Minimum Temperature Time Series Research Articles

Investigation of Temperature Multifractrality According to Zugspitze Weather Station Data

The main multifractal properties of time series of mean, maximum and minimum daily temperatures are analyzed using the method of multifractal fluctuation analysis. As initial data, we used the results of instrumental temperature observations made at the Zugspitze meteorological station in the period from August 1, 1900 to January 31, 2023. In general, variations in the mean, maximum and minimum daily temperatures demonstrate multifractal behavior, especially for small time scales, up to about 90 days An analysis of the generalized Hurst exponent found that the considered time series have a long-term positive correlation and that the multifractality is weaker with large fluctuations. The singularity spectrum for all time series is truncated to the left, which means that the time series have a multifractal structure that is insensitive to local fluctuations of large values.

Variations in Maximum and Minimum Temperature in Mount Qomolangma during 1971–2020

Based on the daily maximum and minimum temperature observational data during 1971–2020, the variabilities of the maximum and minimum temperature of Mount Qomolangma are analyzed. The daily maximum temperature is 25.8 °C and the daily minimum temperature is −31.4 °C during the study period in Mount Qomolangma. Overall, there has been an upward trend with decadal laps for both maximum and minimum temperature. On monthly, seasonal, and annual scales, neither maximum temperature nor minimum temperature time series exhibit an increasing trend from 1971 to 2020. The increasing trends in monthly minimum temperature are even more pronounced than those in maximum temperature. Abrupt changes are noted in both monthly, seasonal, and annual maximum and minimum temperature time series. Specifically, an abrupt change in annual maximum temperature occurred in the 1980s, while an abrupt change in annual minimum temperature occurred in the 1990s. Differences between the north and south slope of Mount Qomolangma are evident, with temperature fluctuations of the north slope being more extreme than those of south slope. The seasonal and annual maximum temperature of the north slope is higher than that of the south slope, except for winter, and the seasonal and annual minimum temperatures of the north slope are all lower than those of the south slope. The tendences of maximum and minimum temperatures in the north slope are more dominant than those in the south slope. The findings are beneficial for understanding the characteristics of local climate change on the Tibetan plateau and to underscore the significant role of Mount Qomolangma in the context of global warming.

Analysis of Rainfall and Temperature Trends and Their Implications on Green Gram Production in the Arid and Semi-Arid Lands of Kenya

Farmers have been facing numerous challenges in the production of green gram with declining yields being the main challenge. This can be attributed to the declining rainfall amounts and unreliable rainfall patterns, increased temperatures and high infestation of the crop by pests and diseases. In this study, climate data of the Makueni County on rainfall and temperature from 1991 to 2020 were obtained from the Kenya Meteorology Department. The data were analyzed in three sets; monthly, annual-monthly and seasonal time series. The analysis focuses on descriptive statistics and trend analysis. The results showed that there was no significant trend in the rainfall data at all three levels; monthly, annual-monthly and seasonal. The absence of a significant trend was associated with the high variability in the time series; hence, instability and unpredictability. The temperature time series exhibited positive linear trends where the maximum temperature increased by 0.003°C and the minimum temperature increased by 0.001°C per month. Such trends were detected in selected monthly as well as seasonal minimum and maximum temperature time series. The unpredictability of rainfall patterns and consistently but gradually rising temperature levels could be attributed to climate change. The crop optimum growing conditions are not attained, therefore, reducing production. Rapid deployment of integrated climate-smart farming techniques and adaptation strategies in the region is recommended for the green gram value chain to build resilience in the production of green gram to achieve sustainable yields.

