Climate Change Uncertainty Research Articles

Assessing the cost-effectiveness of Nature-based Solutions under climate change uncertainty and learning

Assessing the cost-effectiveness of Nature-based Solutions under climate change uncertainty and learning

Quantification and allocation of uncertainties of climate change impacts on hydropower potential under 1.5 °C and 2.0 °C global warming levels in the headwaters of the Benue River Basin, Cameroon

Hydropower is the world's largest producer of renewable energy and represents more than 43% of the low-carbon energy. However, it is sensitive to climate variability and change. This study evaluates the climate change impacts on hydropower potential in the headwaters of the Benue River Basin (HBRB) under 1.5 °C and 2.0 °C global warming levels (GWLs) and quantifies the main sources of uncertainty in the modeling chain. Precipitation and temperature from 17 members of the Coordinated Regional Downscaling Experiment over the Africa domain (CORDEX-Africa) under two representative concentration pathways (RCPs 4.5 and 8.5) were used to run two calibrated Lumped-conceptual hydrological models (HMs) (Hydrologiska Byrans Vattenavdelning (HBV-Light) and HYdrological MODel (HYMOD)). An analysis of variance (ANOVA) decomposition was used to quantify the uncertainties related to each impact modeling chain step in the hydropower potential calculation process. Results reveal a high uncertainty in both climatic and hydrologic parameters. The change in precipitation associated with an increase in potential evapotranspiration (PET) causes a significant decrease in hydropower generation associated with a large uncertainty range. The ANOVA sensitivity test reveals that the dominant contributing source to hydropower projections uncertainty varies with GWL. Given the likely breach of GWL 1.5 by the early 2030s, these findings contribute information for consideration in water and energy planning in the region over the next decade, and stresses that these considerations are urgent for the socioeconomic well-being of the region.

Climate Smart Agriculture (CSA) for Sustainable Agriculture Nexus: A Tool for Transforming Food Systems

Climate Smart Agriculture (CSA) is a global strategy for enhancing food productivity amidst climate change uncertainties in the 21st century. CSA improves farmers’ incomes, reduces greenhouse emissions, and farming systems become resilient to climate change. Despite the vital role that CSA plays in the development of the agricultural industry and the economy, the extent to which CSA is related to sustainable agriculture (SA) is not well documented. Is CSA the same as SA? If they are the same, do CSA practices impose mitigation requirements for developing countries like Uganda? Studies or research on CSA and SA unfortunately have certain shortcomings. Lack of this knowledge makes it difficult to plan investments and develop policies that will increase farmers’ resilience to climate change and variability to improve SA. This study is aimed at assessing how CSA links to SA and whether the two contribute to climate change mitigation requirements. It was found that CSA and SA are also related in a way that the latter leads to lowering greenhouse gas emissions hence mitigating climate change. CSA and SA share a common principal goal of achieving food security. It was concluded that developing countries are the worst affected by the negative impacts of climate change and don’t have the adaptive capacity to respond to climate change effects.

Climate change and Japanese economic policy uncertainty: Asymmetric analysis

Japan has always formulated various corresponding policies in response to climate change. This paper explores the asymmetric impact of climate change on Japan's economic policy uncertainty (JEPU) by comparing text-based indicators (climate policy uncertainty, CPU) and physical indicators (El Niño Southern Oscillation, ENSO). The asymmetric Granger test finds that CPU has no significant impact on JEPU. However, positive ENSO shocks can lead to an increase in JEPU. Different ENSO shocks may differently drive JEPU in all runs. Thus, this kind of text-based indicator may not be appropriate when exploring the impact of climate change on Japanese economic policy.

