Climate Change: Use of Non-Homogeneous Poisson Processes for Climate Data in Presence of a Change-Point

Author(s):  
Jorge Alberto Achcar ◽  
Ricardo Puziol de Oliveira
Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1548
Author(s):  
Suresh Marahatta ◽  
Deepak Aryal ◽  
Laxmi Prasad Devkota ◽  
Utsav Bhattarai ◽  
Dibesh Shrestha

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.


Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1187
Author(s):  
Wouter Julius Smolenaars ◽  
Spyridon Paparrizos ◽  
Saskia Werners ◽  
Fulco Ludwig

In recent decades, multiple flood events have had a devastating impact on soybean production in Argentina. Recent advances suggest that the frequency and intensity of destructive flood events on the Argentinian Pampas will increase under pressure from climate change. This paper provides bottom-up insight into the flood risk for soybean production systems under climate change and the suitability of adaptation strategies in two of the most flood-prone areas of the Pampas region. The flood risk perceptions of soybean producers were explored through interviews, translated into climatic indicators and then studied using a multi-model climate data analysis. Soybean producers perceived the present flood risk for rural accessibility to be of the highest concern, especially during the harvest and sowing seasons when heavy machinery needs to reach soybean lots. An analysis of climatic change projections found a rising trend in annual and harvest precipitation and a slight drying trend during the sowing season. This indicates that the flood risk for harvest accessibility may increase under climate change. Several adaptation strategies were identified that can systemically address flood risks, but these require collaborative action and cannot be undertaken by individual producers. The results suggest that if cooperative adaptation efforts are not made in the short term, the continued increase in flood risk may force soybean producers in the case study locations to shift away from soybean towards more robust land uses.


2020 ◽  
Vol 6 (1) ◽  
pp. 1-25
Author(s):  
Wadii Snaibi

AbstractThe high plateaus of eastern Morocco are already suffering from the adverse impacts of climate change (CC), as the local populations’ livelihoods depend mainly on extensive sheep farming and therefore on natural resources. This research identifies breeders’ perceptions about CC, examines whether they correspond to the recorded climate data and analyses endogenous adaptation practices taking into account the agroecological characteristics of the studied sites and the difference between breeders’ categories based on the size of owned sheep herd. Data on perceptions and adaptation were analyzed using the Chi-square independence and Kruskal-Wallis tests. Climate data were investigated through Mann-Kendall, Pettitt and Buishand tests.Herders’ perceptions are in line with the climate analysis in term of nature and direction of observed climate variations (downward trend in rainfall and upward in temperature). In addition, there is a significant difference in the adoption frequency of adaptive strategies between the studied agroecological sub-zones (χ2 = 14.525, p <.05) due to their contrasting biophysical and socioeconomic conditions, as well as among breeders’ categories (χ2 = 10.568, p < .05) which attributed mainly to the size of sheep flock. Policy options aimed to enhance local-level adaptation should formulate site-specific adaptation programs and prioritise the small-scale herders.


Author(s):  
Ivo Machar ◽  
Marián Halás ◽  
Zdeněk Opršal

Regional climate changes impacts induce vegetation zones shift to higher altitudes in temperate landscape. This paper deals with applying of regional biogeography model of climate conditions for vegetation zones in Czechia to doctoral programme Regional Geography in Palacky University Olomouc. The model is based on general knowledge of landscape vegetation zonation. Climate data for model come from predicted validated climate database under RCP8.5 scenario since 2100. Ecological data are included in the Biogeography Register database (geobiocoenological data related to landscape for cadastral areas of the Czech Republic). Mathematical principles of modelling are based on set of software solutions with GIS. Students use the model in the frame of the course “Special Approaches to Landscape Research” not only for regional scenarios climate change impacts in landscape scale, but also for assessment of climate conditions for growing capability of agricultural crops or forest trees under climate change on regional level.


2021 ◽  
Author(s):  
Erik Engström ◽  
Cesar Azorin-Molina ◽  
Lennart Wern ◽  
Sverker Hellström ◽  
Christophe Sturm ◽  
...  

