Total Precipitation Data Research Articles

Let It Snow: Intercomparison of Various Total and Snow Precipitation Data over the Tibetan Plateau

The Global Precipitation Measurement Mission (GPM) improved spaceborne precipitation data. The GPM dual-frequency precipitation radar (DPR) provides information on total precipitation (TP), snowfall precipitation (SF) and snowfall flags (surface snowfall flag (SSF) and phase near surface (PNS)), among other variables. Especially snowfall data were hardly validated. This study compares GPM DPR TP, SF and snowfall flags on the Tibetan Plateau (TiP) against TP and SF from six well-known model-based data sets used as ground truth: ERA 5, ERA 5 land, ERA Interim, MERRA 2, JRA 55 and HAR V2. The reanalysis data were checked for consistency. The results show overall high agreement in the cross-correlation with each other. The reanalysis data were compared to the GPM DPR snowfall flags, TP and SF. The intercomparison performs poorly for the GPM DPR snowfall flags (HSS = 0.06 for TP, HSS = 0.23 for SF), TP (HSS = 0.13) and SF (HSS = 0.31). Some studies proved temporal or spatial mismatches between spaceborne measurements and other data. We tested whether increasing the time lag of the reanalysis data (+/−three hours) or including the GPM DPR neighbor pixels (3 × 3 pixel window) improves the results. The intercomparison with the GPM DPR snowfall flags using the temporal adjustment improved the results significantly (HSS = 0.21 for TP, HSS = 0.41 for SF), whereas the spatial adjustment resulted only in small improvements (HSS = 0.12 for TP, HSS = 0.29 for SF). The intercomparison of the GPM DPR TP and SF was improved by temporal (HSS = 0.3 for TP, HSS = 0.48 for SF) and spatial adjustment (HSS = 0.35 for TP, HSS = 0.59 for SF).

A Quality Control System for Historical In Situ Precipitation Data

ABSTRACT In this study, a comprehensive quality control (QC) system for in-situ precipitation data records was developed and applied to Canadian in situ precipitation datasets. The system includes a pair of screening procedures to screen for two types of random errors: one procedure is applied to the untransformed monthly total precipitation data series, which is good at finding erroneous data of unusually large values; another is applied to the log-transformed monthly precipitation data (in mm) series, log(P + 0.1), which is good at identifying erroneous zero or near-zero monthly total precipitation amounts. The system then applies three QC (threshold, kriging, and temporal) tests and a decision-making process to confirm whether the screened suspects are erroneous. There is generally good agreement between all the QC tests, while the decision-making process yields the most accurate results when compared to the manually reviewed results. The QC work on Canadian precipitation data sets revealed that it is necessary to apply a pair of screening procedures to identify both types of random errors. All the monthly values identified to be erroneous are set to missing, and so are the corresponding daily values, while keeping records of the original data.

Changes in precipitation and air temperature over Turkey using innovative trend pivot analysis method

Changes in precipitation and air temperature over Turkey using innovative trend pivot analysis method

Evaluation of long-term meteorological drought in the Aras and Coruh Basins with Crossing Empirical Trend Analysis

Drought is one of the most devastating disasters. Although the devastating effects of drought appear after many years, it is possible to evaluate drought based on meteorological parameters to control these negativities. In recent years, graphical trend analysis methods have been used to evaluate drought using meteorological parameters. The Crossing Empirical Trend Analysis method is the most recent trend analysis method and one of these graphical methods. Using monthly total precipitation data measured between 1969 and 2020 at 12 different meteorological observation stations within the borders of the Aras and Coruh basins, located northeast of Turkey, the basin's meteorological drought analysis was analyzed through the standardized precipitation index. In the study, the trend of meteorological drought in the basin was determined using the CETA method. As a result of the study, it was determined that there was an increasing trend in the majority of drought values in the Slope Upper and CETA Pivot analysis and a decreasing trend in the Slope Lower values. According to the Sen Slope analysis, there was no significant change in trends (34% increased, 35% decreased, and 31% no trend). Ardahan station is the most sensitive station regarding meteorological drought among the stations examined. It was concluded that the months with the highest increase trend in the Aras and Coruh basins are May, June and November.