Partial climatic risk screening, adaptation and livelihoods in a coastal urban area in Ghana

Coastal urban areas worldwide are increasingly becoming convergence points for climatic hazards, demographic shifts, and spatial development. However, the presence of societal demands that impact both livelihoods and urban planning in response to climatic hazards undermines the potential positive outcomes. This research, conducted in a coastal urban area of Ghana's Greater Accra Region, utilized the Community-based Risk Screening Tool – Adaptation and Livelihoods (CRiSTAL) developed by the International Institute for Sustainable Development (IISD) to analyze the experiences of climatic hazards and overall livelihoods of the population. The study employed Ghana's Census Sampling Frame for implementation, and Participatory Learning Approaches, to collect data which it identified floods, heavy storms, and heat stress as the most significant hazards. These hazards greatly influenced the population's physical, social, and financial livelihood assets, resulting in losses and damages caused by heavy rains, storms, and subsequent floods. Extreme heat also had a notable impact on human and financial resources. The local population prioritized human mobility and livelihood diversification as important adaptation strategies. The findings have important policy implications, highlighting the need to address barriers and disruptions in resilience-building and sustainability efforts, emphasizing the significance of prioritizing policy investment and considering climate change uncertainties in planning towards minimizing “urban climate policy inhibition” (Urban-CPI). The study also revealed valuable lessons, such as CRiSTAL's ability to bridge the gap between climatic risk and livelihood issues, bringing them closer to communities and enhancing preparedness to adapt to climatic risks and impacts on livelihoods.

A sixfold urban design framework to assess climate resilience: Generative transformation in Negril, Jamaica.

The uncertainty of climate change's impacts hinders adaptation actions, particularly micro-scale urban design interventions. This paper proposes a sixfold urban design framework to assess and enhance the resilience of urban form to climate change, where urban form refers to the patterns of streets, buildings, and land uses. The framework is then applied to Long Bay in Negril, Jamaica-a coastal area that incorporates the complex interactions between urbanization and a highly vulnerable socio-ecological system to climate change-related hazards, primarily sea-level rise. Empirical evidence from 19 in-depth interviews with planning and design professionals and development actors, in situ observations, and morphological analyses reveal that Long Bay's current adaptation strategies heavily rely on bounce-back resilience measures that predominantly consider the impacts of extreme climatic events rather than slow-onset ones. Such strategies abet current tourism-driven development patterns while overlooking Long Bay's inherent abilities for generative transformation and incremental changes to meet climatic uncertainty. Instead, this study's findings highlight how generative urban form transformation would better equip Long Bay to cope with future uncertainty-climatic or other.

Real time control of nature-based solutions: Towards Smart Solutions and Digital Twins in Rangsit Area, Thailand

The intensity and frequency of hydro-meteorological hazards have increased due to fast-growing urbanisation activities and climate change. Hybrid approaches that combine grey infrastructure and Nature-Based Solutions (NBSs) have been applied as an adaptive and resilient strategy to cope with climate change uncertainties and incorporate other co-benefits. This research aims to investigate the feasibility of Real Time Control (RTC) for NBS operation in order to reduce flooding and improve their effectiveness. The study area is the irrigation and drainage system of the Rangsit Area in Thailand. The results show that during the normal flood events, the RTC system effectively reduces water level at the Western Raphiphat Canal Station compared to the system without RTC or with additional storage. Moreover, the RTC system facilitates achieving the required minimum volume and increasing the volume in the retentions. These findings highlight the potential of using RTC to improve the irrigation and drainage system operation as well as NBS implementation to reduce flooding. The RTC system can also assists in equitable water distribution between Klongs and retention areas, while also increasing the water storage in the retention areas. This additional water storage can be utilized for agricultural purposes, providing further benefits. These results represent an essential starting point for the development of Smart Solutions and Digital Twins in utilizing Real-Time Control for flood reduction and water allocation in the Rangsit Area in Thailand.

Organizational heuristics and firms' sensemaking for climate change adaptation

AbstractThe complexity and uncertainty of climate change pose unique challenges to the development of corporate adaptation strategies. Climate adaptation requires organizations to rely on sensemaking to understand climate events, implications for their operations, and develop a response. Organizational heuristics can support sensemaking by simplifying decisions and reducing cognitive effort but also hinder it by creating bias and errors that lead to inefficient decisions. This paper analyzes how firms use organizational heuristics when making decisions on climate change by empirically investigating the adaptation responses of key infrastructure providers in the United Kingdom. Looking at selection, prioritization, procedural, and temporal heuristics, we examine how firms make sense of climate events and develop their responses accordingly. The analysis shows that while these rules‐of‐thumb are sensible in some instances, they can create biases, too, that may deflect responsibility or create a false sense of security leading to inefficient adaptation decisions.

