Richard M. Vogel
Water Science and Technology · 2001–2020
A researcher in the Science corpus: 10 papers; trusts 19 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Water Science and Technology — 39 citations into it; #395 of 1,711 by trust here, #584 by citations
- Global and Planetary Change — 26 citations into it; #4,595 of 10,225 by trust here, #3,398 by citations
- Environmental Engineering — 22 citations into it; #3,352 of 5,479 by trust here, #4,892 by citations
- Statistics and Probability — 8 citations into it; #10,778 of 12,932 by trust here, #11,487 by citations
- Artificial Intelligence — 7 citations into it; #28,751 of 35,345 by trust here, #33,294 by citations
- Ocean Engineering — 5 citations into it
Whom Richard M. Vogel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Keith Beven
- Qingyun Duan
- Bryan Tolson
- Elijah Polak
- Christine Annette Shoemaker
- Soroosh Sorooshian
- Hoshin V. Gupta
- Tom Fawcett
- P. Mugunthan
- William S. Cleveland
Who trusts Richard M. Vogel overall
- Dingbao Wang
- Yin Tang
- Keith Beven
- Hoshin V. Gupta
- Jasper A. Vrugt
- Weihua Li
- Aynom T. Teweldebrhan
- Zhangjun Liu
- D. Ganora
- Giovanna Menardi
Papers from Richard M. Vogel's perspective
Ordered by how strongly the researchers Richard M. Vogel trusts endorse each paper. Same corpus, a different order for every researcher.
- The future of distributed models: model calibration and uncertainty prediction.
- Effective and efficient global optimization for conceptual rainfall‐runoff models
- Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology
- Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology
- Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters
- Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter
- Do Nash values have value?
- Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling
- Assessing the impacts of parameter uncertainty for computationally expensive groundwater models
- Dynamically dimensioned search algorithm for computationally efficient watershed model calibration
Their papers
- Sampling Bias and Class Imbalance in Maximum-likelihood Logistic Regression (2011) — 134 citations
- Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data (2020) — 110 citations
- Regional flow duration curves: Geostatistical techniques versus multivariate regression (2015) — 48 citations
- Appraisal of the generalized likelihood uncertainty estimation (GLUE) method (2008) — 415 citations
- Probabilistic Behavior of Water-Quality Loads (2005) — 49 citations
- A stochastic index flow model of flow duration curves (2004) — 106 citations
- Validation of a watershed model without calibration (2003) — 76 citations
- Impact of Streamflow Persistence on Hydrologic Design (2001) — 59 citations
- The utility of L-moment ratio diagrams for selecting a regional probability distribution (2001) — 141 citations
- Frequency of record‐breaking floods in the United States (2001) — 68 citations