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Study region: Mokgam River watershed, South Korea Study focus: In this study, the uncertainty contribution of three sources and their interaction effects on future climate and runoff projections were quantified. General circulation models (GCMs), shared socioeconomic pathways (SSPs), and bias correction (BC) methods were considered as the three sources. 20 GCMs under four SSPs (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) were used to project the future climate of the study area. Seven BC methods were used to adjust the GCMs’ daily climate data. The storm water management model (SWMM) was used as a hydrological model to simulate runoff, incorporating both natural and conduit flows according to GCMs’ climate projection. The normalized Nash-Sutcliffe efficiency (NNSE), normalized root mean square error (NRMSE), Kling-Gupta efficiency (KGE), and modified index of agreement (MD) were used to evaluate the performance of the GCMs’ climate simulations and the SWMM runoff simulations, which were based on the GCMs’ climate data. The analysis of variance (ANOVA) method was used to quantify the uncertainty. New hydrological insights for the study region: The results showed that the assumptions of the BC method had a significant impact on the variation in climate and runoff projections. In the uncertainty of future climate and runoff projection results, BC methods exhibited the predominant contribution, while SSPs showed the least contribution. However, the uncertainty contribution from SSPs and GCMs was predominant in temperature projections, and these results could vary depending on the assumptions and the number of BC methods used. Overall, this study emphasizes not only the influence of GCMs but also the impact of BC methods on future climate and runoff projections.

Abstract This study estimated global climate change signals at different latitudes for four main Shared Socioeconomic Pathways (SSPs). Five evaluation metrics were integrated using the Technique for Order of Preference by Similarity to Ideal Solution to quantify the historical reproducibility of 25 CMIP6 General Circulation Models (GCMs) with Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature as the reference. The most suitable GCMs for simulating climate over different latitudes, selected based on evaluation metrics, were used to prepare a multimodel ensemble and project the future annual and seasonal precipitation and temperature in the near (2031–2065) and far future (2066–2100). The results showed that GCMs estimated the historical mean temperature efficiently but underestimated the monthly precipitation compared to the reference data. The changes in precipitation and temperature at mid‐latitudes (N45.5°–60°) showed the highest variability for all scenarios. The maximum increases in both climate variables for SSP5‐8.5 were 80.5% and 4.8% at N45.5°–60°, respectively. In contrast, the temperature and precipitation at S30.5°–45° revealed a decreasing pattern. Mid‐latitude winter (S30.5°–45°) would be drier in the future than in the base period (1980–2014). This study showed that precipitation variability and the mean temperature in the northern hemisphere would be larger for SSPs with higher radiative forcing. Therefore, the results of this study help improve knowledge of global future climate change by latitudes.

Abstract Reliable projection of evapotranspiration (ET) is important for planning sustainable water management for the agriculture field in the context of climate change. A global dataset of monthly climate variables was generated to estimate potential ET (PET) using 14 General Circulation Models (GCMs) for four main shared socioeconomic pathways (SSPs). The generated dataset has a spatial resolution of 0.5° × 0.5° and a period ranging from 1950 to 2100 and can estimate historical and future PET using the Penman-Monteith method. Furthermore, this dataset can be applied to various PET estimation methods based on climate variables. This paper presents that the dataset generated to estimate future PET could reflect the greenhouse gas concentration level of the SSP scenarios in latitude bands. Therefore, this dataset can provide vital information for users to select appropriate GCMs for estimating reasonable PETs and help determine bias correction methods to reduce between observation and model based on the scale of climate variables in each GCM.

In this study, the uncertainty in runoff simulations using hydrological models was quantified based on the selection of five evaluation metrics and calibration data length. The calibration data length was considered to vary from 1 to 11 years, and runoff analysis was performed using a soil and water assessment tool (SWAT). SWAT parameter optimization was then performed using R-SWAT. The results show that the uncertainty was lower when using a calibration data length of five to seven years, with seven years achieving the lowest uncertainty. Runoff simulations using a calibration data length of more than seven years yielded higher uncertainty overall but lower uncertainty for extreme runoff simulations compared to parameters with less than five years of calibration data. Different uncertainty evaluation metrics show different levels of uncertainty, which means it is necessary to consider multiple evaluation metrics rather than relying on any one single metric. Among the evaluation metrics, the Nash–Sutcliffe model efficiency coefficient (NSE) and normalized root-mean-squared error (NRMSE) had large uncertainties at short calibration data lengths, whereas the Kling–Gupta efficiency (KGE) and Percent Bias (Pbias) had large uncertainties at long calibration data lengths.

