Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework

Abstract

Abstract. Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce performance degradation. Many existing studies model the time-varying parameters as functions of physically based covariates; however, a major challenge remains in finding effective information to control the large uncertainties that are linked to the additional parameters within the functions. This paper formulated the time-varying parameters for a lumped hydrological model as explicit functions of temporal covariates and used a hierarchical Bayesian (HB) framework to incorporate the spatial coherence of adjacent catchments to improve the robustness of the projection performance. Four modeling scenarios with different spatial coherence schemes and one scenario with a stationary scheme for model parameters were used to explore the transferability of hydrological models under contrasting climatic conditions. Three spatially adjacent catchments in southeast Australia were selected as case studies to examine the validity of the proposed method. Results showed that (1) the time-varying function improved the model performance but also amplified the projection uncertainty compared with the stationary setting of model parameters, (2) the proposed HB method successfully reduced the projection uncertainty and improved the robustness of model performance, and (3) model parameters calibrated over dry years were not suitable for predicting runoff over wet years because of a large degradation in projection performance. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.

The Influence of a Prolonged Meteorological Drought on the Catchment Water Storage Capacity: A Hydrological Model Perspective

“…Thus, the 211 method proposed by Saft et al (2015) was introduced in this study to define the meteorological 212 drought period. A more detailed process of the identification method of dry period can be obtained 213 in research by Saft et al (2015) and our previous study (Pan et al, 2019b). water storage capacity, is the most sensitive parameter in the structure of the GR4J model.…”

“…Furthermore, recent literature indicates that the improved probabilities of the extreme events 60 have been projected in many parts around the world because of the increasing anthropogenic 61 interference and global climate change, which implies the more severe droughts in the future (Dai,62 2012; Dai, 2011;Pan et al, 2019b). Unlike other natural hazards, e.g., flood, cyclonic storms and 63 earthquakes, the drought may have a much longer duration.…”

Response of catchment water storage capacity to the prolonged meteorological drought and asymptotic climate variation

“…For instance, significant variations (i.e., less runoff than expected) in hydrological behaviour were reported during the decade-long millennium drought of many catchments in south-eastern Australia compared with the previous wet period (Saft et al, 2016). Seasonally asymptotic variation have been identified in many catchments in America (Deng et al, 2018;Pan et al, 2019a), Asia (Deng et al, 2016) and Australia (Pan et al, 2019b).…”

“…through certain criteria. Thus, the functional form that achieved the best simulation performance would be recognized as the best item to represent the potential changes in the catchment property (Jeremiah et al, 2013;Westra et al, 2014;Wright et al, 2015;Guo et al, 2017;Pan et al, 2019a;Pan et al, 2019b). For example, Westra et al (2014) found that the streamflow prediction of the Scott Creek catchment in South Australia was significantly improved if the CWSC is allowed to change over time as a combined function.…”

Analysis of Characteristics and Driving Factors of Land-Use Changes in the Tarim River Basin from 1990 to 2018

“…Additionally, research conducted by Guillaume is of great significance in dealing with the impact of land-use transformation on a wide range of potential ecologies [19]. Generally speaking, it is clear from the literature that global climate change and human activities are the two main factors affecting LUCC [20]. Deepening the analysis of the temporal and spatial evolution and driving factors of land use can improve our understanding of sustainable development and help us better examine the relationship between people and land use.…”