CLIMBra - Climate Change Dataset for Brazil

General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systematic biases, requiring a bias correction for any further application. Here, we provide a dataset based on an ensemble of 19 bias-corrected CMIP6 climate models projections for the Brazilian territory based on the SSP2-4.5 and SSP5-8.5 scenarios. We used the Quantile Delta Mapping approach to bias-correct daily time-series of precipitation, maximum and minimum temperature, solar net radiation, near-surface wind speed, and relative humidity. The bias-corrected dataset is available for both historical (1980–2013) and future (2015–2100) simulations at a 0.25° × 0.25° spatial resolution. Besides the gridded product, we provide area-averaged projections for 735 catchments included in the Catchments Attributes for Brazil (CABra) dataset. The dataset provides important variables commonly used in environmental and hydroclimatological studies, paving the way for the development of high-quality research on climate change impacts in Brazil.

Gender-responsive approaches to rapid climate warming among smallholder farmers

Climate change impact has no global and gender boundary, yet how gender norms mediate crop farmers' responsive approaches to rapid climate warming received little attention. Men and women crop smallholder farmers in Nasarawa state experience the impacts of rapid climate warming differently. This study examines the gender-responsive approaches to rapid climate warming among crop smallholder farmers in Nasarawa. Temperature data for 36 years was used to establish change. Also, a survey of semi-strcutured questionnaire with in-depth interviews (IDIs) was conducted among 52 smallholder farmers consisting of men and women in agriculture. Participants for the study were selected using a purposive sampling technique. Descriptive statistics, including time series analysis and frequency distribution was used to analyse the temperature data in R. The results from the analysis of temperature data are on graphs and charts. While data from the questionnaire were analysed using descriptive statistics, content analysis was used to analyse the transcripts of IDI and further presented in themes. The results from this study revealed that there has been an additional increase of 1.14°C in the minimum temperature of the study area over the study period. Analysis showed a statistical trend in the time series of the maximum temperature from January to May throughout the study period and no statistical trend in the minimum temperature time series data for all months (January to December. More so, 63% of respondents perceived both heatwaves and dry spells, while 21% perceived only heatwaves. Also from the results, 33% of the respondents perceived only dry spells as an indication of rapid warming. From the results, the major adaptation strategies used by farmers are traditional irrigation (56%), sustainable forestry (44%), and cover cropping (54%). The results indicated that 66% of women adopted the traditional irrigation practice and 75% practised cover cropping. On the other hand, a larger percentage of men (74%) practised sustainable forestry. Findings showed that gender norms constrain the adoption of responsive approaches which could be as a result of unequal access to climate interventions in agriculture. This study recommends the equitable distribution of climate intervention and capacity-building programs among men and women to help them build resilience to rapid climate warming.

Recent Trends of Mean Maximum and Minimum Air Temperatures in Bangladesh

The annual, seasonal, and monthly maximum and minimum air temperature time series for 34 stations in Bangladesh have been examined during the period 1981– 2020 in this study. Three statistical tests were used in the analysis namely Mann–Kendall, Sen’s slope estimator, and linear regression, respectively. 47% of stations experienced significantly rising maximum air temperature trends during the pre-monsoon, 100% during the monsoon, 74% during the post-monsoon, and 24% during the winter season, respectively. Similar findings have been made about minimum air temperatures during the pre-monsoon, monsoon, post-monsoon, and winter seasons in 41, 91, 41, and 35% of stations over Bangladesh. According to the linear regression technique, the significant increasing trends of the maximum air temperatures at Patuakhali and Mongla stations are 0.83°C/decade and 0.82°C/decade in February and October, respectively. Furthermore, at Sandwip and M. Court, the significant increasing minimum air temperature trends in July and March are 0.26°C/decade and 0.66°C/decade, respectively. The highest monthly maximum and minimum air temperature time series value falls (decreases) were seen in January over the stations at Mongla (-0.58°C/decade) and Sandwip (-0.63°C/decade), respectively. This information will be useful in developing adaptation plans to mitigate the harmful effects of climate change. Vulnerability assessments, disaster management, enhanced structure design, institutional reform, and antiextreme climate engineering are some of the viable climate change adaptation approaches in Bangladesh due to rising temperatures.

