Spatio Temporal Variability of Rainfall Across Western Libya from 1979 to 2009

2020 ◽  
Vol 6 (3) ◽  
pp. 235-244
Author(s):  
Ali Salem Eddenjal
Keyword(s):  
Time Series ◽  
Rainfall Variability ◽  
Statistical Significance ◽  
Regional Scale ◽  
Policy Makers ◽  
Rain Gauges ◽  
Spatio Temporal ◽  
Annual Scale ◽  
Increasing Trends ◽  
The Impact

Aims: To understand the links between climate variability and hydrology in western Libya. Background: This study represents the first comprehensive assessment of rainfall variability in western Libya at a regional scale. Objective: To assess temporal and spatial variability of rainfall in western Libya, based on data (1979-2009) from 16 rain gauges. Methods: The non-parametric Mann-Kendall method and Sen’s slop estimator were used to define changes in rainfall series and their statistical significance. Results: Coastal and mountainous time series showed decreasing trends at the annual, autumn, and spring scales, with very few exceptions. Notably, winter showed increasing trends, with the significant values of 1.94 and 0.88 mm/year at Sirt and Nalut, respectively. Desert stations showed increasing trends, especially at the annual scale, with the greatest significant increase on the order of 1.19 mm/year in Ghadames. For the regional rainfall trend analysis, annual, spring and autumn rainfalls decreased in the coastal and mountainous zones, with the highest significant decrease of 1.94 mm/year. Again, winter rainfall showed increasing trend over the whole study domain. Conclusion: Although most time series showed a tendency towards more drier conditions, most of the detected trends were statistically non-significant. This study will provide guidance for policy makers in their future planning to mitigate the impact of drought.

scholarly journals Rainfall spatio-temporal correlation and intermittency structure from micro-γ to meso-β scale in the Netherlands

2021 ◽  
Author(s):  
Thomas C. van Leth ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
Remko Uijlenhoet
Keyword(s):  
The Netherlands ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Temporal Structure ◽  
Temporal Correlation ◽  
Exponential Model ◽  
Rain Gauges ◽  
Wide Range ◽  
Spatio Temporal ◽  
Two Parameter

AbstractWe investigate the spatio-temporal structure of rainfall at spatial scales from 7m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatio-temporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.

scholarly journals Rainfall regime trends at annual and monthly scales in Catalonia (NE Spain) and indications of CO2 emissions effects

2021 ◽  
Author(s):  
Xavier Lana ◽  
M. Carmen Casas-Castillo ◽  
Raül Rodríguez-Solà ◽  
Carina Serra ◽  
M. Dolors Martínez ◽  
...  
Keyword(s):  
Time Trends ◽  
Statistical Significance ◽  
Threshold Level ◽  
Western Mediterranean ◽  
Rainfall Regime ◽  
Rain Gauges ◽  
Dry Spells ◽  
Greenhouse Effects ◽  
Annual Scale ◽  
Ne Spain

AbstractThe pluviometric regime in the Western Mediterranean and concretely in Catalonia (NE Spain) is characterised by irregular amounts at monthly and annual scales, sometimes with copious short episodes causing floods and, conversely, sometimes with long dry spells exceeding 1 month length, depending on the chosen threshold level to define the dry episode. Taking advantage of a dense network of rain gauges, most of them with records length of 50–60 years and some others exceeding 85 years, the evolution of these monthly and annual amounts is quantified by means of their time trends, statistical significance and several irregularity parameters. In agreement with the evolution of the CO2 emissions into the atmosphere and the increasing concentration, in parts per million (ppm), of this greenhouse gas, different time trends at annual scale have been detected up to approximately years 1960–1970 in comparison with the interval 1960–1970 to nowadays. Consequently, besides the greenhouse effects on the temperature regime, the influence on the pluviometric regime could not be negligible. Graphical abstract

Commercial microwave links instead of rain gauges: fiction or reality?

