scholarly journals Prioritization of water management under climate change and urbanization using multi-criteria decision making methods

2012 ◽  
Vol 16 (3) ◽  
pp. 801-814 ◽  
Author(s):  
J.-S. Yang ◽  
E.-S. Chung ◽  
S.-U. Kim ◽  
T.-W. Kim
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Driving Force ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Decision Criterion ◽  
Climate Change Scenarios ◽  
Multi Criteria Decision Making ◽  
Cost Component ◽  
Hydrological Simulation

Abstract. This paper quantifies the transformed effectiveness of alternatives for watershed management caused by climate change and urbanization and prioritizes five options using multi-criteria decision making techniques. The climate change scenarios (A1B and A2) were obtained by using a statistical downscaling model (SDSM), and the urbanization scenario by surveying the existing urban planning. The flow and biochemical oxygen demand (BOD) concentration duration curves were derived, and the numbers of days required to satisfy the environmental flow requirement and the target BOD concentration were counted using the Hydrological Simulation Program-Fortran (HSPF) model. In addition, five feasible alternatives were prioritized by using multi-criteria decision making techniques, based on the driving force-pressure-state-impact-response (DPSIR) framework and cost component. Finally, a sensitivity analysis approach for MCDM methods was conducted to reduce the uncertainty of weights. The result indicates that the most sensitive decision criterion is cost, followed by criteria response, driving force, impact, state and pressure in that order. As it is certain that the importance of cost component is over 0.127, construction of a small wastewater treatment plant will be the most preferred alternative in this application.

scholarly journals Prioritization of water management under climate change and urbanization using multi-criteria decision making methods

2011 ◽  
Vol 8 (6) ◽  
pp. 9889-9925 ◽  
Author(s):  
J.-S. Yang ◽  
E.-S. Chung ◽  
S.-U. Kim ◽  
T.-W. Kim ◽  
Y. D. Kim
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Driving Force ◽  
Oxygen Demand ◽  
Decision Criterion ◽  
Climate Change Scenarios ◽  
Multi Criteria Decision Making ◽  
Cost Component ◽  
Hydrological Simulation ◽  
Hspf Model

Abstract. This paper quantifies the transformed effectiveness of alternatives for watershed management caused by climate change and urbanization and prioritizes five options using multi-criteria decision making techniques. The climate change scenarios (A1B and A2) were obtained by using a statistical downscaling model (SDSM), and the urbanization scenario by surveying the existing urban planning. The flow and biochemical oxygen demand (BOD) concentration duration curves were derived, and the numbers of days required to satisfy the environmental flow requirement and the target BOD concentration were counted using the Hydrological Simulation Program-Fortran (HSPF) model. In addition, five feasible alternatives were prioritized by using multi-criteria decision making techniques, based on the driving force-pressure-state-impact-response (DPSIR) framework and cost component. Finally, a sensitivity analysis approach for MCDM methods was conducted to reduce the uncertainty of weights. The result indicates that the most sensitive decision criterion is cost, followed by criteria response, driving force, impact, state and pressure in that order. Since it is certain that the importance of cost component is over 0.127, use of the groundwater collected by subway stations will be the most preferred alternative in this application.

scholarly journals Hydrologic sensitivity of the Upper San Joaquin River Watershed in California to climate change scenarios

2012 ◽  
Vol 44 (4) ◽  
pp. 723-736 ◽  
Author(s):  
Zili He ◽  
Zhi Wang ◽  
C. John Suen ◽  
Xiaoyi Ma
Keyword(s):  
Climate Change ◽  
Sierra Nevada ◽  
Climate Change Scenarios ◽  
Intergovernmental Panel ◽  
Hydrological Simulation ◽  
River Watershed ◽  
System Sensitivity ◽  
Hspf Model ◽  
San Joaquin River ◽  
San Joaquin River Watershed

To examine the hydrological system sensitivity of the southern Sierra Nevada Mountains of California to climate change scenarios (CCS), five headwater basins in the snow-dominated Upper San Joaquin River Watershed (USJRW) were selected for hydrologic simulations using the Hydrological Simulation Program-Fortran (HSPF) model. A pre-specified set of CCS as projected by the Intergovernmental Panel on Climate Change (IPCC) were adopted as inputs for the hydrologic analysis. These scenarios include temperature increases between 1.5 and 4.5 °C and precipitation variation between 80 and 120% of the baseline conditions. The HSPF model was calibrated and validated with measured historical data. It was then used to simulate the hydrologic responses of the watershed to the projected CCS. Results indicate that the streamflow of USJRW is sensitive to the projected climate change. The total volume of annual streamflow would vary between −41 and +16% compared to the baseline years (1970–1990). Even if the precipitation remains unchanged, the total annual flow would still decrease by 8–23% due to temperature increases. A larger portion of the streamflow would occur earlier in the water year by 15–46 days due to the temperature increases, causing higher seasonal variability of streamflow.

