Seasonal Precipitation Data Research Articles

Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection‐Permitting Regional Climate Model Simulations in a Complex Topographical Region

ABSTRACTIn mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to the lack of high‐altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection‐Permitting Regional Climate Model (CP‐RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long‐term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross‐validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long‐term seasonal precipitation from observations and CP‐RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP‐RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude‐precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP‐RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.

Analysis of seasonal rainfall variability with innovative graphical methods of Konya Closed Basin, Türkiye

This study looked into the variability of seasonal rainfall in the semi-arid Konya Closed Basin (KCB), a water-limited region with high exploitation demand for water resources due to agricultural activities. In the study, seasonal precipitation data of eleven stations in the basin for the period 1971–2020 were used and analyzed with four trend methods (Şen-ITA, CWTSD, IPTA, and TPSC). Visual and statistical findings from the analyses showed generally increasing trends in winter and summer precipitation and decreasing trends in spring precipitation in the basin. In autumn precipitation, according to Şen-ITA, there is an increasing trend in seven stations and a decreasing trend in four stations, while according to CWTSD, there is an increasing trend in six stations and a decreasing trend in five stations. Based on the arithmetic mean IPTA graphs, Winter-Spring transitions in all stations occur from a rising trend region to a falling trend region, but Spring-Summer transitions take the reverse direction. In the standard deviation IPTA graphs, similar transitions in the trend regions are predominant. To the mean and standard deviation TPSC graphs, most of the stations generally have similar behavior. Spring-Summer transition arrows are dominant in Zone III, while Summer-Autumn and Autumn-Winter transitions are dominant in Zone I. Although Winter-Spring transitions are variable, they are more intense in Zone I and III. CWTSD and Şen-ITA methods are also consistent with the results obtained from IPTA graphs, both visually and numerically.

Assessment of Hydrometeorological Impacts of Climate Change on Water Bodies in Northern Kazakhstan

This article examines the impact of climate change on the hydrometeorological indicators of some lakes and reservoirs in the Akmola and North Kazakhstan regions. Two meteorological variables’ annual and seasonal trends at three weather stations in 1986–2023 were analyzed. The non-parametric Mann–Kendall and Sen’s slope methods were used to determine the presence of a positive or negative trend in weather data and their statistical significance. Hydrometric indicators were studied using the ArcGIS 10.8 program from 1995 to 2023. The results indicate an increasing average spring air temperature, with an annual rise of 0.08–0.09 °C. A significant trend in increasing average annual precipitation was observed in Saumalkol, with a rise of 4.7 mm per year. In contrast, no significant trends were found in the annual and seasonal precipitation data for Sergeyevka. It was also found that the area of Lake Saumalkol increased by 1.6% due to a rise in annual precipitation. In contrast, the area of Lake Kopa decreased by 6.04% because of an increase in the annual average temperature.

Discrepancies in precipitation changes over the Southwest River Basin of China based on ISIMIP3b

Selecting appropriate global climate models (GCMs) is crucial for minimizing uncertainty in regional climate projections under future scenarios. Previous studies have predominantly assessed the modeling capability of GCMs for regional precipitation climatology and its long-term patterns based on annual and seasonal precipitation data. Building upon these, we primally evaluated the performance of five GCMs from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) in simulating precipitation concentration and its variations in the Southwest River Basin (SWRB) of China using the precipitation concentration index (PCI). The results indicate that: (1) The 5 GCMs generally capture the spatial distribution of annual average precipitation in the SWRB but significantly overestimate its magnitude, with a maximum regional average deviation of 207.80 mm. Furthermore, all models tend to overestimate the overall drying trend in the SWRB and show limited capability in simulating interdecadal variations of annual precipitation. (2) While the 5 GCMs reasonably simulate the spatial distribution of annual average PCI in the SWRB, they tend to overestimate its values, with a maximum regional average deviation of 1.54. Additionally, their simulation performance in capturing PCI trends and interdecadal variations is also limited. (3) The 5 GCMs tend to overestimate seasonal precipitation in the SWRB, with the best simulation performance for the distribution of autumn precipitation, followed by spring and summer, and the poorest for winter. Significant differences exist in the simulation performance of the models for seasonal precipitation proportions, which result in discrepancies in the models’ representation of PCI. Moreover, the models’ poor simulation performance of PCI trends is partly due to their inadequate modeling of trends in seasonal precipitation proportions. The findings will contribute to laying the foundation for meteorological hydrological research and water resource management in the SWRB.

