Annual Precipitation Time Series Research Articles

A simplistic approach for monitoring meteorological drought over arid regions: a case study of Rajasthan, India

The commonly used precipitation-based drought indices typically rely on probability distribution functions that can be suitable when the data exhibit minimal discrepancies. However, in arid and semi-arid regions, the precipitation data often display significant discrepancies due to highly irregular rainfall patterns. Consequently, imposing any probability distributions on the data for drought analysis in such regions may not be effective. To address this issue, this study employs a novel drought index called the Discrepancy Precipitation Index (DPI), specifically designed for arid regions. Unlike traditional methods, the DPI does not impose a probability distribution on the precipitation data; instead, it relies on the discrepancy between the data and the mean value. Drought severity classifications (i.e., Drought-I, Drought-II, and Drought-III) are proposed based on the DPI values. The DPI is used to characterize and assess the meteorological drought years based on annual and monsoonal precipitation over nineteen districts in Western Rajasthan, India, during 1901–2019. Additionally, a novel statistic called Discrepancy Measure (DM) is employed to assess the degree of discrepancy in the precipitation climatology of the districts for annual and monsoon precipitation time series. Based on annual precipitation, Jaisalmer district exhibited the highest number of historical drought years (35), whereas three districts, i.e., Jhunjhunu, Dausa, and Bhilwara exhibited the lowest number of drought years (11). Similarly, based on monsoon precipitation, Jaisalmer and Bhilwara encountered the highest (34) and the lowest (11) number of drought years, respectively. The return period of Drought-II is lower for monsoon precipitation-based DPI as compared to that of the annual precipitation-based DPI for all the districts. The DM and DPI-based total number of droughts are found to be strongly correlated for both annual and monsoon precipitation. The DM value is highest for Jaisalmer and lowest for Bhilwara district. The findings reveal DPI as an efficient tool for assessing drought years, particularly in arid climatic conditions. Moreover, as the DM value increases for a precipitation series, the DPI becomes more effective in capturing drought events.

Analysis model of flood spatiotemporal change characteristics based on k-means clustering algorithm

<p>In order to analyze the spatiotemporal change characteristics of regional flood disaster, analyze the main change characteristics and hydrological response of precipitation distribution in Guangxi, and improve the comprehensive utilization efficiency of water resources, this paper introduces the k-means clustering algorithm to design an analysis model of spatiotemporal change characteristics of flood disaster. First of all, obtain the flood data in Guangxi and the observation data of the national meteorological station within 5 km, complete the collection of the basic data of mountain flood disasters. Based on the collected data, an analysis was conducted on the spatial and temporal distribution of flash floods in the Guangxi region. Secondly, the Kriging spatial interpolation method was used to analyze the spatial distribution of precipitation data in Guangxi. The Mann-Kendall trend test was then employed to examine the trend of precipitation-related statistical parameters over time. Additionally, wavelet theory was applied to analyze the time series of annual precipitation and precipitation with different durations in Guangxi. Subsequently, the k-means clustering algorithm was introduced to construct a model for analyzing the spatiotemporal characteristics of flood changes, determining the concentration and duration of precipitation in different years in the region. Finally, analyze the spatiotemporal change characteristics of flood events in different seasons under various indicators, and realize the analysis of flood spatiotemporal change characteristics. The research results indicate that the Frank Copula function fits the best correlation between annual precipitation and temperature, and can better characterize the correlation between the two. The Frank Copula function has the best fitting effect on the correlation between precipitation and temperature in autumn in Guilin, summer in Nanning, and summer and winter in Beihai. In Guangxi Zhuang Autonomous Region, the annual precipitation shows a gradually decreasing trend, especially at R and P stations. In summary, the Frank Copula function can effectively characterize the correlation and trend of precipitation and temperature in different seasons and regions of Guangxi.</p>

Mapping cultivation potential index of rainfed wheat from climatic perspective using Shannon entropy catastrophe scheme