Spatiotemporal analysis of precipitation variability in an endorheic basin of Turkey with coordinated regional climate downscaling experiment data

Understanding how temporal precipitation variability will behave in the future is crucial. Change point detection and sub-trend analysis were done on monthly total precipitation data acquired from the Coordinated Regional Climate Downscaling Experiment project in this study. Sub-periods have been determined based on the natural structure of the data set, as opposed to standard sub-period determination approaches. For the period 1971–2100, six different time series were used under three global climate models (CNRM-CERFACS-CNRM-CM5, ICHEC-EC-EARTH, MPI-M-MPI-ESM-LR) and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5). The study area has been selected to be the Konya Endorheic Basin in Turkey, which has been significantly influenced by climate change and human impacts. For change point detection, the Pruned Exact Linear Time method (PELT) and Binary Segmentation (BS) were employed, for trend analysis, Mann Kendall (MK) and Şen-Innovative Trend Analysis (Şen-ITA), and for both change point detection and sub-trend analysis, the Onyutha Trend Test (OTT) was utilized. The findings revealed four change points and five sub-periods. Summer precipitation has reduced considerably at a 95% significance level. There is strong evidence that precipitation has decreased between 1981 and 2070. Precipitation decreases similarly in the middle basin for the 1981–2000 and 2041–2070 years.

Convective Properties and Lightning Activity in Different Categories of Thunderstorms over the Beijing Area during Five Warm Seasons

Based on comprehensive observations, including total lightning, Doppler radar, precipitation, and other meteorological data, the variations in thunderstorm properties and lightning activity of different categories for thunderstorms over the Beijing area during five warm seasons were investigated. According to the morphology of radar echo, thunderstorms were classified into five categories, including single convective cells, multi-cells, linear mesoscale convective system (MCS), nonlinear MCS, and weak convective precipitation system (WCPS). The diurnal variability of lightning, thunderstorm occurrence, and precipitation showed late-afternoon maxima, with the peak time of lightning frequency occurring before that of precipitation. Despite WCPS having the lowest lightning frequency, the percentage of +CG/CG was the highest with large peak currents. The convective available potential energy (CAPE) of linear MCS, multi-cells, nonlinear MCS, single cells, and WCPS categories followed a pattern from largest to smallest. Meanwhile, warm cloud depth (WCD) exhibited a smaller value in the well-organized thunderstorm categories and a larger value in the WCPS. The topographic forcing mechanism and large wind gradient along mountain slopes facilitated convection occurrence and enhancement, further promoting lightning production. Meanwhile, the nocturnal convection mechanism significantly impacted the activity of nonlinear MCS and WCPS.

Drought Assessment and Monitoring for Rajshahi District Using Meteorological Drought Indices

An analysis for drought condition has been made in the Rajshahi district of Bangladesh. The assessment of drought for the country has become an urgent need. The agriculture of the country is dependent on the amount of precipitation. As drought and precipitation are inversely related so it is desired to know the condition of Rajshahi. The main characteristics that define drought are the absence of precipitation for a lengthy period and a low distribution of rainfall in contrast to the normal climatic condition. The monthly maximum average temperature data, monthly minimum average temperature data and total monthly precipitation data for the last 48 years (1971-2019) from Bangladesh Meteorological Department (BMD) are utilized for the analysis process. An online software named DrinC is preferred due to its open-source platform and its user-friendly interface. The meteorological data serve as inputs for the software. The Deciles Index (DI), the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) have been used to identify the drought condition of Rajshahi. The values of the DI and SPI are generated from the precipitation data. However, for the generation of the RDI, the precipitation data as well as the potential evapo-transpiration (PET) data, which is an intermediate data that is generated from the two different types of temperature data via DrinC software are used. The values of SPI, RDI and DI values are presented in a tabular as well as in graphical form. From the comparison of these data and from the study of the recurrence interval the state and the repetition of drought in Rajshahi district can be determined.