Investigating the Applicability of a Global Average Calibration Line for Ambient Size-Resolved Cloud Condensation Nuclei (CCN) Measurements: A Technical Note

Abstract Aerosol–cloud–precipitation interaction represents the largest uncertainty in climate change’s current and future understanding. Therefore, aerosol properties affecting the cloud and precipitation formation and their accurate estimation is a first step in developing improved parameterizations for the prognostic climate models. Over the last couple of decades, a commercially available Cloud Condensation Nuclei Counter (CCNC) has been deployed in the field and laboratory for characterizing CCN properties of ambient or atmospherically relevant laboratory-generated aerosols. However, most of the CCN measurements performed in the field are often compounded with the erroneous estimation of CCN concentration and other parameters due to a lack of robust and accurate CCNC calibration. CCNC is not a plug-and-play instrument and requires prudent calibration and operation, to avoid erroneous data and added parameterization uncertainties. In this work, we propose and demonstrate the usability of a global calibration equation derived from CCNC calibration experiments from 8 contrasting global environments. Significant correlation was observed between the global calibration and each of the 16 individual experiments. A significant improvement in the correlation was observed when the calibration experiments were separated for high-altitude measurements. Using these equations, we further derived the effective hygroscopicity parameter and found lower relative uncertainty in the hygroscopicity parameter at higher effective supersaturation. Our results signify that altitude-based pressure change could be of importance for accurate calibration at high-altitude locations. Our results are consistent with previous studies emphasizing the criticality of the accurate CCN calibration for the lower supersaturations.

A Two-Stage Investment Decision-Making Model for Urban Rail Transit Drainage Renovation

Climate change is the main cause of frequent extreme weather and natural disasters. Therefore, effective climate adaptation strategies for urban rail transit (URT) should be adopted to cope with extreme precipitation events (EPEs). This study proposes a decision-making model based on climate change for drainage renovation, which consists of an optimal renovation sequence model and an optimal investment timing model. This study analyzes the inundation risk of each station and its node importance in the URT network and then uses a multi-attribute decision analysis (MADA) to determine the optimal renovation sequence. This study also uses a real options pricing approach to calculate the value of an option in order to defer the renovation project and determine the optimal investment timing. Then, the Beijing Urban Rail Transit (BURT) is taken as an example to conduct an empirical analysis of the proposed model. Considering the uncertainty of climate change and the complexity of the URT network, the model can obtain the optimal renovation sequence and the investment timing of each station, which is expected to provide a decision-making tool for urban governments to formulate an optimal plan that strengthens the prevention of flooding disasters.

Climate Future: Averting and Adapting to Climate Change

Robert Mendelsohn of Yale School of the Environment, Department of Economics, and Yale School of Management reviews “Climate Future: Averting and Adapting to Climate Change” by Robert S. Pindyck. The Econlit abstract of this book begins: “Explores the extent and nature of the uncertainty of climate change's impact on the economy and society, promoting the argument that climate policy should focus on adaptation in preparation for the unlikelihood of sufficiently reducing greenhouse gas emissions.”

Identical hierarchy of physical drought types for climate change signals and uncertainty

Climate change may have different impacts on different types of drought through its influence on the mechanisms of the propagation of a precipitation lack into a hydrological or agricultural drought. The involvement of additional processes in runoff and soil moisture modeling potentially leads to discrepancies in the projection uncertainties and signal-to-noise ratios between different drought types. This global study compares climate change signals, uncertainty, and signal-to-noise ratios between meteorological, hydrological, and agricultural droughts characterized by standardized precipitation index (SPI), standardized runoff index (SRI), and standardized soil moisture index (SSI), respectively. The comparison is made for five drought characteristics including median and peak intensity, median and longest duration, and frequency using 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) models for four Shared Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. We find that the spatial extent and magnitude in all five drought characteristics increase from meteorological to hydrological to agricultural drought. This increase manifests itself, however, at the expense of augmented uncertainty, to the extent that uncertainty for agricultural drought is up to sevenfold larger compared to meteorological drought. Despite the augmentation of uncertainty from meteorological to agricultural drought, the hierarchy of drought types for climate change signals still holds for the spatial extent of significant signal-to-noise ratios.