Validation of observed gridded precipitation datasets sourced from satellites or reanalysis over Africa remains a challenge due to the dearth of in-situ products that can act as a true estimate. To address this gap, this study compares the performance of different precipitation products (gauge, reanalysis, and satellite-based) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over Africa. Satellite products are classified as corrected (incorporating gauge observations into their algorithms) or uncorrected, which implies that temporal variations depend entirely on the satellite. The main aim is to identify regions where precipitation products depict minimal uncertainties, supporting the use of the datasets in understanding precipitation variability in the specific regions. This is achieved by applying the triple collocation approach, which takes advantage of three collocated datasets of the same variable to derive the mean square error without requiring knowledge of the true value. The results show that light precipitation (1–5 mm d ^−1 ) was prevalent in most regions of Africa during the study duration (2001–2016). Estimating the spatial distribution of daily precipitation greater than the 90th percentiles suggests that extreme precipitation is mainly detected over the Central Africa region and coastal regions of West Africa, where the majority of uncorrected satellite products show consistent performance. The satellite product CMORPH_V1_RAW shows higher estimates of 90th percentile precipitation among the uncorrected satellite products. The ability of precipitation products to detect rainy or non-rainy days shows that corrected satellite products depict notable agreement for probability of detection and false alarm ratio over most regions of Africa. Overall, better performance is demonstrated by the IMERG products, ARCv2, CHIRPSv2 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0 and REGEN_ALL (gauge) during the study period. Among the reanalysis products, ERA5 datasets shows good performance in estimating daily precipitation over Africa. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage by different stakeholders.

Measurement(s) water-based rainfall Technology Type(s) weather station

Assessment of possible changes in crops water stress due to climate alteration is essential for agricultural planning, particularly in arid regions where water supply is the major challenge for agricultural development. This study aims to project climatic water availability (CWA) and crop water demand (CWD) to outline the possible future agricultural water stress of Iraq for different radiative concentration pathways (RCPs). The ensemble means of downscaled precipitation and temperature projections of the selected global climate models (GCMs) were used in a simple water balance model for this purpose. The modified Mann–Kendall (mMK) trend test was employed to estimate the tendency in CWA and the Wilcoxon rank test to evaluate CWD alteration in three future time horizons compared to the base period (1971–2000). The results revealed a decrease in CWA at a rate of up to −34/year during 2010–2099 for RCP8.5. The largest declination would be in summer (−29/year) and an insignificant decrease in winter (−1.3/year). The study also showed an increase in CWD of all major crops for all scenarios. The highest increase in CWD would be for summer crops, approximately 320 mm, and the lowest for winter crops, nearly 32 mm for RCP8.5 in the far future (2070–2099). The decrease in CWA and increase in CWD would cause a sharp rise in crop water stress in Iraq. This study indicates that the increase in temperature is the main reason for a large increase in CWD and increased agricultural water stress in Iraq.

This study proposed a deep learning-based model to estimate stream water-use rate (WUR) using precipitation (P) and potential evapotranspiration (PET). Correlations were explored to identify relationships among accumulated meteorological variables for various time durations (three-, four-, five-, and six-month cumulative) and WUR, which revealed that three-month cumulative meteorological variables and WUR were highly correlated. A deep belief network (DBN) based on iterating parameter tuning was developed to estimate WUR using P, PET, and antecedent stream water-use rate (DWUR). The training and validation periods were 2011–2016, and 2017–2019, respectively. The results showed that the PET-DWUR based model provided better performances in Nash–Sutcliff efficiency (NSE), root mean square error (RMSE), and determination coefficient (R2) than the P-PET-DWUR and P-DWUR models. The framework in this study can provide a forecast model for deficiencies of stream water use coupled with a weather forecast model.

