Subjective decisions in hydrologic model calibration can have drastic impacts on our understanding of basin processes and simulated fluxes. Here, we present a multicase calibration approach to determine three pillars of an appropriate hydrological model configuration, i.e. calibration data length, spin-up period, and spatial resolution, using a spatially distributed meso-scale hydrological model (mHM) together with a dynamically dimensioned search (DDS) algorithm and Nash-Sutcliffe efficiency (NSE) for the Moselle basin. The results show that a 10-year calibration data length, 2-year spin-up period, and 4-km model resolution are appropriate for the Moselle basin to reduce the computational burden while simulating streamflow with a decent performance. Although the calibration data length and spatial resolution are related to the extent and quality of the data, and the spin-up period is basin dependent, analysing the combined effects further allowed us to understand the interactions of these three usually overlooked pillars in the mHM configuration.
ABSTRACT The popular approach to select a suitable distribution to characterize extreme rainfall events relies on the assessment of its descriptive performance. This study examines an alternative approach to this task that evaluates, in addition to the descriptive performance of the models, their performance in estimating out-of-sample events (predictive performance). With a numerical experiment and a study case in São Paulo state, Brazil, we evaluated the adequacy of seven probability distributions widely used in hydrological analysis to characterize extreme events in the region and compared the selection process of both popular and altenative frameworks. The results indicate that (1) the popular approach is not capable of selecting distributions with good predictive performance and (2) combining different predictive and descriptive tests can improve the reliability of extreme event prediction. The proposed framework allowed the assessment of model suitability from a regional perspective, identifying the Generalized Extreme Value (GEV) distribution as the most adequate to characterize extreme rainfall events in the region.
The standard method for characterizing the variability of overland flow focuses primarily on a single scale, usually the smallest scale available (i.e. the highest resolution). However, the extremes of overland flow are generally variable over a wide range of scales. Thus, the smallest scale has no specific hydrological significance, and a scale-independent characterization is more physically relevant. This study investigated the spatial variability of overland flows and evaluated the hydrological performance of the Nature-Based Solutions scenarios in terms of a possible improvement of the morphological functioning of the catchment at several scales.
We are very grateful to the authors Aksoy and Cavus for their constructive contributions to the discussion of our earlier paper "Drought assessment in a south Mediterranean transboundary catchment." Our paper focused on the variability of rainfall and drought patterns in the Medjerda catchment in Tunisia and analysed the underlying causes resulting in these changes. Scope exists to improve the integration of the space-time variability in the drought assessment methodology. Different drought indices and statistical methods are employed in the study. The discussion of the article concentrates almost exclusively on the use of the Standardized Precipitation Index (SPI) and some mistyped equations. In this reply, we clarify the problematic points of misunderstanding and discuss the classification of droughts based on the SPI values.
This study investigated the lake–aquifer hydraulic interactions in Lake Urmia (LU) as the second largest hypersaline lake in the world. Due to the scarcity of hydrogeological data required for modelling, a method based on Darcy’s Law and lake water budget was used to quantify the lake–aquifer interaction. Long-term ground- and satellite-based hydrological datasets over the time frame 2001–2019 were used. Results indicate that the groundwater flux between LU and the aquifers controls 18.74 ± 1.67% of the lake’s water storage. While 10 out of 14 adjacent aquifers recharge LU at a rate of less than 180 m3/m.month, one phreatic aquifer recharges the LU up to 1400 m3/m.month. Two aquifers are recharged from the seawater of LU at a rate of 110–640 m3/m.month. The results of this study lead to a holistic understanding of the hydraulic interaction of LU–aquifers, which is essential for the sustainable management of both settings.