scholarly journals Management implications based on diversity patterns under climate change scenarios in a continental island biodiversity hotspot

2020 ◽  
Author(s):  
Konstantinos Kougioumoutzis ◽  
Ioannis P. Kokkoris ◽  
Maria Panitsa ◽  
Panayiotis Trigas ◽  
Arne Strid ◽  
...  
Keyword(s):  
Climate Change ◽  
Environmental Management ◽  
Decision Making ◽  
Cost Effective ◽  
Biodiversity Hotspot ◽  
Ex Situ ◽  
Climate Change Scenarios ◽  
Island Biodiversity ◽  
The Impact ◽  
Effective Conservation

AbstractIn the Anthropocene era, climate change poses a great challenge in environmental management and decision-making for species and habitat conservation. To support decision-making, many studies exist regarding the expected vegetation changes and the impacts of climate change on European plants, yet none has investigated how climate change will affect the extinction risk of the entire endemic flora of an island biodiversity hotspot, with intense human disturbance. Our aim is to assess, in an integrated manner, the impact of climate change on the biodiversity and biogeographical patterns of Crete and to provide a case-study upon which a cost-effective and climate-smart conservation planning strategy might be set. We employed a variety of macroecological analyses and estimated the current and future biodiversity, conservation and extinction hotspots in Crete, as well as the factors that may have shaped these distribution patterns. We also evaluated the effectiveness of climate refugia and the NATURA 2000 network (PAs) on protecting the most vulnerable species and identified the taxa that should be of conservation priority based on the Evolutionary Distinct and Globally Endangered (EDGE) index, during any environmental management process. The highlands of Cretan mountain massifs have served as both diversity cradles and museums, due to their stable climate and high topographical heterogeneity. They are also identified as biodiversity hotspots, as well as areas of high conservation and evolutionary value, due their high EDGE scores. Due to the ‘escalator to extinction’ phenomenon and the subsequent biotic homogenization, these areas are projected to become diversity ‘death-zones’ in the near future and should thus be prioritized in terms of conservation efforts and by decision makers. In-situ conservation focusing at micro-reserves and ex-situ conservation practices should be considered as an insurance policy against such biodiversity losses, which constitute cost-effective conservation measures. Scientists and authorities should aim the conservation effort at areas with overlaps among PAs and climate refugia, characterized by high diversity and EDGE scores. These areas may constitute Anthropocene refugia. Thus, this climate-smart, cost-effective conservation-prioritization planning will allow the preservation of evolutionary heritage, trait diversity and future services for human well-being and acts as a pilot for similar regions worldwide.

A Bayesian-inferred physical module to estimate robust mitigation pathways with cost-benefit IAMs

2021 ◽  
Author(s):  
Thomas Gasser ◽  
Artem Baklanov ◽  
Armon Rezai ◽  
Côme Chéritel ◽  
Michael Obersteiner
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Monte Carlo ◽  
Carbon Cycle ◽  
Cost Benefit ◽  
Full Rank ◽  
Climate Change Scenarios ◽  
Bayesian Calibration ◽  
Prior Distributions ◽  
Developed Surface

<p>Cost-benefit integrated assessment models (IAMs) include a simplified representation of both the anthropogenic and natural components of the Earth system, and of the interactions and feedbacks between them. As such, they embed economic- and physics-based equations, and the uncertainty in one domain will inevitably affect the other. Most often, however, the physical uncertainty is explored by testing the sensitivity of the optimal mitigation pathway to a few key physical parameters; but for robust decision-making, the optimal pathway itself should ideally embed the uncertainty.</p><p>Here, we present a new physical module for cost-benefit IAMs that is based on state-of-the-art climate sciences. The module follows well-established formulations that were deemed a good trade-off between simplicity and accuracy. Therefore, its overall complexity remains low, as is necessary to be used with optimisation algorithms, but able to reproduce the behaviour of more complex CMIP models. It is made of four components that all exhibit a degree of non-linearity: global climate response, ocean carbon cycle, land carbon cycle, and permafrost carbon system. (Two impact components were also developed: surface ocean acidification, and sea-level rise response.)</p><p>The calibration of this new module is done through Bayesian inference. Prior distributions of the module’s parameters are taken from CMIP multi-model ensembles, and prior distributions of historical constraints are taken from observational datasets (such as global mean surface temperature) and other synthesis exercises (such as IPCC reports or the global carbon budget). The Bayesian calibration itself is done with a full-rank automatic differentiation variational inference (ADVI) algorithm, which leads to posterior distributions of parameters that are consistent with observations. Additionally, the full-rank ADVI algorithm also finds correlations between parameters (i.e. co-distributions) that tend to further reduce the uncertainty in projected climate change.</p><p>We then implement this new module within the DICE model (that is likely the most widely used cost-benefit IAM), and we demonstrate a significant improvement of the physical modelling, and thus of the IAM’s results. We run a Monte Carlo ensemble of 4000 elements taken from the Bayesian calibration, to properly sample the physical uncertainty in the optimal mitigation pathway simulated by DICE. Notably, our new module leads to a social cost of carbon (SCC) of 26 USD / tCO2 (90% range: 13–43), which is lower than 37 USD / tCO2 in the original model.</p><p>This Monte Carlo approach is not a robust one, however, and a final simulation is run to estimate one <em>unique</em> mitigation pathway shared across all 4000 states of the world (by maximizing the total welfare). This <em>robust</em> mitigation pathway is therefore a unique solution that embeds the physical uncertainty, and it is different from the average pathway of the Monte Carlo ensemble. The unicity of the solution (and its lack of explicit uncertainty) makes it very attractive for decision-making and communication purposes. We posit this robust approach could be applied with the cost-optimal IAMs that are used by the IPCC to create and investigate climate change scenarios.</p>

