KliWES 2.0 – Klimawandel und Wasserhaushalt: Methodikoptimierung der Wasserhaushaltsmodellierung, Fortschreibung von Modelleingangsdaten, sachsenweite Wasserhaushaltsmodellierung für Ist-Zustand und Szenarien sowie Weiterentwicklung der KliWES-Internetanwendung im Wasserhaushaltsportal Sachsen

Die Schriftenreihe informiert über die Fortschreibung des Projektes „Klimawandel und Wasserhaushalt in Sachsen“ (KlIWES-2.0) mit einem weiterentwickelten ARCEGMO- Modell- Konzept. Zunächst erfolgte eine Neuberechnung des IST- Wasserhaushaltes bis 2015. Ergänzend wurden mit acht ausgewählten WEREX-VI- Klima- Realisierungen künftige Wasserhaushalts- Entwicklungen (bis 2100) modelliert. Die Ergebnisse zeigen überwiegend ein weiter abnehmende Abfluss- Dargebots- Entwicklung in den Gewässereinzugsgebieten. Über das neue Anwendungs- Tool „KliWES-2.0“ sind die Ergebnisse im „Wasserhaushaltsportal Sachsen“ für einen breiten Nutzerkreis auch webbasiert verfügbar. Redaktionsschluss: 10.06.202

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.HighlightsWe propose a new approach to deriving calibration recommendations for system modelsApproach is based on analyzing calibration in multi-model simulation exercisesResulting recommendations are holistic and anchored in actual practiceWe apply the approach to calibration of crop models used to simulate phenologyRecommendations concern: objective function, parameters to estimate, software usedCompeting Interest StatementThe authors have declared no competing interest

Diagnosis of Model Errors With a Sliding Time‐Window Bayesian Analysis

Deterministic hydrological models with uncertain, but inferred‐to‐be‐time‐invariant parameters typically show time‐dependent model errors. Such errors can occur if a hydrological process is active in certain time periods in nature, but is not resolved by the model or by its input. Such missing processes could become visible during calibration as time‐dependent best‐fit values of model parameters. We propose a formal time‐windowed Bayesian analysis to diagnose this type of model error, formalizing the question “In which period of the calibration time‐series does the model statistically disqualify itself as quasi‐true?” Using Bayesian model evidence (BME) as model performance metric, we determine how much the data in time windows of the calibration time‐series support or refute the model. Then, we track BME over sliding time windows to obtain a dynamic, time‐windowed BME (tBME) and search for sudden decreases that indicate an onset of model error. tBME also allows us to perform a formal, sliding likelihood‐ratio test of the model against the data. Our proposed approach is designed to detect error occurrence on various temporal scales, which is especially useful in hydrological modeling. We illustrate this by applying our proposed method to soil moisture modeling. We test tBME as model error indicator on several synthetic and real‐world test cases that we designed to vary in error sources (structure and input) and error time scales. Results prove the successful detection errors in dynamic models. Moreover, the time sequence of posterior parameter distributions helps to investigate the reasons for model error and provide guidance for model improvement.Key Points: We propose a data‐driven method for model‐structural error detection. Our method rests on a statistically rigorous Bayesian framework without prior assumptions about error sources or patterns. We confirm successful error detection on various temporal scales in synthetic test cases and present insights from a real‐world case study.German Research Foundation (DFG)Cluster of ExcellenceUniversity of Stuttgarthttps://doi.org/10.18419/darus-183