scholarly journals Trends in Precipitation Extremes in the Zhujiang River Basin, South China

2011 ◽  
Vol 24 (3) ◽  
pp. 750-761 ◽  
Author(s):  
Marco Gemmer ◽  
Thomas Fischer ◽  
Tong Jiang ◽  
Buda Su ◽  
Lü Liu Liu
Keyword(s):  
River Basin ◽  
South China ◽  
Wind Direction ◽  
Daily Precipitation ◽  
Monthly Precipitation ◽  
Maximum Precipitation ◽  
Daily Precipitation Data ◽  
Precipitation Indices ◽  
The Mean ◽  
Zhujiang River

Abstract Spatial and temporal characteristics of precipitation trends in the Zhujiang River basin, South China, are analyzed. Nonparametric trend tests are applied to daily precipitation data from 192 weather stations between 1961 and 2007 for the following indices: annual, monthly, and daily precipitation; annual and monthly number of rain days and precipitation intensity; annual and monthly maximum precipitation; 5-day maximum precipitation, number of rainstorms with >50 mm day−1, and peaks over thresholds (90th, 95th, and 99th percentile). The results show that few stations experienced trends in the precipitation indices on an annual basis. On a monthly basis, significant positive and negative trends above the 90% confidence level appear in all months except December. Trends in the indices of monthly precipitation, rain intensity, rain days, and monthly maximum precipitation show very similar characteristics. They experience the most distinct negative (positive) trends in October (January). A change of the mean wind direction by 50° from east-southeast to east-northeast explains the downward trend in precipitation in October. Dry October months (months with low precipitation indices) can be observed when the mean wind direction is east-northeast (arid) instead of the prevailing mean wind direction, east-southeast (moist). The former is typical for the East Asian winter monsoon (EAWM). Nearly 90% of the driest October months can be explained by wind directions typical for the EAWM. The upward trend in precipitation indices in January cannot be explained by changes in large-scale circulation. The analysis of the precipitation indices delivers more detailed information on observed changes than other studies in the same area. This can be attributed to the higher station density, the quality of daily data, and the focus on monthly trends in the current study.

scholarly journals Bias Correction of Zero-Inflated RCM Precipitation Fields: A Copula-Based Scheme for Both Mean and Extreme Conditions

2019 ◽  
Vol 20 (4) ◽  
pp. 595-611 ◽  
Author(s):  
Rajib Maity ◽  
Mayank Suman ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Joint Distribution ◽  
Daily Precipitation ◽  
Extreme Values ◽  
Entire Study ◽  
Daily Precipitation Data ◽  
Zero Values ◽  
The Mean

Abstract Changes in extreme precipitation due to climate change often require the application of methods to bias correct simulated atmospheric fields, including extremes. Most existing bias correction techniques (i) only focus on the bias in the mean value or on the extreme values separately, and (ii) exclude zero values from analysis, even though their presence is significant in daily precipitation. We developed a copula-based bias correction scheme that is suitable for zero-inflated daily precipitation data to correct the bias in mean as well as in extreme precipitation at any specific statistical quantile. In considering the whole of Germany as a test bed, the proposed scheme is found to work well across the entire study area, including the German Alpine regions. The joint distribution between observed and regional climate model (RCM)-derived precipitation is developed through copulas. In particular, the joint distribution is modified to make it discrete at zero in order to account for zero values. The benefit of considering zero precipitation values is revealed through the improved performance of bias correction both in the mean and extreme values. Second, the quantile that best captures the bias (whether in the mean or any extreme value) is determined for a specific location and varies spatially and seasonally. This relaxation in selecting the location-specific optimal quantile renders the proposed methodology spatially transferable. By acknowledging possible changes in extreme precipitation due to climate change, the proposed scheme is expected to be suitable for climate change impact assessments for extreme events worldwide.

scholarly journals Evaluating the GPCC Full Data Daily Analysis Version 2018 through ETCCDI indices and comparison with station observations over mainland of China

2020 ◽  
Vol 142 (3-4) ◽  
pp. 835-845
Author(s):  
Yu Yu ◽  
Udo Schneider ◽  
Su Yang ◽  
Andreas Becker ◽  
Zhihua Ren
Keyword(s):  
Daily Precipitation ◽  
Correlation Coefficients ◽  
Full Data ◽  
Global Precipitation Climatology Centre ◽  
Daily Precipitation Data ◽  
Distance Weighting ◽  
The Mainland Of China ◽  
The Mean ◽  
Global Precipitation

