Spatial pattern-based performance evaluation and uncertainty analysis of a distributed hydrological model

Advancement in data acquisition techniques (e.g., satellites) has provided enormous opportunities to improve the predictions of distributed hydrological models by incorporating new types of information. Moreover, such advancements have paved the way for the spatial scale evaluation rather than the conventional temporal evaluation of models for more accurate water management by considering the spatial variability. The present study focuses on the spatial pattern-oriented model evaluation by intercomparing the satellite remote sensing data-driven patterns of actual evapotranspiration (AET) and soil moisture with simulated patterns from a hydrological model for the 19 276 km(2) Subarnarekha basin in Eastern India. Pattern comparison is carried out by incorporating three spatial performance metrics as diagnostic tools, that is, joint empirical orthogonal functions (EOF), fractional skill score (FSS), and spatial efficiency (SPAEF). Moreover, the present study made a novel contribution to spatial pattern evaluation by incorporating the uncertainty associated with the patterns through the Quantile Regression' technique. The EOF analysis indicated a significant similarity between simulated and observed patterns. High scores for both the FSS and SPAEF metrics also revealed similarities between simulated and observed spatial patterns. Overall, the results show an acceptable spatial pattern performance of the hydrological model mimicking the observed spatial patterns across time. The uncertainty analysis represents reasonable predictive ranges, with a 95 per cent prediction uncertainty (PPU) band enclosing 80%-95% of AET and 79%-92% of the soil moisture observations. The present study demonstrated a framework for improving the basin's overall water balance analysis by incorporating a spatial pattern-based evaluation of hydrological models.

Automated summary

The study evaluates the spatial performance of a hydrological model by comparing the simulated patterns with satellite remote sensing data-driven patterns. Spatial performance metrics and uncertainty analysis are used for the evaluation. The results show acceptable spatial pattern performance of the hydrological model and provide reasonable predictive ranges.