scholarly journals Impact Assessment of Flood Damage in Urban Areas Using RCP 8.5 Climate Change Scenarios and Building Inventory

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 756
Author(s):  
Dong-Ho Kang ◽  
Dong-Ho Nam ◽  
Se-Jin Jeung ◽  
Byung-Sik Kim
Keyword(s):  
Climate Change ◽  
Flood Damage ◽  
Rainfall Data ◽  
Urban Stream ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Damage Area ◽  
Building Inventory ◽  
Damage Rate ◽  
Rainfall Frequency

Korea has frequent flood damage due to localized torrential rain and typhoons as a result of climate change, which causes many casualties and property damage. In particular, much damage occurs due to urban inundation caused by stream flooding as a result of climate change. Thus, this study aims to analyze the effect of climate change on flood damage targeting the Wonjucheon basin, which is an urban stream flowing the city. For future rainfall data, RCP (Representative Concentration Pathways) 8.5 climate change scenario data was used, statistical detailed using SDQDM (Spatial Disaggregation with Quantile Delta Mapping) techniques, and daily data was downscaled using Copula model. In general, the flood damage rate is calculated by using the area ratio according to the land use in the administrative district, but in this study, the flood damage rate is calculated using the flood damage rate proposed in the multi-dimensional flood damage analysis using Building Inventory. Using the created future rainfall data and current data, the runoff in the Wonjucheon basin, Wonju-si, South Korea, by rainfall frequency was calculated through the Spatial Runoff Assessment Tool (S-RAT) model, which was a distributed rainfall-runoff model. The runoff was calculated using 100-year and 200-year frequency rainfalls for a four-hour duration and the flood damage area was calculated by applying the calculated runoff to the Flo-2D model, was developed by Federal Emergency Management Agency (FEMA) in United State of America, which was a flood inundation model. As a result of calculating the amount of discharge, it was analyzed that the average amount of discharge increased by 16% over the 100-year, 200-year frequency. The calculated result of the flood damage area was analyzed and the analysis results showed that the future flood damage area increased by around 30% at the 100-year frequency and around 15% at the 200-year frequency. The estimated flood damage by rainfall frequency was calculated using the flood damage area by frequency and multi-dimensional analysis, and the analysis result exhibited that the damage increased by around 23% at the 100-year frequency and around 45% at the 200-year frequency.

scholarly journals Climate change impacts the protective effect of forests: A case study in Switzerland

2021 ◽  
Author(s):  
Christine Moos ◽  
Antoine Guisan ◽  
Christophe F. Randin ◽  
Heike Lischke
Keyword(s):  
Climate Change ◽  
Protective Effect ◽  
Climate Warming ◽  
Swiss Alps ◽  
Present Species ◽  
Future Research ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Ground Truth Data

Abstract In steep terrain, forests play an important role as natural means of protection against natural hazards, such as rockfall. Due to climate warming, significant changes in the protection service of these forests have to be expected in future. Shifts of current to more drought adapted species may result in temporary or even irreversible losses in the risk reduction provided by these forests. In this study, we assessed how the protective effect against rockfall of a protection forest in the western part of the Valais in the Swiss Alps may change in future, by combining dynamic forest modelling with a quantitative risk analysis. Current and future forest development was modelled with the spatially explicit forest model TreeMig for a moderate (RCP4.5) and an extreme (RCP8.5) climate change scenario. The simulated forest scenarios were compared to ground-truth data from the current forest complex. We quantified the protective effect of the different forest scenarios based on the reduction of rockfall risk for people and infrastructure at the bottom of the slope. Rockfall risk was calculated on the basis of three-dimensional rockfall simulations. The forest simulations predicted a clear decrease in basal area of most of the currently present species in future. The forest turned into a Q. pubescens dominated forest, for both climate scenarios, and mixed with P. sylvestris in RCP4.5. F. sylvatica completely disappeared in RCP8.5. With climate warming, a clear increase in risk is expected for both climate change scenarios. In the long-term (> 100 years), a stabilization of risk, or even a slight decline may be expected due to an increase in biomass of the trees. The results of this study further indicate that regular forest interventions may promote regeneration and thus accelerate the shift in species distribution. Future research should address the long-term effect of different forest management strategies on the protection service of forests under climate change.

scholarly journals Future Runoff Analysis in the Mekong River Basin under a Climate Change Scenario Using Deep Learning

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1556 ◽  
Author(s):  
Daeeop Lee ◽  
Giha Lee ◽  
Seongwon Kim ◽  
Sungho Jung
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Change Scenario ◽  
Swat Model ◽  
Mekong River ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
Runoff Prediction ◽  
Runoff Analysis

