Improving the flood prediction capability of the Xinanjiang model in ungauged nested catchments by coupling it with the geomorphologic instantaneous unit hydrograph

- We test the flood prediction capability of the Xinanjiang model in ungauged case.
- We couple the Xinanjiang model with the GIUH method to achieve improved prediction.
- The topographic index and GIUH can be regarded as the similarity indicators.
- Geomorphologic features should be considered for regionalizing model parameters.

SummaryImproving the predictive capabilities of rainfall-runoff models in ungauged catchments is a challenging task but has important theoretical and practical significance. In this study, we investigated the predictive capabilities of the conceptual Xinanjiang model (XAJ), which is widely used for flood forecasting and simulation in China, in ungauged catchments. We further produced a hybrid rainfall-runoff model (named XAJ-GIUH) by coupling the XAJ model with the geomorphologic instantaneous unit hydrograph (GIUH) to achieve improved flood predictions in ungauged catchments. The flood prediction capabilities of the original XAJ model and the XAJ-GIUH model were investigated and compared at an hourly time scale in a mountainous catchment with six nested catchments located in the south of Anhui province, China. The two models were first calibrated for each individual catchment by comparing with the observed streamflows. Then, the nested catchments were treated as ungauged and modeled using the parameter values regionalized by transposition from the downstream catchments. The results show that the performance of both models is comparable and satisfactory on different catchment scales in the case that model parameters are calibrated in each catchment. However, the models perform differently in the case that model parameters are transposed from the downstream catchments. The XAJ-GIUH model produced markedly improved estimates of peak discharge and peak time as compared to the original XAJ model in the latter case, indicating that the runoff routing is a major uncertainty source for the application of the XAJ model in this case. Coupling XAJ with topography-based GIUH has the potential to substantially improve the flood prediction capability of the XAJ model in ungauged catchments. Our analysis further reveals that the models do not necessarily perform better when the parameter values are transposed from closer donor catchment. Instead, adopting the parameter values from the catchment with more similar topographic characteristics is more likely to produce better model performance. This study implicates the necessity of including remotely sensed geomorphologic characteristics of ungauged catchments to improve flood predictions in these regions.