The SCS-CN method is a globally known procedure used primarily for direct-runoff estimates. It also is integrated in many modelling applications. However, the method was developed in specific geographical conditions, often making its universal applicability problematic. This study aims to determine appropriate values of initial abstraction coefficients λ and curve numbers (CNs), based on measured data in five experimental catchments in the Czech Republic, well representing the physiographic conditions in Central Europe, to improve direct-runoff estimates. Captured rainfall-runoff events were split into calibration and validation datasets. The calibration dataset was analysed by applying three approaches: (1) Modifying λ, both discrete and interpolated, using the tabulated CN values; (2) event analysis based on accumulated rainfall depth at the moment runoff starts to form; and (3) model fitting, an iterative procedure, to search for a pair of λ, S (CN, respectively). To assess individual rainfall characteristics’ possible influence, a principal component analysis and cluster analysis were conducted. The results indicate that the CN method in its traditional arrangement is not very applicable in the five experimental catchments and demands corresponding modifications to determine λ and CN (or S, respectively). Both λ and CN should be viewed as flexible, catchment-dependent (regional) parameters, rather than fixed values. The acquired findings show the need for a systematic yet site-specific revision of the traditional CN method, which may help to improve the accuracy of CN-based rainfall-runoff modelling.

Rainfall-runoff events significantly influence water runoff and the loss of pollutants from tile-drained agricultural land. We monitored ten small (4 to 38 ha) tile-drained catchments in Czechia for three to five years (2012 to 2016). The discharge was measured continuously; a regular 14-day scheme of water quality monitoring was accompanied with event sampling provided by automatic samplers in 20 to 120 min intervals. A new semi-automated algorithm was developed for the identification of runoff events (RE) based on discharge and water temperature changes. We then quantified the share of RE on the total runoff and the N and P losses, and we compared six methods for nutrient load estimation on an annual and monthly basis. The results showed considerable differences among the monitored sites, seasons, and applied methods. The share of RE on N loads was on average 5% to 30% of the total annual load, whereas for P (dissolved and total), the share of RE was on average 10% to 80% on the total annual load. The most precise method for nutrient load estimation included the RE. The methods based on point monitoring of the discharge and water quality underestimated the loads of N by 10% to 20% and of P by 30% to 80%. The acquired findings are crucial for the improvement of nutrient load assessment in tile-drained catchments, as well as for the design of various mitigation measures on tile-drained agricultural land.