Uncertainties of Climate Change on Suitable Habitat of Calligonum mongolicum Turcz. (Polygonaceae) in Desert Areas of Central Asia

Understanding the climatically suitable habitat of species plays a vital role in the sustainable use and management of target species. Calligonum mongolicum Turcz., a native shrub species found in desert areas of Central Asia, is generally considered as one of the top four tree species for desertification control. However, previous works on suitable habitat simulation had focused mainly on either the national or specific geographical scales rather than entire biota scales, which have underestimated the climatic tolerance of the species. Furthermore, the uncertainty outcomes of climate change were largely ignored. With these questions, the arid regions of Central Asia were selected as our research background area. Occurrence data of C. mongolicum were obtained from various sources, such as the Global Biodiversity Information Facility, the Chinese Virtual Herbarium, and the iPlant website. The maximum entropy model (MaxEnt) was used to simulate the suitable habitat change dynamics under various climate change scenarios [5 general circulation models (GCMs) × 3 shared socioeconomic pathways (SSPs)]. The uncertainty of climate change induced by GCMs and SSPs were decomposed by the two-way ANOVA method. Our results show that hydrological-related variables are more important for the species’ habitat suitability than thermal-related variables. The climatic threshold for the core suitable habitat was 1–30 mm for precipitation of the coldest quarter, 14–401 mm for annual precipitation, −16.01–12.42 °C for mean temperature of the driest quarter, 9.48–32.63 °C for mean temperature of the wettest quarter, and −25.01–−9.77 °C for the minimum temperature of the coldest month. The size of suitable habitat was about 287.4 × 104 km2 under the current climate condition, located in China and Mongolia. Climate change has less impact on the total area size, but it has bigger impacts on the gain area and loss area sizes. The loss area is mainly located in the southeast boundaries, whereas the gain area is mainly located in Mongolia and the Qinghai-Tibet Plateau. The decomposition uncertainty of climate change indicates that GCMs could explain 14.5%, 66.4%, and 97.0% of total variation, respectively, and SSPs could explain 85.5%, 33.6%, and 3.0% of the total variation for gain, loss, and total habitat sizes, respectively. Our work clearly demonstrates that while C. mongolicum has great planting potential in Central Asia under various climate change scenarios, the sensitive areas possess large uncertainties requiring long-term climate monitoring for afforestation projects.

Data-Driven Modelling of Soil Moisture Dynamics for Smart Irrigation Scheduling

In the face of increasing water scarcity and uncertainties of climate change, improving crop water use efficiency and productivity, while minimizing negative environmental impacts, is becoming crucial to meet the surging global food demand. Smart irrigation has a potential of improving water use efficiency in precision agriculture especially when efficient irrigation control strategies are adopted. Conventionally, irrigation systems rely on heuristic methods to schedule irrigation which either leads to over-irrigation or under-irrigation. This influences the crop physiological characteristics as well as the water use efficiency. To tackle this menace, model-based irrigation management has been overemphasized. A closed-loop irrigation control strategy relies on a mathematical model of the system for irrigation scheduling decisions. In this study, a data-driven approach was used to learn soil moisture dynamics from a drip irrigated tomato in an open field agricultural system. A total number of 9674 data samples were collected using an ATMOS41 weather station, TERROS 12 soil moisture sensor and a YFS-201 flow sensor for crop evapotranspiration and precipitation, soil moisture and irrigation volumes respectively. Data driven modelling was then performed using the system identification toolbox in a MATLAB environment. The model formulation was a multi-input single-output (MISO) system with reference evapotranspiration, irrigation and rainfall as inputs and soil moisture as the output. Different model structures including transfer functions, state space models, polynomial models and ARX models were evaluated. Model performance was evaluated using the mean square error (MSE), final prediction error (FPE) and estimated fit of the model approaches. Simulation results indicate that the soil moisture dynamics model provides a satisfactory approximation of the process dynamics with a state space model giving an estimated fit of 97.04 %, MSE and FPE of 1.74×10−7 and 1.75×10−7 respectively. This model will be used to design a model predictive controller for smart irrigation scheduling in open field environmental conditions.