Mapping the Cultivation Potential Index (CPI) for a specific crop is a topical research activity in agro-climate studies, which can lead to environmental and water resources protection and agriculture efficiency increase. This study formulates a framework to delineate the CPI of rainfed wheat from the climatic perspective of precipitation and temperature. Four precipitation layers were prepared using the frequency analysis of annual, autumn, spring, and June precipitation time series; also, three temperature layers comprise autumn, spring, and June temperatures obtained by remote sensing techniques. The Shannon-Entropy Catastrophe Scheme (ECS) was also formulated to combine seven layers of precipitation and temperature besides the land use layer while decreasing the inherent subjectivity. The formulated methodology was implemented in the Maragheh city, northwest of Iran. Some contrasts exist between the spatial distributions of precipitation in different time scales, which necessitates the formulation of ECS as a multi-criteria decision-making technique. ECS identifies the eastern half of the study area as the medium to relatively high CPI, which can be employed as a decision support system for planning and managing agriculture.

Long-range, time-varying statistical prediction of annual precipitation in a Mediterranean remote site

In the Mediterranean basin, climate change signals are often representative of atmospheric transients in precipitation patterns. Remote mountaintop rainfall stations are far from human influence and can more easily unveil climate signals to improve the accuracy of long-term forecasts. In this study, the world’s longest annual precipitation time-series (1884–2021) from a remote station, the Montevergine site (1284 m a.s.l.) in southern Italy, was investigated to explain its forecast performance in the coming decades, offering a representative case study for the central Mediterranean. For this purpose, a Seasonal AutoRegressive-exogenous Time Varying process with Exponential Generalised Autoregressive Conditional Heteroscedasticity (SARX(TVAR)-EGARCH) model was developed for the training period 1884–1991, validated for the interval 1992–2021, and used to make forecasts for the time-horizon 2022–2051, with the support of an exogenous variable (dipole mode index). Throughout this forecast period, the dominant feature is the emergence of an incipient and strong upward drought trend in precipitation until 2035. After this change-point, rainfall increases again, more slightly, but with considerable values towards the end of the forecast period. Although uncertainties remain, the results are promising and encourage the use of SARX(TVAR)-EGARCH in climate studies and forecasts in mountain sites.

Statistical Model for Annual Trends and Magnitude of Climatic Variability across Locations from the Malwa Plateau Agroclimatic Zone of Madhya Pradesh, India

Global climate change has affected the local weather patterns in different regions, especially in India. To manage the water resources effectively, it is important to quantify the local changes in the climatic variables. One of the key variables is precipitation, which is often used as an indicator of climate change. This study aims to examine the temporal variability of rainfall in the Malwa Agroclimatic Zone from 1991 to 2020 (30 years) and to assess its impact on the hydrological status of the districts in this zone. The Mann-Kendall and Sen's tests are used to detect the trend and magnitude of change in the annual precipitation time series. The autocorrelation effects are removed before applying the Mann-Kendall test. The results show that on an annual basis, Indore (Z 1.21 & Q 7.86), Mandsaur (Z 0.82 & Q 3.697), Neemuch (Z 1.03 & Q 3.488), Rajgarh (Z 0.93 & Q 0.5658), Ratalm (Z 0.64 & Q 3.525), Shajapur (Z = 0.57 & Q 3.164) and Ujjain (Z 0.11 & Q 0.692) have an increasing but not statistically significant trend in rainfall, while Dewas (Z -0.07 and Q -0.189) has a decreasing but not statistically significant trend in rainfall. The annual and monthly maximum temperature decreased in the Indore district, while the minimum temperature showed an increasing and decreasing trend. Dewas district also had a decreasing maximum temperature and an increasing minimum temperature. Ujjain district had a decreasing trend for maximum temperature and a significant annual increasing trend for minimum temperature. Shajapur district had a significant annual decreasing trend for maximum temperature and a significant annual and monthly (January and September) increasing trend for minimum temperature. Neemuch district had a significant annual increasing trend for both maximum and minimum temperature, as well as a significant monthly (January) increasing trend for minimum temperature. Ratlam district had a significant annual and monthly (January and September) increasing trend for minimum temperature. Mandsaur and Rajgarh districts had non-significant increasing trends for both maximum and minimum temperature, except for significant monthly (January and September) increasing trends.

