Assessing Hydrologic Impact of Climate Change with Uncertainty Estimates: Bayesian Neural Network Approach

Abstract

A major challenge in assessing the hydrologic effect of climate change remains the estimation of uncertainties associated with different sources, such as the global climate models, emission scenarios, downscaling methods, and hydrologic models. There is a demand for an efficient and easy-to-use rainfall-runoff modeling tool that can capture the different sources of uncertainties to generate future flow simulations that can be used for decision making. To manage the large range of uncertainties in the climate change impact study on water resources, a neural network-based rainfall-runoff model-namely, Bayesian neural network (BNN)-is proposed. The BNN model is used with Canadian Centre for Climate Modelling and Analysis Coupled GCM, versions 1 and 2 (CGCM1 and CGCM2, respectively) with two emission scenarios, Intergovernmental Panel on Climate Change (IPCC) IS92a and Special Report on Emissions Scenarios (SRES) B2. One widely used statistical downscaling model (SDSM) is used in the analysis. The study is undertaken to simulate daily river flow and daily reservoir inflow in the Serpent and the Chute-du-Diable watersheds, respectively, in northeastern Canada. It is found that the uncertainty bands of the mean ensemble flow (i.e., flow simulated using the mean of the ensemble members of downscaled meteorological variables) is able to mostly encompass all other flows simulated with various individual downscaled meteorological ensemble members whichever CGCM or emission scenario is used. In addition, the uncertainty bands are also able to typically encompass most of the flows simulated with another rainfall-runoff model, namely, Hydrologiska Byrå ns Vattenbalansavdelning (HBV). The study results suggest that the BNN model could be used as an effective hydrological modeling tool in assessing the hydrologic effect of climate change with uncertainty estimates in the form of confidence intervals. It could be a good alternative method where resources are not available to implement the general multimodel ensembles approach. The BNN approach makes the climate change impact study on water resources with uncertainty estimate relatively simple, cost effective, and time efficient.

The Statistical DownScaling Model: insights from one decade of application

“…Climate model uncertainty has become a major focus for downscaling research: according to both Scopus and WoS this topic accounts for over 20% of published output since 1993 (and ∼25% of all SDSM outputs). Studies typically quantify uncertainties arising from permutations of emission scenario, GCM, downscaling technique, and impacts model (Wilby and Harris, 2006;Dibike et al, 2008;Prudhomme and Davies, 2009;Khan and Coulibaly, 2010;Chen et al, 2011). The trend in downscaling research is towards more exhaustive analysis of uncertainty (Harding et al, 2012), something which presents SDSM-users with a dilemma since only a few forcing scenarios are provided through the CCCSN portal.…”

An Efficient Weather Forecasting System using Radial Basis Function Neural Network

“…Extra hidden neurons raise the network's ability to extract higherorder statistics from (input) data. Furthermore a network is said to be fully connected if every node in each layer of the network is connected to every other node in the adjacent forward layer (Khan and Coulibaly, 2010). The network "learns" by adjusting the interconnections (called weights) between layers.…”

Investigating the Spatial and Temporal Variability of Precipitation using Entropy Theory

“…The distribution of precipitation spatially and temporally directly affects the hydrological cycle within a watershed, altering flow regimes and complicating the accurate predictions of flow (Das et al 2008;Boyer et al 2010;Coulibaly 2006;Khan and Coulibaly 2010;Liu and Cui 2011;Trenouth et al 2013;Thompson et al 2016). An understanding of precipitation variability is also needed for flood and drought preparedness planning.…”