Estimating the uncertainty of Australian area‐average temperature anomalies

AbstractAn important metric for communicating the context and impact of climate change is regionally or nationally averaged temperature anomalies. Quantifying the associated uncertainty adds confidence in the reporting of temperature records and trends. For area‐averaged temperature anomalies derived from point‐source observations we model the uncertainty by a three‐step process: (1) observation random error; (2) spatial analysis grid point uncertainty; and (3) area‐average uncertainty. Each step should reflect the method used to calculate the area average. For Australian area‐average temperature anomalies, the observation random error is modelled as the sum of the sampling uncertainty and the uncertainty in the adjustment process. Daily maximum and minimum temperature time series from the 104 non‐urban locations of the Australian Climate Observation Reference Network—Surface Air Temperature (ACORN‐SAT) version 2 dataset for 1981–2010 are used to estimate the sampling uncertainty, based on predicted location pair zero‐distance correlations having values of less than 1.0. ACORN‐SAT version 2 breakpoint information is used to estimate the uncertainty from the selection of reference stations used in the detection and homogenization processes. Monthly area‐average temperature uncertainty is estimated for the 1910–2018 period from Monte Carlo simulations using the observation uncertainty and the available ACORN‐SAT network for each month. The uncertainty in annual area‐average temperature anomalies is then obtained from the monthly uncertainties and inter‐month correlations modelled with a periodic auto‐regressive model. The ±2σ annual mean temperature area‐average uncertainty is ±0.084°C in 2018 and ± 0.176°C in 1910. The larger value in 1910 is primarily due to decreased ACORN‐SAT coverage over areas of inland Australia. The Australian annual mean temperature change and associated uncertainty from ACORN‐SAT version 2 over 1910–2018 is found to be 1.42 ± 0.28°C. This is consistent with global air temperature warming by around 1°C since 1850.

Simulation of the impact of climate change on runoff and drought in an arid and semiarid basin (the Hablehroud, Iran)

This study evaluates the impact of climate change (CC) on runoff and hydrological drought trends in the Hablehroud river basin in central Iran. We used a daily time series of minimum temperature (Tmin), maximum temperature (Tmax), and precipitation (PCP) for the baseline period (1982–2005) analysis. For future projections, we used the output of 23 CMIP5 GCMs and two scenarios, RCP 4.5 and RCP 8.5; then, PCP, Tmin, and Tmax were projected in the future period (2025–2048). The GCMs were weighed based on the K-nearest neighbors algorithm. The results indicated a rising temperature in all months and increasing PCP in most months throughout the Hablehroud river basin's areas for the future period. The highest increase in the Tmin and Tmax in the south of the river basin under the RCP 8.5 scenario, respectively, was 1.87 °C and 1.80 °C. Furthermore, the highest reduction in the PCP was 54.88% in August under the RCP 4.5 scenario. The river flow was simulated by the IHACRES rainfall-runoff model. The annual runoff under the scenarios RCP 4.5 and RCP 8.5 declined by 11.44% and 13.13%, respectively. The basin runoff had a downward trend at the baseline period; however, it will have a downward trend in the RCP 4.5 scenario and an upward trend in the RCP 8.5 scenario for the future period. This study also analyzed drought by calculating the streamflow drought index for different time scales. Overall, the Hablehroud river basin will face short-term and medium-term hydrological drought in the future period.

Time-series forecasting models

Statistical and mathematical models of forecasting are of paramount importance for the understanding and study of databases, especially when applied to data of climatological variables, which enables the atmospheric study of a city or region, enabling greater management of the anthropic activities and actions that suffer the direct or indirect influence of meteorological parameters, such as precipitation and temperature. Therefore, this article aimed to analyze the behavior of monthly time series of Average Minimum Temperature, Average Maximum Temperature, Average Compensated Temperature, and Total Precipitation in Belém (Pará, Brazil) on data provided by INMET, for the production and application forecasting models. A 30-year time series was considered for the four variables, from January 1990 to December 2020. The Box and Jenkins methodology was used to determine the statistical models, and during their applications, models of the SARIMA and Holt-Winters class were estimated. For the selection of the models, analyzes of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Autocorrelation Correlogram (ACF), and Partial Autocorrelation (PACF) and tests such as Ljung-Box and Shapiro-Wilk were performed, in addition to Mean Square Error (NDE) and Absolute Percent Error Mean (MPAE) to find the best accuracy in the predictions. It was possible to find three SARIMA models: (0,1,2) (1,1,0) [12], (1,1,1) (0,0,1) [12], (0,1,2) (1,1,0) [12]; and a Holt-Winters model with additive seasonality. Thus, we found forecasts close to the real data for the four-time series worked from the SARIMA and Holt-Winters models, which indicates the feasibility of its applicability in the study of weather forecasting in the city of Belém. However, it is necessary to apply other possible statistical models, which may present more accurate forecasts.