Abstract The new 1° × 1° resolution global Full Data Daily Analysis Version 2018 published by the Global Precipitation Climatology Centre (GPCC) of Deutscher Wetterdienst was compared with an analysis of the measurements from the national dataset over the mainland of China with regard to four of the 27 ETCCDI indices (http://etccdi.pacificclimate.org/list_27_indices.shtml) commonly used to determine extreme precipitation (Rx5day, R10mm, CDD and SDII). After extreme value check, integrity check, and homogeneity check, the observations from 2327 surface stations covering the years from 1982 to 2016 fulfilled the criteria for the evaluation. The in situ daily precipitation data were interpolated onto a 1° × 1° grid over the mainland of China by employing Shepard’s angular and distance weighting algorithm. The four aforementioned indices were then calculated on the national station–based analysis being referred to as STA. Moreover, the aforementioned gridded GPCC Full Data Daily product was directly utilized to calculate the same indices (FDDA). The China national means of Rx5day, R10mm, CDD and SDII calculated from FDDA and STA had similar variations and trends with high correlation coefficients, and the mean biases between FDDA and STA were 2.5 mm, 1.2 days, 0.0 day and 0.3 mm respectively. The trends of Rx5day, R10mm and SDII are increasing, whereas the trend of CDD is negative. The distributions of the grid mean and the grid trends of indices over China from FDDA and STA show similar patterns too, indicating that the FDDA shows a surprisingly high fidelity in reproducing almost the same patterns in the four ETCCDI indices chosen compared with the STA-based analysis.

scholarly journals Identifying hydro-meteorological events from precipitation extremes indices and other sources over northern Namibia, Cuvelai Basin

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Frans C. Persendt ◽  
Christopher Gomez ◽  
Peyman Zawar-Reza
Keyword(s):  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Economic Losses ◽  
Precipitation Data ◽  
Rainfall Station ◽  
Extreme Precipitation Events ◽  
Daily Precipitation Data ◽  
Dry Spell ◽  
Flood Disasters ◽  
Precipitation Indices

Worldwide, more than 40% of all natural hazards and about half of all deaths are the result of flood disasters. In northern Namibia flood disasters have increased dramatically over the past half-century, along with associated economic losses and fatalities. There is a growing concern to identify these extreme precipitation events that result in many hydro-meteorological disasters. This study presents an up to date and broad analysis of the trends of hydrometeorological events using extreme daily precipitation indices, daily precipitation data from the Grootfontein rainfall station (1917–present), regionally averaged climatologies from the gauged gridded Climate Research Unit (CRU) product, archived disasters by global disaster databases, published disaster events in literature as well as events listed by Mendelsohn, Jarvis and Robertson (2013) for the data-sparse Cuvelai river basin (CRB). The listed events that have many missing data gaps were used to reference and validate results obtained from other sources in this study. A suite of ten climate change extreme precipitation indices derived from daily precipitation data (Grootfontein rainfall station), were calculated and analysed. The results in this study highlighted years that had major hydro-meteorological events during periods where no data are available. Furthermore, the results underlined decrease in both the annual precipitation as well as the annual total wet days of precipitation, whilst it found increases in the longest annual dry spell indicating more extreme dry seasons. These findings can help to improve flood risk management policies by providing timely information on historic hydro-meteorological hazard events that are essential for early warning and forecasting.

scholarly journals Probabilistic Projections of Climate Change for the Mid-Atlantic Region of the United States: Validation of Precipitation Downscaling during the Historical Era*

2012 ◽  
Vol 25 (2) ◽  
pp. 509-526 ◽  
Author(s):  
Liang Ning ◽  
Michael E. Mann ◽  
Robert Crane ◽  
Thorsten Wagener
Keyword(s):  
United States ◽  
General Circulation ◽  
Large Scale ◽  
Daily Precipitation ◽  
Circulation Model ◽  
The United States ◽  
Monthly Precipitation ◽  
Atlantic Region ◽  
Daily Precipitation Data ◽  
Training Interval

Abstract This study uses a statistical downscaling method based on self-organizing maps (SOMs) to produce high-resolution, downscaled precipitation estimates over the state of Pennsylvania in the mid-Atlantic region of the United States. The SOMs approach derives a transfer function between large-scale mean atmospheric states and local meteorological variables (daily point precipitation values) of interest. First, the SOM was trained using seven coarsely resolved atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis dataset to model observed daily precipitation data from 17 stations across Pennsylvania for the period 1979–2005. Employing the same coarsely resolved variables from nine general circulation model (GCM) simulations taken from the historical analysis of the Coupled Model Intercomparison Project, phase 3 (CMIP3), the trained SOM was subsequently applied to simulate daily precipitation at the same 17 sites for the period 1961–2000. The SOM analysis indicates that the nine model simulations exhibit similar synoptic-scale features to the (NCEP) observations over the 1979–2007 training interval. An analysis of the sea level pressure signatures and the precipitation distribution corresponding to the trained SOM shows that it is effective in differentiating characteristic synoptic circulation patterns and associated precipitation. The downscaling approach provides a faithful reproduction of the observed probability distributions and temporal characteristics of precipitation on both daily and monthly time scales. The downscaled precipitation field shows significant improvement over the raw GCM precipitation fields with regard to observed average monthly precipitation amounts, average monthly number of rainy days, and standard deviations of monthly precipitation amounts, although certain caveats are noted.

