Special Issue "Climate Extremes: Human-Environment Consequences and Adaptation Measures"

A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 9923

Special Issue Editor

Dr. Alban Kuriqi
E-Mail Website
Guest Editor
CERIS—Civil Engineering Research and Innovation for Sustainability, Instituto Superior Tecnico, University of Lisbon. Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
Interests: renewable energy; hydropower impacts; water management; ecohydrology; ecohydraulic; river restoration; climate change
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Special Issue Information

Dear Colleagues,

Climate change due to global warming is no longer a modeling assumption but something that is happening and affecting our lives in different aspects. Climate change affects the temporal and spatial variability of meteorological components, such as precipitation, temperature, and alters the hydrologic cycle, i.e., evapotranspiration, runoff, flow discharge in rivers, and groundwater budget, at different levels. These meteorohydrologic alterations result in extreme events such as floods and droughts, which in turn lead to numerous devastating consequences in the environment and in humans. Flushing away entire settlements, wildfires, agriculture droughts, and hydrologic droughts, impairing society and the ecosystem at different trophic levels are some of the most common consequences. Both flood and drought event frequency has been continuously increasing in recent decades. Therefore, it is imperative to improve our knowledge of management and adaptation measure policies. This Special Issue aims in particular to stimulate interdisciplinary research among different fields such as economics, hydrology, integrated water resource management and transboundary water cooperation, integrated flood and drought risk management, geology, geotechnics, natural hazard policies and legislation, sociology, geography, and their interactions in different regions of the world. Moreover, this Special Issue aims to generate cutting-edge knowledge, methods, and procedures of extreme event management. Innovative measures to mitigate and adapt to the effects of extreme events are essential, in particular, in sensitive areas. A better understanding of extreme event mechanisms is essential to obtain strategically relevant information that supports correct decision making and implementation of appropriate environmental adaptation and/or protection measures. In a broad view, interesting topics include but are not limited to floods, landslides, meteorological droughts, hydrological droughts, agriculture droughts, wildfires, several other climate-related hazards like typhoons/cyclones/hurricanes, heatwaves, and most importantly adaptation measures related to respective extremes. Thus, it is the right moment to focus on new research areas that link sustainable development, climate change, and disaster risks.


Dr. Alban Kuriqi
Guest Editor

Manuscript Submission Information

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Keywords

  • floods
  • droughts
  • landslides
  • debris flows
  • wildfires
  • ecology
  • water resource
  • urban flooding
  • urban stormwater
  • urban hydrological modeling
  • climate change drivers
  • climate change adaptation
  • the resilience of agricultural production
  • adaptation measures
  • mitigation measures
  • hydrology

Published Papers (8 papers)

