A decade of Predictions in Ungauged Basins (PUB)—a review
Abstract The Prediction in Ungauged Basins (PUB) initiative of the International Association
of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium …
of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium …
Characterising performance of environmental models
In order to use environmental models effectively for management and decision-making, it is
vital to establish an appropriate level of confidence in their performance. This paper reviews …
vital to establish an appropriate level of confidence in their performance. This paper reviews …
[HTML][HTML] Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual …
Long short-term memory (LSTM) models are recurrent neural networks from the field of deep
learning (DL) which have shown promise for time series modelling, especially in conditions …
learning (DL) which have shown promise for time series modelling, especially in conditions …
Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments
A Ritter, R Muñoz-Carpena - Journal of Hydrology, 2013 - Elsevier
Success in the use of computer models for simulating environmental variables and
processes requires objective model calibration and verification procedures. Several …
processes requires objective model calibration and verification procedures. Several …
Improved estimators of model performance efficiency for skewed hydrologic data
JR Lamontagne, CA Barber… - Water Resources …, 2020 - Wiley Online Library
Abstract The Nash‐Sutcliffe efficiency (NSE) and the Kling‐Gupta efficiency (KGE) are now
the most widely used indices in hydrology for evaluation of the goodness of fit between …
the most widely used indices in hydrology for evaluation of the goodness of fit between …
[HTML][HTML] How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction
The skill of a forecast can be assessed by comparing the relative proximity of both the
forecast and a benchmark to the observations. Example benchmarks include climatology or …
forecast and a benchmark to the observations. Example benchmarks include climatology or …
Lessons learnt from checking the quality of openly accessible river flow data worldwide
Advances in open data science serve large-scale model developments and, subsequently,
hydroclimate services. Local river flow observations are key in hydrology but data sharing …
hydroclimate services. Local river flow observations are key in hydrology but data sharing …
A comprehensive comparison of four input variable selection methods for artificial neural network flow forecasting models
Artificial neural networks (ANNs) are increasingly used for flood forecasting. The
performance of these models relies on the selection of appropriate inputs. However, Input …
performance of these models relies on the selection of appropriate inputs. However, Input …
Benchmarking Data-Driven Rainfall-Runoff Models in Great Britain: A comparison of LSTM-based models with four lumped conceptual models
Long short-term memory (LSTM) models are recurrent neural networks from the field of deep
learning (DL) which have shown promise for time series modelling, especially in conditions …
learning (DL) which have shown promise for time series modelling, especially in conditions …
[HTML][HTML] Pitfalls in hydrologic model calibration in a data scarce environment with a strong seasonality: Experience from the Adyar catchment, India
Process-based hydrologic models can provide necessary information for water resources
management. However, the reliability of hydrological models depends on the availability of …
management. However, the reliability of hydrological models depends on the availability of …