A Gauge-Based Analysis of Daily Precipitation over East Asia

2007 ◽  
Vol 8 (3) ◽  
pp. 607-626 ◽  
Author(s):  
Pingping Xie ◽  
Mingyue Chen ◽  
Song Yang ◽  
Akiyo Yatagai ◽  
Tadahiro Hayasaka ◽  
...  
Keyword(s):  
East Asia ◽  
Daily Precipitation ◽  
Interpolation Method ◽  
Optimal Interpolation ◽  
Network Density ◽  
Monthly Precipitation ◽  
Orographic Effects ◽  
Precipitation Estimates ◽  
The Mean ◽  
Validation Tests

Abstract A new gauge-based analysis of daily precipitation has been constructed on a 0.5° latitude–longitude grid over East Asia (5°–60°N, 65°–155°E) for a 26-yr period from 1978 to 2003 using gauge observations at over 2200 stations collected from several individual sources. First, analyzed fields of daily climatology are computed by interpolating station climatology defined as the summation of the first six harmonics of the 365-calendar-day time series of the mean daily values averaged over a 20-yr period from 1978 to 1997. These fields of daily climatology are then adjusted by the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) monthly precipitation climatology to correct the bias caused by orographic effects. Gridded fields of the ratio of daily precipitation to the daily climatology are created by interpolating the corresponding station values using the optimal interpolation method. Analyses of total daily precipitation are finally calculated by multiplying the daily climatology by the daily ratio. Cross-validation tests indicated that this gauge-based analysis has high quantitative quality with a negligible bias and a correlation coefficient of ∼0.6 for comparisons between withdrawn station data and the analysis at a 0.05° latitude–longitude grid box. The quality of the analysis increases with the gauge network density. The mean distribution and annual cycle of this new gauge analysis present similar patterns but with more detailed structures and slightly larger magnitude compared to other published monthly gauge analyses over the region. The East Asia gauge analysis is applied to verify the performance of five satellite-based precipitation estimates. This examination reveals the regionally and seasonally dependent performance of the satellite products with the best statistics observed for relatively wet regions. Further improvements of the daily gauge analysis are underway to increase the gauge network density and to refine the algorithm to better deal with the orographic effects especially over South and Southeast Asia.

scholarly journals Probability Distribution of Precipitation Extremes for Weather Index–Based Insurance in the Zhujiang River Basin, South China

2012 ◽  
Vol 13 (3) ◽  
pp. 1023-1037 ◽  
Author(s):  
Thomas Fischer ◽  
Buda Su ◽  
Yong Luo ◽  
Thomas Scholten
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Distribution Functions ◽  
Precipitation Extremes ◽  
Economic Losses ◽  
Return Periods ◽  
Weather Index ◽  
Maximum Precipitation ◽  
Insurance Program ◽  
Zhujiang River

Abstract In a changing climate, understanding the frequency of weather extremes is crucial to improving the management of the associated risks. The concept of weather index–based insurance is introduced as a new approach in weather risk adaptation. It can decrease the vulnerability to precipitation extremes that cause floods and economic losses in the Zhujiang River basin. The probability of precipitation extremes is a key input and the probability distribution of annual precipitation extremes is analyzed with four distribution functions [gamma 3, generalized extreme value (GEV), generalized Pareto, and Wakeby]. Three goodness-of-fit tests (Kolmogorov–Smirnov, Anderson–Darling, and Chi Squared) are applied to the distribution functions for annual time series (1961–2007) of 192 meteorological stations. The results show that maximum precipitation and 5-day-maximum precipitation are best described by the Wakeby distribution. On a basin scale, the GEV is the most reliable and robust distribution for estimating precipitation indexes for an index-based insurance program in the Zhujiang River basin. However, each station has to be analyzed individually as GEV is not always the best-fitting distribution function. Based on the distribution functions, spatiotemporal characteristics of return periods for maximum precipitation and 5-day-maximum precipitation are determined. The return levels of the 25- and 50-yr return periods show similar spatial pattern: they are higher in the southeast and lower in the southwest of the basin. This spatial distribution is in line with the annual averages. The statistical distribution of precipitation indexes delivers important information for a theoretical weather index–based insurance program.