Assessing ecosystem vulnerability under severe uncertainty of global climate change

Assessing the vulnerability and adaptive capacity of species, communities, and ecosystems is essential for successful conservation. Climate change, however, induces extreme uncertainty in various pathways of assessments, which hampers robust decision-making for conservation. Here, we developed a framework that allows us to quantify the level of acceptable uncertainty as a metric of ecosystem robustness, considering the uncertainty due to climate change. Under the framework, utilizing a key concept from info-gap decision theory, vulnerability is measured as the inverse of maximum acceptable uncertainty to fulfill the minimum required goal for conservation. We applied the framework to 42 natural forest ecosystems and assessed their acceptable uncertainties in terms of maintenance of species richness and forest functional type. Based on best-guess estimate of future temperature in various GCM models and RCP scenarios, and assuming that tree species survival is primarily determined by mean annual temperature, we performed simulations with increasing deviation from the best-guess temperature. Our simulations indicated that the acceptable uncertainty varied greatly among the forest plots, presumably reflecting the distribution of ecological traits and niches among species within the communities. Our framework provides acceptable uncertainty as an operational metric of ecosystem robustness under uncertainty, while incorporating both system properties and socioeconomic conditions. We argue that our framework can enhance social consensus building and decision-making in the face of the extreme uncertainty induced by global climate change.

A Review and Comparative Analysis of IWCM Concepts in Australia and Similar Jurisdictions

Interpretations of integrated water cycle management (IWCM) differ across jurisdictions. This paper discusses 10 interpretations of the IWCM concept globally, in Australia and in jurisdictions similar to Australia. Five interpretations of many IWCM versions in Australia are reviewed. This strategic concept aims to address the internal challenges of managing water demand and supply, achieving appropriate disposal and/or wastewater recycling for re-use and distribution networks and providing services at an affordable rate, per changing community needs. The IWCM concept is also recognised as a resource planning tool to address external challenges, such as the uncertainties of climate change, the circular economy and resilience. All 10 IWCM concepts reviewed in this paper acknowledge governance and stakeholders to be of primary importance: governance to drive the conceptual interpretation and stakeholders to develop, drive, implement and promote IWCM as adept at addressing local challenges. The two global interpretations place primary importance on governance, stakeholder engagement and natural resource management, whereas the local interpretations place equally high importance on water critical infrastructure and water economy. Technology, which is changing at an unprecedented pace, is considered, but not as an immediate or primary challenge. These differences are mainly attributed to the organisations’ responsibilities and constraints, which drive IWCM concept design.

Evaluating uncertainty in climate change impacts on peak discharge and flood volume in the Qaran Talar watershed, Iran

Abstract This study evaluates the effects of climate change (CC) on runoff properties over the case study of the Qaran Talar watershed located in Iran. To consider the two main sources of uncertainty, i.e., Greenhouse Gases emission (GHG) scenarios and outputs of Atmosphere-Ocean General Circulation Models (AOGCMs), a daily rainfall time series was generated for two future periods (2021-2050 and 2070-2099) at three risk levels of 0.1, 0.25 and 0.50. 22 AOGCMs outputs following two emission scenarios (RCP4.5 and RCP8.5) were used. The results showed that the uncertainty of climate change scenarios was primarily owing to the uncertainty of GCMs outputs. Regarding the 2021-2050 period, under both emission scenarios, the increases in peak discharge and flood volume (FV) were estimated to reach 70, 50, and 30% at three risk levels of 0.1, 0.25 and 0.50, respectively, compared to the recent past period. As the current century draws to a close, the difference between the results of the two emission scenarios becomes apparent so that in the far-future period (2070-2099), the RCP8.5 scenario estimates and FV more than the RCP4.5 scenario does. Additionally, it was found that the uncertainty caused by AOGCMs was more than that by GHG emission scenarios.

