scholarly journals Climate change effect on decline groundwater level using Entropy wavelet (case study of Khorramabad city)

2021 ◽  
Author(s):  
Reza Hassanzadeh ◽  
Mehdi Komasi ◽  
Alireza Derikvand
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Hydrological Cycle ◽  
Climatic Factors ◽  
Global Climate ◽  
Affecting Factors ◽  
The Third ◽  
Temperature And Precipitation ◽  
Time Period ◽  
The Impact

Abstract Changing global climate predicts a warmer future which may alter the hydrological cycle, surface water as well as groundwater resource. The Entropy wavelet criterion is a new indicator to analyze the time series fluctuations. In this study, the effective factors of decreasing the groundwater level in Khorramabad city during the years 2005–2018 have been evaluated by the use of Entropy wavelet criterion. In general, it can be said that the decreasing of Entropy wavelet criterion or time series complexity of a phenomenon shows the time series decrease of fluctuations natural amounts, which it leads to an unfavorable trend. In this regard, in order to identify the affecting factors of the groundwater level decrease in Khorramabad, the groundwater level has been divided into 4 time periods, and after being investigated, the monthly time series of runoff, temperature, and precipitation of this city were divided into 4 periods. Each of these subset were decomposed into other several subsets at different time scales under the wavelet transform, and finally, after calculation of the normalized wavelet energy for this subset, its Entropy wavelet criterion was calculated for each period. Investigation of Entropy wavelet complexity shows a 21.3% decrease in groundwater level in the second period, but in the third and fourth periods, it has increased by 145 and 272%, respectively. Also, according to the results of Entropy wavelet changes analyzing for the precipitation time series, 35.2, 32.8, and 10.06% decrease in the second, third, and fourth periods were shown. The air temperature time series complexity decreased of 26.8% only in the third time period and in the second and fourth period, it shows an increase of 29.65 and 34.7%, respectively. However, the runoff time series did not show any reduction complexity according to the entropy wavelet criterion. These results indicate that the impact of climatic factors has been more effective than human factors in reducing the groundwater level of Khorramabad.

Homogenization of long-term snow observations

2020 ◽  
Author(s):  
Gernot Resch ◽  
Barbara Chimani ◽  
Roland Koch ◽  
Wolfgang Schöner ◽  
Christoph Marty
Keyword(s):  
Time Series ◽  
Snow Depth ◽  
Global Climate ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
Innovative Methods ◽  
Weather And Climate ◽  
Seasonal Basis ◽  
The Impact

<p>Climate data contains vital information about the global climate system. To get the desired information out of measurements, they have to be homogenous, where the variability of a time series is only caused by variations in weather and climate and not due to external influences.</p><p>Snow is an important component of this system, treated as one of the most obvious visual evidences of climate change and important for countries with mountainous environments. But most of the existing tools and algorithms that are being used for homogenization have been developed for air temperature and precipitation, whereas their application to snow depth measurements has only been rarely attempted. Until now, there have only been smaller efforts to develop methods and tools for snow series.</p><p>We are trying to break new ground by developing innovative methods that can be applied to the homogenization of longterm snow observations, as well as to demonstrate the impact of the developed adjustments on climatologies and trends. For that, we are using daily longterm snow measurements of the two most frequently measured parameters, snow depth (HS) and new snow height (HN) from the Swiss-Austrian domain.</p><p>As a first approach, we are applying the existing methods PRODIGE for the detection of multiple inhomogeneities and INTERP for the calculation of corrections with a quantile-mapping approach on a seasonal basis on selected time series.</p>

scholarly journals Projected distribution of the warm season droughts over the territory of Ukraine in 2021-2050

2020 ◽  
Keyword(s):  
Time Series ◽  
Hydrological Cycle ◽  
Warm Season ◽  
Data Access ◽  
Natural Phenomenon ◽  
Drought Frequency ◽  
Temperature And Precipitation ◽  
Precipitation Regimes ◽  
Dry Seasons ◽  
The Impact