Proposal of a new method for drought analysis

Abstract Below-average precipitation and above-average air temperature are important factors in the occurrence and intensity of drought. In the context of global climate change, air temperature increase, as a key climatological parameter, has to be considered when calculating the drought index. We introduce a new method of drought analysis, relying on standardized values of precipitation and mean air temperatures for a certain period. The standardized value is calculated by subtracting the average value for each period from each measured value and dividing the obtained value by the standard deviation of the sample. Next, the New Drought Index (NDI) is calculated by subtracting the standardized temperature value from the standardized precipitation value. NDI values were determined for the monthly and annual precipitation time series and mean monthly and annual air temperatures measured at the stations Split-Marjan and Zagreb-Grič between 1948 and 2020. The NDI indicates that the risk of drought has intensified significantly in recent decades, which may be related to the effect of global warming.

Changes in the associations between malaria incidence and climatic factors across malaria endemic countries in Africa and Asia-Pacific region

Empirical evidence supporting the associations between malaria incidence and climatic factors has remained controversial, buffering the progress in the Global Malaria Program that aims to eliminate 90% of the world malaria burden by 2030. This study has aimed to evaluate the nature and extent at which these relations are maintained across all the malaria endemic countries of Africa and Asia-Pacific region. We have utilized the last two decades of malaria incidence, annual minimum temperature, and annual precipitation time series data (2000–2020) for the two most malaria-impacted regions. These data were fitted in the generalized linear model and the mixed effects model. The results showed that there exists a large heterogeneity in malaria incidence across the countries, and between the regions. Last two decadal tendencies showed significant reductions in the disease burden in almost all the countries in the Asia Pacific, with several exceptions or relatively slowed reductions in the Africa. We found significant changes in the positive linear associations between malaria incidence, annual minimum temperature, and annual precipitation across African countries. Many Asia-Pacific countries namely Bangladesh, Bhutan, Indonesia, South Korea, Nepal, Thailand, Vietnam showed negative effects in the associations with the annual precipitation. This study indicates that increasing temperature within the range of 12–30 °C can generate positive effects on malaria incidence, but the nature and extent of precipitation effects vary across countries and at a large regional scale.

A Comparison Study of Observed and the CMIP5 Modelled Precipitation over Iraq 1941–2005

This paper presents an analysis of the annual precipitation observed by a network of 30 rain gauges in Iraq over a 65-year period (1941–2005). The simulated precipitation from 18 climate models in the CMIP5 project is investigated over the same area and time window. The Mann–Kendall test is used to assess the strength and the significance of the trends (if any) in both the simulations and the observations. Several exploratory techniques are used to identify the similarity (or disagreement) in the probability distributions that are fitted to both datasets. While the results show that large biases exist in the projected rainfall data compared with the observation, a clear agreement is also observed between the observed and modelled annual precipitation time series with respect to the direction of the trends of annual precipitation over the period.

Large infrequent rain events dominate the hydroclimate of Rapa Nui (Easter Island)

The history of the Polynesian civilization on Rapa Nui (Easter Island) over the Common Era has come to exemplify the fragile relationship humans have with their environment. Social dynamics, deforestation, land degradation, and climatic shifts have all been proposed as important parts of the settlement history and societal transformations on Rapa Nui. Furthermore, climate dynamics of the Southeast Pacific have major global implications. While the wetlands of Rapa Nui contain critical sedimentological archives for reconstructing past hydrological change on the island, connections between the island’s hydroclimate and fundamental aspects of regional climatology are poorly understood. Here we present a hydroclimatology of Rapa Nui showing that there is a clear seasonal cycle of precipitation, with wet months receiving almost twice as much precipitation as dry months. This seasonal cycle can be explained by the seasonal shifts in the location and strength of the climatological south Pacific subtropical anticyclone. For interannual precipitation variability, we find that the occurrence of infrequent, large rain events explains 92% of the variance of the observed annual mean precipitation time series. Approximately one third (33%) of these events are associated with atmospheric rivers, 21% are associated with classic cold-front synoptic systems, and the remainder are characterized by cut-off lows and other synoptic-scale storm systems. As a group, these large rain events are most strongly controlled by the longitudinal position of the south Pacific subtropical anticyclone. The longitudinal location of this anticyclone explains 21% of the variance in the frequency of large rain events, while the remaining variance is left unexplained by any other major atmosphere-ocean dynamics. We find that over the observational era there appears to be no linear relationship between the number of large rain events and any other major climate phenomena. With the south Pacific subtropical anticyclone projected to strengthen and expand westward under global warming, our results imply that Rapa Nui will experience an increase in the number of dry years in the future.