2014 ◽  
Vol 71 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Petr Sýkora ◽  
David Stránský ◽  
Vojtěch Bareš
Keyword(s):  
Rainfall Variability ◽  
A Priori ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Spatio Temporal ◽  
Areal Rainfall ◽  
Quantitative Precipitation Estimates ◽  
Commercial Microwave Links ◽  
Unique Dataset

Commercial microwave links (MWLs) were suggested about a decade ago as a new source for quantitative precipitation estimates (QPEs). Meanwhile, the theory is well understood and rainfall monitoring with MWLs is on its way to being a mature technology, with several well-documented case studies, which investigate QPEs from multiple MWLs on the mesoscale. However, the potential of MWLs to observe microscale rainfall variability, which is important for urban hydrology, has not been investigated yet. In this paper, we assess the potential of MWLs to capture the spatio-temporal rainfall dynamics over small catchments of a few square kilometres. Specifically, we investigate the influence of different MWL topologies on areal rainfall estimation, which is important for experimental design or to a priori check the feasibility of using MWLs. In a dedicated case study in Prague, Czech Republic, we collected a unique dataset of 14 MWL signals with a temporal resolution of a few seconds and compared the QPEs from the MWLs to reference rainfall from multiple rain gauges. Our results show that, although QPEs from most MWLs are probably positively biased, they capture spatio-temporal rainfall variability on the microscale very well. Thus, they have great potential to improve runoff predictions. This is especially beneficial for heavy rainfall, which is usually decisive for urban drainage design.

scholarly journals Compound Droughts and Heat Waves in China

2019 ◽  
Vol 11 (12) ◽  
pp. 3270 ◽  
Author(s):  
Lei Ye ◽  
Ke Shi ◽  
Zhuohang Xin ◽  
Chao Wang ◽  
Chi Zhang
Keyword(s):  
Heat Wave ◽  
North China ◽  
Heat Waves ◽  
Northwest China ◽  
Central China ◽  
Spatio Temporal ◽  
The 1960S ◽  
Increasing Trends ◽  
Climate Events ◽  
The Impact

Droughts and heat waves both are natural extreme climate events occurring in most parts of the world. To understand the spatio-temporal characteristics of droughts and heat waves in China, we examine changes in droughts, heat waves, and the compound of both during 1961–2017 based on high resolution gridded monthly sc_PDSI and daily temperature data. Results show that North China and Northwest China are the two regions that experience the most frequent droughts, while Central China is the least drought-affected region. Significant drought decreasing trends were mostly observed Qinghai, Xinjiang, and Tibet provinces, while the belt region between Yunnan and Heilongjiang provinces experienced significant drought increasing trends. Heat waves occur more frequently than droughts, and the increase of heat wave occurrence is also more obvious. The increasing of heat wave occurrence since the 2000s has been unprecedented. The compound droughts and heat waves were mild from the 1960s to 1980s, and began to increase in 1990s. Furthermore, the significant increasing trends of the percentage of compound droughts and heat waves to droughts are observed in entire China, and more than 90% drought occurrences are accompanied by one or more heat waves in the 2010s. The results highlight the increased percentage of compound droughts and heat waves and call for improved efforts on assessing the impact of compound extremes, especially in an era of changing climate.

scholarly journals Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

2020 ◽  
Vol 12 (22) ◽  
pp. 3798
Author(s):  
Lei Ma ◽  
Michael Schmitt ◽  
Xiaoxiang Zhu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Series Data ◽  
Spectral Bands ◽  
Object Based ◽  
Spatio Temporal ◽  
Optical Satellite Images ◽  
The Impact

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.

scholarly journals Drifting Effects of NOAA Satellites on Long-Term Active Fire Records of Europe

2019 ◽  
Vol 11 (4) ◽  
pp. 467 ◽  
Author(s):  
Helga Weber ◽  
Stefan Wunderle
Keyword(s):  
Time Series ◽  
Explicit Knowledge ◽  
Regional Scale ◽  
Error Sources ◽  
Fire Cycle ◽  
Active Fire ◽  
Maximum Delay ◽  
Orbital Drift ◽  
The Impact