The occurrence and severity of agricultural droughts may not be dependent upon climatic variables alone. Rather increasingly, drought is affected by human interventions such as irrigation. Anthropogenic activity has introduced uncertainty in the assessment of current drought and future drought risk in many parts of the world; neither climatic nor remote sensing data alone are able to assess drought conditions effectively. In response, we present a simple approach to assess drought by combining a remote sensing-based drought index, the Temperature Vegetation Dryness Index (TVDI), climate data (i.e., rainfall and temperature), and field observations to evaluate recent drought conditions in northwestern Bangladesh (NWB). Applying this approach, we gained five insights: (i) the TVDI successfully indicated the drought conditions of NWB and agrees with field observations, (ii) the integrated use of TVDI and climate data (such as rainfall and temperature) provides the best understanding of the difference between meteorological drought and droughts resulting from surface moisture conditions, (iii) the TVDI results agree with rainfall data (r2 = 0.40 in March and r2 = 46 in April) in a part of the study area (NWB) where irrigation is not available, (iv) the TVDI can be used along with climate data to predict the potential risk of drought, and (v) while meteorological drought exists due to low rainfall and high temperature in this NWB in pre-monsoon season, because of widespread irrigation practices, meteorological drought is unable to trigger agricultural drought over most parts of the study area. The findings imply that there is a potential risk of drought in NWB, since any disruption of irrigation water supply could trigger a severe agricultural drought over the whole region. This is similar to what is currently observed over a small part of NWB.

Rain barrels can be technically shared through social practices or mutual agreement between individual households. This study proposes the evaluation system for a rain barrel sharing network (RBSN) considering three performance criteria of reliability, resiliency, and vulnerability, under plausible climate change scenarios. First, this study shows how the system can be improved in terms of the performance criteria using historical daily rainfall data based on the storage-reliability-yield relationship. This study then examined how the benefits from RBSN are affected by climate change after 100 years. Three climate change scenarios (A1B, A2 and B2) and three global circulation models were used for this purpose. The results showed that the reliability and vulnerability are improved due to sharing and their improvements become larger under climate change conditions. In contrast, the resiliency reduces slightly due to sharing and its reduction is attenuated under climate change conditions. In particular, vulnerability will be reduced significantly under climate change. These results suggest that the sharing of various water resources systems can be an effective climate change adaptation strategy that reduces vulnerability and increases the reliability of the system.

This study developed a robust parameter set (ROPS) selection framework for a rainfall-runoff model that considers multi-events using the Pareto optimum and minimax regret approach (MRA). The calibrated parameter sets based on the Nash-Sutcliffe coefficient (NSE) for two events were derived using a genetic algorithm. We generated 41 combinations for weighting values between two events for the multi-event objective function and derived 41 Pareto optimum points that were considered as the ROPS candidates. Then, two different approaches for parameter selection were proposed to determine the ROPS among the candidates: one uses NSE only and the other uses four performance measures (NSE, peak flow error, root mean square error and percentage of bias). In the NSE-only method, five events, including two events from the calibration set and three events from the evaluation set, were used, and the ROPS was selected based on the regrets of both the calibration and the evaluation sets. In the multiple (i.e., four) performance measure method, only three events from the evaluation set were used and the ROPS was determined based on the regrets of twelve different cases, including three events with four measures. As a result, while single- and multi-event optimizations produced satisfying results for the calibration events, the optimized parameters from the single-event calibration do not perform well for another event, even one with the same criteria, such as NSE. The results of this study suggest that the optimized parameter set from the well-weighted objective function can successfully simulate not only hydrographs in general but also others, such as peak flow. In addition, the ROPS can be selected by considering the multiple performance measures of multiple validation events, as well as the NSE only of multiple calibration and validation events. Note that the study provides a framework that could be performed reasonably well with a limited number of events. While the computational resources might not be a limiting factor these days, it is still valuable to have such a tool for several reasons: one could utilize it for an operational decision making support tool, as the full searches for an optimal set of parameters might not be performed in the operational facility. It could also be used in a situation where one has a limited number of good-quality observational data for some reason.

This study developed a methodological framework to update the rainfall intensity-duration-frequency (IDF) curves under climate change scenarios. A model output statistics (MOS) method is used to downscale the daily rainfall of general circulation models (GCMs), and an artificial neural network (ANN) is employed for the disaggregation of projected daily rainfall to hourly maximum rainfall, which is then used for the development of IDF curves. Finally, the 1st quartiles, medians, and 3rd quartiles of projected rainfall intensities are estimated for developing IDF curves with uncertainty level. Eight GCM simulations under two radiative concentration pathways (RCP) scenarios, namely, RCP 4.5 and RCP 8.5, are used in the proposed framework for the projection of IDF curves with related uncertainties for peninsular Malaysia. The projection of rainfall revealed an increase in the annual average rainfall throughout the present century. The comparison of the projected IDF curves for the period 2006⁻2099 with that obtained using GCM hindcasts for the based period (1971⁻2005) revealed an increase in rainfall intensity for shorter durations and a decrease for longer durations. The uncertainty in rainfall intensity for different return periods for shorter duration is found to be 2 to 6 times more compared to longer duration rainfall, which indicates that a large increase in rainfall intensity for short durations projected by GCMs is highly uncertain for peninsular Malaysia. The IDF curves developed in this study can be used for the planning of climate resilient urban water storm water management infrastructure in Peninsular Malaysia.