Recent Changes in Temperature and Precipitation of the Summer and Autumn Seasons over Fujian Province, China

Based on the observation data of daily temperature and precipitation in summer and autumn of 68 representative meteorological stations in Fujian Province from 1971 to 2018, using the climate Tendency Rate, Mann-Kendall trend test, Morlet wavelet analysis and other methods, this paper analyzes the variation trends of air temperature and annual precipitation and the wavelet periodic variation characteristics of annual precipitation time series in summer and autumn of Fujian Province over a period of approximately 48 years. The results show that over the approximately 48 years, the temperature and precipitation in summer and autumn in Fujian showed an obvious upward trend, which had a mutation around 2000, but the mutation time was different, and the precipitation was slightly earlier. The annual temperature and precipitation in summer and autumn experienced three oscillations on the 28a scale. In the 28a time scale of summer autumn seasonal oscillation, there are three negative centers and two positive centers. According to the characteristics of annual average temperature and annual precipitation in the first major cycle, the annual precipitation in summer and autumn will continue to increase in the future.

Geo-statistical assessment of meteorological drought in the context of climate change: case of the Macta basin (Northwest of Algeria)

Northwestern Algeria experiences a high number of spatial and temporal variations in precipitation. This phenomenon predominantly alters the duration as well as the frequency of drought in this region. The objective of this study is to detail the geographical variations in terms of drought intensity, duration, magnitude and frequency in the Macta basin using time series of rainfall data for 42 years (1970–2011) from 42 rainfall stations. The methodology employed lies in analyzing the spatial and temporal variations of rainfall along with the computation of the standardized precipitation index (SPI) to assess the spatial–temporal variations of meteorological drought over different time scales. The Sen’s slope test estimator, Mann–Kendall (MK) test, and the recently developed Innovative Trend Analysis (ITA) method were used for the purpose of evaluating the precipitation trend. The application of the above-mentioned methods has shown a very notable increasing trend during autumn and a decreasing trend during spring precipitation. In this scenario, the annual, winter, and summer precipitation time series experienced a mixed (increasing as well as decreasing) trend. The magnitude of the highest increasing (decreasing) precipitation trend was found at 2.20 mm/season (− 2.55 mm/season) during autumn (spring) season. The annual precipitation trend was in the range of − 4.93 mm/year and 5.29 mm/year. A decreasing trend was observed from both MK and ITA tests in the northern part of the Mediterranean Sea, near the coastal region in Macta basin, whereas the increasing trend was observed in the south. In drought analysis, the long-term frequency drought showed that both northern and western regions faced drought conditions most of the time in comparison with southern and eastern parts of the study area. This infers that the northern and western parts lack long-term water resources. Further, the northern part of the basin usually experiences severe drought conditions when compared to other parts. Drought duration varies from 9 to 37 months with an average of 20 months. Thus, this area is cited to be highly sensitive and prone to droughts compared to other parts of the basin. These findings would be very useful in applying drought adaptation policies by water resource managers in the Macta basin.

A multi‐century meteo‐hydrological analysis for the Adda river basin (Central Alps). Part II: Daily runoff (1845–2016) at different scales

AbstractA high‐quality daily runoff time series of the Lake Como inflow and outflow, the longest for Italian Alps, was reconstructed for the 1845–2016 period in the Adda river basin. It was compared with contemporary monthly precipitation and temperature observations and estimated potential evapotranspiration losses. Trend analyses were conducted for daily flow maxima and 7‐day duration minima of inflows into the lake showing a non‐significant decrease and a significant increase, respectively. Although the annual precipitation time series exhibits a non‐significant decrease, annual runoff volumes decrease with a rate of −136 mm⋅century−1, with a significance level of 5%. Possible causes of variability of rainfall and runoff as North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation and Western Mediterranean Oscillation indexes and sunspot activity were also explored. Wavelet spectra analyses of monthly precipitation and runoff show some changes in the energy both at small and large scales and are effective in pointing out phenomena as droughts and the effects of dams' regulation. Conversely, wavelet coherence spectra indicate a weak correlation of NAO and sunspots with precipitation. In addition, the analysis of temperature and potential evapotranspiration tendencies suggests that the decrease of runoff has to be ascribed mostly to anthropogenic factors, including water abstraction for irrigation and increased evapotranspiration losses due to natural afforestation and, only in part, to climatic variability.