A Machine Learning Sustainable Development Goals Model for Water Resources Serious Gaming. Case Study:  Combeima River, Colombia.

2021 ◽  
Author(s):  
Laura Viviana Garzon Useche ◽  
Karel Aldrin Sánchez Hernández ◽  
Gerald Augusto Corzo Pérez ◽  
German Ricardo Santos Granados
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Land Use ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Climate Adaptation ◽  
Complex Model ◽  
Mobile App ◽  
Climate Change Scenarios ◽  
Development Goals

<p>The importance of knowing and representing rural and urban development in water management is vital for its sustainability.  An essential part of the management required that stakeholders are more aware of the consequences of decisions and in some way, can link decisions towards sustainability.  For this, a mobile app serious game called Water Citizens has been proposed as knowledge dissemination and to provide a better understanding of the way decisions affect Sustainable Development Goals (SDGs). A complex model of a pilot region (Combeima in Ibague, Colombia) has been developed, and the model results are few into equations to estimate fluctuations of SDGs in the region. Running this complex model in real-time, for a mobile application, requires an extensive high-performance computing system linked to large and complex network setup. To solve this problem, a fast yet accurate surrogate model is proposed.</p><p>Therefore, this study contemplates an analysis of methods to forecast sustainable development indicators evaluated through climate change scenarios for a period between 1989-2039. The proposed scenarios associated the public health, livestock, agriculture, engineering, education and environment sectors with climate variables, climate change projections, land cover and land use, water demands (domestic, agricultural and livestock) and water quality (BOD and TSS). Generating the possibility that each player can make decisions that represent the actions that affect or contribute to the demand, availability and quality of water in the region.</p><p>Consequently, a set of indicators were selected to recreate the dimensions of each sector and reflect its relationship with the Sustainable Development Objectives, as opposed to the decisions made by each player. In addition, three categories were considered for the levels of sustainability: low (0.0 - 0.33), medium (0.34 - 0.66) and high (0.67 - 1.0) for the calculated SDG values. </p><p>Self-learning techniques have been employed in the analysis of decision-making problems. In this study, the nearest K neighbours (k-NN) and a multilayer perceptron network (MLP) were used. Through an analysis based on the responses of the players and sustainability indexes, a multiple correlation analysis was developed in order to consolidate the learning dataset, which was randomly partitioned in proportions 0.7 and 0.3 for the training and test subsets respectively. Subsequently, the model fit and performance was carried out, analysing the MSE error metric and confusion matrix.</p><p>Finally, the results of this study will allow to determine the potential of supervised learning models as a decision-making tool for the evaluation of sustainable development, as well as to obtain a better abstraction and representation of the water resource to the challenges related to climate adaptation and water sustainability measures of citizen action, besides generating new approaches for the use of artificial intelligence in land use planning and climate adaptation processes.</p>

Hydrological simulation of Po River (North Italy) discharge under climate change scenarios using the RCM COSMO-CLM

2015 ◽  
Vol 521-522 ◽  
pp. 346-358 ◽  
Author(s):  
R. Vezzoli ◽  
P. Mercogliano ◽  
S. Pecora ◽  
A.L. Zollo ◽  
C. Cacciamani
Keyword(s):  
Climate Change ◽  
Climate Change Scenarios ◽  
Po River ◽  
Hydrological Simulation
Sign in / Sign up

Export Citation Format

Share Document