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Research

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Article
Spatial and Temporal Assessment of Remotely Sensed Land Surface Temperature Variability in Afghanistan during 2000–2021
Climate 2022, 10(7), 111; https://doi.org/10.3390/cli10070111 - 19 Jul 2022
Viewed by 418
Abstract
The dynamics of land surface temperature (LST) in Afghanistan in the period 2000–2021 were investigated, and the impact of the factors such as soil moisture, precipitation, and vegetation coverage on LST was assessed. The remotely sensed soil moisture data from Land Data Assimilation [...] Read more.
The dynamics of land surface temperature (LST) in Afghanistan in the period 2000–2021 were investigated, and the impact of the factors such as soil moisture, precipitation, and vegetation coverage on LST was assessed. The remotely sensed soil moisture data from Land Data Assimilation System (FLDAS), precipitation data from Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), and NDVI and LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used. The correlations between these data were analyzed using the regression method. The result shows that the LST in Afghanistan has a slightly decreasing but insignificant trend during the study period (R = 0.2, p-value = 0.25), while vegetation coverage, precipitation, and soil moisture had an increasing trend. It was revealed that soil moisture has the highest impact on LST (R = −0.71, p-value = 0.0007), and the soil moisture, precipitation, and vegetation coverage explain almost 80% of spring (R2 = 0.73) and summer (R2 = 0.76) LST variability in Afghanistan. The LST variability analysis performed separately for Afghanistan’s river subbasins shows that the LST of the Amu Darya subbasin had an upward trend in the study period, while for the Kabul subbasin, the trend was downward. Full article
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Article
Towards a Flood Assessment Product for the Humanitarian and Disaster Management Sectors Based on GNSS Bistatic Radar Measurements
Climate 2022, 10(5), 77; https://doi.org/10.3390/cli10050077 - 23 May 2022
Viewed by 925
Abstract
This manuscript focuses on the need for tailoring flood assessment products to decision making within the humanitarian sector. Decision-makers often struggle to extract all of the information contained in scientific products, either because they come from different fields of expertise or because they [...] Read more.
This manuscript focuses on the need for tailoring flood assessment products to decision making within the humanitarian sector. Decision-makers often struggle to extract all of the information contained in scientific products, either because they come from different fields of expertise or because they have different needs that are not captured in the results or the processing of the data. Here we define the key elements of a flood assessment product designed for the humanitarian sector. From a remote sensing perspective, in order to assess flooding, the measurement sampling properties, i.e., spatial resolution and temporal repeat, are key. We have therefore implemented a methodology through the processing and interpretation of the measurements from the Cyclone Global Navigation Satellite System (CYGNSS) mission. CYGNSS measurements are usually parametrized in various possible observables. Those observables are then linked to the surface characteristics, such as, in this case, the presence of inundation in the CYGNSS footprint. Our methodology includes the variability of the pixels in landscapes with infrastructure, rivers, agricultural fields, rural areas, and other elements characteristic of the agricultural-urban interface. We provide an original methodology that uses CYGNSS mission bistatic radar measurements and an artificial intelligence classification algorithm based on statistical properties of the land pixels through a k-means clustering strategy to detect and monitor flooding events, as well as to characterize the land surface prior to and post flooding events. The novel methodology to derive a flooding product is then evaluated towards the needs of the humanitarian sector by a cognizant link (a translator) between technologists or scientists and decision-makers. The inclusion of humanitarian needs into product development following the advice of a cognizant link is novel to the applications developed employing GNSS bistatic radar data. Full article
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Article
Expectations of Future Natural Hazards in Human Adaptation to Concurrent Extreme Events in the Colorado River Basin
Climate 2022, 10(2), 27; https://doi.org/10.3390/cli10020027 - 18 Feb 2022
Viewed by 907
Abstract
Human adaptation to climate change is the outcome of long-term decisions continuously made and revised by local communities. Adaptation choices can be represented by economic investment models in which the often large upfront cost of adaptation is offset by the future benefits of [...] Read more.
Human adaptation to climate change is the outcome of long-term decisions continuously made and revised by local communities. Adaptation choices can be represented by economic investment models in which the often large upfront cost of adaptation is offset by the future benefits of avoiding losses due to future natural hazards. In this context, we investigate the role that expectations of future natural hazards have on adaptation in the Colorado River basin of the USA. We apply an innovative approach that quantifies the impacts of changes in concurrent climate extremes, with a focus on flooding events. By including the expectation of future natural hazards in adaptation models, we examine how public policies can focus on this component to support local community adaptation efforts. Findings indicate that considering the concurrent distribution of several variables makes quantification and prediction of extremes easier, more realistic, and consequently improves our capability to model human systems adaptation. Hazard expectation is a leading force in adaptation. Even without assuming increases in exposure, the Colorado River basin is expected to face harsh increases in damage from flooding events unless local communities are able to incorporate climate change and expected increases in extremes in their adaptation planning and decision making. Full article
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Article
Assessing Changes in 21st Century Mean and Extreme Climate of the Sacramento–San Joaquin Delta in California
Climate 2022, 10(2), 16; https://doi.org/10.3390/cli10020016 - 29 Jan 2022
Viewed by 1044
Abstract
This work aims to assess potential changes in the mean and extreme precipitation and temperature across the Sacramento–San Joaquin Delta (Delta) in California in the 21st century. The study employs operative climate model projections from the Coupled Model Inter-comparison Project Phase 5 (CMIP5). [...] Read more.