Spatio-Temporal Trends of Monthly and Annual Precipitation in Aguascalientes, Mexico

The objective of this research was to analyze the temporal patterns of monthly and annual precipitation at 36 weather stations of Aguascalientes, Mexico. The precipitation trend was determined by the Mann–Kendall method and the rate of change with the Theil–Sen estimator. In total, 468 time series were analyzed, 432 out of them were monthly, and 36 were annual. Out of the total monthly precipitation time series, 42 series showed a statistically significant trend (p ≤ 0.05), from which 8/34 showed a statistically significant negative/positive trend. The statistically significant negative trends of monthly precipitation occurred in January, April, October, and December. These trends denoted more significant irrigation water use, higher water extractions from the aquifers in autumn–winter, more significant drought occurrence, low forest productivity, higher wildfire risk, and greater frost risk. The statistically significant positive trends occurred in May, June, July, August, and September; to a certain extent, these would contribute to the hydrology, agriculture, and ecosystem but also could provoke problems due to water excess. In some months, the annual precipitation variability and El Niño-Southern Oscillation (ENSO) were statistically correlated, so it could be established that in Aguascalientes, this phenomenon is one of the causes of the yearly precipitation variation. Out of the total annual precipitation time series, only nine series were statistically significant positive; eight out of them originated by the augments of monthly precipitation. Thirteen weather stations showed statistically significant trends in the total precipitation of the growing season (May, June, July, August, and September); these stations are located in regions of irrigated agriculture. The precipitation decrease in dry months can be mitigated using shorter cycle varieties with lower water consumption, irrigation methods with high efficiency, and repairing irrigation infrastructure. The precipitation increase in humid months can be used to store water and use it during the dry season, and its adverse effects can be palliated with the use of varieties resistant to root diseases and lodging. The results of this work will be beneficial in the management of agriculture, hydrology, and water resources of Aguascalientes and in neighboring arid regions affected by climate change.

Long-Term Rainfall Trends and Future Projections over Xijiang River Basin, China

Precipitation trend detection is vital for water resources development and decision support systems. This study predicts the climate change impacts on long-term precipitation trends. It deals with the analysis of observed historical (1960–2010) and arithmetic mean method in assembling precipitation from CMIP5 Global Climate Models (GCMs) datasets for a future period (2020–2099) under four emission scenarios. Daily precipitation data of 32 weather stations in the Xijiang River Basin were provided by National Meteorological Information Centre (NMIC) of the China Meteorological Administration (CMA) and Global Climate Models (GCMs) with all four emission scenarios statistically downscaled using Bias Correction Special Disaggregation (BCSD) and applied for bias correction via Climate Change Toolkit (CCT). Nonparametric Mann–Kendall test was applied for statistical significance trend analysis while the magnitude of the trends was determined by nonparametric Sen’s estimator method on a monthly scale to detect monotonic trends in annual and seasonal precipitation time series. The results showed a declined trend was observed for the past 50 years over the basin with negative values of MK test (Z) and Sen’s slope Q. Historical GCMs precipitation detected decreasing trends except for NoerESM1-M which observed slightly increasing trends. The results are further validated by historical precipitation recorded by the Climate Research Unit (CRU-TS-3.1). The future scenarios will likely be positive trends for annual rainfall. Significant positive trends were observed in monsoon and winter seasons while premonsoon and postmonsoon seasons will likely be slightly downward trends. The 2040s will likely observe the lowest increase of 6.6% while the 2050s will observe the highest increase of 11.5% over the 21st century under future scenarios. However, due to the uncertainties in CMIP5, the future precipitation projections should be interpreted with caution. Thus, it could be concluded that the trend of change in precipitation around the Xijiang River Basin is on the increase under future scenarios. The results can be valuable to water resources and agriculture management policies, as well as the approach for managing floods and droughts under the perspective of global climate change.

