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Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables. [ABSTRACT FROM AUTHOR]

Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas and are suitable for representing the physics of the hydrologic model, but they might fail in competition with their more advanced counterparts. In contrast, deep learning (DL) approaches provide a great computational capability for streamflow simulation, but they rely on data characteristics and the physics of the issue cannot be fully understood. To overcome these limitations, the current study provided a novel framework based on a combination of conceptual and DL techniques for enhancing the accuracy of streamflow simulation in a snow-covered basin. In this regard, the current study simulated daily streamflow in the Kalixälven river basin in northern Sweden by integrating a snow-based conceptual hydrological model (MISD) with a DL model. Daily precipitation, air temperature (average, minimum, and maximum), dew point temperature, evapotranspiration, relative humidity, sunshine duration, global solar radiation, and atmospheric pressure data were used as inputs for the DL model to examine the effect of each meteorological variable on the streamflow simulation. Results proved that adding meteorological variables to the conceptual hydrological model underframe of parallel settings can improve the accuracy of streamflow simulating by the DL model. The MISD model simulated streamflow had an MAE = 8.33 (cms), r = 0.88, and NSE = 0.77 for the validation phase. The proposed deep-conceptual learning-based framework also performed better than the standalone MISD model; the DL method had an MAE = 7.89 (cms), r = 0.90, and NSE = 0.80 for the validation phase when meteorological variables and MISD results were combined as inputs for the DL model. The integrated rainfall-runoff model proposed in this research is a new concept in rainfall-runoff modeling which can be used for accurate streamflow simulations. [ABSTRACT FROM AUTHOR]

This study investigated the applicability and performance of a hydrological model (namely FLEXG) for simulating streamflow in boreal catchment, exemplified by the representative Krycklan catchment. The FLEXG simulated daily streamflow for the whole catchment and its sub-catchments. The Kling-Gupta Efficiency (KGE) and Nash-Sutcliffe Efficiency (NSE) demonstrated favorable model performance during the calibration period (KGE = 0.88 and NSE = 0.75) and the subsequent validation (KGE = 0.81 and NSE = 0.63) period for the Krycklan catchment. Notably, a high degree of congruence was observed across eight sub-catchments, with KGE values consistently exceeding the threshold of 0.7 during both calibration and validation periods. These findings underscore the FLEXG model's aptitude for simulating streamflow within boreal catchments, as evidenced by its ability to capture seasonal patterns across the catchment. Additionally, regression analysis was conducted to examine the intricate relationships between model parameters and varied catchment characteristics. These catchment characteristics were identified as pivotal factors influencing streamflow modelling in the study region. Specifically, it was established that catchment size shows a negative correlation with the splitter D (R2 = 0.7), a parameter intimately associated with both fast and slow recession periods. The proportion of forest cover was negatively correlated with the slow response reservoir recession coefficient, K f (R2 = 0.57), whereas the proportion of wetland cover displayed a positive correlation with K s (R2 = 0.54). These correlations underscore the substantive impact of land use patterns on streamflow generation dynamics within the boreal catchment context. • Evaluation of the FLEXG model for streamflow simulation in a nested boreal catchment. • FLEXG achieved good performance (KGE ≥0.75) in most sub-catchments. • Analysis of similarities and differences of model parameters in sub-catchments. • Quantification of impacts of catchment characteristics on hydrologic signatures. • Comparison of the performance of FLEXG and previous modelling studies in Krycklan. [ABSTRACT FROM AUTHOR]

Glaciers are one of the main sources of freshwater in cold regions. The glacier melting process can significantly impact the glacier mass balance (GMB) and contribute a large amount of runoff in cold regions. This study applied the recently developed semi-distributed glacio-hydrological conceptual model (FLEXG) to understand the glacier melting process and the effect of topography on GMB in the Torne River basin, northern Sweden. The study simulated glacier and snow accumulation and ablation, as well as runoff from the glacier and non-glacier areas of the basin using the FLEXG model for the time period 1989–2018. The FLEXG model considers the influence of topography on runoff generation, and in this study the basin was classified into 143 zones depending on elevation and aspect. In order to gain a comprehensive view of the performance of the FLEXG model, the classical lumped hydrological model HBV was used and compared with the FLEXG model in simulating total streamflow and peak runoff at the outlet of the basin. Our results revealed that the FLEXG model performed well in reproducing the streamflow (also better than the HBV model) with metric Kling-Gupta Efficiency (KGE) of 0.80 and 0.71 for the calibration and validation periods, respectively. We also found that the FLEXG model performs better in peak runoff simulation than the HBV model. The FLEXG simulated snow cover area proportion agreed well with the MODIS satellite snow cover product (R2 = 0.60 and RMSE = 28%). The GMB in different elevation zones was simulated, and a downward trend was found for GMB changes during the study period because of climate change. [ABSTRACT FROM AUTHOR]