Vulnerability of barley yields in Québec.

Climate projections across Québec indicate increased water stress and recurrent vulnerability of cropping systems. In recent years, reports of droughts and water stress have been recorded across the province. Many parts of Québec have experienced droughts in the past few years, which have had uninvestigated impacts on crops. These droughts have been described as some of the most significant in the last 80 years. On the positive side, climate change is likely to trigger shorter winters and therefore longer growing seasons for several crops. However, for crops like maple syrup, the regions suitable for their cultivation will shift northwards. Despite these projections, studies monitoring the susceptibility of barley to environmental changes, climate variability, and adaptive capacity across Québec are still limited. This study aims to provide a provincial-scale portrait of vulnerability of barley in Québec based on historical growing season precipitation, barley yield, and socioeconomic data (literacy and poverty rates) using a composite statistical model. Growing season precipitation data were downloaded from Ouranos. Barley yield data were collected from the Institut de la Statistique du Québec, and the socio-demographic data were collected from the Advisory Council of Poverty and the Institut de la Statistique du Québec. A vulnerability index with sub-indices (sensitivity, exposure, and adaptive capacity) is deployed. It is hypothesised that (1) vulnerability is inversely associated with adaptive capacity, and (2) the peripheral regions of Québec are more vulnerable and less adaptive to climate stressors. Initial results show that when the vulnerability index for barley is more prominent, the associated index of adaptive capacity is relatively lower. A significant gradient between the peripheral and southern regions of Québec is observed, with vulnerability lowest around Montreal/Laval and gradually increasing towards the peripheral regions. A better understanding of vulnerability warrants a change in approach from focusing solely on climate-related variables to integrating socioeconomic proxies. The challenge, however, has been how to introduce these socioeconomic proxies into empirical and process-based crop models.

Comparing standardised precipitation and barley yield across Quebec.

Climate change and variability continue to affect crop production across the world in general and in Quebec in particular. Therefore, it is important to better understand this climate-yield nexus. Unfortunately, in Quebec, there are currently no studies that analyse both precipitation and barley yield. This study aims at filling this research gap by comparing standardised precipitation (SPI) against barley yield gaps across Quebec for three peripheral and three southern/central regions of Quebec. The study uses growing season precipitation data from Ouranos and barley yield data from Institut de la Statistique du Québec. This work deploys the use of standardised index (SPI)index and a machine learningyield gapalgorithm (actual barley yield minus projected barley yield) to provide a provincial portrait of the relationships between SPI and barley yield. The results show that the peripheral regions record below zero SPIs (Abitibi-Temiscamingue - 0.48, Saguenay-Lac-Saint-Jean - 0.14 and Outaouais - 0.10) and more yield gap years, while the southern/central regions record positive SPIs (Estrie 1.17, Centre du Quebec 0.86 and Monteregie: 0.33) and fewer years with yield gaps. This shows that there is a south-north gradient in the variations of SPI and yield gaps. The SPI and yield patterns can be explained by prolonged winters further north of Quebec and recently winters with insufficient snow cover which triggers rapid snow melt and thus shortening the growing season for barley. Policy actions around drought-resistant varieties within a co-creation context and more research that explores daily and monthly liquid precipitation totals during the growing season of the crop need to be explored. There is also a need to better understand the economic costs and benefits of the associated yield gapsas well as the impacts of temperature.