A Spatiotemporal Classification of the Peruvian Precipitations Between 1990 and 2015

Precipitation and its variations have great importance in water resource management and sustainable development. In this study, the Peruvian precipitations between January 1990 to October 2015, were used. The precipitations were classified under spatial, temporal, and spatiotemporal classes. For this aim, properties of the precipitation time series including the monthly mean, monthly standard deviation, and principal components at monthly and annual scale were evaluated. Results were projected on a map using the Kriging method. Later, the double mass curves of the monthly precipitation time series were used to classify the temporal changes in the precipitations. Thereafter, the Spearman rank-order correlation was used to evaluate the spatiotemporal changes in monthly and annual precipitation time series by projected t-values on the Peruvian map. Finally, precipitations time series were plotted against Koppen–Geiger climate class of each station and several large scale oscillations namely North Atlantic Oscillation (NAO), El Nino/Southern Oscillation (ENSO), Atlantic Multi-decadal Oscillation (AMO), and Pacific Decadal Oscillation (PDO) simultaneously. It was concluded that there are at least three major climatic regions in the country. Spatial classes, depicts that the Andes Ranges is a major role player in the climate of the country while the ENSO and PDO are the main drivers of the precipitation extremes. Results also indicated to an ascending changes in the amount of precipitation from west to east, while a descending changes were observed at Amazon forest near San Ramon.

Variation Trends of Precipitation and Runoff in the Jinsha River Basin, China: 1961-2015

Data analysis and mathematical statistics are used to analyze the Interannual variation of runoff and precipitation in the Jinsha River basin from 1961 to 2015. The area average annual precipitation showed no significant upward trend, and the annual runoff showed no significant downward trend. The results of cumulative anomaly test, MK mutation test and sliding t test are consistent, there were three mutation years of 1984, 1997 and 2005 in the time series of annual precipitation and runoff in the Jinsha River basin. This study also provides necessary hydrometeorological basic information for quantitative assessment of the contribution rate of climate change and human activities to runoff change.

Статистична оцінка часових змін річних сум опадів рівнинної території України

The field of precipitation of the plain territory of Ukraine is investigated according to the data of evenly spaced 18 weather stations. The annual precipitation is analyzed for periods of different duration (from the beginning of observations at the station until 2015) and for the period 1961-2015. The main statistical characteristics are calculated, the patterns of their changes in the study area are shown. Gradient of decrease in multi-year annual precipitation for 1961-2015 (650 to 400 mm) directed from the northwest to the south and southeast of the country. The value of positive skewness and kurtosis, the coefficient of variation (0.16-0.26), on the contrary, increases in this direction. The standard deviation (91-137 mm) is maximum in the southwest and in the center of the plain part of Ukraine. It was determined that the distribution of annual precipitation can be considered normal, mainly with positive skewness and kurtosis. Multi-year fluctuations in annual precipitation are approximated by linear trend equations and a polynomial of the 6th degree. Regions with a negative and positive linear trend of annual precipitation in 1961-2015 were identified. A downward trend in precipitation was noted at stations located in a “strip” from the southwest (Chernivtsi) to the northeast (Sumy) through the center of Ukraine. In the south-west of this region (Vinnytsia), the decrease in precipitation is the greatest: the negative linear trend is statistically significant, the slope of the trend is -2.35, the coefficient of determination is 0.14; mean annual precipitation for 1991-2015 compared to 1961-1990 less by 10.5%, 53.4 mm. In the rest of the plain territory of the country, there was a tendency towards an increase in precipitation, but the positive trend for all stations is statistically insignificant. The absence of statistically significant linear trends (except for Vinnytsia) can be explained by the relative stability of the multi-year precipitation regime during this period. The use of a more complex approximation and a long time series of observations increased the trend approximation confidence, but the influence of these factors is not unambiguous for all weather stations. On the graphs of polynomial trends, the cycle manifestation in the time series of annual precipitation depends on the length of the observation series and decreases from west to east of Ukraine. The duration of the cycles is 25-30 and 35-40 years when determined according to the data of 1961-2015, and from 70 to 90 and 120 years according to the series of observations more than 100 years. In 2016-2025, as shown by estimates by the equations of polynomials of the 6th degree, a decrease in annual precipitation will prevail on the plain territory of Ukraine compared to 1961-2015. The largest decrease (by 10-13%) is likely in the central regions (Poltava, Dnipro). an increase (by 5%) - in the southwest (Vinnitsa, Chernivtsi).