This study presented the conceptual framework of the water-management analysis module (WMAM) to derive effective physical specifications for the design and planning of low-impact development (LID) practices using the storm-water management model (SWMM). This decision-support system can be used for six LID types and has the following key capabilities: determining relevant LID design parameters within the SWMM that critically influence the hydrological cycle components using a simple sensitivity analysis and determining the best hydrological values for LID planning specification. This study analyzed a highly urbanized university campus as a case study to determine the design and planning specifications for an infiltration trench and permeable pavement. In addition, the performance of different LID practices during high-intensity rainfall events was compared. The results indicate that the WMAM can be very useful in determining many LID design and planning parameters.

This study developed a multi-criteria decision analysis (MCDA) framework to prioritize sites and types of low impact development (LID) practices. This framework was systemized as a web-based system coupled with the Storm Water Management Model (SWMM). Using TOPSIS method, which is a type of MCDA method, multiple types and sites of designated LID practices are prioritized. This system is named the Water Management Prioritization Module (WMPM). WMPM can simultaneously determine the priority of multiple LID types and sites. In this study, an infiltration trench and permeable pavement were considered for multiple sub-catchments in South Korea to demonstrate the WMPM procedures. The TOPSIS method was manually incorporated to select the vulnerable target sub-catchments and to prioritize the LID planning scenarios for multiple types and sites considering social, hydrologic and physical-geometric factors. In this application, the Delphi method and entropy theory were used to determine the subjective and objective weights, respectively.

Developers are increasingly looking for the best management practices to reduce the risk of floods in rapidly growing urban areas. Low impact development (LID) is regarded as one of the most suitable solutions for urban stormwater management and thus the U.S. EPA’s (Environmental Protection Agency) SWMM5.1 (Storm Water Management Model) added the hydrological simulation function for LID structures in 2009. However, SWMM5.1 cannot consider the optimal or best physical specifications of LID design and planning fitted to a study area, nor can it instantly derive the best combination for multiple LID designs and plans. Therefore, in this study, a web-based decision support system (DSS) for the EPA’s SWMM 5.1, referred to as the Water Management Analysis Module (WMAM) is used to decide the most effective specifications of design and planning parameters for LID structure. This study was carried out over an urban catchment of University Technology Malaysia campus located in Johor, Malaysia. The hydrologic cycles with and without LID were simulated using EPA SWMM5.1. The sensitivity analysis and multiple scenario analysis were performed using WMAM. As a result, the effective specification of LID design parameters indicates that peak flow is reduced to 20.95% and 17.5% for two sub-catchments, S1 (highest by area) and S6 (lowest by area) by installing an LID structure. Thus, this study provides a tool for the best solution for what values for physical parameters will be the best for a specified LID type and what capacities can achieve the particular objectives using WMAM.

The climate modelling community has trialled a large number metrics to evaluate the temporal performance of the Global Circulation Models (GCMs) for the selection of GCMs, while very little attention has been given to spatial performance of GCMs which is equally important. This study evaluated the performance of 20 Coupled Model Intercomparison Project 5 (CMIP5) GCMs pertaining to their skills in simulating mean annual, monsoon and winter precipitation over Pakistan using state-of-the-art spatial metrics; SPAtial EFficiency, Goodman--Kruskal's lambda, Fractions Skill Score, Cramer's V, Mapcurves, and Kling-Gupta efficiency for the period 1961-2005. The multi-model ensemble (MME) precipitation was generated through intelligent merging of simulated precipitation of selected GCMs employing Random Forest (RF) regression and Simple Mean (SM). The results indicated some differences in the ranks of GCMs for different metrics. The overall ranks indicated NorESM1-M, CESM1-CAM5, GFDL-CM3 and GFDL-ESM2G as the best GCMs in simulating the spatial patterns of mean annual, monsoon and winter precipitation over Pakistan. MME precipitation generated based on the best performing GCMs showed more similarities with observed precipitation compared to precipitation simulated by individual GCMs. The MME developed using RF displayed better performance than the MME-based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation. The approach suggested in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections. [ABSTRACT FROM AUTHOR]