Identifying yield and growing season precipitation gaps for maize and millet in Cameroon

Abstract Climate change drives huge differences between the actual and projected yield and growing season precipitation. Therefore, this work identifies yield and precipitation gaps for maize and millet at the national and subnational scales as well as policy considerations for agricultural policy experts that can mitigate these gaps. Yield data for the national and subnational scale analyses were obtained for the period 1961–2021 from the FAOSTAT and the Ministry of Agriculture (MINAGRI)/IRAD of Cameroon, respectively. Growing season precipitation data for the national and subnational scales were collected from the World Bank climate change portal and the Climate Research Unit (CRU). Various machine learning algorithms were used to bias-adjust the data and to compute the potential yield and growing season precipitation from which the yield and precipitation gaps were computed. The results show a positive correlation between yield and precipitation gaps, with millet depicting the strongest correlation. The average yield gap for maize is 0.55 t/ha, higher than the average yield gap for millet that is 0.28 t/ha. Not all years with yield gaps are correlated with precipitation gaps. The average precipitation gap for maize is 108 mm/year, and it is higher than the 101 mm/year recorded for millet.

Precipitation trend identification with a modified innovative trend analysis technique over Lake Issyk-Kul, Kyrgyzstan

Abstract The main concern of this study is using a new type of innovative trend analysis (ITA) method with a particular graphical illustration. It compares the results with the classic MK trend test at a 95% confidence level. Among the 15 annual and seasonal data series (3 weather stations annually, spring, summer, autumn, and winter) studied, the MK trend test found significant increasing trends of 3 data series (20%). However, using the new ITA method, 6 data series (40%) ‘high’ and ‘low’ simultaneously showed a significant increasing trend. The new ITA method can also detect all significant trends identified by the MK trend test. As for the new ITA method, the ‘high’ values of the 12 data series (80%) exhibited significant increasing patterns, and the 9 data series (60%) displayed significant increasing patterns for ‘low’ values. According to the ‘low’ and ‘high’ values, a gain one data series (6.7%) manifested significant decreasing trends. These results detailed annual and seasonal precipitation data series patterns by evaluating ‘low’ and ‘high’ values. The findings also demonstrated that the new method runs counter to the previous ITA.

Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil

Southeastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project rises as a convenient yet few exploited alternative. Precisely, this study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward’s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter period.

Trend analysis of annual and seasonal precipitation data in Arcadia region (Greece)

Trend analysis of annual and seasonal precipitation data in Arcadia region (Greece)

Forecasting occurrence and quantity of monthly precipitation simultaneously while accounting for complex serial correlation

AbstractTweedie's compound Poisson regression models have been introduced in recent years to model and predict daily, monthly and seasonal precipitation data. Tweedie's compound Poisson regression analysis of precipitation data captures the relationship between the mean structure of precipitation and covariates while accounting for its right‐skewness and zero‐inflation appropriately, and thus extends trend analysis stage of classical time series techniques to handle right‐skewed time series data with possible zero‐inflation. A distinctive advantage of Tweedie's compound Poisson modelling of precipitation data is that the occurrence and quantity of precipitation can be simultaneously modelled using a single distribution; however, this approach ignores serial correlation between precipitation data observed over time. In this study, we propose to accommodate complex correlation structures of time series precipitation data using the Autoregressive Integrated Moving Average (ARIMA) models at the next stage as done in the classical time series analysis. Our analyses of monthly precipitation data in Australia demonstrated the usefulness of our two‐stage approach to prediction of precipitation.

