Related Experiment Video
Updated: Feb 1, 2026
06:37
Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
Published on: November 13, 2017
9.7K
Recent insights on uncertainties present in integrated catchment water quality modelling
Franz Tscheikner-Gratl1, Vasilis Bellos2, Alma Schellart3
1Water Management Department, Civil Engineering and Geosciences, TU Delft, Stevinweg 1, 2628 CN, Delft, the Netherlands; Integral Design and Management, Civil Engineering and Geosciences, TU Delft, Stevinweg 1, 2628 CN, Delft, the Netherlands.
Water Research
|December 15, 2018
Summary
This study explores uncertainty in integrated catchment modeling, comparing linked versus fully integrated approaches. Findings suggest linked models may not increase uncertainty and can even reduce it for water quality parameters.
Area of Science:
- Environmental Science
- Hydrology
- Water Quality Modeling
Background:
- Current knowledge on uncertainties in sub-models of integrated catchment models is limited.
- Existing frameworks for analyzing uncertainty in hydrological models are insufficient.
- The QUICS project (Quantifying Uncertainty in Integrated Catchment Studies) provides practical experience.
Purpose of the Study:
- To stimulate discussion on uncertainty in integrated catchment modeling based on QUICS project experiences.
- To compare integrated versus linked sub-model approaches for catchment modeling.
- To investigate the impact of model linkage on overall uncertainty and acceptable complexity.
Main Methods:
- Review of current knowledge on uncertainties in integrated catchment model sub-models.
- Comparison of building fully integrated models versus linking separate sub-models.
- Analysis of uncertainty implications of model linkage and complexity.
Main Results:
- Model linkage does not necessarily increase uncertainty; a compensation effect may decrease overall uncertainty.
- Uncertainty in key water quality parameters can decrease when using linked models.
- Challenges in applying uncertainty analysis to integrated catchment water quality modeling were identified.
Conclusions:
- The study questions the focus on linkage uncertainty, suggesting a shift towards model structure uncertainty.
- Linked models may offer a viable approach to managing and potentially reducing uncertainty in water quality predictions.
- Recommendations for future research in integrated catchment water quality modeling uncertainty are provided.
Related Concept Videos
01:19Quality of Water
560
In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
560
01:14Testing Water Quality
389
When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
389
04:08The Uncertainty Principle
31.8K
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
31.8K
02:46Uncertainty in Measurement: Reading Instruments
51.8K
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
51.8K
00:59Uncertainty: Overview
1.7K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.7K
03:37Uncertainty in Measurement: Accuracy and Precision
101.1K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
101.1K