Nonstationary analysis of the extreme temperatures in Turkey

Record-breaking extreme temperatures have been measured in the last two decades all over Turkey, with recent studies detecting positive trends in extreme temperature time series. In this study, nonstationary extreme value analysis was performed on extreme temperature time series obtained from fifty stations scattered over the seven geographical regions of Turkey. Basic characterization of the data set was defined through outlier detection, homogeneity, trend detection, and stationarity tests. Trend-including non-stationary extreme temperature time series were analyzed with non-stationary Generalized Extreme Value distribution. Three main physical drivers were considered as the leading causes that trigger the observed trends in extreme temperatures over Turkey: time, teleconnection patterns of the Arctic Oscillations, and those of the North Atlantic Oscillations. The results showed that most of the absolute annual minimum and maximum temperature time series are inhomogeneous while the possible breakpoints date back to the1970s and 1990s, respectively. More than half of the absolute annual maximum time series (26/50 and many of the absolute annual minimum time series (21/50) showed a positive trend. No negative trend was detected in the extreme temperature time series. Based on the frequency analysis of the 21 annual maximum time series, the non-stationary estimations of 50-year return levels were detected to be higher than in the stationary model (between 0.44 °C and 3.73 °C). The return levels in 15 of the 20 minimum temperature time series increased from 0.11 °C up to 12.28 °C. Elevation increases the nonstationarity impact on absolute minimum temperatures and decreases it on absolute maximums. The findings in this study indicate that the consideration of non-stationarity in extreme temperature time series is a necessity during return level estimations over the study area.

First and last frost date determinations based on meteorological observations in Japan: trend analysis and estimation scheme construction

We investigated the first and last frost dates using meteorological observation data from Japan. First, we identified long-term trends of retardation (mean: +0.224 day/year) in the first frost date in fall and advancement (−0.228 day/year) in the last frost date in spring using historical frost observation data from 1951 to 2010. Trends determined over 20-year subperiods were distinct from long-term trends and were sensitive to decadal changes in daily minimum air temperature. Second, we proposed a scheme to infer the first and last frost dates from the time series of daily minimum air temperature. After optimization, the first (last) cold date when the daily minimum temperature fell below a temperature criterion of ca. 2 °C yielded the best estimate of the first (last) frost date with an error of 2 days in most cases. However, the overall root mean square errors were 13–16 days because some cases with significant misfits deteriorated the values. Both wind speed and humidity shifted the criterion. Sites with strong winds contributed to a decrease in the temperature criterion. Because this scheme only requires the daily minimum air temperature, it is widely applicable to the quantitative evaluation of the first and last frost dates from given meteorological or climate projection data.

