Assessing climate change vulnerability with group multi-criteria decision making approaches

This study developed an approach to assess the vulnerability to climate change and variability using various group multi-criteria decision-making (MCDM) methods and identified the sources of uncertainty in assessments. MCDM methods include the weighted sum method, one of the most common MCDM methods, the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy-based TOPSIS, TOPSIS in a group-decision environment, and TOPSIS combined with the voting methods (Borda count and Copeland’s methods). The approach was applied to a water-resource system in South Korea, and the assessment was performed at the province level by categorizing water resources into water supply and conservation, flood control and water-quality sectors according to their management objectives. Key indicators for each category were profiled with the Delphi surveys, a series of questionnaires interspersed with controlled opinion feedback. The sectoral vulnerability scores were further aggregated into one composite score for water-resource vulnerability. Rankings among different MCDM methods varied in different degrees, but noticeable differences in the rankings from the fuzzy- and non-fuzzy-based methods suggested that the uncertainty with crisp data, rather widely used, should be acknowledged in vulnerability assessment. Also rankings from the voting-based methods did not differ much from those from non-voting-based (i.e., average-based) methods. Vulnerability rankings varied significantly among the different sectors of the water-resource systems, highlighting the need to assess the vulnerability of water-resource systems according to objectives, even though one composite index is often used for simplicity.     

Integrated assessment of climate change and urbanization impact on adaptation strategies: a case study in two small Korean watersheds

This study develops an integrated approach to assess climate change and urbanization impacts on adaptation strategies in watersheds. We considered the two adaptation strategies for two small watersheds in Korea: the redevelopment of an existing reservoir and the reuse of highly treated wastewater treatment plant (WWTP) effluent. Climate change scenarios were obtained by statistically downscaling the predicted precipitation and temperature with a global climate model (A1B and A2), and urbanization scenarios were derived by estimating the impervious area ratios with an impervious cover model. With the climate change and urbanization scenarios, we used the Hydrological Simulation Program-Fortran model to derive the flow and biochemical oxygen demand (BOD) concentration (conc.) duration curves, and calculate the numbers of days satisfying environmental requirement for instreamflow and the target BOD conc. Climate change reduced the effectiveness of the adaptation strategies with respect to low flow and BOD conc., whereas urbanization generally increased their effectiveness. Climate change had a greater impact on the effectiveness of the adaptation strategies for BOD conc. than for low flow, whereas urbanization had a greater impact on low flow. Comparing impacts of two strategies, a larger decrease in the effectiveness was observed for the WWTP effluent reuse strategy in response to climate change and urbanization. However, the consistent trends cannot be found with ease if climate change and urbanization happens jointly.     

Deep-learning based projection of change in irrigation water-use under RCP 8.5

Stream water-use is essential for both agricultural and hydrological management and yet not many studies have explored its non-stationarity and nonlinearity with meteorological variables. This study proposed a deep-learning based model to estimate agricultural water withdrawal using hydro-meteorological variables, which projected the changes of agricultural water withdrawal influenced by climate change of future. The relationships between meteorological variables and stream water-use rate (WUR) were quantified using a deep belief network (DBN). The influences of precipitation, potential evapotranspiration, and monthly averaged WUR on the performance of the developed DBN model were tested. As a result, this DBN with potential evapotranspiration (PET) provided better performances than precipitation to estimate the WUR. The PET of multi-model scenarios for Representative Concentration Pathways 8.5 would be increased as time goes by, and thus leads to increase WUR estimated by DBN in three basins, located in South Korea during the future period. On the contrary, water availability expected to decrease compared to the current. Therefore, managing water-uses and improving efficiencies can be prepared for the change in agricultural water-use by climate change in the future.     

Distributional changes in rainfall and river flow in Sarawak,Malaysia

Climate change may not change the rainfall mean, but the variability and extremes. Therefore, it is required to explore the possible distributional changes of rainfall characteristics over time. The objective of present study is to assess the distributional changes in annual and northeast monsoon rainfall (November-January) and river flow in Sarawak where small changes in rainfall or river flow variability/distribution may have severe implications on ecology and agriculture. A quantile regression-based approach was used to assess the changes of scale and location of empirical probability density function over the period 1980-2014 at 31 observational stations. The results indicate that diverse variation patterns exist at all stations for annual rainfall but mainly increasing quantile trend at the lowers, and higher quantiles for the month of January and December. The significant increase in annual rainfall is found mostly in the north and central-coastal region and monsoon month rainfalls in the interior and north of Sarawak. Trends in river flow data show that changes in rainfall distribution have affected higher quantiles of river flow in monsoon months at some of the basins and therefore more flooding. The study reveals that quantile trend can provide more information of rainfall change which may be useful for climate change mitigation and adaptation planning.     

Comparison of precipitation projections of CMIP5 and CMIP6 global climate models over Yulin,China

Theoretical and Applied Climatology - This study compared precipitation projections of CMIP5 and CMIP6 GCMs over Yulin City, China. The performance of CMIP5 and CMIP6 GCMs in replicating Global...