In establishing adequate climate change policies regarding water resource development and management, the most essential step is performing a rainfall-runoff analysis. To this end, although several physical models have been developed and tested in many studies, they require a complex grid-based parameterization that uses climate, topography, land-use, and geology data to simulate spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty originating from insufficient data quality and quantity, unreliable parameters, and imperfect model structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based black-box method. Future runoff variations were simulated by applying a climate change scenario. To assess the applicability of the LSTM model, its result was compared with a runoff analysis using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005), verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data were fed into the algorithms, with the exception of the observed discharge and temperature data, which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the SWAT model showed a Nash–Sutcliffe efficiency (NSE) value of 0.84, while the LSTM model showed a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model. However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction presents a higher reproducibility than that of the SWAT model in simulating runoff variation according to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff time-series are available.

scholarly journals Using species distribution models to infer potential climate change-induced range shifts of freshwater fish in south-eastern Australia

2011 ◽  
Vol 62 (9) ◽  
pp. 1043 ◽  
Author(s):  
Nick Bond ◽  
Jim Thomson ◽  
Paul Reich ◽  
Janet Stein
Keyword(s):  
Climate Change ◽  
Freshwater Fish ◽  
Species Distribution ◽  
Climate Change Scenario ◽  
Species Distribution Models ◽  
Range Shifts ◽  
Future Climate Change ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Distribution Models

There are few quantitative predictions for the impacts of climate change on freshwater fish in Australia. We developed species distribution models (SDMs) linking historical fish distributions for 43 species from Victorian streams to a suite of hydro-climatic and catchment predictors, and applied these models to explore predicted range shifts under future climate-change scenarios. Here, we present summary results for the 43 species, together with a more detailed analysis for a subset of species with distinct distributions in relation to temperature and hydrology. Range shifts increased from the lower to upper climate-change scenarios, with most species predicted to undergo some degree of range shift. Changes in total occupancy ranged from –38% to +63% under the lower climate-change scenario to –47% to +182% under the upper climate-change scenario. We do, however, caution that range expansions are more putative than range contractions, because the effects of barriers, limited dispersal and potential life-history factors are likely to exclude some areas from being colonised. As well as potentially informing more mechanistic modelling approaches, quantitative predictions such as these should be seen as representing hypotheses to be tested and discussed, and should be valuable for informing long-term strategies to protect aquatic biota.

scholarly journals ADAPTATIONS ASSESSMENT ON THE IMPACTS OF FLOODING UNDER CURRENT CONDITION AND CLIMATE CHANGE SCENARIO, CASE STUDY: CENTINI VILLAGE, INDONESIA

2017 ◽  
Vol 19 (3) ◽  
pp. 163 ◽  
Author(s):  
Adjie Pamungkas ◽  
Sarah Bekessy ◽  
Ruth Lane
Keyword(s):  
Climate Change ◽  
Climate Change Scenario ◽  
Recovery Process ◽  
Future Climate Change ◽  
Small Scale ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Community Vulnerability ◽  
Vulnerability Model

Reducing community vulnerability to flooding is increasingly important given predicted intensive flood events in many parts of the world. We built a community vulnerability model to explore the effectiveness of a range of proactive and reactive adaptations to reduce community vulnerability to flood. The model consists of floods, victims, housings, responses, savings, expenditure and income sub models. We explore the robustness of adaptations under current conditions and under a range of future climate change scenarios. We present results of this model for a case study of Centini Village in Lamongan Municipality, Indonesia, which is highly vulnerable to the impacts of annual small-scale and infrequent extreme floods.  We compare 11 proactive adaptations using indicators of victims, damage/losses and recovery process to reflect the level of vulnerability. We find that reforestation and flood infrastructure redevelopment are the most effective proactive adaptations for minimising vulnerability to flood under current condition. Under climate change scenario, the floods are predicted to increase 17% on the average and 5% on the maximum measurements. The increasing floods result reforestation is the only effective adaptations in the future under climate change scenario.

scholarly journals Case for Climate Smart Agriculture in Addressing the Threat of Climate Change

2021 ◽  
pp. 131-145
Author(s):  
John Saviour Yaw Eleblu ◽  
Eugene Tenkorang Darko ◽  
Eric Yirenkyi Danquah
Keyword(s):  
Climate Change ◽  
Crop Production ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Food Crop ◽  
Adverse Conditions ◽  
Climate Change And Variability ◽  
Food Crop Production ◽  
Smart Agriculture ◽  
Available Information