Relationships between weather and yield anomalies vary with crop type and latitude in Sweden

CONTEXTInformation on how crop yields are affected by weather variations and extreme weather is needed to develop climate adaptation measures for arable cropping systems. Here, we analysed the effects of weather anomalies and soil texture on crop yield anomalies across Sweden from 1965 to 2020. OBJECTIVEThe aims of this study were to (i) assess the effects of temperature and precipitation anomalies and extreme weather on crop yield anomalies for major field crops across Sweden, (ii) quantify how crop responses to weather anomalies vary along the north-south climate gradient across Sweden, and (iii) elucidate the impacts of soil texture on yield responses to weather anomalies. METHODSWe used daily mean air temperature, daily total precipitation, soil texture and crop yield data from public databases covering all 21 counties in Sweden. Yield data was detrended to account for the effects of agricultural intensification on crop productivity. To assess seasonal weather influences on crop yields, temporal trends of daily average temperature and daily total precipitation were detrended for each season containing a three-month period. We also used a water balance index and a heat wave index to evaluate the impact of extreme weather. RESULTS AND CONCLUSIONSOur analyses showed that years with extreme weather during summer (i.e. heat waves, drought or water excess) resulted in the largest negative yield anomalies. Spring-sown crops were more negatively affected by extreme weather compared to autumn-sown crops, which we associate with differences in the lengths of the growth period for autumn- and spring-sown crops. Effects of soil texture on yield anomalies were found for spring-sown cereals, where negative effects of drought were exacerbated with increasing sand content. Moreover, we showed that the effects of weather conditions on crop yield anomalies differed between different regions within the country. In northern Sweden, crop yields were more sensitive to excess water, while drought effects were more pronounced in southern Sweden. Similarly, increased summer temperatures favoured crop yields in northern Sweden but had a negative impact on crop yields in the southern part of the country. SIGNIFICANCEOur study demonstrates that weather impacts on yields vary between crops and locations, and that adaptation to future climate will require crop- and site-specific strategies.

Bulanık Mantık Yaklaşımı Kullanılarak Kızılırmak Nehri Aylık Ortalama Akımlarının Tahmini

River flow values are used in the design and operation of hydraulic structures. Determining the correct flow value is important in terms of controlling water movements in the operation of hydraulic structures, irrigation of agricultural lands, hydroelectric production, environmental protection and flood control. In the literature, different methods are used to predict possible river flows using the available data. The fuzzy logic approach is a kind of intelligent system method used in solving problems involving uncertainty. The method has been widely used in the modeling of hydrological data for 2000’s. In this study, the fuzzy logic method was applied to estimate the flow data of Yamula Station on the Kızılırmak River in the Kızılırmak basin, one of the largest basins in Turkey. In addition to these flow station data, the monthly average temperature and monthly total precipitation data of the Kayseri meteorology station, which affects the station flows, were also used for modeling. Three different models were created for the flow estimates. In these models, temperature and precipitation data were selected as input values and river flow data were chosen as output values. In the models, 1982-2012 data of the stations were used. Model output data were tested with data set of 2013, 2014 and 2015. As a result, it has been seen that the fuzzy logic approach gave healthy results when both temperature and precipitation data were used as inputs.

Evaluation of annual total precipitation in the transboundary Euphrates–Tigris River Basin of Türkiye using innovative graphical and statistical trend approaches

The Euphrates–Tigris River Basin (ETRB), one of the largest river basins in the Middle East, is also among the most risky transboundary basins in the world. ETRB has a critical importance for the region both politically and economically due to its location. Evaluating the increasing regional impacts of climate change is even more important for the sustainable management of water and soil resources, especially in transboundary basins such as ETRB. Türkiye is one of the most important riparian countries of the ETRB and the Türkiye part of ETRB constitutes the headwater of the basin. In this study, the temporal variability of the annual total precipitation data for the period 1965–2020 of eighteen stations located in the Türkiye part of the ETRB was investigated. Classical Mann–Kendall (MK) test was used to statistically determine the monotonic trend of precipitation. In addition to the MK method, analyses were carried out with three innovative trend methods, which have the ability to interpret trends both statistically and graphically. These innovative trend methods are Şen innovative trend analysis (Şen-ITA), Onyutha trend test (OTT) and trend analysis with combination of Wilcoxon test and scatter diagram (CWTSD). The results obtained show that there is a decreasing trend in annual total precipitation in ETRB according to all trend methods generally used for the examined period. In addition, the results obtained from the relatively new OTT and CWTSD methods show strong consistency with the results of the other two methods. The advantages such as performing numerical and visual trend analysis with innovative OTT and CWTSD methods, identifying trends in low–medium–high value data and detecting sub-trends have shown that these methods can be used as an alternative to the widely used MK and Şen-ITA.