Arctic Ocean Precipitation From Atmospheric Reanalyses and Comparisons With North Pole Drifting Station Records

AbstractWe evaluate fields of Arctic Ocean precipitation from six atmospheric reanalyses: NASA MERRA, NASA MERRA2, NOAA CFSR/CFSv2, ECMWF ERA‐Interim, ECMWF ERA5, and JMAO JRA55. The study is motivated by recognition that precipitation fields from reanalyses can serve as the key input into snow on sea ice accumulation models, supporting retrievals of sea ice thickness from satellite altimeter systems. Time series of annual precipitation over the central Arctic Ocean correlate well between all reanalyses, and they all capture the basic spatial and seasonal patterns of Arctic precipitation. However, they differ strongly with respect to precipitation amount: CFSR and MERRA2 are wetter than the other reanalyses. All reanalyses depict that the majority of total annual precipitation over the central Arctic Ocean comes from small events, less than 1 mm/day. Validation is challenged by sparse observations, uncertain adjustments for gauge undercatch, and other issues. However, given that correlations with data from Russian North Pole drifting station records are all roughly equal and that they depict similar interannual variability at the regional scale, all of the reanalyses appear suitable for supporting retrievals of ice thickness (provided that appropriate bias corrections can be applied). However, only CFSR, MERRA2, ERA5, and JRA55 will continue to generate output beyond 2019.

Annual Green Water Resources and Vegetation Resilience Indicators: Definitions, Mutual Relationships, and Future Climate Projections

Satellites offer a privileged view on terrestrial ecosystems and a unique possibility to evaluate their status, their resilience and the reliability of the services they provide. In this study, we introduce two indicators for estimating the resilience of terrestrial ecosystems from the local to the global levels. We use the Normalized Differential Vegetation Index (NDVI) time series to estimate annual vegetation primary production resilience. We use annual precipitation time series to estimate annual green water resource resilience. Resilience estimation is achieved through the annual production resilience indicator, originally developed in agricultural science, which is formally derived from the original ecological definition of resilience i.e., the largest stress that the system can absorb without losing its function. Interestingly, we find coherent relationships between annual green water resource resilience and vegetation primary production resilience over a wide range of world biomes, suggesting that green water resource resilience contributes to determining vegetation primary production resilience. Finally, we estimate the changes of green water resource resilience due to climate change using results from the sixth phase of the Coupled Model Inter-comparison Project (CMIP6) and discuss the potential consequences of global warming for ecosystem service reliability.

Homogeneity tests and non-parametric analyses of tendencies in precipitation time series in Keszthely, Western Hungary

This study aims to investigate the precipitation trends in Keszthely (Western Hungary, Central Europe) through an examination of historical climate data covering the past almost one and a half centuries. Pettitt’s test for homogeneity was employed to detect change points in the time series of monthly, seasonal and annual precipitation records. Change points and monotonic trends were analysed separately in annual, seasonal and monthly time series of precipitation. While no break points could be detected in the annual precipitation series, a significant decreasing trend of 0.2–0.7 mm/year was highlighted statistically using the autocorrelated Mann-Kendall trend test. Significant change points were found in those time series in which significant tendencies had been detected in previous studies. These points fell in spring and winter for the seasonal series, and October for the monthly series. The question therefore arises of whether these trends are the result of a shift in the mean. The downward and upward shift in the mean in the case of spring and winter seasonal amounts, respectively, leads to a suspicion that changes in precipitation are also in progress in these seasons. The study concludes that homogeneity tests are of great importance in such analyses, because they may help to avoid false trend detections.