scholarly journals Assessment of the Impact of Climate Change on Drought Characteristics in the Hwanghae Plain, North Korea Using Time Series SPI and SPEI: 1981–2100

Water ◽  
2017 ◽  
Vol 9 (8) ◽  
pp. 579 ◽  
Author(s):  
Sang-Hyun Lee ◽  
Seung-Hwan Yoo ◽  
Jin-Yong Choi ◽  
Seungjong Bae
Keyword(s):  
Climate Change ◽  
Time Series ◽  
North Korea ◽  
Impact Of Climate Change ◽  
Drought Characteristics ◽  
The Impact

scholarly journals Bayesian time-series analyses reveal a complex impact of climate change on Classic Maya conflict

2019 ◽  
Author(s):  
William Carleton ◽  
Dave Campbell ◽  
Mark Collard
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Classic Maya ◽  
Classic Period ◽  
Climate Data ◽  
Late Classic ◽  
Impact Of Climate Change ◽  
Increasing Temperature ◽  
Bayesian Time Series ◽  
The Impact

Researchers disagree about the impact of climate change on conflict among the Maya during the Classic period (ca. 250-900 CE). Some contend that increasing aridity exacerbated conflict, while others have found that increasing temperature ramped up conflict. Here, we report a study in which we sought to resolve this disagreement. We collated annually-resolved conflict and climate data, and then created a Bayesian time-series model for analysing count-based prehistoric and historic data. We carried out three analyses, one covering more or less the whole of the Classic Period (292-900 CE), one focused on the Early Classic (292-600 CE), and one that concentrated on the Late Classic (600-900 CE). Our analyses indicated that climate change likely did impact Classic Maya conflict levels, but our results differed from those of previous studies in two important ways. First, we found that the impact of climate change is only evident during the Late Classic. Second, we found that while increasing summer temperature exacerbated conflict, increasing aridity suppressed it. Thus, our study offers a new, more complex perspective on Classic Maya climate-conflict dynamics. It also has implications for our understanding of other aspects of Classic Maya history and for the debate about the likely impact of the current bout of climate change on conflict levels.

Modelling high-resolution rainfall extremes in a changing climate

2021 ◽  
Author(s):  
Ting-Yu Dai ◽  
Li-Pen Wang
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Rainfall Time Series ◽  
Step Size ◽  
Monthly Basis ◽  
Climate Conditions ◽  
Impact Of Climate Change ◽  
Time Series Modelling ◽  
Rainfall Extremes ◽  
The Impact

<p> </p><p>Stochastic modelling is an increasingly popular method to generate long rainfall time series as input for the subsequent hydrological applications, such as the design of urban drainage system. It aims to resemble the physical process of rainfall using parameters with physical meanings, instead of its statistical features. There are, however, two main challenges yet to be overcome in stochastic rainfall modelling. These are 1) reproduction of rainfall extremes at sub-hourly timescales, and 2) incorporation of the impact of climate change.</p><p>Some recent breakthroughs have been made to address the first challenge. Onof and Wang (2020) reformulated the equations of the randomised Bartlett-Lewis rectangular pulse (BLRP) models and showed that the improved models can well preserving rainfall extremes at sub-hourly (5- and 10-min) and hourly timescales. </p><p>The second challenge is however yet to be explored. Cross et al. (2020) recently presented a multivariate regression method that associates BLRP parameters to temperature estimates on a monthly basis, attempting to capture the dynamics of the underlying climate. However, the concept of ‘calendar month’ - an artificial period of time - was still employed to represent natural seasonality. This may fail capturing the natural shift and length difference of seasons between years. To address the above drawback, it is critical to ‘relax’ the concept of calendar month, so that the most similar climate conditions between different years can be better identified. </p><p>An innovative approach is proposed in this work to circumvent the above drawback, where two main improvements are implemented. First, instead of following calendar month, we slice the original rainfall time series using an overlapping moving window with 30-day window width and 10-day step size. This enables a stronger continuity in representing climate variations. Second, the dynamic time warping (DTW) algorithm is employed to quantify the similarity of climate conditions between different years. DTW is a widely-used algorithm in measuring the similarity between two time series, and is known to be less sensitive to the distortion in time axis as compared to the Euclidean distance metrics. Then, based upon DTW measures, we can identify the historical periods with the most similar climate conditions to the target ones. The statistical properties of the local gauge data for these specific periods are used to build the BLRP model in a dynamic fashion. </p><p>Selected atmospheric variables (including geopotential, temperature, U-component of wind, and V-component of wind ) from the ERA5 re-analysis datasets and five-minute rainfall data from 6 long recording rain gauges in Germany (one with 69 years of data; others with 49 years) are used to test the impact of the proposed approach. Preliminary results show that the statistical behaviours of newly identified periods of data are more analogous to the target period as compared to those identified from the traditional method relying on calendar month. This demonstrates the potential to use the proposed new approach to better incorporating the impact of climate change into stochastic rainfall time series modelling. </p>