Introduction. Drought is a natural phenomenon that occurs in all climates, and is one of the most relevant natural hazards, which propagates through the full hydrological cycle and affects large areas, often with long-term economic and environmental impacts. A prolonged deficit in precipitation over a defined region cause a meteorological drought, while the other types of drought describe secondary effects on specific ecological and economic compartments. Recent trends in the drought distribution and intensity shows that Europe splits into two big areas, in which the southern and western regions have positive trends of drought frequency, duration, and severity, and the northern and eastern regions show a decrease in this parameters. Regarding the long events, territory of Ukraine belongs to the areas in which a prominent decrease in drought frequency, duration and severity are fixed. But positive trends in the drought characteristics are observed on the Black Sea coast, also in the Carpathian region, many droughts occurred in the past three decades. The purpose of this study to examine the results of analysis of the spatiotemporal distribution of warm season droughts (April-October) across the administrative areas of Ukraine in 2021-2050 under the climate scenarios RCP4.5 and RCP6.0 with them comparing. Data and methods. Drought estimation was performed using the SPEI index (the Standardized Precipitation Evapotranspiration index). The inclusion of temperature through the potential evapotranspiration (PET) along with precipitation data allows SPEI to account for the impact of temperature regime on a drought situation. A drought episode for given time scale is defined as a period, in which SPEI is continuously negative and reaches a value of –1.0 or less. In this study, the gridded fields of monthly air temperature and precipitation intensity from multimodel sets of global CMP5 models are taken for calculations of SPEI. Data access was made through the Climate Explorer. All data were averaged over the area of each of 25 administrative regions of Ukraine. Research results. Analysis of the time series of the calculated SPEI index for both scenarios showed that in all regions of Ukraine there will be a tendency to transition from moderately wet conditions in 2021-2035 to droughty conditions in 2037-2050. In the first half of the study period drought is expected near 2024, as well as in 2030-2033 almost in all provinces except southern areas. In the second half of the period prolonged seasonal drought is projected in 2044-2047 over all Ukraine and in some areas drought may reach an extreme intensity. Decade analysis of the SPEI7 time series showed that in both scenarios in all regions of Ukraine, the least number of dry seasons is expected from 2021 to 2030. The highest number of dry seasons in this period may reach up to 4-5 cases per 10 years in the western regions under RCP6.0 scenario. In other regions the number of dry warm seasons will be 2-3 cases per 10 years. In the period from 2031 to 2040, the number of dry seasons will increase substantially in all regions of Ukraine under RCP4.5 scenario, in which their number will be 5-6 cases per 10 years. Under RCP6.0 scenario, an increase in the number of droughts will be observed in all areas except the western regions, where will be from 2 to 4 dry seasons per 10 years. In the last decade from 2041 to 2050, in both scenarios, the number of dry seasons will increase throughout Ukraine compared to the previous decade. Under RCP6.0 scenario, the greatest increase is projected in the north of the country and in some other regions throughout Ukraine, where the maximum number of seasons with droughts will reached up 8-10 cases per 10 years. Conclusions. Comparison of the SPEI7 time series for both scenarios showed that under RCP6.0 scenario the transition from wet conditions to dry conditions is projected during long period from 2030 to the early 2040s. In this time, small interannual variations of the SPEI index across all regions are expected, and only in the last decade the dry seasons will prevail. In addition, in the RCP6.0 scenario, maxima of drought frequency are expected in few different regions of the country, compared with the RCP4.5 scenario, which indicates significant scenarios' differences in the predicted state of the regional atmospheric circulation determined the temperature and precipitation regimes in the future.

scholarly journals Response of altitudinal vegetation belts of the Tianshan Mountains in northwestern China to climate change during 1989–2015

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Zhang ◽  
Lu-yu Liu ◽  
Yi Liu ◽  
Man Zhang ◽  
Cheng-bang An
Keyword(s):  
Climate Change ◽  
Global Climate Change ◽  
Global Climate ◽  
Animal Husbandry ◽  
Forest Tree ◽  
Tianshan Mountains ◽  
Northwestern China ◽  
Temperature And Precipitation ◽  
Altitudinal Belts ◽  
The Impact