Stochastic investigation of daily air temperature extremes from a global ground station network

Near-surface air temperature is one of the most widely studied hydroclimatic variables, as both its regular and extremal behaviors are of paramount importance to human life. Following the global warming observed in the past decades and the advent of the anthropogenic climate change debate, interest in temperature’s variability and extremes has been rising. It has since become clear that it is imperative not only to identify the exact shape of the temperature’s distribution tails, but also to understand their temporal evolution. Here, we investigate the stochastic behavior of near-surface air temperature using the newly developed estimation tool of Knowable (K-)moments. K-moments, because of their property to substitute higher-order deviations from the mean with the distribution function, enable reliable estimation and an effective alternative to order statistics and, particularly for the outliers-prone distribution tails. We compile a large set of daily timeseries (30–200 years) of average, maximum and minimum air temperature, which we standardize with respect to the monthly variability of each record. Our focus is placed on the maximum and minimum temperatures, because they are more reliably measured than the average, yet very rarely analyzed in the literature. We examine segments of each timeseries using consecutive rolling 30-year periods, from which we extract extreme values corresponding to specific return period levels. Results suggest that the average and minimum temperature tend to increase, while overall the maximum temperature is slightly decreasing. Furthermore, we model the temperature timeseries as a filtered Hurst-Kolmogorov process and use Monte Carlo simulation to produce synthetic records with similar stochastic properties through the explicit Symmetric Moving Average scheme. We subsequently evaluate how the patterns observed in the longest records can be reproduced by the synthetic series.

Modelling and Forecasting of Monthly Rainfall and Temperature Time Series Using SARIMA for Trend Detection- A Case Study of Umiam, Meghalaya (India)

Seasonal Auto Regressive Integrative Moving Average Models (SARIMA) were developed for monthly rainfall, mean monthly maximum and minimum temperature time series for Umiam (Barapani), Meghalaya (India). The best model was selected based on the minimum values of AIC and BIC criteria as well as based on observing the ACF and PACF plot of residuals. SARIMA (5,1,2) x (1,1,1)12, SARIMA (2,1,2) x (2,1,1)12, SARIMA (6,1,4) x (2,1,3)12 models were found to be the best fit model for the monthly rainfall, mean monthly maximum and minimum temperatures time series respectively. The adequacy of the SARIMA models was also verified using the Ljung-Box (Q) statistic test. McLeod-Li test and Engle’s ARCH LM test were carried out for residuals. The results indicated that there was no Arch effect in the established SARIMA models and models can be used for forecasting the future values for the year 2013 to 2028. The determination of trend in monthly rainfall, mean maximum and minimum temperatures in the forecasted series were done using different trend analysis techniques. For monthly rainfall and mean monthly minimum temperature time series, all the selected methods supported no significant trend. However, in the case of mean monthly maximum temperature time series, three selected methods supported falling trend.

Projected temperature increases over northern Ghana

This study reports on projected temperature increases and their likely implication on smallholder farming systems across the northern regions of Ghana. Aggregated climate data from 1961 to 2000 show that the mean annual temperature across Ghana rose by 1.0 ℃, with the greatest rate of change in the northern–eastern part of the country. For the development of the downscaled climate projection, five regional climate models (RCMs) forced with three global climate models (GCMs) were used from CORDEX and CMIP5, respectively. Eight stations spatially spread across the study locations were selected for the study based on the quality and completeness of data. The study used daily, minimum and maximum temperature station data from 1980 to 2014 obtained from the Ghana Meteorological Agency. The downscaling was done for the emission scenarios pathways—Representative Concentration Pathways (RCP) 2.6 and 8.5—from the selected RCMs using the station data. Time series of minimum and maximum temperatures averaged for the eight stations was analysed from 1961 to 2080. Results indicate temperatures are likely to increase over northern Ghana with minimum air temperature increasing by 0.5 °C under RCP 2.6 and 2.5 °C under RCP 8.5. Maximum air temperatures are likely to increase by 1 °C under RCP 2.6 and 2 °C under RCP 8.5 by the year 2080. Warming rates per year for the stations reveal that minimum temperatures are warming faster than maximum temperatures. The 99th percentile, which is a measure of heat waves, shows a close relationship between the minimum temperatures for the RCP 2.6 and RCP 8.5 pathways in all the stations except for Wenchi and Bolgatanga. Each pathway has a variation between 22.5 °C and 30 °C. The lowest projected value of the 99th percentile maximum temperature is 35 °C in Wenchi, and the highest projected is above 43 °C in Navrongo. Smallholder food crop farming systems are likely to be negatively impacted, with direct risks of low crop yield unless measures like irrigation and careful crop selection are implemented.

