Jim Freer

Water Science and Technology · 2001–2019

A researcher in the Science corpus: 7 papers; trusts 8 researchers and is trusted by 68 researchers here, as inferred from the citation graph.

Subfields

• Water Science and Technology — 35 citations into it; #14 of 1,711 by trust here, #224 by citations
• Environmental Engineering — 32 citations into it; #87 of 5,479 by trust here, #364 by citations
• Statistics and Probability — 26 citations into it
• Global and Planetary Change — 24 citations into it; #151 of 10,225 by trust here, #1,457 by citations
• Civil and Structural Engineering — 9 citations into it

Whom Jim Freer trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Keith Beven
2. Hoshin V. Gupta
3. Thorsten Wagener
4. Dmitri Kavetski
5. Emanuele Borgonovo
6. B. Schaefli
7. Qingyun Duan
8. Andrea Saltelli
9. George A. Kuczera
10. Jasper A. Vrugt

Who trusts Jim Freer overall

1. Keith Beven
2. Jasper A. Vrugt
3. S. Christensen
4. Hoshin V. Gupta
5. M. Clark
6. Wen Wang
7. Misgana K. Muleta
8. Bryan Tolson
9. Mojtaba Sadegh
10. Thomas Wöhling

Papers from Jim Freer's perspective

Ordered by how strongly the researchers Jim Freer trusts endorse each paper. Same corpus, a different order for every researcher.

1. Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis
2. Probabilistic sensitivity analysis of complex models: a Bayesian approach
3. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology
4. Global sensitivity analysis using polynomial chaos expansions (Reliability Engineering & System Safety)
5. Bayesian calibration of computer models
6. Shaping the next one hundred years
7. From screening to quantitative sensitivity analysis. A unified approach (Computer Physics Communications)
8. A new uncertainty importance measure (Reliability Engineering & System Safety)
9. Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters
10. Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling

Their papers

• Sensitivity analysis of environmental models: A systematic review with practical workflow (2014) — 1,428 citations
• Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores (2019) — 1,148 citations
• Ensemble evaluation of hydrological model hypotheses (2010) — 106 citations
• Weather radar and hydrology (2009) — 52 citations
• Comment on "Hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology" by Pietro Mantovan and Ezio Todini (2007) — 110 citations
• Stochastic capture zone delineation within the generalized likelihood uncertainty estimation methodology: Conditioning on head observations (2001) — 112 citations
• Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology (2001) — 2,210 citations