A Novel Bias Correction Method for Extreme Events

When one is using climate simulation outputs, one critical issue to consider is the systematic bias affecting the modelled data. The bias correction of modelled data is often used when one is using impact models to assess the effect of climate events on human activities. However, the efficacy of most of the currently available methods is reduced in the case of extreme events because of the limited number of data for these low probability and high impact events. In this study, a novel bias correction methodology is proposed, which corrects the bias of extreme events. To do so, we extended one of the most popular bias correction techniques, i.e., quantile mapping (QM), by improving the description of extremes through a generalised extreme value distribution (GEV) fitting. The technique was applied to the daily mean temperature and total precipitation data from three seasonal forecasting systems: SEAS5, System7 and GCFS2.1. The bias correction efficiency was tested over the Southern African Development Community (SADC) region, which includes 15 Southern African countries. The performance was verified by comparing each of the three models with a reference dataset, the ECMWF reanalysis ERA5. The results reveal that this novel technique significantly reduces the systematic biases in the forecasting models, yielding further improvements over the classic QM. For both the mean temperature and total precipitation, the bias correction produces a decrease in the Root Mean Squared Error (RMSE) and in the bias between the simulated and the reference data. After bias correcting the data, the ensemble forecasts members that correctly predict the temperature extreme increases. On the other hand, the number of members identifying precipitation extremes decreases after the bias correction.

Analysis of Precipitation Data Using Innovative Trend Pivot Analysis Method and Trend Polygon Star Concept: A Case Study of Soan River Basin, Potohar Pakistan

Abstract The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, the environment, and water resources. In this study, the innovative trend pivot analysis method (ITPAM) and trend polygon star concept method were applied for precipitation trend detection at 11 stations located in the Soan River basin (SRB), Potohar region, Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting, and calculating monthly shifts under different trend behaviors, that is, increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows a complex nature of behavior, and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first dataset and the second dataset in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behavior in the water resources and agricultural sectors. Significance Statement The monthly polygonal trends with risk graphs of total monthly precipitation data depicted a clear picture of climate change effects in the Potohar region of Pakistan. The monsoonal rainfall showed a significant decreasing trend in highland stations and an increasing trend in lowland stations, indicating a shifting pattern of monsoonal rainfall from highland to lowland areas.

Investigation of meteorological drought characteristics of the great man-made river region: a case study of drought in Libya

Drought is a hidden natural hazard that involves complex climatic systems and has significant environmental and social consequences. Due to the current state of catastrophic climatic occurrences, there has been an increased interest in monitoring droughts in recent years. This study conducted meteorological drought analysis for five monitoring stations in the Great Man-Made River region located in Libya. The Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) methods were used to determine meteorological droughts utilizing monthly total precipitation data, and mean monthly temperatures and monthly total precipitation data, respectively. The drought analysis of the Great Man-Made River region using DrinC software for 1-, 3-, 6-, and 12-month SPI values were researched. According to the SPI-12-month index values, the driest period was determined by 86% in the Tripoli Airport and Nalut station, and the least dry period was determined in the Sirt station by 39%. It was found that the year 2000–2001 was one of the driest years in all studied stations, and the other years with high drought rates were 1981–1982, 1984–1985, and 1992–1993.

