There is sense in every model: Discover it with SPOTPY

Uncertainty in hydrological modeling has an important effect on the reliability of predictions, such as droughts and floods. Uncertainty quantification can expose parameter sensitivities and structural flaws, enabling better calibration and robust risk assessments. However, the use and implementation of suitable methods often hinder good research and thus good model results.

Building on a decade of community work, SPOTPY has evolved into a widely used tool for covering a wide range of peer-reviewed hydrological calibration, uncertainty, and sensitivity analysis techniques. Here, the latest advances in research and practice are presented, featuring an expanded set of optimization algorithms, hydrological performance metrics, and high-throughput workflows that make rigorous parameter exploration accessible, ranging from desktop studies to large computing clusters.

The new release strengthens SPOTPY’s role as a “single entry point” for testing alternative calibration strategies for any hydrological or ecohydrological model. A redesigned model interface simplifies the coupling of external models (from simple conceptual bucket models to fully distributed land‑surface models), while improved I/O handling and database backends streamline storage of millions of simulations for posterior analysis. The availability of global and local optimization methods has been extended and harmonized: alongside classic algorithms such as SCE‑UA, DREAM, ROPE and Monte Carlo sampling, users can now flexibly switch between multi‑objective and single‑objective formulations and customize stopping criteria to balance convergence and computational cost.

For performance evaluation, SPOTPY now offers an enriched library of objective functions and hydrological signatures tailored to discharge and ecohydrological time series, from classical Kling–Gupta Efficiency (parametric and non‑parametric) to hydrological signature-based flow percentile‑based indicators. All metrics are fully integrated into calibration, sensitivity analysis, and uncertainty assessment workflows so that users can, for example, calibrate to traditional goodness‑of‑fit while simultaneously tracking regime‑oriented diagnostics that are critical for low‑flow, flood, or water‑quality applications. Recent case studies demonstrate how these capabilities help quantify trade‑offs between parameter identifiability and process realism in hydrological models under changing climate and land‑use conditions, for which an overview will be presented.

Furthermore, the new features will be illustrated through real-world hydrological applications, highlighting practical guidance on algorithm choice, the diagnostic use of hydrological signatures, and robust uncertainty communication. However, as not every model produces the expected result on the first try, a discussion ground will be provided for problems that are frequently encountered in hydrological modeling.