Aavudai Anandhi

Examination of change factor methodologies for climate change impact assessment

[1] A variety of methods are available to estimate values of meteorological variables at future times and at spatial scales that are appropriate for local climate change impact assessment. One commonly used method is Change Factor Methodology (CFM), sometimes referred to as delta change factor methodology. Although more sophisticated methods exist, CFM is still widely applicable and used in impact analysis studies. While there are a number of different ways by which change factors (CFs) can be calculated and used to estimate future climate scenarios, there are no clear guidelines available in the literature to decide which methodologies are most suitable for different applications. In this study several categories of CFM (additive versus multiplicative and single versus multiple) for a number of climate variables are compared and contrasted. The study employs several theoretical case studies, as well as a real example from Cannonsville watershed, which supplies water to New York City, USA. Results show that in cases when the frequency distribution of Global Climate Model (GCM) baseline climate is close to the frequency distribution of observed climate, or when the frequency distribution of GCM future climate is close to the frequency distribution of GCM baseline climate, additive and multiplicative single CFMs provide comparable results. Two options to guide the choice of CFM are

Investigating the impact of climate change on New York City’s primary water supply

Future climate scenarios projected by three different General Circulation Models and a delta-change methodology are used as input to the Generalized Watershed Loading Functions – Variable Source Area (GWLF-VSA) watershed model to simulate future inflows to reservoirs that are part of the New York City water supply system (NYCWSS). These inflows are in turn used as part of the NYC OASIS model designed to simulate operations for the NYCWSS. In this study future demands and operation rules are assumed stationary and future climate variability is based on historical data to which change factors were applied in order to develop the future scenarios. Our results for the West of Hudson portion of the NYCWSS suggest that future climate change will impact regional hydrology on a seasonal basis. The combined effect of projected increases in winter air temperatures, increased winter rain, and earlier snowmelt results in more runoff occurring during winter and slightly less runoff in early spring, increased spring and summer evapotranspiration, and reduction in number of days the system is under drought conditions. At subsystem level reservoir storages, water releases and spills appear to be higher and less variable during the winter months and are slightly reduced during summer. Under the projected future climate and assumptions in this study the NYC reservoir system continues to show high resilience, high annual reliability and relatively low vulnerability.

Extreme Daily Rainfall Event Distribution Patterns in Kansas

AbstractThe Rainfall Frequency Atlas (TP40) was last updated for Kansas in 1961, using weather data from 1911 to 1958. Rainfall information contained in the atlas is the basis for important engineering and hydrologic design decisions in the state. With growing concern about the effects of global climate change and predictions of more extreme weather events, it is necessary to explore rainfall distribution patterns using the most current and complete data available. In this study, extreme rainfall frequency was analyzed using daily precipitation data (1920–2009) from 24 stations in Kansas and 15 stations from adjacent states. The Weibull distribution was used to calculate the precipitation probability distribution frequency at each station. Weather station point data were spatially interpolated using kriging. The overall analysis showed an increase in extreme precipitation events in Kansas with extreme event values tending to increase in magnitude from the northwest to southeast part of the state. Comparin...

A system’s approach to assess the exposure of agricultural production to climate change and variability

Estimating the exposure of agriculture to climate variability and change can help us understand key vulnerabilities and improve adaptive capacity, which is vital to secure and increase world food production to feed its growing population. A number of indices to estimate exposure are available in literature. However, testing or validating them is difficult and reveals a considerable variability, and no systematic methodology has been developed to guide users in selecting indices for particular applications. This need is addressed in this paper by developing a flowchart from a conceptual model that uses a system’s approach. Also, we compare five approaches to estimate exposure indices (EIs) to study the exposure of agriculture to climate variability and change: single stressor-mean climate, single stressor-extreme climate, multiple stressor-mean climate, multiple stressor-extreme climate; and combinations of the above approaches. The developed flowchart requires gathering information on the region of study, including its agriculture, stressor(s), climate factor(s) (CF), period of interest and the method of aggregation. The flowchart was applied to a case study in Kansas to better understand the five approaches to estimate EIs and the implications of the choices made in each step on the estimated the exposure. The flowchart provides options that guide EI estimation by selecting the most appropriate stressor(s), associated CF(s), and aggregation methods when a detailed methodological analysis is possible, or proposes a default method when data or resources do not allow a detailed analysis. Climate adaptation involves integration of a multitude of factors across complex systems. A more standardized approach to assessing exposure can promote information sharing across different locations and systems as this rapidly evolving area of study moves forward.