Entropy Based Regional Precipitation Prediction in the Case of Gediz River Basin

The Shannon’s entropy concept is defined to measure the information content of hydrological processes in hydrology and water resources. Entropy concept has also provided an opportunity to solve several related topics in water resources engineering. The presented study aims to define the regional distribution of the expected long-term annual total precipitation, by using the entropy concept. For this purpose, the frequency analysis of the observed long-term total monthly precipitation data for each station is analyzed, and entropy values are calculated using the frequency histogram called " Intensity Entropy – IE". By using the IE values, it is possible to define the regional information of long-term expected precipitation even if the gauging stations have different observation periods without missing any information of available data. In addition, there is no need to complete the data of the missing observation years (months) to define the precipitation-elevation relations for producing an isometric map. In regional analysis, it is possible to create isoentropy map, by using the determined IE values for each gauging station. The IE method is performed and isoentropy map is created for Gediz Basin as a case study, and the a priori conditions of using IE method results for regional information are discussed in the paper.

Performance assessment of spatio-temporal regression kriging with GAMLSS models as trends

The main objective of this study is to propose different probabilistic models for adjusting the trend component, since it significantly influences the quality of the spatio-temporal interpolation of rainfalls. We used the monthly total precipitation data of the São Francisco River Basin (SFRB) for the period of 31 years, 1989-2019. The SFRB occupies 8% of the whole Brazilian territory, mostly located in the Northeast Brazilian region. For the trend component, we propose the fitted GAMLSS models by comparing different probability distribution families, which in most cases include the characteristics of these data. The results indicate the existence of a spatio-temporal pattern of the residues obtained from the adjustment of the trend with zero adjusted Gamma distribution for the accumulated monthly precipitation. The adjustment revealed a spatial dependence of up to 873 km between the pluviometric stations and temporal autocorrelation of approximately 1.6 months. The methodology used in this study enabled us to create rainfall maps, interpolating unobserved locations in differences years. The projection of these maps to the SFRB is considered extremely important for planning and implementing activities related to water resources across the river basin.

The Innovative Polygon Trend Analysis (IPTA) as a Simple Qualitative Method to Detect Changes in Environment—Example Detecting Trends of the Total Monthly Precipitation in Semiarid Area

Precipitation is a crucial component of the water cycle, and its unpredictability may dramatically influence agriculture, ecosystems, and water resource management. On the other hand, climate variability has caused water scarcity in many countries in recent years. Therefore, it is extremely important to analyze future changes of precipitation data in countries facing climate change. In this study, the Innovative Polygon Trend Analysis (IPTA) method was applied for precipitation trend detection at seven stations located in the Wadi Sly basin, in Algeria, during a 50-year period (1968–2018). In particular, the IPTA method was applied separately for both arithmetic mean and standard deviation. Additionally, results from the IPTA method were compared to the results of trend analysis based on the Mann–Kendall test and the Sen’s slope estimator. For the different stations, the first results showed that there is no regular polygon in the IPTA graphics, thus indicating that precipitation data varies by years. As an example, IPTA result plots of both the arithmetic mean and standard deviation data for the Saadia station consist of many polygons. This result means that the monthly total precipitation data is not constant and the data is unstable. In any case, the application of the IPTA method showed different trend behaviors, with a precipitation increase in some stations and decrease in others. This increasing and decreasing variability emerges from climate change. IPTA results point to a greater focus on flood risk management in severe seasons and drought risk management in transitional seasons across the Wadi Sly basin. When comparing the results of trend analysis from the IPTA method and the rest of the analyzed tests, good agreement was shown between all methods. This shows that the IPTA method can be used for preliminary analysis trends of monthly precipitation.