Time series analysis of climate variables using seasonal ARIMA approach

The dynamic structure of climate is governed by changes in precipitation and temperature and can be studied by time series analysis of these factors. This paper describes investigation of time series and seasonal analysis of the monthly mean minimum and maximum temperatures and the precipitation for the Bhagirathi river basin situated in the state of Uttarakhand, India. The data used is from the year 1901–2000 (100 years). The seasonal ARIMA (SARIMA) model was used and forecasting was done for next 20 years (2001–2020). The auto-regressive (p) integrated (d) moving average (q) (ARIMA) model is based on Box Jenkins approach which forecasts the future trends by making the data stationary and removing the seasonality. It was found that the most appropriate model for time series analysis of precipitation data was SARIMA(0,1,1) (0,1,1)12 (with constant) and of temperature data was SARIMA(0,1,0) (0,1,1)12 (with constant). The model prediction results show that the forecast data fits well with the trend in the data. However, over-predictions are found in extreme rainfall events and temperature results. The information of pattern and trends can assist as a prediction tool for development of better water management practices in the area.

Assessing the sensitivity of standardized precipitation evapotranspiration index to three potential evapotranspiration models in Nigeria

Drought indices are very essential in monitoring and planning for drought occurrences. Though many indices have been established over the years, it is expected that proper investigation should be done using these indices to know the ones that can better characterize the drought condition of a region or country. The current study evaluated the performance of three potential evapotranspiration models (Hargreaves, Penman-Monteith and Thornthwaite) as inputs into Standardized Precipitation Evapotranspiration Index (SPEI) in regards to identified historic drought events for five agroecological zones in Nigeria. The computation of the indices was done using either one or more of the monthly time series of rainfall, minimum, maximum and mean temperature, sunshine hours and wind speed from 1981 – 2015 at five timescales (1, 3, 6, 12 and 24 months). Although all the models correlated significantly with one another, SPEI-H (Standardized Precipitation Evapotranspiration Hargreaves) vs SPEI-P (Standardized Precipitation Evapotranspiration Penman-Monteith) show higher correlation for all timesteps than SPEI-H vs SPEI-T (Standardized Precipitation Evapotranspiration Thornthwaite) and SPEI-P vs SPEI-T. The efficiency of the indices in identifying historic drought episodes: 1983 – 1985, 2007 and 2011 was examined using information obtained from Emergency Events Database (EM-DAT) and previous literature on drought studies in Nigeria. Drought onsets, severity and duration determined by the indices were compared with that of the EM-DAT. All the three indices show earlier onset for both 3- and 12- month timesteps than that of EM-DAT, but indicated a significant difference in severity and duration during the 1983 – 1985 drought episode for 12-month timestep. Though SPEI-P has a higher comparative predictive ability as indicated by EM-DAT, it was discovered that SPEI-H is a good alternative for regions where there is a paucity of data.

21st Century Climate Change Projections of Precipitation and Temperature in Jordan

In this paper the climatic trends of the important climatic factors in Jordan mainly temperature and precipitation have been investigated, and the baseline scenarios (business as usual) of the climatic have been developed. Precipitation, maximum temperature, minimum temperature and mean temperature time series at selected eight climatic stations have been used to develop the business as usual (BAU) and the future climate scenarios in Jordan. Trend analysis revealed obvious decreasing trends in the precipitation time series of most of the stations, the decrease in precipitation started in the decade 1960’s. While temperatures showed increasing trends, the minimum temperature has increased more than the maximum temperature. Climate baseline scenarios of the daily maximum temperature, minimum temperature, mean temperature and precipitation at the 8 locations in Jordan have been generated for the 55 years baseline period 1961 – 2014 using the NCEP re-analysis data, Hadley General Circulation Model (HadCM3) and the Statistical Downscaling Model (SDSM). Two scenario periods, the 2050-2065, and the 2080-2099 were considered. Recent projections of global climate changes in response to increasing greenhouse gas concentrations in the atmosphere include warming in Jordan from about +2.5°C to +5°C by end of century. All projection scenarios indicate a reduction in annual precipitation range from 10% to about 37%. For precipitation, the analysis of the results from the SDSM does not lead to an identical conclusion among all stations.