AbstractWithin the mountain altitudinal vegetation belts, the shift of forest tree lines and subalpine steppe belts to high altitudes constitutes an obvious response to global climate change. However, whether or not similar changes occur in steppe belts (low altitude) and nival belts in different areas within mountain systems remain undetermined. It is also unknown if these, responses to climate change are consistent. Here, using Landsat remote sensing images from 1989 to 2015, we obtained the spatial distribution of altitudinal vegetation belts in different periods of the Tianshan Mountains in Northwestern China. We suggest that the responses from different altitudinal vegetation belts to global climate change are different. The changes in the vegetation belts at low altitudes are spatially different. In high-altitude regions (higher than the forest belts), however, the trend of different altitudinal belts is consistent. Specifically, we focused on analyses of the impact of changes in temperature and precipitation on the nival belts, desert steppe belts, and montane steppe belts. The results demonstrated that the temperature in the study area exhibited an increasing trend, and is the main factor of altitudinal vegetation belts change in the Tianshan Mountains. In the context of a significant increase in temperature, the upper limit of the montane steppe in the eastern and central parts will shift to lower altitudes, which may limit the development of local animal husbandry. The montane steppe in the west, however, exhibits the opposite trend, which may augment the carrying capacity of pastures and promote the development of local animal husbandry. The lower limit of the nival belt will further increase in all studied areas, which may lead to an increase in surface runoff in the central and western regions.

The impact of climate change on cocoa production in West Africa

2014 ◽  
Vol 6 (3) ◽  
pp. 296-314 ◽  
Author(s):  
Kenneth Ofori-Boateng ◽  
Baba Insah
Keyword(s):  
Climate Change ◽  
West Africa ◽  
Climatic Factors ◽  
Extreme Temperature ◽  
Yield Response ◽  
Content Type ◽  
Impact Of Climate Change ◽  
Temperature And Precipitation ◽  
Cocoa Production ◽  
The Impact

Purpose – The study aimed at examining the current and future impact of climate change on cocoa production in West Africa. Design/methodology/approach – A translog production function based on crop yield response framework was used. A panel model was estimated using data drawn from cocoa-producing countries in West Africa. An in-sample simulation was used to determine the predictive power of the model. In addition, an out-sample simulation revealed the effect of future trends of temperature and precipitation on cocoa output. Findings – Temperature and precipitation play a considerable role in cocoa production in West Africa. It was established that extreme temperature adversely affected cocoa output in the sub-region. Furthermore, increasing temperature and declining precipitation trends will reduce cocoa output in the future. Practical implications – An important implication of this study is the recognition that lagging effects are the determinants of cocoa output and not coincident effects. This finds support from the agronomic point of view considering the gestation period of the cocoa crop. Originality/value – Although several studies have been carried out in this area, this study modeled and estimated the interacting effects of factors that influence cocoa production. This is closer to reality, as climatic factors and agricultural inputs combine to yield output.

scholarly journals The Impact of Oceanic Near-Inertial Waves on Climate

2013 ◽  
Vol 26 (9) ◽  
pp. 2833-2844 ◽  
Author(s):  
Markus Jochum ◽  
Bruce P. Briegleb ◽  
Gokhan Danabasoglu ◽  
William G. Large ◽  
Nancy J. Norton ◽  
...  
Keyword(s):  
Mixed Layer ◽  
Global Climate ◽  
Climate Impact ◽  
Surface Boundary ◽  
Inertial Waves ◽  
Surface Boundary Layer ◽  
Velocity Signal ◽  
Climate System Model ◽  
Temperature And Precipitation ◽  
The Impact