AbstractClimate smart agriculture (CSA) embodies a blend of innovations, practices, systems, and investment programmes that are used to mitigate against the adverse effects of climate change and variability on agriculture for sustained food production. Food crop production under various climate change scenarios requires the use of improved technologies that are called climate smart agriculture to ensure increased productivity under adverse conditions of increased global temperatures, frequent and more intense storms, floods and drought stresses. This chapter summarizes available information on climate change and climate smart agriculture technologies. It is important to evaluate each climate change scenario and provide technologies that farmers, research scientists, and policy drivers can use to create the desired climate smart agriculture given the array of tools and resources available.

scholarly journals Evaluation of the trophic status in a Mediterranean reservoir under climate change: An integrated modelling approach

2020 ◽  
Author(s):  
Carina Almeida ◽  
Paulo Branco ◽  
Pedro Segurado ◽  
Tiago B. Ramos ◽  
Teresa Ferreira ◽  
...  
Keyword(s):  
Climate Change ◽  
Nutrient Dynamics ◽  
Trophic Status ◽  
Integrated Modelling ◽  
Phosphorus Concentration ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Total Phosphorus Concentration ◽  
Southern Portugal ◽  
Modelling Approach

Abstract This study describes an integrated modelling approach to better understand the trophic status of the Montargil reservoir (southern Portugal) under climate change scenarios. The SWAT and CE-QUAL-W2 models were applied to the basin and reservoir, respectively, for simulating water and nutrient dynamics while considering one climatic scenario and two decadal timelines (2025–2034 and 2055–2064). Model simulations showed that the dissolved oxygen concentration in the reservoir's hypolimnion is expected to decrease by 60% in both decadal timelines, while the chlorophyll-a concentration in the reservoir's epiliminion is expected to increase by 25%. The total phosphorus concentration (TP) is predicted to increase in the water column surface by 63% and in the hypolimion by 90% during the 2030 timeline. These results are even more severe during the 2060 timeline. Under this climate change scenario, the reservoir showed an eutrophic state during 70–80% of both timelines. Even considering measures that involve decreases in 30 to 35% of water use, the eutrophic state is not expected to improve.

scholarly journals Methods for the Design of Climate Change Scenario in Slovakia for the 21St Century

2009 ◽  
Vol 1 (1) ◽  
pp. 77-90 ◽  
Author(s):  
Marian Melo ◽  
Milan Lapin ◽  
Ingrid Damborska
Keyword(s):  
Climate Change ◽  
Time Series ◽  
21St Century ◽  
Climate Change Scenario ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Precipitation Time Series ◽  
Impact Studies ◽  
Temperature And Precipitation ◽  
Mean Temperature

Abstract In this paper methods of climate-change scenario projection in Slovakia for the 21st century are outlined. Temperature and precipitation time series of the Hurbanovo Observatory in 1871-2007 (Slovak Hydrometeorological Institute) and data from four global GCMs (GISS 1998, CGCM1, CGCM2, HadCM3) are utilized for the design of climate change scenarios. Selected results of different climate change scenarios (based on different methods) for the region of Slovakia (up to 2100) are presented. The increase in annual mean temperature is about 3°C, though the results are ambiguous in the case of precipitation. These scenarios are required by users in impact studies, mainly from the hydrology, agriculture and forestry sectors.

scholarly journals Future Representation of Species’ Climatic Niches in Protected Areas: A Case Study With Austrian Endemics

2021 ◽  
Vol 9 ◽  
Author(s):  
Philipp Semenchuk ◽  
Dietmar Moser ◽  
Franz Essl ◽  
Stefan Schindler ◽  
Johannes Wessely ◽  
...  
Keyword(s):  
Climate Change ◽  
Endemic Species ◽  
Intraspecific Diversity ◽  
Change Scenario ◽  
Mean Annual Temperature ◽  
Climate Change Scenarios ◽  
Evolutionary Potential ◽  
Area Of Occupancy ◽  
The Future ◽  
Bivariate Density

