An index-based robust decision making framework for watershed management in a changing climate

Application of various cytotoxic endpoints for the toxicity prioritization of fine dust (PM2.5) sources using a multi-criteria decision-making approach

Fine dust (PM2.5) is generated from various sources, and many studies have reported on the sources of PM2.5. However, the current research on PM2.5 toxicity based on its sources is insufficient. In this study, we developed a framework for the prioritization of fine dust (PM2.5) sources on the basis of the multi-endpoint toxicities using the multi-criteria decision-making method (MCDM). To obtain the multi-endpoint toxicities of PM2.5 sources, cell mortality, reactive oxygen species (ROS), inflammation and mutagenicity were measured for diesel exhaust particles (DEP), gasoline exhaust particles (GEP), rice straw burning particles (RBP), coal combustion particles (CCP) and tunnel dust particles (TDP). The integrative toxicity score (ITS) of the PM2.5 source was calculated using MCDM, which consist of four steps: (1) defining the decision-making matrix, (2) normalization and weighting, (3) calculating the ITS (linear aggregation) and (4) a global sensitivity analysis. The indicator of cell mortality had the highest weight (0.3780) followed by inflammation (0.2471), ROS (0.2178) and mutagenicity (0.1571). Additionally, the ITS based on the sources contributing to PM2.5 resulted in the following order: DEP (0.89), GEP (0.44), RBP (0.40), CCP (0.23) and TDP (0.06). The relative toxicity index (RTI), which represents the ratio of toxicity due to the difference in sources, increases as the contribution of the highly toxic sources increases. The RTI over 1 is closely associated with an increased contribution from highly toxic sources, such as diesel exhaust, gasoline exhaust and biomass burning. It is necessary to investigate the toxicity of various PM2.5 sources and PM2.5 risk based on the sources.

Ensemble streamflow projections for a small watershed with HSPF model

A watershed modeling tool, Hydrological Simulation Program-FORTRAN (HSPF), was utilized to model the hydrological processes in the agricultural Sarısu watershed in western Turkey. The meteorological input data were statistically downscaled time series from General Circulation Model simulations. The input data were constructed as an ensemble of 400 individual time series of temperature, precipitation, dewpoint temperature, solar radiation, potential evapotranspiration, cloudiness, and wind velocity, as required by HSPF. The ensemble was divided into four subsets, each comprising of 100 time series, of different Special Report on Emissions Scenarios. Yearly and monthly total streamflow time series were obtained from the calibrated and validated HSPF model spanning a period of 116 years between the water years of 1984 and 2099. The projections in the watershed showed a median increase of 3 °C in yearly average temperatures between the beginning and end 30-year periods of the 116-year simulation periods based on 400 ensemble members while the corresponding change in total yearly precipitation was - 71 mm. These changes led to a decrease in yearly streamflows by 40% which reflected itself to varying degrees in monthly flows. Correlations were established between the principal drivers of the watershed hydrological cycle, namely temperature and precipitation, and streamflow. The results showed that the changes in the climatic conditions will greatly affect water-related issues in the watershed and emphasize the necessity of preparing carefully to adapt to a warmer and drier climate.

An innovative method based on cloud model learning to identify high-risk pollution intervals of storm-flow on an urban catchment scale