Impacts of urbanization and climate change on surface runoff in Thu Duc City, Vietnam

Thu Duc City (TDC), a sub-city of Ho Chi Minh City (HCMC), Vietnam, has undergone rapid transformation in land use - land cover (LULC) over the past few decades. Flooding is one of the biggest challenges to the sustainable development of the city. Quantifying excess runoff induced by urbanization and climate change is essential for developing an efficient flood risk management strategy. This research aims to evaluate the effects of the impervious surface expansion (a key indicator of urbanization) and the rainfall increase caused by climate change on the excess runoff depth in TDC. The dataset derived from LANDSAT satellite images was used to explore the spatial-temporal dynamics of impervious surface areas from 1985 to 2020. The EPA-SWMM urban rainfall-runoff model was employed to investigate the effects of increased impervious surface and climate change on excess runoff depth. The satellite data showed that TDC has 24% of the impermeable surface area currently, much lower than 75.6% in areas located in the central districts such as Nhieu Loc – Thi Nghe (NL-TN) basin, and that impermeable areas have expanded rapidly in the north-south and west-east directions since 2010. The simulated results indicated that impervious surface development increased excess runoff depth by 14.0mm between 1985 and 2020. It also showed that increasing rainfall due to climate change could raise the average excess runoff depth by 18.2%, 64.0%, and 49.4%, respectively, at the beginning, the middle, and the end of the century compared to the current stage. It suggests mainstreaming stormwater retention and detention systems into urban development plans for adapting to the future uncertainties of urbanization and climate change. It also recommends that the impervious surface areas must be maintained within design thresholds to control runoff and mitigate floods in practice. The research findings could assist policymakers and stakeholders in formulating a long-term vision to propose an efficient urban stormwater management strategy in TDC and similar growing cities.

Applications of CNOP-P Method to Predictability Studies of Terrestrial Ecosystems

In this paper, recent research on terrestrial ecosystem predictability using the conditional nonlinear optimal parameter perturbation (CNOP-P) method is summarized. The main findings include the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, the identification of key physical parameters that lead to large uncertainties in terrestrial ecosystem modeling and prediction, and the evaluation of the simulation ability and prediction skill of terrestrial ecosystems by reducing key physical parameter errors. The study areas included the Inner Mongolia region, north–south transect of eastern China, and Qinghai–Tibet Plateau region. The periods of the studies were from 1961 to 1970 for the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, and from 1951 to 2000 for the identification of the most sensitive combinations of physical parameters. Climatic Research Unit (CRU) data were employed. The numerical results indicate the important role of nonlinear changes in climate variability due to the occurrences of extreme events characterized by CNOP-P in the abrupt grassland ecosystem equilibrium state and formation of carbon sinks in China. Second, the most sensitive combinations of physical parameters to the uncertainties in simulations and predictions of terrestrial ecosystems identified by the CNOP-P method were more sensitive than those obtained by traditional methods (e.g., one-at-a-time (OAT) and stochastic methods). Furthermore, the improvement extent of the simulation ability and prediction skill of terrestrial ecosystems by reducing the errors of the sensitive physical parameter combinations identified by the CNOP-P method was higher than that by the traditional methods.

Real options analysis for urban flood mitigation under environmental change

As urban flooding becomes more frequent and severe due to climate change and accelerated urbanization, effective urban flood control measures are needed to mitigate urban flooding under environmental change. However, few studies consider both climate change and land use change and formulate urban flood control plans for the uncertainty of climate change. This study proposes a real options method based on trinomial tree model to design urban flood control measures under environmental changes. Future rainfall was predicted based on the SDSM model. Land use changes for 2035 and 2050 were predicted using the CA-Markov model. Considering the uncertainty of future climate change, future urban flood control measures based on the real options method were developed, and to compare and analyze them with fixed investment method. The research results show that rainfall and construction land in the study area will increase in the future. The benefit of real options retrofit scheme is higher than the fixed investment retrofit scheme and climate change uncertainty has a greater impact on flood control planning decisions. The research framework proposed in this paper provides an effective basis for the formulation of urban flood control strategies and the design of flood control measures under environmental change.