This work aims to assess potential changes in the mean and extreme precipitation and temperature across the Sacramento–San Joaquin Delta (Delta) in California in the 21st century. The study employs operative climate model projections from the Coupled Model Inter-comparison Project Phase 5 (CMIP5). Specifically, 64 individual downscaled daily projections (1/16 degree, approximately 6 by 6 km) on precipitation and temperature from 32 Global Circulation Models (GCMs) under two emission scenarios (RCP 4.5 and RCP 8.5) from 2020–2099 are utilized for the analysis. The results indicate increasing warming (in mean, minimum, and maximum temperature) further into the future under both emission scenarios. Warming also exhibits a strong seasonality, with winters expecting lower and summers expecting higher increases in temperature. In contrast, for mean annual total precipitation, there is no consistent wetter or drier signal. On average, the changes in annual total precipitation are minimal. However, dry season precipitation is projected to decline. The study also shows that the number of wet days is projected to decrease while the number of very wet (daily precipitation over 10 mm) and extremely wet (daily precipitation over 20 mm) days is projected to increase. Moreover, the study illustrates that only about half of the changes in total annual precipitation are projected to come from changes in the wettest 10% of wet days. In contrast, a majority of changes in variance of the annual precipitation comes from changes in variance of the wettest 10% of the wet days. This suggests that fluctuations in large storms are projected to dictate the variability of precipitation in the Delta. Additionally, a general upward trend in dry conditions measured by the Standardized Precipitation-Evapotranspiration Index is expected during the projection period. The trending signal is stronger at multi-year temporal scales (one to four years) and under the higher emission scenario. These change patterns are generally similar across three sub-regions of the Delta (i.e., North, South, and West), even though some changes in the South Delta are the most pronounced. This study further discusses challenges posed by these changes to the Delta’s water supply and ecosystems, along with the Delta’s resiliency and potential ways to address these challenges. Full article
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Article
Predicting Water Availability in Water Bodies under the Influence of Precipitation and Water Management Actions Using VAR/VECM/LSTM
Climate 2021, 9(9), 144; https://doi.org/10.3390/cli9090144 - 21 Sep 2021
Cited by 2 | Viewed by 1105
Abstract
Recently, awareness about the significance of water management has risen as population growth and global warming increase, and economic activities and land use continue to stress our water resources. In addition, global water sustenance efforts are crippled by capital-intensive water treatments and water [...] Read more.
Recently, awareness about the significance of water management has risen as population growth and global warming increase, and economic activities and land use continue to stress our water resources. In addition, global water sustenance efforts are crippled by capital-intensive water treatments and water reclamation projects. In this paper, a study of water bodies to predict the amount of water in each water body using identifiable unique features and to assess the behavior of these features on others in the event of shock was undertaken. A comparative study, using a parametric model, was conducted among Vector Autoregression (VAR), the Vector Error Correction Model (VECM), and the Long Short-Term Memory (LSTM) model for determining the change in water level and water flow of water bodies. Besides, orthogonalized impulse responses (OIR) and forecast error variance decompositions (FEVD) explaining the evolution of water levels and flow rates, the study shows the significance of VAR/VECM models over LSTM. It was found that on some water bodies, the VAR model gave reliable results. In contrast, water bodies such as water springs gave mixed results of VAR/VECM. Full article
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Article
Flood Risk Assessment under Climate Change: The Petite Nation River Watershed
Climate 2021, 9(8), 125; https://doi.org/10.3390/cli9080125 - 05 Aug 2021
Cited by 4 | Viewed by 1805
Abstract
In Canada, climate change is expected to increase the extreme precipitation events by magnitude and frequency, leading to more intense and frequent river flooding. In this study, we attempt to map the flood hazard and damage under projected climate scenarios (2050 and 2080). [...] Read more.
In Canada, climate change is expected to increase the extreme precipitation events by magnitude and frequency, leading to more intense and frequent river flooding. In this study, we attempt to map the flood hazard and damage under projected climate scenarios (2050 and 2080). The study was performed in the two most populated municipalities of the Petite Nation River Watershed, located in southern Quebec (Canada). The methodology follows a modelling approach, in which climate projections are derived from the Hydroclimatic Atlas of Southern Quebec following two representative concentration pathways (RCPs) scenarios, i.e., RCP 4.5 and RCP 8.5. These projections are used to predict future river flows. A frequency analysis was carried out with historical data of the peak flow (period 1969–2018) to derive different return periods (2, 20, and 100 years), which were then fed into the GARI tool (Gestion et Analyse du Risque d’Inondation). This tool is used to simulate flood hazard maps and to quantify future flood risk changes. Projected flood hazard (extent and depth) and damage maps were produced for the two municipalities under current and for future scenarios. The results indicate that the flood frequencies are expected to show a minor decrease in peak flows in the basin at the time horizons, 2050 and 2080. In addition, the depth and inundation areas will not significantly change for two time horizons, but instead show a minor decrease. Similarly, the projected flood damage changes in monetary losses are projected to decrease in the future. The results of this study allow one to identify present and future flood hazards and vulnerabilities, and should help decision-makers and the public to better understand the significance of climate change on flood risk in the Petite Nation River watershed. Full article
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Article
Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models
Climate 2021, 9(2), 28; https://doi.org/10.3390/cli9020028 - 01 Feb 2021
Cited by 12 | Viewed by 1492
Abstract
Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference [...] Read more.
Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study. Full article
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Review