Explicit knowledge of different error sources in long-term climate records from space is required to understand and mitigate their impacts on resulting time series. Imagery of the heritage Advanced Very High Resolution Radiometer (AVHRR) provides unique potential for climate research dating back to the 1980s, flying onboard a series of successive National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites. However, the NOAA satellites are affected by severe orbital drift that results in spurious trends in time series. We identified the impact and extent of the orbital drift in 1 km AVHRR long-term active fire data. This record contains data of European fire activity from 1985–2016 and was analyzed on a regional scale and extended across Europe. Inconsistent sampling of the diurnal active fire cycle due to orbital drift with a maximum delay of ∼5 h over NOAA-14 lifetime revealed a ∼90% decline in the number of observed fires. However, interregional results were less conclusive and other error sources as well as interannual variability were more pronounced. Solar illumination, measured by the sun zenith angle (SZA), related changes in background temperatures were significant for all regions and afternoon satellites with major changes in −0.03 to −0.09 K deg − 1 for ▵ B T 34 (p ≤ 0 . 001). Based on example scenes, we simulated the influence of changing temperatures related to changes in the SZA on the detection of active fires. These simulations showed a profound influence of the active fire detection capabilities dependent on biome and land cover characteristics. The strong decrease in the relative changes in the apparent number of active fires calculated over the satellites lifetime highlights that a correction of the orbital drift effect is essential even over short time periods.

Spatio-temporal cluster analyses of landslides in Italy at national and regional scale

2020 ◽  
Author(s):  
Marj Tonini ◽  
Kim Romailler ◽  
Gaetano Pecoraro ◽  
Michele Calvello
Keyword(s):  
Cluster Analysis ◽  
Point Processes ◽  
Statistical Significance ◽  
Regional Scale ◽  
Regional Analysis ◽  
Scan Statistics ◽  
Economic Losses ◽  
Campania Region ◽  
Temporal Cluster ◽  
Spatio Temporal

<p><strong>Keywords:</strong> Landslides, FraneItalia, cluster analysis, spatio-temporal point process</p><p>In Italy landslides pose a significant and widespread risk, resulting in a large number of casualties and huge economic losses. Landslide inventories are critical to support investigations of where and when landslides have happened and may occur in the future, i.e. to establish reliable correlations between triggering factors and landslide occurrences. To deal with this issue, statistical methods originally developed for spatio-temporal stochastic point processes can be useful for identifying correlations between events in space and time and detecting a significant excess of cases within large landslide datasets.</p><p>In the present study, the authors propose an approach to analyze and visualize spatio-temporal clusters of landslides occurred in Italy in the period 2010-2017, considering the weather warning zones as territorial units. Besides, a regional analysis was conducted in Campania region considering the municipalities as territorial units. Data on landslide occurrences derived from the FraneItalia catalog, an inventory retrieved from online Italian news. The database contains 8931 landslides, grouped in 4231 single events and 938 areal events (records referring to multiple landslides triggered by the same cause in the same geographic area). Analyses were performed both annually, considering each year individually, and globally, considering the entire frame period. We applied the spatio-temporal scan statistics permutation model (STPSS, integrated in SaTScan<sup>TM</sup> software), which allowed detecting clusters’ location and estimating their statistical significance. STPSS is based on cylindrical moving windows which scan the area across the space and in time counting the number of observed and expected occurrences and computing the likelihood ratio. The statistical inference (p-value) is evaluated by Monte Carlo sampling and finally the most likely clusters in the real and randomly generated datasets are compared.</p><p>Although more detailed analyses are required for the determination of cause-effect relationships among landslides and other variables, some relations with the local topographic and meteorological conditions can already be argued. At national scale, spatio-temporal clusters of landslides are mainly recurrent in two zones: the area enclosing Liguria Region – Northern Tuscany at north-west and the area between Abruzzo and Molise regions at centre-east. During the year, landslide clusters are particularly abundant between October and March. as most of the events in the FraneItalia catalog are rainfall-induced, strongly influenced by seasonal rainfall patterns. Concerning the regional analysis, most of the clusters are located in the Lattari mountains, the Pizzo d’Alvano massif and the Picentini mountains, areas highly susceptible to landslide occurrence due to geomorphological factors.</p><p>In conclusion, the application of spatio-temporal cluster analysis at various scale allowed the identification of frame periods with greater landslide activity. The question of whether this increase in activity depends climate conditions or topographic factors is still open and request further investigations.</p><p>REFERENCES</p><p>Calvello, M., Pecoraro, G. FraneItalia: a catalog of recent Italian landslides. <em>Geoenvironmental Disasters</em>. 5: 13 (2018)</p><p>Tonini, M. & Cama, M. Spatio-temporal pattern distribution of landslides causing damage in Switzerland. <em>Landslides</em> 16 (2019)</p>

scholarly journals Comparison of Six Statistical Methods for Interrupted time Series Studies: Empirical Evaluation of 190 Published Series