Abstract The Community Climate System Model, version 4 (CCSM4) is used to assess the climate impact of wind-generated near-inertial waves (NIWs). Even with high-frequency coupling, CCSM4 underestimates the strength of NIWs, so that a parameterization for NIWs is developed and included into CCSM4. Numerous assumptions enter this parameterization, the core of which is that the NIW velocity signal is detected during the model integration, and amplified in the shear computation of the ocean surface boundary layer module. It is found that NIWs deepen the ocean mixed layer by up to 30%, but they contribute little to the ventilation and mixing of the ocean below the thermocline. However, the deepening of the tropical mixed layer by NIWs leads to a change in tropical sea surface temperature and precipitation. Atmospheric teleconnections then change the global sea level pressure fields so that the midlatitude westerlies become weaker. Unfortunately, the magnitude of the real air-sea flux of NIW energy is poorly constrained by observations; this makes the quantitative assessment of their climate impact rather uncertain. Thus, a major result of the present study is that because of its importance for global climate the uncertainty in the observed tropical NIW energy has to be reduced.

scholarly journals Sources of predictability over the Mediterranean at seasonal time-scale: building up an empirical forecasting model

2021 ◽  
Author(s):  
Esteban Rodríguez-Guisado ◽  
Ernesto Rodríguez-Camino
Keyword(s):  
Time Series ◽  
Global Climate ◽  
Supplementary Information ◽  
Climate Indices ◽  
Least Squares Regression ◽  
Dynamical Models ◽  
Flexible Tool ◽  
Probabilistic Forecasts ◽  
The Mediterranean ◽  
The Impact

<p>Although most operational seasonal forecasting systems are based on dynamical models, empirical forecasting systems, built on statistical relationships between present and future at seasonal time horizons conditions of the climate system, provide a feasible and realistic alternative and a source of supplementary information. Here, a new empirical model based on partial least squares regression is presented. Originally designed as a flexible tool, the model can be run with many configurations including different predictands, resolutions, leads and aggregation times. To be able of producing forecast for any selected configuration, the model automatically selects predictors from an initial pool, containing global climate indices and specific predictors for the Mediterranean region unveiled in the frame of the MEDSCOPE project. Additionally, the model explores spatial fields, generating time series based on spatial averages of areas well correlated with the predictand. These time series are added to the initial pool of candidate predictors.  We present here results from a configuration producing probabilistic forecasts of seasonal (3 month averages) temperature and precipitation, their verification and comparison against a selection of state-of-the-art seasonal forecast systems based on dynamical models in a hindcast period (1994-2015). The model is able to produce spatially coherent anomaly patterns, and reach levels of skill comparable to those based on dynamical models. As predictors can be easily removed or incorporated, the model can provide information on the impact of a particular predictor on skill, so it can be used to help in the search and understanding of new sources of predictability. Evaluation of soil moisture impact on summer temperature predictability is shown as an example</p>

scholarly journals Permafrost seasonal surface changes revealed from Sentinel-1 InSAR time-series, Yamal peninsula

2020 ◽  
Vol 382 ◽  
pp. 183-187
Author(s):  
Kanayim Teshebaeva ◽  
Ko J. van Huissteden ◽  
Alexander V. Puzanov ◽  
Dmitry N. Balykin ◽  
Anton I. Sinitsky ◽  
...  
Keyword(s):  
Time Series ◽  
Active Layer ◽  
Global Climate ◽  
Yamal Peninsula ◽  
Active Layers ◽  
Eurasian Continent ◽  
Time Period ◽  
Sar Imagery ◽  
Displacement Patterns ◽  
Surface Changes

Abstract. Widespread thawing of permafrost in the northern Eurasian continent causes severe problems for infrastructure and global climate. We test the potential of Sentinel-1 SAR imagery to enhance detection of permafrost surface changes in the Siberian lowlands of the northern Eurasian continent at the Yamal peninsula site. We used InSAR time-series technique to detect seasonal surface movements related to permafrost active layer changes. The satellite InSAR time-series analysis has detected continuous movements, subsidence in three zones, which have occurred during the time period from 2017 to 2018. Observed subsidence zones show up to 180 mm yr−1 rates of seasonal active layers changes. These seasonal ground displacement patterns align well with lithology and linked to anthropogenic impact on the permafrost surface changes in the area. The results show that Sentinel-1 mission is of great importance for the longer-term monitoring of active layer thickening in permafrost regions. The combined analyses of the obtained InSAR time series with additional field observations may support regular process monitoring as part of a global warming risk assessment.

