products against Rain Gauge Observations along the Sichuan–Tibet Railway

Being constructed in southwestern China, the Sichuan–Tibet Railway(STR) travels across the eastern Tibetan Plateau where there is the most complex terrain and changeable weather in the world. Due to sparse ground-based observations over the Tibetan Plateau, precipitation products retrieved by remote sensing are more widely used;however,satellite-based precipitation products(SPPs) have not yet been strictly and systematically evaluated along the STR.This study aims to evaluate the performance of six SPPs by a series of metrics with available ground observations along the STR during 1998–2020. The six SPPs include the datasets derived from the Tropical Rainfall Measuring Mission(TRMM), Climate Prediction Center morphing technique(CMORPH), Global Precipitation Measurement(GPM), Global Satellite Mapping of Precipitation(GSMaP), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks(PERSIANN), and Fengyun-2 satellites precipitation estimate(FY2PRE). The results indicate that most of the SPPs can capture the precipitation characteristics on multiple timescales(monthly,daily, hourly, and diurnal cycle) as shown by the evaluated metrics. The probability density functions of the daily and hourly precipitation are also well represented by the SPPs, and 30 mm day^(-1) and 16 mm h^(-1) are identified as the daily and hourly thresholds of extreme precipitation events along the STR. The best SPP varies at different timescales:GPM and GSMaP are suitable for the monthly and daily scale, and FY2PRE and GPM are suited to the hourly scale.In general, GPM is relatively optimum on multiple timescales, and PERSIANN gives the worst performance. In addition, the SPPs perform worse at higher altitudes and for more intense precipitation. Overall, the results from this study are expected to provide essential reference for using the SPPs in meteorological services and disaster prevention in the STR construction and its future operation.

Are regional precipitation-productivity relationships robust to decadal-scale dry period?

Precipitation(PPT)is the primary climatic determinant of plant growth and aboveground net primary productivity(ANPP)for many of the world’s major terrestrial ecosystems.Thus,relationships between PPT and productivity can provide insight into how changes in climate may alter ecosystem functions globally.Spatial PPT–ANPP relationships for grasslands are found remarkably similar around the world,but whether and how they change during periods of extended climatic anomalies remain unknown.Here,we quantifed how regional-scale PPTANPP relationships vary between an extended wet and a dry period by taking advantage of a 35-year record of PPT and NDVI(as a surrogate for ANPP)at 1700 sites in the temperate grasslands of northern China.We found a sharp decrease in the strength of the spatial PPT–ANPP relationship during an 11-year period of below average PPT.We attributed the collapse of this relationship to asynchrony in the responses of different grassland types to this decadal period of increased aridity.Our results challenge the robustness of regional PPT–productivity if aridity in grasslands is increased globally by climate change.