2020 ◽  
Author(s):  
Simon Turner ◽  
Amalia Karahalios ◽  
Andrew Forbes ◽  
Monica Taljaard ◽  
Jeremy Grimshaw ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Method ◽  
Statistical Methods ◽  
Statistical Significance ◽  
Empirical Evaluation ◽  
Interrupted Time Series ◽  
Series Data ◽  
Standard Errors ◽  
P Values ◽  
The Impact

Abstract Background The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets. MethodsA random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods. ResultsFrom the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4% to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series. ConclusionsThe choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.

scholarly journals Analysis of rainfall trends over Tripura

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 27-36
Author(s):  
RANJAN PHUKAN ◽  
D. SAHA
Keyword(s):  
Heavy Rainfall ◽  
Monsoon Season ◽  
Rainfall Variability ◽  
Kendall Test ◽  
Annual Rainfall ◽  
The North ◽  
Rainfall Trends ◽  
Annual Scale ◽  
Increasing Trends ◽  
Rainy Days

Rainfall in India has very high temporal and spatial variability. The rainfall variability affects the livelihood and food habits of people from different regions. In this study, the rainfall trends in two stations in the north-eastern state of Tripura, namely Agartala and Kailashahar have been studied for the period 1955-2017. The state experiences an annual mean of more than 2000 mm of rainfall, out of which, about 60% occurs during the monsoon season and about 30% in pre-monsoon. An attempt has been made to analyze the trends in seasonal and annual rainfall, rainy days and heavy rainfall in the two stations, during the same period.Non-parametric Mann-Kendall test has been used to find out the significance of these trends. Both increasing and decreasing trends are observed over the two stations. Increasing trends in rainfall, rainy days and heavy rainfall are found at Agartala during pre-monsoon season and decreasing trends in all other seasons and at annual scale. At Kailashahar, rainfall amount (rainy days & heavy rainfall) is found to be increasing during pre-monsoon and monsoon seasons (pre-monsoon season). At annual scale also, rainfall and rainy days show increasing trends at Kailashahar. The parameters are showing decreasing trends during all other seasons at the station. Rainy days over Agartala show a significantly decreasing trend in monsoon, whereas no other trend is found to be significant over both the stations.  

scholarly journals Impact of Rain Gauges Distribution on the Runoff Simulation of a Small Mountain Catchment in Southern Ecuador

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1169 ◽  
Author(s):  
Adrián Sucozhañay ◽  
Rolando Célleri
Keyword(s):  
Hydrological Modeling ◽  
Rainfall Variability ◽  
Model Performance ◽  
Rainfall Data ◽  
Model Structure ◽  
Mountain Regions ◽  
Rainfall Estimation ◽  
Runoff Simulation ◽  
Rain Gauges ◽  
The Impact

In places with high spatiotemporal rainfall variability, such as mountain regions, input data could be a large source of uncertainty in hydrological modeling. Here we evaluate the impact of rainfall estimation on runoff modeling in a small páramo catchment located in the Zhurucay Ecohydrological Observatory (7.53 km2) in the Ecuadorian Andes, using a network of 12 rain gauges. First, the HBV-light semidistributed model was analyzed in order to select the best model structure to represent the observed runoff and its subflow components. Then, we developed six rainfall monitoring scenarios to evaluate the impact of spatial rainfall estimation in model performance and parameters. Finally, we explored how a model calibrated with far-from-perfect rainfall estimation would perform using new improved rainfall data. Results show that while all model structures were able to represent the overall runoff, the standard model structure outperformed the others for simulating subflow components. Model performance (NSeff) was improved by increasing the quality of spatial rainfall estimation from 0.31 to 0.80 and from 0.14 to 0.73 for calibration and validation period, respectively. Finally, improved rainfall data enhanced the runoff simulation from a model calibrated with scarce rainfall data (NSeff 0.14) from 0.49 to 0.60. These results confirm that in mountain regions model uncertainty is highly related to spatial rainfall and, therefore, to the number and location of rain gauges.

Sign in / Sign up

Export Citation Format

Share Document