&lt;p&gt;Here we present the progress of the first work package (WP1) of the project &amp;#8220;Assessing centennial wind speed variability from a historical weather data rescue project in Sweden&amp;#8221; (WINDGUST), funded by FORMAS &amp;#8211; A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously introduced in EGU2019-17792-1 and EGU2020-3491. In a global climate change, one of the major uncertainties on the causes driving the climate variability of winds (i.e., the &amp;#8220;stilling&amp;#8221; phenomenon and the recent &amp;#8220;recovery&amp;#8221; since the 2010s) is mainly due to short availability (i.e., since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).&lt;/p&gt;&lt;p&gt;The WINDGUST is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden.&lt;/p&gt;&lt;p&gt;During 2020, we worked in WP1 to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. In the process we followed the &amp;#8220;Guidelines on Best Practices for Climate Data Rescue&amp;#8221; of the World Meteorological Organization. Our protocol consisted on: (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template. We will report the advances and current status, challenges and experiences learned during the development of WP1. Until new year 2020/2021 eight out of thirteen selected stations spanning over the years 1925 to 1948 have been scanned and digitized by three staff members of SMHI during 1,660 manhours.&lt;/p&gt;


2021 ◽  
Author(s):  
Anna Maria De Girolamo ◽  
Youssef Brouziyne ◽  
Lahcen Benaabidate ◽  
Aziz Aboubdillah ◽  
Ali El Bilali ◽  
...  

&lt;p&gt;The non-perennial streams and rivers are predominant in the Mediterranean region and play an important ecological role in the ecosystem diversity in this region. This class of streams is particularly vulnerable to climate change effects that are expected to amplify further under most climatic projections. Understanding the potential response of the hydrologic regime attributes to climatic stress helps in planning better conservation and management strategies. Bouregreg watershed (BW) in Morocco, is a strategic watershed for the region with a developed non-perennial stream network, and with typical assets and challenges of most Mediterranean watersheds. In this study, a hybrid modeling approach, based on the Soil and Water Assessment Tool (SWAT) model and Indicator of Hydrologic Alteration (IHA) program, was used to simulate the response of BW's stream network to climate change during the period: 2035-2050. Downscaled daily climate data from the global circulation model CNRM-CM5 were used to force the hybrid modeling framework over the study area. Results showed that, under the changing climate, the magnitude of the alteration will be different across the stream network; however, almost the entire flow regime attributes will be affected. Under the RCP8.5 scenario, the average number of zero-flow days will rise up from 3 to 17.5 days per year in some streams, the timing of the maximum flow was calculated to occur earlier by 17 days than in baseline, and the timing of the minimal flow should occur later by 170 days in some streams. The used modeling approach in this study contributed in identifying the most vulnerable streams in the BW to climate change for potential prioritization in conservation plans.&lt;/p&gt;