Assessment and monitoring of drought in Chitradurga district of Karnataka using different drought indices


 Drought is a natural disaster due to less precipitation than the normal that can occur irrespective of climate regimes. Impact assessment of drought and monitoring are the most important mitigation stratregies to combat the drought effects. As the single index cannot assess all the drought conditions, in the present study multi-variate indices approach has been used to assess and monitor drought. Five indices were assessed using precipitation data such as deciles index (DI), percent normal (PN), China-Z index (CZI), Z-Score index (ZSI) and standardized precipitation index (SPI). Monthly total precipitation data was used to calculate drought events occurred during the period 1967–2017 in different talukas of Chitradurga district of Karnataka, India. The assessment revealed that SPI, CZI and ZSI performance was similar in identifying drought. PN was very much responsive for the rainfall events that occurred during the particular year however, it exhibited variations in dry conditions. DI was not that much satisfactory in identifying drought conditions. Among the five indices assessed, SPI seems to be the best indicator to predict the drought onset than the other four drought indices. Therefore SPI can be recommended for assessing and monitoring the drought in Chitradurga district of Karnataka, India.

Determining the relationship of evapotranspiration with precipitation and temperature over Turkey

Evapotranspiration (ET), which is a combination of the words evaporation and transpiration, is expressed as the sum of water due to water consumption and evaporation in plants. In this study, the NASA Drought Early Warning Systems Network Land Data Assimilation System model (FLDAS NOAH) was used to determine evapotranspiration data for the 2018-2019 water years (October 2017-September 2019) on Turkey. In addition, NASA Global Land Data Assimilation System (GLDAS NOAH) was used for temperature data. Total precipitation data with corrected error rates are taken from MERRA-2 Model M2TMNXFLX. The relationship between the determined monthly total average evapotranspiration values and the monthly average precipitation and monthly average temperature values was determined by ArcGIS. It is important to examine the local evaporation and transpiration conditions in more detail in the regions where water resources planning will be made. The importance of water holding capacity in plants in determining agricultural and hydrological drought can be explained by evapotranspiration.

Long‐term precipitation and stream discharge records at seven forested experimental watersheds along a latitudinal transect in Japan: Jozankei, Kamabuchi, Takaragawa, Tsukuba, Tatsunokuchi‐yama, Kahoku and Sarukawa

AbstractThis data note introduces a database of long‐term daily total precipitation and stream discharge data for seven forested watersheds in Japan that have been continuously monitored by the Forestry and Forest Products Research Institute. Three of the watersheds started data collection in the 1930s. Forest cover across the sites ranges from cool to warm temperate regions with the latitude spanning from 31 to 44° N and annual precipitation ranging from 1200 to 3000 mm yr−1. The effects of vegetation change via clearcutting, thinning and forest fire (among other stressors) on stream discharge can be analysed from the long‐term observation sites. Moreover, this multi‐site dataset allows for inter‐ and intra‐site comparisons of annual water loss (difference of annual precipitation and stream discharge). These long‐term datasets can provide comprehensive insights into the effects of climate change and other stressors on forested ecosystems, not only in Japan but across a spectrum of forest types, if combined with other long‐term records from other forested watersheds across the world.

Application of non-Gaussian multidimensional autoregressive model for climate data prediction

From the point of view of agriculture, ecology, or environmental engineering, the capability of forecasting meteorological variables in the long and short term is crucial. Short-term forecasts enabling the planning of field work in agriculture, management of mass events, or tourism are important, while long-term forecasts related to advancing climate change are also very interesting. In the literature, there are known many approaches that can be used to forecast climate time series. The most common is based on the statistical modelling of the corresponding data, and the prediction is made on the fitted model. There are known one-dimensional approaches, where single variables are modeled separately; however, in the last decade, there appears a new trend which assumes the importance of the relationship between different time series. This is the approach considered in this paper. We propose to examine the climate data (temperature and precipitation) using the multidimensional vector autoregressive model (VAR). However, because in the time series we observe non-Gaussian behaviour, the classical VAR model can not be applied and the multidimensional Gaussian noise is replaced by the alpha -stable one. This model was previously analyzed by the authors in the context of financial data description where also non-Gaussian characteristics are observed. The main goal of this paper is to answer the question whether there are reasons to go from the Gaussian model to the generalized models, like alpha -stable based. The second purpose is to link total precipitation data with temperature time series. In the classical approach, precipitation was treated as a variable not correlated with temperature, which, as we will show in the paper, is inconsistent with reality. We hope the presented in this paper results open new areas of interest related to climate data modelling and prediction.