An integrated framework is presented for sustainability-based flood hazard mapping of the Swannanoa River watershed in the state of North Carolina, U.S. The framework uses a hydrologic model for rainfall-runoff transformation, a two-dimensional unsteady hydraulic model flood simulation and a GIS-based multi-criteria decision-making technique for flood hazard mapping. Economic, social, and environmental flood hazards are taken into account. The importance of each hazard is quantified through a survey to the experts. Utilizing the proposed framework, sustainability-based flood hazard mapping is performed for the 100-year design event. As a result, the overall flood hazard is provided in each geographic location. The sensitivity of the overall hazard with respect to the weights of the three hazard components were also investigated. While the conventional flood management approach is to assess the environmental impacts of mitigation measures after a set of feasible options are selected, the presented framework incorporates the environmental impacts into the analysis concurrently with the economic and social influences. Thereby, it provides a more sustainable perspective of flood management and can greatly help the decision makers to make better-informed decisions by clearly understanding the impacts of flooding on economy, society and environment. [ABSTRACT FROM AUTHOR]

Developers are increasingly looking for the best management practices to reduce the risk of floods in rapidly growing urban areas. Low impact development (LID) is regarded as one of the most suitable solutions for urban stormwater management and thus the U.S. EPA's (Environmental Protection Agency) SWMM5.1 (Storm Water Management Model) added the hydrological simulation function for LID structures in 2009. However, SWMM5.1 cannot consider the optimal or best physical specifications of LID design and planning fitted to a study area, nor can it instantly derive the best combination for multiple LID designs and plans. Therefore, in this study, a web-based decision support system (DSS) for the EPA'sSWMM5.1, referred to as the Water Management Analysis Module (WMAM) is used to decide the most effective specifications of design and planning parameters for LID structure. This study was carried out over an urban catchment of University Technology Malaysia campus located in Johor, Malaysia. The hydrologic cycles with and without LID were simulated using EPA SWMM5.1. The sensitivity analysis and multiple scenario analysis were performed using WMAM. As a result, the effective specification of LID design parameters indicates that peak flow is reduced to 20.95% and 17.5% for two sub-catchments, S1 (highest by area) and S6 (lowest by area) by installing an LID structure. Thus, this study provides a tool for the best solution for what values for physical parameters will be the best for a specified LID type and what capacities can achieve the particular objectives using WMAM. [ABSTRACT FROM AUTHOR]

This study developed a multi-criteria approach to spatially assess the robust water resource vulnerability in sub-basins and applied it to the Han River basin. The Intergovernmental Panel on Climate Change (IPCC) suggested three factors of vulnerability; namely, exposure, sensitivity and adaptive capacity were used in this study with respect to water quantity and quality. In this study, 16 water quantity indicators and 13 water quality indicators were selected to identify the vulnerability using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Environmental and socioeconomic data were obtained from the national statistics database, and hydrological data were simulated using the calibrated Soil andWater Assessment Tool (SWAT) model. Expert surveys and Shannon entropy method were used to determine subjective and objective weights for all indicators, individually. As a result, water quantity-vulnerable sub-basins were associated with high water use and water leakage ratios. Water quality-vulnerable sub-basins were associated with relatively high values of maximum consecutive dry days and heatwave days. The water quantity indices of both weighting methods showed relatively similar spatial distributions, while the distribution of water quality indices was distinct. These results suggest that considering different weighting methods is important for assessing the robust water resource vulnerability of sub-basins. [ABSTRACT FROM AUTHOR]

