Synergistic Calibration of a Hydrological Model Using Discharge and Remotely Sensed Soil Moisture in the Paraná River Basin

Hydrological models are useful tools for water resources studies, yet their calibration is still a challenge, especially if aiming at improved estimates of multiple components the cycle. This has led hydrologic community to look ways constrain with variables. Remote sensing soil moisture very promising in this sense, large areas which field observations may be unevenly distributed. However, use such data calibrate hydrological synergistic way not well understood, tropical humid as those found South America. Here, we perform scenarios multiobjective model optimization situ discharge and SMOS L4 root zone product Upper Paraná River Basin America (drainage area > 900,000 km²), 136 river gauges used. An additional scenario used compare relative impacts using all small subset containing nine only. Across basin, joint (CAL-DS) leads precision accuracy both The discharges estimated by CAL-DS (median KGE improvement was 0.14) accurate obtained only equal 0.14), while retrieval practically 0.11) that 0.13). Nonetheless, individual rates able retrieve satisfactory estimates, vice versa. These results show complementarity between these two variables highlight benefits considering framework. It also shown that, instead optimization, estimate reasonable moisture, although relatively less accurately than entire dataset. In summary, study shows poorly gauged basins, few capable providing basin-wide more preferable performing discharge-oriented process.