Influence of Soil and Land‐Use/Land‐Cover Data Quality on <scp>SWAT</scp> Parameter Transferability in Small Subtropical Agricultural Watersheds

ABSTRACT Regionalisation of hydrological model parameters is a cornerstone of Prediction in Ungauged Basins (PUB), yet the level of input‐data detail required for reliable parameter transfer remains unclear, particularly in intensively managed agricultural landscapes with intra‐annual land‐use/land‐cover (LULC) dynamics associated with multi‐cropping systems. While recent studies have primarily focused on refining transfer techniques—such as proximity, similarity, regression, fuzzy clustering, or machine learning—the role of watershed descriptor quality has rarely been explored. Here, we evaluated how soil and LULC data quality control the transferability of SWAT parameters through a factorial proxy‐basin experiment in two small subtropical agricultural watersheds in southern Brazil (WS80: 0.80 km 2 , WS140: 1.39 km 2 ). Four scenarios crossed two levels of soil data quality (global HWSD at 1:5000000 vs. locally sampled at approximately 1:50000) with two levels of LULC representation (static default management vs. dynamic, EVI2‐derived crop–livestock rotations). Calibration performance improved in WS80 from NSE = 0.16 under coarse soil and static LULC to NSE = 0.75 when dynamic LULC was included. Under direct parameter transfer, the combined fine‐soil and dynamic‐LULC scenario achieved satisfactory NSE in both target directions (0.56 and 0.55), compared with NSE = 0.06–0.25 under the baseline global‐soil/static‐LULC scenario. Post‐transfer PBIAS ranged from −67.2% to +48.8% across coarse‐soil scenarios, whereas the fine‐soil/dynamic‐LULC scenario reduced PBIAS to −39.9% and + 28.7%. Our findings show that, in small managed agricultural watersheds, improving the physical realism of watershed descriptors can substantially increase SWAT parameter transferability, and that both soil and dynamic LULC upgrades are needed to achieve satisfactory model performance with transferred parameters.