A comprehensive framework model for the trend, period and evaluation of the precipitation enhancement effect: TPEM

Abstract In this study, a framework model (TPEM) for evaluating the temporal and spatial variation of urban precipitation is established. TPEM includes seven calculation methods at the same time. Taking the annual precipitation and flood season precipitation data of eight meteorological stations in Zhengzhou, China, from 1960 to 2020 as an example, the trend and period of rain island effect in Zhengzhou are analyzed from the annual and flood season rainfall scale, and the distance method is introduced to quantitatively evaluate the rain island effect. The results show that the rain island effect in the central urban area of Zhengzhou has a sudden increase trend in 1997 and 2004 respectively, and the increasing trend of rain island effect on the scale of annual and flood season rainfall lasts for four and ten years respectively. At the same time, it has fluctuation periods of 8–10a, 16–18a, 3–5a, 17–20a, 5–7a, and 2–3a respectively. The rain island effect in Zhengzhou is concentrated in the flood season. The research can provide a scientific basis for cities to deal with the rain island effect in the future.

Vulnerability of Barley, Maize, and Wheat Yields to Variations in Growing Season Precipitation in Morocco

Climate change continues to have adverse effects on crop yields in Africa and globally. In Morocco, rising temperatures and declining precipitation are having daunting effects on the vulnerability of crops. This study examines the vulnerability of barley, maize, and wheat to variations in growing season precipitation and socio-economic proxies of adaptive capacity such as literacy and poverty rates at both national and sub-national scales in Morocco. The methodology is based on a composite vulnerability index (vulnerability is a function of exposure, sensitivity, and adaptive capacity). National and sub-national crop yield data used to compute the sensitivity index were downloaded from FAOSTAT and the global crop yield gaps Atlas. The mean annual growing season precipitation data at both the national and sub-national scales used to compute the exposure index were downloaded from the world bank climate portal. Proxy data for adaptive capacity in the form of literacy and poverty rates were downloaded from the world bank, figshare, and MPR archives. The CANESM model was used to validate the crop yield observations. The results show that wheat shows the lowest vulnerability and the highest adaptive capacity, while maize has the highest vulnerability and lowest adaptive capacity. Sub-nationally, vulnerability indexes decrease northwards while adaptive capacity and normalized growing season precipitation increase northwards. Wheat also shows the lowest vulnerability and highest adaptive capacity and normalized growing season precipitation at each latitude northward. Model validation shows that the models used here reproduce most of the spatial patterns of the crops concerned. These findings have implications for climate change adaptation and climate policy in Morocco, as it becomes evident which of these most cultivated crops are more vulnerable nationally and spatially. These results have implications for future research, as it might be important to understand how these crops perform under growing season temperature as well as what future projections and yield gaps can be observed.

Recent Patterns of Exposure, Sensitivity, and Adaptive Capacity of Selected Crops in Cameroon

In most parts of sub-Saharan Africa, precipitation is impacted by climate change. In some countries like Cameroon, it is still not clear how maize, millet and rice will respond to changes in growing season precipitation. This work examines the exposure, sensitivity, and adaptive capacity of the above crops to droughts at both the national and sub-national scale. Crop yield data were culled from FAOSTAT while growing season precipitation data were culled from the database of UNDP/Oxford University and the climate portal of the World Bank. Adaptive capacity proxies (literacy, and poverty rate) were collected from KNOEMA and the African Development Bank. The analysis was performed using the vulnerability index equation. Nationally, millet has the lowest vulnerability and rice has the highest. At the sub-national scale, northern maize has the highest vulnerability followed by western highland rice. It is observed that when scales change, the crops that are vulnerable also change. However, at both levels vulnerability has an inverse relationship with adaptive capacity.