scholarly journals Large scale integrated hydrological modelling of the impact of climate change on the water balance with DANUBIA

2011 ◽  
Vol 7 (1) ◽  
pp. 61-70 ◽  
Author(s):  
M. Prasch ◽  
T. Marke ◽  
U. Strasser ◽  
W. Mauser
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Water Balance ◽  
Large Scale ◽  
Regional Scale ◽  
Future Climate ◽  
Future Climate Change ◽  
Impact Of Climate Change ◽  
Ipcc Sres ◽  
The Impact

Abstract. Future climate change will affect the water availability in large areas. In order to derive appropriate adaptation strategies the impact on the water balance has to be determined on a regional scale in a high spatial and temporal resolution. Within the framework of the BRAHMATWINN project the model system DANUBIA, developed within the project GLOWA Danube (GLOWA Danube, 2010; Mauser and Ludwig, 2002), was applied to calculate the water balance components under past and future climate conditions in the large-scale mountain watersheds of the Upper Danube and the Upper Brahmaputra. To use CLM model output data as meteorological drivers DANUBIA is coupled with the scaling tool SCALMET (Marke, 2008). For the determination of the impact of glacier melt water on the water balance the model SURGES (Weber et al., 2008; Prasch, 2010) is integrated into DANUBIA. In this paper we introduce the hydrological model DANUBIA with the tools SCALMET and SURGES. By means of the distributed hydrological time series for the past from 1971 to 2000 the model performance is presented. In order to determine the impact of climate change on the water balance in both catchments, time series from 2011 to 2080 according to the IPCC SRES emission scenarios A2, A1B, B2 and Commitment are analysed. Together with the socioeconomic outcomes (see Chapter 4) the DANUBIA model results provide the basis for the derivation of Integrated Water Resources Management Strategies to adapt to climate change impacts (see Chapter 9 and 10).

scholarly journals Impact of Climate Change on Hydrologic Extremes in the Upper Basin of the Yellow River Basin of China

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Jun Wang ◽  
Zhongmin Liang ◽  
Dong Wang ◽  
Tian Liu ◽  
Jing Yang
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Yellow River ◽  
Quantitative Estimation ◽  
Impact Of Climate Change ◽  
Flood Volume ◽  
Long Duration ◽  
Rainfall Extremes ◽  
The Impact ◽  
Hydrologic Extremes

To reveal the revolution law of hydrologic extremes in the next 50 years and analyze the impact of climate change on hydrologic extremes, the following main works were carried on: firstly, the long duration (15 d, 30 d, and 60 d) rainfall extremes according to observed time-series and forecast time-series by dynamical climate model product (BCC-CSM-1.1) were deduced, respectively, on the basis that the quantitative estimation of the impact of climate change on rainfall extremes was conducted; secondly, the SWAT model was used to deduce design flood with the input of design rainfall for the next 50 years. On this basis, quantitative estimation of the impact of climate change on long duration flood volume extremes was conducted. It indicates that (1) the value of long duration rainfall extremes for given probabilities (1%, 2%, 5%, and 10%) of the Tangnaihai basin will rise with slight increasing rate from 1% to 6% in the next 50 years and (2) long duration flood volume extremes of given probabilities of the Tangnaihai basin will rise with slight increasing rate from 1% to 6% in the next 50 years. The conclusions may provide technical supports for basin level planning of flood control and hydropower production.

scholarly journals ASSESSING IMPACTS OF CLIMATE CHANGE ON TEAK AND SAL LANDSCAPE USING MODIS TIME SERIES DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 305-313 ◽  
Author(s):  
M. K. Patasaraiya ◽  
B. Sinha ◽  
J. Bisaria ◽  
S. Saran ◽  
R. K. Jaiswal
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Forest Ecosystems ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Central India ◽  
Series Data ◽  
Impact Of Climate Change ◽  
Increasing Trend ◽  
The Impact