A Quality-Controlled Global Sub-daily Precipitation Dataset and Sub-daily Precipitation Indices

2020 ◽  
Author(s):  
Hayley Fowler ◽  
Liz Lewis ◽  
Stephen Blenkinsop ◽  
David Pritchard ◽  
Selma Guerreiro ◽  
...  
Keyword(s):  
Climate Change ◽  
Daily Precipitation ◽  
Climate Extremes ◽  
Research Programme ◽  
Grand Challenge ◽  
Maximum Rainfall ◽  
Daily Precipitation Data ◽  
Hourly Rainfall ◽  
Wide Range ◽  
Precipitation Indices

<p>Extremes of precipitation can cause flooding and droughts which can lead to substantial damages to infrastructure and ecosystems and can result in loss of life. It is still uncertain how hydrological extremes will change with global warming as we do not fully understand the processes that cause extreme precipitation under current climate variability. Progress has been limited so far in this area due to the lack of data available to researchers. The INTENSE project, part of the with the World Climate Research Programme (WCRP)'s Grand Challenge on 'Understanding and Predicting Weather and Climate Extremes', has used a novel and fully-integrated data-modelling approach to provide a step-change in our understanding of the nature and drivers of global sub-daily precipitation extremes and change on societally relevant timescales.</p><p>The first step towards achieving this was to construct a new global sub-daily precipitation dataset. The dataset contains hourly rainfall data from ~25,000 gauges across >200 territories from a wide range of sources. A rigorous, flexible quality-control algorithm has been developed to ensure that the data collected is as accurate as possible. The QC methodology combines a number of checks against neighbouring gauges, known biases and errors, and thresholds based on the Expert Team on Climate Change Detection and Indices (ETCCDI) Climate Change Indices.  An open source version of the QC software will set a new standard for verifying sub-daily precipitation data.</p><p>A set of global sub-daily precipitation indices have also been produced (and will be made freely available later this year) based upon stakeholder recommendations including indices that describe maximum rainfall totals and timing, the intensity, duration and frequency of storms, frequency of storms above specific thresholds and information about the diurnal cycle. The talk will discuss the major findings from the production of these new global sub-daily precipitation indices.</p>

scholarly journals High-Resolution Dynamical Downscaling of Seasonal Precipitation Forecasts for the Hanjiang Basin in China Using the Weather Research and Forecasting Model

2017 ◽  
Vol 56 (5) ◽  
pp. 1515-1536 ◽  
Author(s):  
Yuan Li ◽  
Guihua Lu ◽  
Zhiyong Wu ◽  
Hai He ◽  
Jian He
Keyword(s):  
Correlation Coefficient ◽  
Dynamical Downscaling ◽  
Daily Precipitation ◽  
Wrf Model ◽  
Water Diversion ◽  
Weather Research And Forecasting ◽  
Monthly Precipitation ◽  
Seasonal Precipitation ◽  
Daily Precipitation Data ◽  
Weather Research

AbstractManagement of water resources may benefit from seasonal precipitation forecasts, but for obtaining high enough resolution, dynamical downscaling is necessary. This study investigated the downscaling capability of the Weather Research and Forecasting (WRF) Model ARW, version 3.5, on seasonal precipitation forecasts for the Hanjiang basin in China during 2001–09, which was the water source of the middle route of the South-to-North Water Diversion Project (SNWDP). The WRF Model is forced by the National Centers for Environmental Prediction Operational Climate Forecast System, version 2 (CFSv2), and it performs at a high horizontal resolution of 10 km with four selected convection schemes. The National Oceanic and Atmospheric Administration’s Climate Prediction Center global daily precipitation data were employed to evaluate the WRF Model on multiple scales. On average, when large biases were removed, the WRF Model slightly outperformed the CFSv2 in all seasons, especially summer. In particular, the Kain–Fritsch convective scheme performed best in summer, whereas little difference was found in winter. The WRF Model showed similar results in monthly precipitation, but no time-dependent characteristics were observed for all months. The spatial anomaly correlation coefficient showed greater uncertainty than the bias and the temporal correlation coefficient. In addition, the performance of the WRF Model showed considerable regional variations. The upper basin always showed better agreement with observations than did the middle and lower parts of the basin. A comparison of the forecast and observed daily precipitation revealed that the WRF Model can provide more accurate extreme precipitation information than the CFSv2.

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