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Review
Climate Change Related Catastrophic Rainfall Events and Non-Communicable Respiratory Disease: A Systematic Review of the Literature
Climate 2022, 10(7), 101; https://doi.org/10.3390/cli10070101 - 04 Jul 2022
Viewed by 1067
Abstract
Climate change is increasing the frequency and intensity of extreme precipitation events, the impacts of which disproportionately impact urban populations. Pluvial flooding and flooding related sewer backups are thought to result in an increase in potentially hazardous human-pathogen encounters. However, the extent and [...] Read more.
Climate change is increasing the frequency and intensity of extreme precipitation events, the impacts of which disproportionately impact urban populations. Pluvial flooding and flooding related sewer backups are thought to result in an increase in potentially hazardous human-pathogen encounters. However, the extent and nature of associations between flooding events and non-communicable respiratory diseases such as chronic bronchitis, asthma, and chronic obstructive pulmonary disease (COPD) are not well understood. This research seeks to characterize the state of research on flooding and NCRDs through a systematic review of the scientific literature. We conducted a systematic search of PubMed, Web of Science, and Scopus for published scholarly research papers using the terms flooding, monsoon, and tropical storm with terms for common NCRDs such as asthma, COPD, and chronic bronchitis. Papers were included if they covered research studies on individuals with defined outcomes of flooding events. We excluded review papers, case studies, and opinion pieces. We retrieved 200 articles from PubMed, 268 from Web of Science and 203 from Scopus which comprised 345 unique papers. An initial review of abstracts yielded 38 candidate papers. A full text review of each left 16 papers which were included for the review. All papers except for one found a significant association between a severe weather event and increased risk for at least one of the NCRDs included in this research. Our findings further suggest that extreme weather events may worsen pre-existing respiratory conditions and increase the risk of development of asthma. Future work should focus on more precisely defining measure of health outcomes using validated tools to describe asthma and COPD exacerbations. Research efforts should also work to collect granular data on patients’ health status and family history and assess possible confounding and mediating factors such as neighborhood water mitigation infrastructure, housing conditions, pollen counts, and other environmental variables. Full article
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