How to Calculate Hydrological Performance Metrics in Excel Step by Step Tutorial
Evaluating hydrological model performance requires more than just one metric. In this Excel-based tutorial, we walk through 10 key performance metrics used in hydrological modelling, and show how to calculate and interpret them using observed vs simulated hydrographs.
You’ll learn how to compute and understand:
• Nash–Sutcliffe Efficiency (NSE)
• Log-NSE (low-flow performance)
• Kling–Gupta Efficiency (KGE)
• R-squared (R²)
• Root Mean Square Error (RMSE)
• Mean Absolute Error (MAE)
• Percent Bias (PBIAS)
• Volumetric Efficiency (VE)
• Peak Flow Bias (PFB)
• Timing of Peak Error (TPE)
We also explain:
• What each metric actually measures
• Which parts of the hydrograph they are sensitive to (peaks, low flows, timing, volume)
• Why relying on a single metric can be misleading
• How to interpret metrics in the context of model performance
This video is ideal for hydrologists, water engineers, researchers, and students working on rainfall–runoff modelling, flood modelling, and model calibration.
Also watch our first episode on performance metrics to know more about the metrics:
Hydrological Model Performance Metrics Explained: NSE, KGE, RMSE & More
• Hydrological Model Performance Metrics Exp...
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⏱️ Timestamps
00:00 – Introduction: Why Use Multiple Performance Metrics?
00:29 – Numerical Example (Start with visual inspection!)
01:06 – Data Preparation in Excel
04:00 – Calculating NSE & Log-NSE
05:27 – KGE and Its Components
07:45 – R², RMSE, and MAE Calculated
09:55 – Bias Metrics (PBIAS, VE)
11:40 – Peak and Timing Metrics (PFB, TPE)
12:57 – Key Takeaways from the Analysis
13:37 – Concluding Notes
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🔑 Keywords
#hydrologicalmodelling #hydrologytutorial #rainfallrunoffmodel #modelperformance #nse #rmse #kge #pbias #peakestimation #modelcalibration #civilengineering