Heejun Chang

Spatial analysis of water quality trends in the Han River basin, South Korea

Spatial patterns of water quality trends for 118 sites in the Han River basin of South Korea were examined for eight parameters-temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (TP), and total nitrogen (TN). A non-parametric seasonal Mann-Kendalls test determined the significance of trends for each parameter for each site between 1993 and 2002. There are no significant trends in temperature, but TN concentrations increased for the majority of the monitoring stations. DO, BOD, COD, pH, SS, and TP show increasing or decreasing trends with approximately half of the stations exhibiting no trends. Urban land cover is positively associated with increases in water pollution and included as an important explanatory variable for the variations in all water quality parameters except pH. Topography and soil factors further explain the spatial variations in pH, COD, BOD, and SS. BOD, COD, SS, and TP variations are consistently better explained by 100m buffer scale analysis, but DO are better explained by the whole basin scale analysis. Local water quality management or geology could further explain some variations of water quality. Non-point-source pollution exhibits strong positive spatial autocorrelation as measured by Morans I, indicating that the incorporation of spatial dimensions into water quality assessment enhances our understanding of spatial patterns of water quality. The spatial regression models, compared to ordinary least square (OLS) models, always better explain the variations in water quality. This study suggests that spatial analysis of watershed data at different scales should be a vital part of identifying the fundamental spatio-temporal distribution of water quality.

A review of hydrological modelling of basin-scale climate change and urban development impacts

Hydrological modelling is a valuable tool for researchers in geography and other disciplines for studying the processes governing impacts of climate change and urban development on water resources and for projecting potential ranges of impacts from scenarios of future change. Modelling is an inherently probabilistic exercise, with uncertainty amplified at each stage of the process, from scenario generation to issues of scale, to simulation of hydrological processes, to management impacts. At the basin scale, significant factors affecting hydrological impacts of climate change include latitude, topography, geology, and land use. Under scenarios of future climate change, many basins are likely to experience changes not only in their mean hydrology, but also in the frequency and magnitude of extreme hydrological events. Impacts of climate change on water quality are largely determined by hydrological changes and by the nature of pollutants as flushingor dilution-controlled. The most significant impact of urban development on water resources is an increase in overall surface runoff and the flashiness of the storm hydrograph. The increase in impervious surface area associated with urban development also contributes to degradation of water quality as a result of non-point source pollution. Modelling studies on the combined impacts of climate change and urban development have found that either change may be more significant, depending on scenario assumptions and basin characteristics, and that each type of change may amplify or ameliorate the effects of the other. Hydrological impacts of climate change and urban development are likely to significantly affect future water resource management.

Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales.

The relationship among land cover, topography, built structure and stream water quality in the Portland Metro region of Oregon and Clark County, Washington areas, USA, is analyzed using ordinary least squares (OLS) and geographically weighted (GWR) multiple regression models. Two scales of analysis, a sectional watershed and a buffer, offered a local and a global investigation of the sources of stream pollutants. Model accuracy, measured by R(2) values, fluctuated according to the scale, season, and regression method used. While most wet season water quality parameters are associated with urban land covers, most dry season water quality parameters are related topographic features such as elevation and slope. GWR models, which take into consideration local relations of spatial autocorrelation, had stronger results than OLS regression models. In the multiple regression models, sectioned watershed results were consistently better than the sectioned buffer results, except for dry season pH and stream temperature parameters. This suggests that while riparian land cover does have an effect on water quality, a wider contributing area needs to be included in order to account for distant sources of pollutants.

Spatial Variations of Single-Family Residential Water Consumption in Portland, Oregon

Although water demand theories identify price structures, technology, and individual behavior as determinants of water demand, limited theoretical or empirical evidence suggests a link between urban development patterns and water use. To assess the role of urban development patterns on water demand, we used GIS and statistical models to analyze single-family residential water consumption in the Portland, Oregon, metropolitan area. Our results show that residential water consumption per household at the census block group scale is best explained by average building size, followed by building density and building age, with low water consumption areas clustering together and typically located in high-density and older neighborhoods. Accounting for spatial dependence among residuals, explanatory variables explain up to 87% of variations in water consumption. Our results help to develop a water demand framework that incorporates existing factors with urban development policies to more effectively manage limited water and land resources.

Valuing ecological systems and services

Making trade-offs between ecological services and other contributors to human well-being is a difficult but critical process that requires valuation. This allows both better recognition of the ecological, social, and economic trade-offs and also allows us to bill those who use up or destroy ecological services and reward those that produce or enhance them. It also aids improved ecosystems policy. In this paper we clarify some of the controversies in defining the contributions to human well-being from functioning ecosystems, many of which people are not even aware of. We go on to describe the applicability of the various valuation methods that can be used in estimating the benefits of ecosystem services. Finally, we describe some recent case studies and lay out the research agenda for ecosystem services analysis, modeling, and valuation going forward.

Basin Hydrologic Response to Changes in Climate and Land Use: the Conestoga River Basin, Pennsylvania

Climate change and land-use change can significantly affect the hydrology of a drainage basin. This study examines changes in streamflow under three environmental scenarios in the Conestoga River basin and its five sub-basins using AVGWLF, a combined model of ArcView GIS and Generalized Watershed Loading Function. The results of the study indicate that climate change scenarios (Hadley Centre model and Canadian Centre model) are projected to increase spring streamflow by 5% to 18%. Mean annual stream-flow is projected to decrease under the Canadian Centre model, while it is projected to increase by 11% under the Hadley Centre model. Urban growth is only projected to increase mean annual streamflow by less than 2%. The magnitude and direction of changes in streamflow are sensitive to watershed size and land-use scenarios. Smaller watersheds with higher proportion of impervious surfaces exhibit greater impacts than larger watersheds with relatively smaller urban areas. Assessing local hydrologic impacts of climate change and land-use change are thus important to identifying subtle changes in streamflow and to establishing future water-management strategies.