2021 ◽  
Author(s):  
Cameron Ross ◽  
Ryley Beddoe ◽  
Greg Siemens

&lt;p&gt;Initialization (spin-up) of a numerical ground temperature model is a critical but often neglected step for solving heat transfer problems in permafrost. Improper initialization can lead to significant underlying model drift in subsequent transient simulations, distorting the effects on ground temperature from future climate change or applied infrastructure. &amp;#160;In a typical spin-up simulation, a year or more of climate data are applied at the surface and cycled repeatedly until ground temperatures are declared to be at equilibrium with the imposed boundary conditions, and independent of the starting conditions.&lt;/p&gt;&lt;p&gt;Spin-up equilibrium is often simply declared after a specified number of spin-up cycles. In few studies, equilibrium is visually confirmed by plotting ground temperatures vs spin-up cycles until temperatures stabilize; or is declared when a certain inter-cycle-temperature-change threshold is met simultaneously at all depths, such as &amp;#8710;T &amp;#8804; 0.01&lt;sup&gt;o&lt;/sup&gt;C per cycle. In this study, we investigate the effectiveness of these methods for determining an equilibrium state in a variety of permafrost models, including shallow and deep (10 &amp;#8211; 200 m), high and low saturation soils (S = 100 and S = 20), and cold and warm permafrost (MAGT = ~-10 &lt;sup&gt;o&lt;/sup&gt;C and &gt;-1 &lt;sup&gt;o&lt;/sup&gt;C). The efficacy of equilibrium criteria 0.01&lt;sup&gt;o&lt;/sup&gt;C/cycle and 0.0001&lt;sup&gt;o&lt;/sup&gt;C/cycle are compared. Both methods are shown to prematurely indicate equilibrium in multiple model scenarios. &amp;#160;Results show that no single criterion can programmatically detect equilibrium in all tested models, and in some scenarios can result in up to 10&lt;sup&gt;o&lt;/sup&gt;C temperature error or 80% less permafrost than at true equilibrium. &amp;#160;A combination of equilibrium criteria and visual confirmation plots is recommended for evaluating and declaring equilibrium in a spin-up simulation.&lt;/p&gt;&lt;p&gt;Long-duration spin-up is particularly important for deep (10+&amp;#160;m) ground models where thermal inertia of underlying permafrost slows the ground temperature response to surface forcing, often requiring hundreds or even thousands of spin-up cycles to establish equilibrium. Subsequent transient analyses also show that use of a properly initialized 100 m permafrost model can reduce the effect of climate change on mean annual ground temperature of cold permafrost by more than 1 &lt;sup&gt;o&lt;/sup&gt;C and 3 &lt;sup&gt;o&lt;/sup&gt;C under RCP2.6 and RCP8.5 climate projections, respectively, when compared to an identical 25 m model. These results have important implications for scientists, engineers and policy makers that rely on model projections of long-term permafrost conditions.&lt;/p&gt;


2018 ◽  
Vol 4 (12) ◽  
pp. eaat4343 ◽  
Author(s):  
Ariel Ortiz-Bobea ◽  
Erwin Knippenberg ◽  
Robert G. Chambers

A pressing question for climate change adaptation is whether ongoing transformations of the agricultural sector affect its ability to cope with climatic variations. We examine this question in the United States, where major increases in productivity have fueled most of agricultural production growth over the past half-century. To quantify the evolving climate sensitivity of the sector and identify its sources, we combine state-level measures of agricultural productivity with detailed climate data for 1960–2004. We find that agriculture is growing more sensitive to climate in Midwestern states for two distinct but compounding reasons: a rising climatic sensitivity of nonirrigated cereal and oilseed crops and a growing specialization in crop production. In contrast, other regions specialize in less climate-sensitive production such as irrigated specialty crops or livestock. Results suggest that reducing vulnerability to climate change should consider the role of policies in inducing regional specialization.


2018 ◽  
Vol 11 (2) ◽  
pp. 420-433 ◽  
Author(s):  
Adem Yavuz Sönmez ◽  
Semih Kale

Abstract The main purpose of this study was to estimate possible climate change effects on the annual streamflow of Filyos River (Turkey). Data for annual streamflow and climatic parameters were obtained from streamflow gauging stations on the river and Bartın, Karabük, Zonguldak meteorological observation stations. Time series analysis was performed on 46 years of annual streamflow data and 57 years of annual mean climatic data from three monitoring stations to understand the trends. Pettitt change-point analysis was applied to determine the change time and trend analysis was performed to forecast trends. To reveal the relationship between climatic parameters and streamflow, correlation tests, namely, Spearman's rho and Kendall's tau were applied. The results of Pettitt change-point analysis pointed to 2000 as the change year for streamflow. Change years for temperature and precipitation were detected as 1997 and 2000, respectively. Trend analysis results indicated decreasing trends in the streamflow and precipitation, and increasing trend in temperature. These changes were found statistically significant for streamflow (p &lt; 0.05) and temperature (p &lt; 0.01). Also, a statistically significant (p &lt; 0.05) correlation was found between streamflow and precipitation. In conclusion, decreasing precipitation and increasing temperature as a result of climate change initiated a decrease in the river streamflow.


2017 ◽  
Vol 8 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.


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