Daily relative humidity projections in an Indian river basin for IPCC SRES scenarios

A two-stage methodology is developed to obtain future projections of daily relative humidity in a river basin for climate change scenarios. In the first stage, Support Vector Machine (SVM) models are developed to downscale nine sets of predictor variables (large-scale atmospheric variables) for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) (A1B, A2, B1, and COMMIT) to RH in a river basin at monthly scale. Uncertainty in the future projections of RH is studied for combinations of SRES scenarios, and predictors selected. Subsequently, in the second stage, the monthly sequences of RH are disaggregated to daily scale using k-nearest neighbor method. The effectiveness of the developed methodology is demonstrated through application to the catchment of Malaprabha reservoir in India. For downscaling, the probable predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis data set for the period 1978–2000 and (2) simulations of the third-generation Canadian Coupled Global Climate Model for the period 1978–2100. The performance of the downscaling and disaggregation models is evaluated by split sample validation. Results show that among the SVM models, the model developed using predictors pertaining to only land location performed better. The RH is projected to increase in the future for A1B and A2 scenarios, while no trend is discerned for B1 and COMMIT.

Can meta-analysis be used as a decision-making tool for developing scenarios and causal chains in eco-hydrological systems? Case study in Florida: Can meta-analysis be used as a decision making tool in eco-hydrological systems?

Ecohydrology. 2018;e1997. https://doi.org/10.1002/eco.1997 Abstract To date, there are a high volume of studies concerning climate change impact assessments in ecosystems. Meta‐analysis, scenario development, and causal chains/loops have been used as tools in these assessments as well as in decision making either individually or combined in pairs. There exists a need to develop decision support tools that improve the linkage between climate‐impacts research and planning, management, adaptation, and mitigation decisions by providing quantitative and timely information to stakeholders and managers. The overall goal is to address this need. A specific objective was to develop a decision support tool in eco‐hydrological applications that combine three components: meta‐analysis, scenario development, and causal chains/loop. The developed tool is novel, warranted, and timely. The use of the tool is demonstrated for Florida. The meta‐analysis of 32 studies revealed precipitation changes ranged between +30% and −40%, and temperature changes ranged from +6°C to −3°C for Florida. Seven incremental scenarios were developed at 10% increments in the precipitation change range and nine scenarios with 1°C increments in the temperature change range (driving forces). The causal chains/loops were developed using Driver‐Pressure‐State‐Impact‐Response framework for selected ecosystems and environment (e.g., agroecosystem, mangroves, water resources, and sea turtles) in Florida. The driving force puts pressure on the ecosystem or environment impacting their state, which in turn had a response (e.g., mitigation and adaptation strategies). The framework used indicators selected from studies on climate impact assessments (meta‐analysis and others) for the selected ecosystems as well as author expertise on the topic to develop the chains/loops. The decision tool is applicable to stakeholders and any ecosystem within and outside of Florida.

Ecohydrological Index, Native Fish, and Climate Trends and Relationships in the Kansas River Basin

This study quantified climatological and hydrological trends and relationships to presence and distribution of two native aquatic species in the Kansas River Basin over the past half-century. Trend analyses were applied to indicators of hydrologic alteration (IHAs) at 34 streamgages over a 50-year period (1962-2012). Results showed a significant negative trend in annual streamflow for 10 of 12 western streamgages (up to -7.65 mm/50 yr) and smaller negative trends for most other streamgages. Significant negative trends in western Basin streamflow were more widespread in summer (12 stations) than winter or spring (6 stations). The negative-trend magnitude and significance decreased from west to east for maximum-flow IHAs. Minimum- flow IHAs, however, significantly decreased at High Plains streamgages but significantly increased at Central Great Plains streamgages. Number of zero-flow days showed positive trends in the High Plains. Most streamgages showed negative trends in low- and high-flow pulse frequency and high-flow pulse duration, and positive trends in low-flow pulse duration. These results were consistent with increasing occurrence of drought. Shift in occurrence from present (1860-1950) to absent (2000-2012) was significantly related (p<0.10) to negative trends of 1-day maximum flows (both species) and indices associated with reduced spawning-season flows for Plains Minnow and shifting annual-flow timing and increased flow intermittency for Common Shiner. Both species were absent for all western Basin sites and had different responses to hydrological index trends at eastern Basin sites. These results demonstrate ecohydrological index changes impact distributions of native fish and suggest target factors for assessment or restoration activities.