Water Resources Management - Singapore University of Technology and Design

Undergraduate course in Water Resources Management offered at Singapore University of Technology and Design in Spring 2015

Quantifying ENSO impacts at the basin scale using the Iterative Input variable Selection algorithm

Medium-to-long range streamflow predictions provide a key assistance in anticipating hydro- climatic adverse events and prompting effective adaptation measures. In this context, recent modelling efforts have been dedicated to seasonal and inter-annual predictions based on the teleconnection between at-site hydrological processes and large-scale, low-frequency climate fluctuations, such as El Nino Southern Oscillation (ENSO). This work proposes a novel procedure for first detecting the impact of ENSO on hydro-meteorological processes at the basin scale, and then quantitatively assessing the potential of ENSO indexes for building medium-to-long range streamflow prediction models. Core of this procedure is the adoption of the Iterative Input variable Selection (IIS) algorithm, which is employed to find the most relevant determinants of streamflow variability and derive predictive models based on the selected inputs. The procedure is tested on two different case studies, the Columbia River (US) and the Williams River (Australia), whose sensitivity to ENSO fluctuations has been documented in previous studies. Results show that IIS outcomes for both case studies are consistent with the results of previous analyses conducted with state-of-the-art detection methods, and that ENSO indexes can effectively be used in both regions to enhance the accuracy of streamflow prediction models

An evaluation framework for input variable selection algorithms for environmental data-driven models

Abstract not availableStefano Galelli, Greer B. Humphrey, Holger R. Maier, Andrea Castelletti, Graeme C. Dandy, Matthew S. Gibb

Droughts, Pluvials, and Wet Season Timing Across the Chao Phraya River Basin: A 254-Year Monthly Reconstruction from Tree Ring Widths and δ18O

Water system operations require subannual streamflow data—e.g., monthly or weekly—that are not readily achievable with conventional streamflow reconstructions from annual tree rings. This mismatch is particularly relevant to highly seasonal rivers such as Thailand's Chao Phraya. Here, we combine tree ring width and stable oxygen isotope ratios (δ18O) from Southeast Asia to produce 254-year, monthly-resolved reconstructions for all four major tributaries of the Chao Phraya. From the reconstructions, we derive subannual streamflow indices to examine past hydrological droughts and pluvials, and find coherence and heterogeneity in their histories. The monthly resolution reveals the spatiotemporal variability in wet season timing, caused by interactions between early summer typhoons, monsoon rains, catchment location, and topography. Monthly-resolved reconstructions, like the ones presented here, not only broaden our understanding of past hydroclimatic variability, but also provide data that are functional to water management and climate-risk analyses, a significant improvement over annual reconstructions

Time to Use Dendrohydrological Data in Water Resources Management?

In this editorial, we argue that the water resources management community should move away from relying only on short instrumental record, and instead incorporate long term tree-ring data into informing water management