scholarly journals Precipitation and temperature regime over Cyprus as a result of global climate change

2010 ◽  
Vol 23 ◽  
pp. 17-24 ◽  
Author(s):  
C. Giannakopoulos ◽  
P. Hadjinicolaou ◽  
E. Kostopoulou ◽  
K. V. Varotsos ◽  
C. Zerefos
Keyword(s):  
Climate Change ◽  
Global Climate Change ◽  
Temperature Regime ◽  
Agricultural Sector ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Precipitation Regime ◽  
Drought Duration ◽  
The Impact

Abstract. In this study, the impact of global climate change on the temperature and precipitation regime over the island of Cyprus has been investigated. The analysis is based on daily output from a regional climate model (RCM) at a high horizontal resolution (25 km) produced within the framework of the EU-funded ENSEMBLES project. The control run represents the base period 1961–1990 and is used here as reference for comparison with future predictions. Two future periods are studied, 2021–2050 and 2071–2100. For the study area and over the study period, an analysis of the changes associated with the temperature regime and the hydrological cycle, such as mean precipitation and drought duration, is presented. Variations in the mean annual and seasonal rainfall are presented. Changes in the number of hot days/warm nights as well as drought duration are also discussed. These changes should be very important to assess future possible water shortages over the island and to provide a basis for associated impacts on the agricultural sector.

scholarly journals Strangely mined bitcoins: Empirical analysis of anomalies in the bitcoin blockchain transaction network

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0258001
Author(s):  
María Óskarsdóttir ◽  
Jacky Mallett
Keyword(s):  
Time Series ◽  
Network Science ◽  
Significant Evidence ◽  
Network Measures ◽  
Blockchain Technology ◽  
Degree Correlation ◽  
Time Period ◽  
Peer Network ◽  
The Impact ◽  
Peer To Peer Network

The blockchain technology introduced by bitcoin, with its decentralised peer-to-peer network and cryptographic protocols, provides a public and accessible database of bitcoin transactions that have attracted interest from both economics and network science as an example of a complex evolving monetary network. Despite the known cryptographic guarantees present in the blockchain, there exists significant evidence of inconsistencies and suspicious behavior in the chain. In this paper, we examine the prevalence and evolution of two types of anomalies occurring in coinbase transactions in blockchain mining, which we reported on in earlier research. We further develop our techniques for investigating the impact of these anomalies on the blockchain transaction network, by building networks induced by anomalous coinbase transactions at regular intervals and calculating a range of network measures, including degree correlation and assortativity, as well as inequality in terms of wealth and anomaly ratio using the Gini coefficient. We obtain time series of network measures calculated over the full transaction network and three sub-networks. Inspecting trends in these time series allows us to identify a period in time with particularly strange transaction behavior. We then perform a frequency analysis of this time period to reveal several blocks of highly anomalous transactions. Our technique represents a novel way of using network science to detect and investigate cryptographic anomalies.

scholarly journals A coupled stochastic rainfall-evapotranspiration model for hydrological impact analysis

2017 ◽  
Author(s):  
Minh Tu Pham ◽  
Hilde Vernieuwe ◽  
Bernard De Baets ◽  
Niko E. C. Verhoest
Keyword(s):  
Time Series ◽  
Impact Analysis ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Rainfall Runoff ◽  
Hydrological Impact ◽  
Long Time ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract. A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such exercise, discharge is often considered, as especially extreme high discharges often cause damage due to the coinciding floods. Investigating extreme discharges generally requires long time series of precipitation and evapotranspiration that are used to force a rainfall-runoff model. However, such kind of data may not be available and one should resort to stochastically-generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events is not well studied. In this paper, stochastically-generated rainfall and coinciding evapotranspiration time series are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically-generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has a large potential for hydrological impact analysis.

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