Identifying maize yield and precipitation gaps in Uganda

In sub-Saharan Africa (SSA), precipitation is an important driver of agricultural production. In Uganda, maize production is essentially rain-fed. However, due to changes in climate, projected maize yield targets have not often been met as actual observed maize yields are often below simulated/projected yields. This outcome has often been attributed to parallel gaps in precipitation. This study aims at identifying maize yield and precipitation gaps in Uganda for the period 1998–2017. Time series historical actual observed maize yield data (hg/ha/year) for the period 1998–2017 were collected from FAOSTAT. Actual observed maize growing season precipitation data were also collected from the climate portal of World Bank Group for the period 1998–2017. The simulated or projected maize yield data and the simulated or projected growing season precipitation data were simulated using a simple linear regression approach. The actual maize yield and actual growing season precipitation data were now compared with the simulated maize yield data and simulated growing season precipitation to establish the yield gaps. The results show that three key periods of maize yield gaps were observed (period one: 1998, period two: 2004–2007 and period three: 2015–2017) with parallel precipitation gaps. However, in the entire series (1998–2017), the years 2008–2009 had no yield gaps yet, precipitation gaps were observed. This implies that precipitation is not the only driver of maize yields in Uganda. In fact, this is supported by a low correlation between precipitation gaps and maize yield gaps of about 6.3%. For a better understanding of cropping systems in SSA, other potential drivers of maize yield gaps in Uganda such as soils, farm inputs, crop pests and diseases, high yielding varieties, literacy, and poverty levels should be considered.

Use of Seasonal Streamflow Forecasts for Flood Mitigation with Adaptive Reservoir Operation: A Case Study of the Chao Phraya River Basin, Thailand, in 2011

Predicting streamflow can help water managers make policy decisions for individual river basins. In 2011, heavy rainfall from May until October resulted in the largest flood event in the history of Thailand. This event created difficulty for water managers, who lacked information to make predictions. Studies on the 2011 Thai flood have proposed alternative reservoir operations for flood mitigation. However, no study to date has used predictive information to determine how to control reservoirs and mitigate such extreme floods. Thus, the objective of this study is to update and develop a method for using streamflow predictive data to support adaptive reservoir operation with the aim of mitigating the 2011 flood. The study area was the Chao Phraya River Basin, one of the most important basins in Thailand. We obtained predictive information from a hydrological model with a reservoir operation module using an ensemble of seasonal precipitation data from the European Centre for Medium–Range Weather Forecasts (ECMWF). The six-month ECMWF prediction period was used to support the operation plan for mitigating flooding in 2011 around each reservoir during the wet season. Decision-making for reservoir operation based on seasonal predictions was conducted on a monthly time scale. The results showed that peak river discharge decreased slightly, by around 4%, when seasonal predictive data were used. Moreover, changing the reservoir operation plan and using seasonal predictions decreased the peak river discharge by around 20%.

Role of Cluster Validity Indices in Delineation of Precipitation Regions

The delineation of precipitation regions is to identify homogeneous zones in which the characteristics of the process are statistically similar. The regionalization process has three main components: (i) delineation of regions using clustering algorithms, (ii) determining the optimal number of regions using cluster validity indices (CVIs), and (iii) validation of regions for homogeneity using L-moments ratio test. The identification of the optimal number of clusters will significantly affect the homogeneity of the regions. The objective of this study is to investigate the performance of the various CVIs in identifying the optimal number of clusters, which maximizes the homogeneity of the precipitation regions. The k-means clustering algorithm is adopted to delineate the regions using location-based attributes for two large areas from Canada, namely, the Prairies and the Great Lakes-St Lawrence lowlands (GL-SL) region. The seasonal precipitation data for 55 years (1951–2005) is derived using high-resolution ANUSPLIN gridded point data for Canada. The results indicate that the optimal number of clusters and the regional homogeneity depends on the CVI adopted. Among 42 cluster indices considered, 15 of them outperform in identifying the homogeneous precipitation regions. The Dunn, D e t _ r a t i o and Trace( W − 1 B ) indices found to be the best for all seasons in both the regions.