<p><strong>Abstract.</strong> Climate change poses a severe threat to the forest ecosystems by impacting its productivity, species composition and forest biodiversity at global and regional level. The scientific community all over the world is using remote sensing techniques to monitor and assess the impact of climate change on forest ecosystems. The consistent time series data provided by MODIS is immensely used for developing a different type of Vegetation indices like NDVI (Normalized difference vegetation indices) products at different spatial and temporal resolution. These vegetation indices have significant potential to detect forest growth and health, vegetation seasonality and different phenological events like budding and flowering. The current study aims to understand the impact of climate change on Teak and Sal forest of STR (Satpura tiger reserve) in central India by using Landsat and MODIS time series data. The rationale for taking STR as study site was to attribute the changes exclusively to climate change as there is no anthropogenic disturbance in STR. A change detection analysis was carried out to detect changes between the period 2017 and 1990 using Landsat data of October month. To understand the inter-annual and seasonal variation of Teak and Sal forests, freely available MOD13Q1 product (250<span class="thinspace"></span>m, 16 days’ interval) was used to extract NDVI values for each month and four seasons (DJF, JJAS, ON, MAM) for the period 2000 to 2015. The climatic data (rainfall and temperature) was sourced from IMD (India Meteorological Department) at different resolutions (1, 0.5 and 0.25 degree) for the given period of the study. A correlation analysis was done to establish a causal relationship between climate variable (temperature and rainfall) and vegetation health (NDVI) on a different temporal scale of annual, seasonal and month. The study found an increasing trend in annual mean temperature and no consistent trend in total annual rainfall over the period 2000 to 2015. The maximum percentage change was observed in minimum temperature over the period 2000 to 2015. The average annual NDVI of Teak and Sal forests showed an increasing trend however, no trend was observed in seasonal and monthly NDVI over the same period. The maximum and minimum NDVI was found in the post-monsoon months (ON) and summer months (MAM) respectively. As STR is a Teak and Sal dominated landscape, the findings of the current study can also be applied in developing silvicultural and adaptation strategies for other Teak and Sal dominated landscapes of central India.</p>

HOW CLIMATE CHANGE CAN INFLUENCE THE AGRICULTURE IN UKRAINE: A REVIEW

2020 ◽  
Author(s):  
N. Maidanovych ◽  
Keyword(s):  
Climate Change ◽  
Crop Yields ◽  
Soil Surface ◽  
Winter Period ◽  
Negative Consequences ◽  
Resource Saving ◽  
Impact Of Climate Change ◽  
Positive Effects ◽  
Average Annual Temperature ◽  
The Impact

The purpose of this work is to review and analyze the main results of modern research on the impact of climate change on the agro-sphere of Ukraine. Results. Analysis of research has shown that the effects of climate change on the agro-sphere are already being felt today and will continue in the future. The observed climate changes in recent decades have already significantly affected the shift in the northern direction of all agro-climatic zones of Europe, including Ukraine. From the point of view of productivity of the agro-sphere of Ukraine, climate change will have both positive and negative consequences. The positives include: improving the conditions of formation and reducing the harvesting time of crop yields; the possibility of effective introduction of late varieties (hybrids), which require more thermal resources; improving the conditions for overwintering crops; increase the efficiency of fertilizer application. Model estimates of the impact of climate change on wheat yields in Ukraine mainly indicate the positive effects of global warming on yields in the medium term, but with an increase in the average annual temperature by 2 ° C above normal, grain yields are expected to decrease. The negative consequences of the impact of climate change on the agrosphere include: increased drought during the growing season; acceleration of humus decomposition in soils; deterioration of soil moisture in the southern regions; deterioration of grain quality and failure to ensure full vernalization of grain; increase in the number of pests, the spread of pathogens of plants and weeds due to favorable conditions for their overwintering; increase in wind and water erosion of the soil caused by an increase in droughts and extreme rainfall; increasing risks of freezing of winter crops due to lack of stable snow cover. Conclusions. Resource-saving agricultural technologies are of particular importance in the context of climate change. They include technologies such as no-till, strip-till, ridge-till, which make it possible to partially store and accumulate mulch on the soil surface, reduce the speed of the surface layer of air and contribute to better preservation of moisture accumulated during the autumn-winter period. And in determining the most effective ways and mechanisms to reduce weather risks for Ukrainian farmers, it is necessary to take into account the world practice of climate-smart technologies.

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