Analysis of spatial patterns of trends in the frequency and intensity of Indian precipitation

bl ‘’kks/k Ik= esa lewps Hkkjr ds 129 ekSle dsanzksa ds fy, 1910 ls 2000 rd dh le;kof/k ds nSfud o"kkZ ds fjdkM+ksaZ dks ,df=r fd;k x;k gS A blds ckn fofHkUu ekSle foKkfud mi[kaM+ksa ds fy, ek/; okf"kZd o"kkZ ds ekuksa ds vuqlkj bu dsanzksa dks ukS fHkUu&fHkUu {ks=ksa esa ck¡Vk x;k gS A izR;sd {ks= ds fy, gj ik¡p izfr’kr ds varjky ij dqy o"kkZ vkSj o"kkZ dh ckjackjrk dk foLr`r fo’ys"k.k fd;k gS A bu ifj.kkeksa ls lkekU;r% Hkkjr ds yxHkx lHkh Hkkxksa esa o"kkZ dh deh dk irk pyk gS tcfd dsoy mRrj if’peh Hkkxksa esa o"kkZ esa o`f) ns[kh xbZ gS A o"kkZ ds izfr lSadM+k oxZ varjkyksa ds vuqlkj fd, x, gekjs fo’ys"k.k ls ;g irk pyrk gS fd fo’ks"k :Ik ls ns’k ds vk/ks Hkkx if’peh {ks= esa vfro`f"V dh ?kVuk,¡ ckj&ckj gksrh gSa A Hkkjrh; o"kkZ ds LFkkfud vk;keksa ij izdk’k Mkyus okys gkmxVu bR;kfn (2001) ds vkbZ- ih- lh- lh- ds oSKkfud ewY;kadu vkSj vU; v/;;uksa ds lkFk gekjs ifj.kke O;kid :Ik ls esy [kkrs gSa A We assembled daily precipitation records for 129 weather stations spread all over India for the time period 1910 to 2000. Next we classified these stations into nine different regions according to the mean annual precipitation values for the different India meteorological sub-divisions. We conducted detailed analysis of total precipitation and the frequency of precipitation for each five-percentile interval for every region. In general, our results show a decrease in precipitation throughout much of India with only the northwest showing an increase. Our analyses by precipitation percentile class intervals show that the most extreme events have become more frequent, particularly in the western half of the country. Our results are broadly consistent with the IPCC Scientific Assessment by Houghton et al. (2001) and other studies focusing on the spatial dimensions of Indian precipitation over time.

Rainfall Trends in Humid Temperate Climate in South America: Possible Effects in Ecosystems of Espinal Ecoregion

In central Argentina, the annual rainfall regime shows increasing since the 2nd half of the 20th century. The aim of this work was to evaluate the long-term changes in the intensity of rainfall in the central-north region of Entre Ríos between 1945 and 2019, based only on daily precipitation records aggregated at yearly, monthly and seasonal levels. We used monthly rainfall data for the period 1945–2019 from 6 localities in Province of Entre Rios, Argentina. The change detection analysis has been conceded using Pettitt’s test, von Neumann ratio test, Buishand’s range test and standard normal homogeneity (SNH) test, while non-parametric tests including linear regression, Mann-Kendall and Spearman rho tests have been applied for trend analysis. Like the regional results, this study observed a sustained increase in monthly rainfall to the breaking point in the 1970s, but then the annual rate of increase was even higher. The average annual rainfall in the region prior to that date was 946 mm, while after the same 1150 mm, equivalent to 21.5% higher than the 1945–1977 average and 8.5% higher according to the historical average 1945–2019.