Sustainable watershed management (SWM) can be achieved through recognition and reflection upon the values of citizens. Collaborative governance consisting of citizens is crucial for successful SWM. Collaborative governance definitely requires an active participatory decision-making process that reflects citizens' preferences. Citizen preference also tends to substantially change with life pattern and life quality. These shifts can be caused by slight variations in both social priorities and personal preferences for SWM. Therefore, collaborative water governance must be frequently renewed in response to citizens' values through the participatory framework. The An'yang Stream in South Korea is generally regarded as a representative urban stream restoration case that has been successfully led by collaborative governance. By conducting individual surveys with citizens on-site, this study addresses how citizens' preferences of the stream's management have changed between 2005 and 2015. In addition, this study used three quantitative hydrologic vulnerability indices: potential flood damage (PFD), potential streamflow depletion (PSD), and potential water quality deterioration (PWQD). They can spatially quantify citizen preference using the Analytic Hierarchy Process (AHP), which can systematically derive citizens' subjective relative-weighted preferences. In the end, this study identified critical differences in priorities in regard to vulnerable areas between in 2005 and in 2015. [ABSTRACT FROM AUTHOR]

This study assessed the water use vulnerability to include the uncertainty of the weighting values of evaluation criteria and the annual variations of performance values using fuzzy TOPSIS coupled with the Shannon entropy method. This procedure was applied to 12 major basins covering about 88% territory of South Korea. Hydrological components were simulated using Soil and Water Assessment Tool (SWAT) of which parameters were optimally calibrated using SWAT-CUP model. The 15 indicators including hydrological and anthropogenic factors were selected, based on three aspects of climate exposure, sensitivity and adaptive capacity. Their weighting values were objectively quantified using the Entropy method. All performance values of 12 basins obtained from statistic Korea and SWAT simulation were normalized with the consideration of the annual variations from 1991 to 2014 using triangular fuzzy numbers (TFNs). Then, Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique was used to quantify the water use vulnerability and rank 12 basins as follows: A12 (Hyeongsan River) > A6 (Seomjin River) > A5 (Youngsan River) > A8 (Mangyung River) > A2 (Ansung River) > A9 (Dongjin River) > A10 (Nakdong River) > A3 (Geum River) > A4 (Sapgyo River) > A11 (Taehwa River) > A7 (Tamjin River) > A1 (Han River). This framework can be used to determine the spatial priority for sustainable water resources plan and applied to derive the climate change vulnerability on sustainable water resources. [ABSTRACT FROM AUTHOR]

The goal of this study is to derive water resource vulnerability characteristics for South Korea according to individual district populations in a changing climate. The definition of water resource vulnerability in this study consists of potential flood damage and potential water scarcity. To quantify these vulnerabilities, key factors, or indicators affecting vulnerability, are integrated with a technique for order of preference by similarity to ideal solution (TOPSIS), which is a multi-criteria decision-making approach to determine the optimal alternative by considering both the best and worst solutions. The weight for each indicator is determined based on both the Delphi technique and Shannon's entropy, which are employed to reduce the uncertainty in the process of determining the weights. The Delphi technique reflects expert opinions, and Shannon's entropy reflects the uncertainty of the performance data. Under A1B climate change scenarios, medium-sized districts (200,000-300,000 inhabitants) are the most vulnerable regarding potential flood damage; the largest districts (exceeding 500,000 inhabitants) are found to be the most vulnerable with respect to potential water scarcity. This result indicates that the local governments of cities or districts with more than 200,000 inhabitants should implement better preventative measures for water resources. In addition, the Delphi and entropy methods show the same rankings for flood vulnerability; however, these approaches produce slightly different rankings regarding water scarcity vulnerability. Therefore, it is suggested that rankings from not only subjective but also objective weights should be considered in making a final decision to implement specific adaptive measures to climate change. [ABSTRACT FROM AUTHOR]