Identifying high-risk storm-flow pollution intervals in an urban watershed is critical for watershed pollution control decision-making. High-risk pollution intervals of storm-flow are defined as storm-flow intervals that contribute more than the background pollutant load, and whose load contribution rank in the top 20%. However, the identification of high-risk pollution intervals is difficult due to variations in the flow-concentration relationship among rain events, uncertainty inherent in stormwater quality data, and physically-based stormwater models requiring a substantial number of parameters. A new method for identifying high-risk pollution intervals during different rain events is proposed. A dataset of the urban watershed located in Shenzhen, southern China, was used to demonstrate the proposed method. A "cut-pool" strategy was initially used to pre-process the dataset for maximizing valuable information hidden in existing datasets and to investigate the impact of rainfall on flow-concentration relationships. Gaussian cloud distribution was then introduced to capture the trend, dispersing extent and randomness of stormwater quality data at any flow interval. Interval Overlapping Ratio (IOR) and Load contribution of storm-flow high-risk pollution intervals was used to assess the performance of the method. Results show that storm-flow high-risk Chemical Oxygen Demand (COD) pollution intervals of the Shiyan watershed was 0.5-1.5 mm under light rain (0-13 mm), 1-3 mm under moderate rain (13-27 mm) and 5-7 mm under heavy rain (27-43 mm). The accuracy of the identified high-risk pollution intervals (IOR) was 63-66% under light rain, 64-67% under moderate rain. Moreover, COD load can be reduced by 44-48% with high-risk storm-flow under light rain; 43-49% under moderate rain; 32% under heavy rain. This method is very useful for effectively controlling storm-flow pollution on an urban catchment scale.

Spatiotemporal variation of watershed health propensity through reliability-resilience-vulnerability based drought index (case study: Shazand Watershed in Iran)

Quantitative response of the watershed health to climate variability is of critical importance for watershed managers. However, existing studies seldom considered the impact of climate variability on watershed health. The present study therefore aimed to analyze the temporal and spatial variability of reliability (R_el), resilience (R_es) and vulnerability (V_ul) indicators in node years of 1986, 1998, 2008 and 2014 in connection with Standardized Precipitation Index (SPI) for 24 sub-watersheds in the Shazand Watershed of Markazi Province in Iran. The analysis was based on rainfall variability as one of the main climatic drivers. To achieve the study purposes, the monthly rainfall time series of eight rain gauge stations distributed across the watershed or neighboring areas were analyzed and corresponding SPIs and R_el R_esV_ul indicators were calculated. Ultimately, the spatial variation of SPI oriented R_el R_esV_ul was mapped for the study watershed using Geographic Information System (GIS). The average and standard deviation of SPI-R_el R_esV_ul index for the study years of 1986, 1998, 2008 and 2014 was obtained 0.240±0.025, 0.290±0.036, 0.077±0.0280 and 0.241±0.081, respectively. In overall, the results of the study proved the spatiotemporal variations of SPI-R_el R_esV_ul watershed health index in the study area. Accordingly, all the sub-watersheds of the Shazand Watershed were grouped in unhealthy and very unhealthy conditions in all the study years. For 1986 and 1998 all the sub-watersheds were assessed in unhealthy status. Whilst, it declined to very unhealthy condition in 2008 and then some 75% of the watershed ultimately referred again to unhealthy and the rest still remained under very unhealthy conditions in 2014.

Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data

An accurate understanding of flood risk and its drivers is crucial for effective risk management. Detailed risk projections, including uncertainties, are however rarely available, particularly in developing countries. This paper presents a method that integrates recent advances in global-scale modeling of flood hazard and land change, which enables the probabilistic analysis of future trends in national-scale flood risk. We demonstrate its application to Indonesia. We develop 1000 spatially-explicit projections of urban expansion from 2000 to 2030 that account for uncertainty associated with population and economic growth projections, as well as uncertainty in where urban land change may occur. The projections show that the urban extent increases by 215%-357% (5th and 95th percentiles). Urban expansion is particularly rapid on Java, which accounts for 79% of the national increase. From 2000 to 2030, increases in exposure will elevate flood risk by, on average, 76% and 120% for river and coastal floods. While sea level rise will further increase the exposure-induced trend by 19%-37%, the response of river floods to climate change is highly uncertain. However, as urban expansion is the main driver of future risk, the implementation of adaptation measures is increasingly urgent, regardless of the wide uncertainty in climate projections. Using probabilistic urban projections, we show that spatial planning can be a very effective adaptation strategy. Our study emphasizes that global data can be used successfully for probabilistic risk assessment in data-scarce countries.