Climate driven species’ range shifts may interfere with existing protected area (PA) networks, resulting in a mismatch between places where species are currently protected and places where they can thrive in the future. Here, we assess the climate-smartness of the Austrian PA network by focusing on endemic species’ climatic niches and their future representation within PAs. We calculated endemic species’ climatic niches and climate space available in PAs within their dispersal reach under current and future climates, with the latter represented by three climate change scenarios and three time-steps (2030, 2050, and 2080). Niches were derived from the area of occupancy of species and the extent of PAs, respectively, and calculated as bivariate density kernels on gradients of mean annual temperature and annual precipitation. We then computed climatic representation of species’ niches in PAs as the proportion of the species’ kernel covered by the PA kernel. We found that under both a medium (RCP 4.5) and severe (RCP 8.5) climate change scenario, representation of endemic species’ climatic niches by PAs will decrease to a sixth for animals and to a third for plants, on average, toward the end of the century. Twenty to thirty percent of Austrian endemic species will then have no representation of their climatic niches in PAs anymore. Species with larger geographical and wider elevational ranges will lose less climatic niche representation. The declining representation of climatic niches in PAs implies that, even if PAs may secure the persistence of a part of these endemics, only a small portion of intraspecific diversity of many species may be represented in PAs in the future. We discuss our findings in the context of the varied elevational gradients found in Austria and suggest that the most promising strategies for safeguarding endemic species’ evolutionary potential are to limit the magnitude of climate change and to reduce other pressures that additionally threaten their survival.

scholarly journals Proportional Variation of Potential Groundwater Recharge as a Result of Climate Change and Land-Use: A Study Case in Mexico

Land ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 364 ◽  
Author(s):  
Jesús Guerrero-Morales ◽  
Carlos R. Fonseca ◽  
Miguel A. Goméz-Albores ◽  
María Laura Sampedro-Rosas ◽  
Sonia Emilia Silva-Gómez
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Groundwater Recharge ◽  
Supervised Classification ◽  
Distributed Model ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Water Vulnerability ◽  
Potential Groundwater ◽  
Proportional Variation

This work proposes a methodology whereby the selection of hydrologic and land-use cover change (LUCC) models allows an assessment of the proportional variation in potential groundwater recharge (PGR) due to both land-use cover change (LUCC) and some climate change scenarios for 2050. The simulation of PGR was made through a distributed model, based on empirical methods and the forecasting of LUCC stemming from a supervised classification with remote sensing techniques, both inside a Geographic Information System. Once the supervised classification was made, a Markov-based model was developed to predict LUCC to 2050. The method was applied in Acapulco, an important tourism center for Mexico. From 1986 to 2017, the urban area increased 5%, and by 2050 was predicted to cover 16%. In this period, a loss of 7 million m3 of PGR was assumed to be caused by the estimated LUCC. From 2017 to 2050, this loss is expected to increase between 73 and 273 million m3 depending on the considered climate change scenario, which is the equivalent amount necessary for satisfying the water needs of 6 million inhabitants. Therefore, modeling the variation in groundwater recharge can be an important tool for identifying water vulnerability, through both climate and land-use change.

scholarly journals Assessing the Impact of Projected Climate Change on Zoo Visitation in Toronto (Canada)

2016 ◽  
Vol 8 (2) ◽  
pp. 30 ◽  
Author(s):  
Micah J. Hewer ◽  
William A. Gough
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Positive Impact ◽  
Global Climate ◽  
Global Climate Models ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Intergovernmental Panel ◽  
Local Climate ◽  
The Impact

Weather and climate have been widely recognised as having an important influence on tourism and recreational activities. However, the nature of these relationships varies depending on the type, timing and location of these activities. Climate change is expected to have considerable and diverse impacts on recreation and tourism. Nonetheless, the potential impact of climate change on zoo visitation has yet to be assessed in a scientific manner. This case study begins by establishing the baseline conditions and statistical relationship between weather and zoo visitation in Toronto, Canada. Regression analysis, relying on historical weather and visitation data, measured at the daily time scale, formed the basis for this analysis. Climate change projections relied on output produced by Global Climate Models (GCMs) for the Intergovernmental Panel on Climate Change’s 2013 Fifth Assessment Report, ranked and selected using the herein defined Selective Ensemble Approach. This seasonal GCM output was then used to inform daily, local, climate change scenarios, generated using Statistical Down-Scaling Model Version 5.2. A series of seasonal models were then used to assess the impact of projected climate change on zoo visitation. While accounting for the negative effects of precipitation and extreme heat, the models suggested that annual visitation to the zoo will likely increase over the course of the 21st century due to projected climate change: from +8% in the 2020s to +18% by the 2080s, for the least change scenario; and from +8% in the 2020s to +34% in the 2080s, for the greatest change scenario. The majority of the positive impact of projected climate change on zoo visitation in Toronto will likely occur in the shoulder season (spring and fall); with only moderate increases in the off season (winter) and potentially negative impacts associated with the peak season (summer), especially if warming exceeds 3.5 °C.

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