Climate change‐sensitive hydrologic design under uncertain future precipitation extremes

Abstract

[1] Precipitation as an important component of hydrologic cycle bears a significant influence on hydrologic design and water resources management. The uncertainties associated with future climate change coupled with limitations of climate change models and uncertainties in projections, our inability to quantify these introduce additional complexities in hydrologic design using future precipitation extremes. A new optimal compromise hydrologic design of a stormsewer system using a fuzzy mixed integer nonlinear mathematical programming (MINLP) model with discrete, binary variables and logical constraints is developed and evaluated in this study. Preferences of hydrologists toward expected uncertain future changes in precipitation extremes are modeled using linear, nonlinear, triangular and Gaussian fuzzy membership functions. Methods for deriving membership functions based on multimodel multiple scenario GCM-based simulations are also suggested. Incorporation of triangular functions in optimization formulation required the use of binary variables. A hypothetical hydrologic design example with realistic parameter values is used to obtain compromise elements of stormsewer system. Results from the study suggest a compromise design is possible based on the preferences and a balance between over and under design of stormsewer infrastructure is achievable. The nature of membership functions reflecting the preferences attached to future extremes influence the optimal solutions obtained resulting in changes in cost-based performance measures.

A holistic, multi-scale dynamic downscaling framework for climate impact assessments and challenges of addressing finer-scale watershed dynamics

“…However, the world is undergoing a period of rapid climate change, rarely experienced in the past [IPCC, 2001;. Thus, assessments of changes in watershed systems in response to climate change have been increasing [Hanson et al, 2012;Safeeqand Fares, 2012;Wangand Alimohammadi, 2012;Beauchamp et al, 2013;Demaria et al, 2013;Lu et al, 2013;Mukundan et al, 2013;Nunes et al, 2013;Patterson et al, 2013;Shen et al, 2013;Teegavarapu, 2013;Wu et al, 2013;Schnorbus et al, 2014].…”

“…First, future regional climate information is downscaled using outputs from 12 General Circulation Models (GCMs) of the third phase of the Coupled Model Intercomparison Project (CMIP3) database, the A1B emission scenario of the Special Report on Emission Scenarios (SRES, [Meehl et al, 2005]). Projections of weather variables for 3 nearby meteorological stations are generated for the present/historic and future periods using a stochastic weather generator, Advanced WEather-GENerator (AWE-GEN), combined with the Monte-Carlo simulation to produce an ensemble of alternatives of future climate [Ivanov et al, 2007;Fatichi et al, 2011;2013]. Next, a number of hydrologic and hydrodynamic metrics of channel flow are resolved by the coupled model, TIN (Triangulated Irregular Network) based Real time Integrated Basin Simulator -Flow, Erosion and Sediment Transport (tRIBS-FEaST), designed to address morphological or ecohydrologic applications.…”

Toward a theoretical framework for integrated modeling of hydrological change

“…A possible physical motivation for hydrological change that attracts the interest of researchers is climate change . There is no doubt that climate has ever been changing, as suggested by paleoclimate proxies which show that the climate has never been static .…”

“…A possible physical motivation for hydrological change that attracts the interest of researchers is climate change. 5,8,[17][18][19][20][21][22][23][24][25][26][27][28] There is no doubt that climate has ever been changing, as suggested by paleoclimate proxies which show that the climate has never been static. 29 Climate change is certainly a potential driver of hydrological change, but the interaction between climate and hydrology is very complex, and attempts to simulate it through climatic models did not provide satisfactory results.…”

Assessment of NARCCAP model in simulating rainfall extremes using a spatially constrained regionalization method

“…Therefore, future projections for more intense heavy precipitation in some regions (e.g. Tebaldi et al, 2006;IPCC, 2007;Fowler and Ekstrom, 2009) could warrant revision of these standards (Peck et al, 2012;Teegavarapu, 2013;Hassanzadeh et al, 2014;Wernstedt and Carlet, 2014). In fact, many studies have used global and Regional Climate Models (RCMs) to estimate potential changes in precipitation intensity that could provide information for local and regional climate adaptation plans (Larsen et al, 2009;Berggren et al, 2012;Cheng and AghaKouchak, 2014).…”