Impacts of Climate Change and Urban Development on Water Resources in the Tualatin River Basin, Oregon

We investigated the relative importance of future climate change and land use change in determining the quantity and quality of freshwater resources in northwestern Oregons Tualatin River Basin using the U.S. Environmental Protection Agencys Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) modeling system. Models were calibrated and validated using historic flow and water quality data between 1990 and 2006. The goodness of fit for the calibrated models was high, with coefficients of determination ranging from 0.72 to 0.93 in the calibration period. The calibrated models were run under a range of eight statistically downscaled climate change, two regional land use change, and four combined scenarios. Results included average increases in winter flows of 10 percent, decreases in summer flows of 37 percent, and increases in fifth-percentile flows of up to 80 percent as a result of climate change in the Tualatin River Basin. For land use change, the results included an increase in annual flows of 21 percent for the development-oriented scenario and a decrease of 16 percent for the conservation-oriented scenario. For combined scenarios of high climate change and high urban development, there is a projected increase in winter flows of up to 71 percent and decrease in summer flows of up to 48 percent. Climate change scenarios were more significant than urban development scenarios in determining basin hydrological response. The results are relevant to regional planners interested in the long-term response of water resources to climate change and land use change at the basin scale.

Water Quality Impacts of Climate and Land Use Changes in Southeastern Pennsylvania

Abstract This study investigates potential changes in nitrogen and phosphorus loads under a warmer and wetter climate, urban growth, and combined changes in the Conestoga River Basin and its five subbasins in southeastern Pennsylvania. A GIS-based hydrochemical model was employed for assessing the sensitivity of the basins to the projected changes in 2030. Under the HadCM2 climate change scenario, mean annual nitrogen and phosphorus loads are expected to increase, with great increases in spring but slight decreases in fall primarily because of changes in monthly precipitation. When climate change and urbanization occur concurrently, mean annual nitrogen loads further increase by 50% in the most urbanizing subbasin. Point source nitrogen control could mitigate negative effects of climate and land use changes, reducing mean annual nitrogen loads to the contemporary baseline level.

Potential Impacts of Climate Change on Flood-Induced Travel Disruptions: A Case Study of Portland, Oregon, USA

This study investigated potential impacts of climate change on travel disruption resulting from road closures in two urban watersheds in the Portland, Oregon, metropolitan area. We used ensemble climate change scenarios, a hydrologic model, a stream channel survey, a hydraulic model, and a travel forecast model to develop an integrated impact assessment method. High-resolution climate change scenarios are based on the combinations of two emission scenarios and eight general circulation models. The Precipitation-Runoff Modeling System was calibrated and validated for the historical period of 1988 and 2006 and simulated for determining the probability of floods for 2020 through 2049. We surveyed stream cross-sections at five road crossings for stream channel geometry and determined flood water surface elevations using the Hydrologic Engineering Centers River Analysis System (HEC-RAS) model. Four of the surveyed bridges and roadways were lower in elevation than the current 100-year flood water surface elevation, leading to relatively frequent nuisance flooding. These roadway flooding events will become more frequent under some climate change scenarios in the future, but climate change impacts will depend on local geomorphic conditions. Whereas vehicle miles traveled was not significantly affected by road closure, vehicle hours delay demonstrated a greater impact from road closures, increasing by 10 percent in the Fanno Creek area. Our research demonstrated the usefulness of the integration of top-down and bottom-up approaches in climate change impact assessment and the need for spatially explicit modeling and participatory planning in flood management and transportation planning under increasing climate uncertainty.

Local landscape predictors of maximum stream temperature and thermal sensitivity in the Columbia River Basin, USA

Stream temperature regimes are important determinants of the health of lotic ecosystems, and a proper understanding of the landscape factors affecting stream temperatures is needed for water managers to make informed decisions. We analyzed spatial patterns of thermal sensitivity (response of stream temperature to changes in air temperature) and maximum stream temperature for 74 stations in the Columbia River basin, to identify landscape factors affecting these two indices of stream temperature regimes. Thermal sensitivity (TS) is largely controlled by distance to the Pacific Coast, base flow index, and contributing area. Maximum stream temperature (Tmax) is mainly controlled by base flow index, percent forest land cover, and stream order. The analysis of four different spatial scales--relative contributing area (RCA) scale, RCA buffered scale, 1 km upstream RCA scale, and 1 km upstream buffer scale--yield different significant factors, with topographic factors such as slope becoming more important at the buffer scale analysis for TS. Geographically weighted regression (GWR), which takes into account spatial non-stationary processes, better predicts the spatial variations of TS and Tmax with higher R(2) and lower residual values than ordinary least squares (OLS) estimates. With different coefficient values over space, GWR models explain approximately up to 62% of the variation in TS and Tmax. Percent forest land cover coefficients had both positive and negative values, suggesting that the relative importance of forest changes over space. Such spatially varying GWR coefficients are associated with land cover, hydroclimate, and topographic variables. OLS estimated regression residuals are positively autocorrelated over space at the RCA scale, while the GWR residuals exhibit no spatial autocorrelation at all scales. GWR models provide useful additional information on the spatial processes generating the variations of TS and Tmax, potentially serving as a useful tool for managing stream temperature across multiple scales.