Long-term temperature and precipitation trends in the Luquillo Mountains, and their relationships to global atmospheric indices used in climate change predictions

Most climate models for the Caribbean predict drying trends, increased temperature, and precipitation variability. We used regression analyses to evaluate how global predictions translated into local temporal patterns of climatic variation within the Luquillo Mountains in Puerto Rico, and which variables were more useful for expressing precipitation and temperature variability trends there. We used cross-correlation analyses to evaluate how local precipitation and temperature data related to regional atmosphere-ocean teleconnections indices (ENSO, NAO, TNA, and AMO). Contrary to regional climate predictions, we found an increase in mean annual precipitation during the dry season, and no trends in annual and wet season precipitation data sets were observed. Precipitation variance increased after 1995, but only when considering dry season data, which showed a decrease in the total number and consecutive days without rain after 1995. In agreement with regional predictions, maximum and minimum annual mean temperatures increased between 1975 and 2015. We found strong positive associations between maximum and minimum temperatures and rainfall distribution (i.e. more wet days, less dry intervals). Precipitation and temperature were more strongly associated to changes in the Tropical Atlantic than to changes in the North Atlantic or the Eastern Pacific. Our downscaling analysis (one station data) shows the relevance of evaluating global climate predictions in specific areas where landscape features can modify the patterns observed at bigger scales. The combination of downscaling analysis and the inclusion of long-term climate trends can facilitate the development of better future scenarios of climate variability and evaluate their importance to local forest ecosystems.

Validation of The Weather Generator CLIGEN with Season Precipitation Data in Tokat Province

In models that predict hydrologic, ecological and product-yield relationship, the climate interface file is widely used. Today, CLIGEN is the most widely used climate model. While this model is extensively used in many countries around the world, it is not used commonly in our country. In this study, daily data belonging to Tokat climate conditions were simulated with CLIGEN. Observed and simulated precipitation was classified as the wet/dry period. The performance of the CLIGEN climate model was evaluated in both wet and dry periods according to the seasons. The relationship between the obtained precipitation data was statistically determined by Kolmogorov-Smirnov and t-test. Tokat province is located in the arid and semi-arid climate zone. The performance of the model in predicting precipitation in all seasons during the dry period was found to be quite successful.

Changes in soil microbial community response to precipitation events in a semi-arid steppe of the Xilin River Basin, China

In the context of climate change, precipitation is predicted to become more intense at the global scale. Such change may alter soil microbial communities and the microbially mediated carbon and nitrogen dynamics. In this study, we experimentally repackaged precipitation patterns during the growing season (from June to September) of 2012 in a semi-arid temperate steppe of the Xilin River Basin in Inner Mongolia of China, based on the 60-year growing season precipitation data. Specifically, a total amount of 240 mm simulated precipitation was assigned to experimental plots by taking the following treatments: (1) P6 (6 extreme precipitation events, near the 1st percentile); (2) P10 (10 extreme precipitation events, near the 5th percentile); (3) P16 (16 moderate precipitation events, near the 50th percentile); and (4) P24 (24 events, 60-year average precipitation, near the 50th percentile). At the end of the growing season, we analyzed soil microbial community structure and biomass, bacterial abundance, fungal abundance and bacterial composition, by using phospholipid fatty acid (PLFA), real-time quantitative polymerase chain reaction (RT-qPCR) and 16S rRNA gene clone library methods. The extreme precipitation events did not change soil microbial community structure (represented by the ratio of PLFA concentration in fungi to PLFA concentration in bacteria, and the ratio of PLFA concentration in gram-positive bacterial biomass to PLFA concentration in gram-negative bacterial biomass). However, the extreme precipitation events significantly increased soil microbial activity (represented by soil microbial biomass nitrogen and soil bacterial 16S rRNA gene copy numbers). Soil fungal community showed no significant response to precipitation events. According to the redundancy analysis, both soil microbial biomass nitrogen and soil ammonium nitrogen (NH4-N) were found to be significant in shaping soil microbial community. Acidobacteria, Actinobacteria and Proteobacteria were the dominant phyla in soil bacterial composition, and responded differently to the extreme precipitation events. Based on the results, we concluded that the extreme precipitation events altered the overall soil microbial activity, but did not impact how the processes would occur, since soil microbial community structure remained unchanged.