A Linear Dynamical Systems Approach to Streamflow Reconstruction Reveals History of Regime Shifts in Northern Thailand

Abstract

Catchment dynamics is not often modeled in streamflow reconstruction studies; yet, the streamflow generation process depends on both catchment state and climatic inputs. To explicitly account for this interaction, we contribute a linear dynamic model, in which streamflow is a function of both catchment state (i.e., wet/dry) and paleoclimatic proxies. The model is learned using a novel variant of the Expectation‐Maximization algorithm, and it is used with a paleo drought record—the Monsoon Asia Drought Atlas—to reconstruct 406 years of streamflow for the Ping River (northern Thailand). Results for the instrumental period show that the dynamic model has higher accuracy than conventional linear regression; all performance scores improve by 45–497%. Furthermore, the reconstructed trajectory of the state variable provides valuable insights about the catchment history—e.g., regime‐like behavior—thereby complementing the information contained in the reconstructed streamflow time series. The proposed technique can replace linear regression, since it only requires information on streamflow and climatic proxies (e.g., tree‐rings, drought indices); furthermore, it is capable of readily generating stochastic streamflow replicates. With a marginal increase in computational requirements, the dynamic model brings more desirable features and value to streamflow reconstructions.

Increased Variability of Thailand's Chao Phraya River Peak Season Flow and Its Association With ENSO Variability: Evidence From Tree Ring δ18O

“…D'Arrigo et al () reconstructed Indonesia's Citarum River based on ring width measurements from regional teak sites that exhibited a strong direct correlation between rainfall and September–November streamflow. More recently, Nguyen and Galelli () used the tree ring width‐derived Monsoon Asia Drought Atlas data (Cook et al, ) to reconstruct the annual flow of north Thailand's Ping River. In comparison, our simple linear regression using the CPRB18O as a predictor resulted in a reconstruction model that accounted for 57% of the variation in CPRPF for the period 1956–1999.…”

“…• Supporting Information S1 successfully applied in many regions of the world for reconstructing streamflow (e.g., D'Arrigo et al, 2011;Derose et al, 2015;Gou et al, 2010;Lara et al, 2008;Meko et al, 2012;Nguyen & Galelli, 2017;Woodhouse et al, 2006). Despite the challenges associated with cross-dating tropical tree rings, a growing body of annually dated tree ring records have emerged from Southeast Asia that make it possible to attempt such a reconstruction for the CPR.…”

“…One promising way to extend the instrumental record of streamflow is to utilize regional tree ring records as proxies for streamflow through the connection to hydroclimate (e.g., Stockton & Jacoby, ). Due to their annual resolution, tree rings are easily comparable with observed streamflow data and have been successfully applied in many regions of the world for reconstructing streamflow (e.g., D'Arrigo et al, ; Derose et al, ; Gou et al, ; Lara et al, ; Meko et al, ; Nguyen & Galelli, ; Woodhouse et al, ). Despite the challenges associated with cross‐dating tropical tree rings, a growing body of annually dated tree ring records have emerged from Southeast Asia that make it possible to attempt such a reconstruction for the CPR.…”

Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model

“…Nguyen and Galelli () used a linear dynamic systems modeling approach to streamflow reconstruction in northern Thailand. Ho et al () demonstrated another modeling approach that was applied to reconstructing streamflow in the Missouri River Basin using a gridded paleo‐proxy called the Living Blended Drought Atlas (LBDA).…”

On the Value of ENSO State for Urban Water Supply System Operators: Opportunities, Trade‐Offs, and Challenges

“…Planning aspects entail designing a portfolio of structural interventions (e.g., water transfers, desalination, and water reuse plants) and financial tools (e.g., contingency funds or index insurances) that secure water supply under critical hydro‐climatological conditions (see Characklis et al, ; Herman et al, ; Kasprzyk et al, ; Zeff et al, , and references therein). On the other hand, management aspects require devising operating strategies that adapt water supply decisions to the sharp trends and regime shifts in streamflow records caused by ENSO (Nguyen & Galelli, ; Turner & Galelli, ). Adaptive management decisions alone may not guarantee full reliability during the worst ENSO episodes, but could help operators hedge against the risk of failure—for example, by cutting back the supply in small and frequent increments at the onset of a critical drought.…”