Mapping Flash Floods in Iraq by Using GIS

This study aims to investigate flash floods in Iraq by plotting the cartographic maps by using synoptic and dynamical analysis of meteorological reanalysis data obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) and statistical analysis of daily precipitation records from the Iraqi Meteorological and Seismology Organization for selected Iraqi stations (Mosul, Kirkuk, Khanaqin, Baghdad and Al-Rutba, Al-Hayy, Al-Nasiriyah, and Basra), as well as the use of geographic information system (GIS) techniques. Three models create to investigate and map flash floods in Iraq. The results of the first model (the longest period of time) shows that the station of Mosul record the longest period for a rainstorm, 9 days in 2014, while the lowest period was in Rutba, 6 days in 2012, and the other stations varied between these two stations. The results of the second model (the highest total rainfall), present that Kirkuk station recorded the highest amount of rain (117.2 mm in 2013), while Al-Rutba station, 47.2 mm in 2011, the lowest station. Finally, the results of the third model (the highest frequency of rainstorms per month) shows that the lowest frequency of rainstorms per month was in Basra, 29 rainstorms in 2009, while Mosul station has 40 rainstorms in 2007 and the other stations within these two values.

Assessment of GPM IMERG Satellite Precipitation Estimation under Complex Climatic and Topographic Conditions

Satellite precipitation estimation provides crucial information for those places lacking rainfall observations from ground–based sensors, especially in terrestrial or marine areas with complex climatic or topographic conditions. This is the case over much of Western China, including Upper and Middle Lancang River Basin (UMLRB), an extremely important transnational river system in Asia (the Lancang–Mekong River Basin) with complex climate and topography that has limited long–term precipitation records and high–elevation data, and no operational weather radars. In this study, we evaluated three GPM IMERG satellite precipitation estimation (IMERG E, IMERG L and IMERG F) over UMLRB in terms of multi–year average precipitation distribution, amplitude consistency, occurrence consistency, and elevation–dependence in both dry and wet seasons. Results demonstrated that monsoon and solid precipitation mainly affected amplitude consistency of precipitation, aerosol affected occurrence consistency of precipitation, and topography and wind–induced errors affected elevation dependence. The amplitude and occurrence consistency of precipitation were best in wet seasons in the Climate Transition Zone and worst in dry seasons in the same zone. Regardless of the elevation–dependence of amplitude or occurrence in dry and wet seasons, the dry season in the Alpine Canyon Area was most positively dependent and most significant. More significant elevation–dependence was correlated with worse IMERG performance. The Local Weighted Regression (LOWERG) model showed a nonlinear relationship between precipitation and elevation in both seasons. The amplitude consistency and occurrence consistency of both seasons worsened with increasing precipitation intensity and was worst for extreme precipitation cases. IMERG F had great potential for application to hydroclimatic research and water resources assessment in the study area. Further research should assess how the dependence of IMERG’s spatial performance on climate and topography could guide improvements in global precipitation assessment algorithms and the study of mountain landslides, floods, and other natural disasters during the monsoon period.

Gap-free global annual soil moisture: 15 km grids for 1991–2018

Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency Climate Change Initiative v4.5) soil moisture dataset, which contains > 40 years of satellite soil moisture global grids with a spatial resolution of ∼ 27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict finer-grained (i.e., downscaled) satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating downscaled soil moisture with and without the support of bioclimatic and soil type information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated prediction variances for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, 987 stations) and in situ precipitation records (171 additional stations) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r= 0.69 to r= 0.87 with root mean squared errors (RMSEs, m3 m−3) around 0.03 and 0.04. Our soil moisture predictions improve (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r= 0.30 (RMSE = 0.09, unbiased RMSE (ubRMSE) = 0.37) to r= 0.66 (RMSE = 0.05, ubRMSE = 0.18) and (b) the correlation with local precipitation records across boreal (from r= < 0.3 up to r= 0.49) or tropical areas (from r= < 0.3 to r= 0.46) which are currently poorly represented in the ISMN. Temporal trends show a decline of global annual soil moisture using (a) data from the ISMN (-1.5[-1.8,-1.24] %), (b) associated locations from the original ESA-CCI dataset (-0.87[-1.54,-0.17] %), (c) associated locations from predictions based on terrain parameters (-0.85[-1.01,-0.49] %), and (d) associated locations from predictions including bioclimatic and soil type information (-0.68[-0.91,-0.45] %). We provide a new soil moisture dataset that has no gaps and higher granularity together with validation methods and a modeling approach that can be applied worldwide (Guevara et al., 2020, https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e).