Impacts of climate changes on the maximum and minimum temperature in Iran

In this paper, trends of minimum and maximum temperatures in Iran were studied using time series of daily minimum and maximum temperatures of 45 meteorological stations from 1976 to 2005 (as the baseline period). Mann-Kendall test, for maximum and minimum temperature, was obtained 1.85 and 3.56, respectively, which was positive and significant. The slope of the trend line for maximum and minimum temperature was obtained 0.23 and 0.39 °C decade−1, respectively. In this study, the trend of extreme temperature indicators was also evaluated. According to the obtained results, in annual terms, TX10, FDO, TN10, and IDO indices have had a negative trend at most stations, but TX90, TR20, TNx, TNx, TXn, TN90, SDI, and SU25 indices showed a positive trend. In the seasonal scale, the indices of cold days (TX10) and cold nights (TN10) showed significant negative trends in most seasons. Warm days (TX90) and warm nights (TN90) showed significant positive trends at most stations. The results of future simulations using several general circulation models in different time periods showed that the highest increase in maximum and minimum temperature related to the RCP8.5 scenario in periods of 2071 to 2099. The results also showed that northwest of Iran would have the highest temperature rise. The results also showed that the probability density function of the minimum and maximum temperatures will shift to warmer temperatures. This could be an indication of climate change in the future decades in Iran.

ARIDITY INDEX BASED ON TEMPERATURE AND RAINFALL DATA FOR KURDISTAN REGION-IRAQ

Aridity index is a numerical indicator of the degree of dryness of the climate at a given location. Time series of annual precipitation, minimum and maximum temperature records for 22 meteorological stations in Kurdistan region-Iraq were collected from different agencies. In Kurdistan region-Iraq neither aridity index non it’s trend have been studied, therefore the purpose of this paper is to document the range of an aridity index, called De Martonne’s aridity index (IDM) and to find the trend of aridity index using Mann-Kendal nonparametric test. Spatial zoning of the region was done using inverse distance weight (IDW) interpolation method by ArcGIS software. Results showed that different types of climate in the region were appeared however most of the study area falls in the range of semi-arid zone. Seasonal aridity showed that Kurdistan region-Iraq has extremely humid climate in winter and arid climate in summer. The De Martonne aridity index, average precipitation and average temperature were drawn for 10, 20 and 30 years according to availability of the data. Aridity index for 30 years from De Martonne were compared with two other indices and the results were very close.  Trend analyses were performed for precipitation, temperature, and aridity index for the average data of 10, 20, and 30 years. Results revealed that for 10 year duration Dukan temperature has significant increasing trend at the 95% confidence level. It can be concluded that the last decade (2006-2015) undergo insignificant increasing in precipitation and temperature. For 20 year duration Dukan and Hawler temperature showed significant increasing trend at the 95% confidence level. For 30 year duration all stations temperature increased, Dukan precipitation showed decreasing trend while Dukan aridity index showed increasing trend significantly at the 95% confidence level. Increased significant trends at the 95% confidence level for temperature appears at both short and long durations (10, 20 and 30 years) while precipitation and aridity index trends appear only in long duration (30 years).

The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset

Abstract. We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation (0 to 6993 m a.s.l.) ranges, and it relies on multiple data sources (including ground data, remote-sensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basin-averaged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydro-climatological indices using these time series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset and promote common standards in large sample studies, we computed most catchment attributes introduced by Addor et al. (2017) in their Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r>0.91) and lower correlation (r<0.76) in hyper-arid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. CAMELS-CL can be visualised from http://camels.cr2.cl and downloaded from https://doi.pangaea.de/10.1594/PANGAEA.894885.