We assessed the relative hydrological impacts of climate change and urbanization using an integrated approach that links the statistical downscaling model (SDSM), the Hydrological Simulation Program-Fortran (HSPF) and the impervious cover model (ICM). A case study of the Anyangcheon watershed, a representative urban region in Korea, illustrates how the proposed framework can be used to analyse the impacts of climate change and urbanization on water quantity and quality. The evaluation criteria were measurements of low flow (99, 95, and 90 percentile flow), high flow (10, 5, and 1 percentile value), pollutant concentration (30, 10, and 1 percentile value), and the numbers of days required to satisfy the target water quantity and quality for a sensitive comparison of subtle impacts of variations in these measures. Nine scenarios, including three climate scenarios (present conditions, A1B, and A2) and three land use change scenarios, were analysed using the HSPF model. The impacts of climate change on low flow (34·1-59·8% increase) and high flow (29·1-37·1% increase) were found to be much greater than those on the biochemical oxygen demand (BOD) (3·8-10·0% decrease). On the other hand, the impacts of urbanization on water quality (19·0-44·6% increase) are more significant than those on high (1·0-4·4% increase) and low flow (11·4-25·6% decrease). Furthermore, low flows are more sensitive to urbanization than high flows. The number of days required to satisfy the target water quantity and quality can be a sensitive criterion to compare the subtle impacts of climate and urbanization on human society, especially as they are much more sensitive than low flow and pollutant concentration. Finally, urbanization has a potent impact on BOD while climate change has a high impact on flow rate. Therefore, the impacts of both climate change and urbanization must be included in watershed management and water resources planning for sustainable development. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The objective of this paper is to introduce a continuous simulation-based screening procedure for ranking urban watershed management alternatives using multi-attribute decision making (MADM). The procedure integrates continuous urban runoff simulation results from the United States Environmental Protection Agency's Storm Water Management Model (SWMM) with the use of an alternative evaluation index (AEI) and MADM techniques, following the driver-pressure-state-impact-response (DPSIR) approach. The analytic hierarchy process estimates the weights of the criteria, and SWMM results are used to quantify the effects of the management alternatives on water quantity and quality metrics. In addition, the tendency of AEI to reflect resident preferences toward management objectives is incorporated to include stakeholder participation in the decision-making process. This systematic decision support process is demonstrated for a Korean urban watershed. According to the AEI, seven alternatives were divided into three groups: poor (0∼0.3), acceptable (0.3∼0.6), and good (0.6∼1). The use of multiple MADM techniques provided a consistency check. The demonstration illustrates the ability of the continuous simulation-based MADM approach to provide decision makers with a ranking of suitable urban watershed management alternatives which incorporate stakeholder feedback. [ABSTRACT FROM AUTHOR]

This paper employs a new estimation method for estimating stage- discharge rating curve parameters. In typical practical applications, the original nonlinear rating curve is transformed into a simple linear regression model by log-transforming the measurement without examining the effect of heteroscedasticity of residuals. Therefore, the model with pseudo-likelihood estimation is developed in this study to deal with heteroscedasticity of residuals in the original non-linear model. The parameters of rating curves and variance functions of errors are simultaneously estimated by the pseudo-likelihood estimation (P-LE) method. Also simulated annealing, a sort of global optimization techniques, is adapted to minimize the log likelihood of the weighted residuals. At first, the developed P-LE model was applied to a hypothetical site where stage-discharge data were generated by incorporating various errors for statistical test. Also, the limit of stages for segmentation is estimated in the process of P-LE using the Heaviside function. For the validation of effects of the developed P-LE model, the results of the conventional log-transformed linear regression (LT-LR) model and the P-LE model are estimated and compared. After statistical simulation, the developed P-LE model is then applied to the real data sets from six gauge stations in the Geum River basin. It can be suggested that this new estimation method is applied to real river sites to successfully determine the weights taking into account error distributions from observed discharge data. [ABSTRACT FROM AUTHOR]

Potential flood damage (PFD), potential streamflow depletion (PSD), potential water quality deterioration (PWQD), and watershed evaluation index (WEI) have been developed to spatially quantify the hydrological vulnerability using multi-criteria decision making (MCDM) techniques. All criteria are selected on the basis of a sustainability evaluation concept (pressure-state-response model), and their weights are estimated by an Analytic Hierarchy Process, which is also a type of MCDM technique. The MCDM techniques used for the evaluation are composite programming, compromise programming, ELECTRE II, Regime method, and Evamix method; these techniques can be classified according to data availability and objectives (prefeasibility and feasibility). Furthermore, the WEI is improved to reflect the preferences of the residents with regard to management objectives through weights (of PFD, PSD, and PWQD) obtained from questionaires of residents. Finally, this study derives a procedure to identify the spatial investment prioritization using four indices. [ABSTRACT FROM AUTHOR]

This article discusses the use of a hydrologic model of runoff estimation for predicting the result of climatic changes in a watershed in Korea. The authors used multiple regression analysis to derive an equation to predict total runoff from historical climate data. They calibrated and validated the computer-based hydrologic model called Soil and Water Assessment Tool (SWAT). They estimated the effects of various climate variations including precipitation, solar radiation and maximum temperature. They also estimated the effects of groundwater withdrawal on streamflow during the dry period. A combined equation described the relationships between the total runoff during the dry period and the climate, urban area proportion, and groundwater withdrawal.