A New Method for Determining an Optimal Diurnal Threshold of GNSS Precipitable Water Vapor for Precipitation Forecasting

Nowadays, precipitable water vapor (PWV) retrieved from ground-based Global Navigation Satellite Systems (GNSS) tracking stations has heralded a new era of GNSS meteorological applications, especially for severe weather prediction. Among the existing models that use PWV timeseries to predict heavy precipitation, the “threshold-based” models, which are based on a set of predefined thresholds for the predictors used in the model for predictions, are effective in heavy precipitation nowcasting. In previous studies, monthly thresholds have been widely accepted due to the monthly patterns of different predictors being fully considered. However, the primary weakness of this type of thresholds lies in their poor prediction results in the transitional periods between two consecutive months. Therefore, in this study, a new method for the determination of an optimal set of diurnal thresholds by adopting a 31-day sliding window was first proposed. Both the monthly and diurnal variation characteristics of the predictors were taken into consideration in the new method. Then, on the strength of the new method, an improved PWV-based model for heavy precipitation prediction was developed using the optimal set of diurnal thresholds determined based on the hourly PWV and precipitation records for the summer over the period 2010–2017 at the co-located HKSC–KP (King’s Park) stations in Hong Kong. The new model was evaluated by comparing its prediction results against the hourly precipitation records for the summer in 2018 and 2019. It is shown that 96.9% of heavy precipitation events were correctly predicted with a lead time of 4.86 h, and the false alarms resulting from the new model were reduced to 25.3%. These results suggest that the inclusion of the diurnal thresholds can significantly improve the prediction performance of the model.

How important is accounting for serial correlation and field significance in trend detection of extreme rainfall occurrences?

&lt;p&gt;A number of studies have shown that the ability of statistical tests to detect trends in hydrologic extremes is negatively affected by (i) the presence of autocorrelation in the time series, and (ii) field significance. Here, we investigate these two issues and evaluate the power of several trend tests using time series of frequencies (or counts) of precipitation extremes from long-term (100 years) precipitation records of 1087 gauges of the Global Historical Climate Network database. For this aim, we design several Monte Carlo experiments based on simulations of random count time series with different levels of autocorrelation and trend. We find the following. (1) The observed records are consistent with the hypothesis of autocorrelation induced by the presence of trends, indicating that the existence of serial correlation does not significantly affect trend detection. (2) Tests based on the linear and Poisson regressions are more powerful that nonparametric tests, such as Mann Kendall. (3) Accounting for field significance improves the interpretation of the results by limiting the rejection of the false null hypothesis. We then use these results to investigate the presence of trends in the observed records. We find that, depending on the quantiles used to define the frequency of precipitation extremes, 34-47% of the selected gages exhibit a statistically significant trend, of which 70-80% are positive and located mainly in United States and Northern Europe. The significant negative trends are mostly located in Southern Australia.&lt;/p&gt;

A global observation-based dataset of sub-daily precipitation indices

&lt;p&gt;Short duration precipitation extremes can lead to severe flash flooding and destructive landslides. Yet many gaps remain in our understanding of these acute precipitation events, partly due to the lack of accessible and high quality sub-daily observational datasets available to researchers. To address this problem, the INTENSE project (leading the GEWEX Hydroclimatology Panel Cross-Cutting Project on Sub-Daily Extremes) has coordinated a major international effort to collate sub-daily precipitation observations from around the world. The resulting Global Sub-Daily Rainfall (GSDR) dataset contains hourly precipitation records from over 20,000 gauges globally. The quality of the raw data underpinning the GSDR dataset is variable, so an automated and wide-ranging quality control procedure has been developed and applied to the records. To facilitate research and other applications of the dataset, we have defined and calculated a novel set of sub-daily precipitation indices. These indices complement and extend the ETCCDI daily precipitation indices by characterising key aspects of shorter duration precipitation variability, including intensity, duration and frequency properties. Project partners and other collaborators continue to augment the resulting indices database by performing the calculations on their own observations and sharing these with the INTENSE project, with new contributors always welcome. This combined effort has led to an extensive observation-based climatology of various sub-daily precipitation characteristics (including extremes) across large parts of the world. These indices will be publicly available for as many gauges as possible, alongside a gridded dataset that also incorporates indices calculated for additional restricted-access gauge records.&lt;/p&gt;