MODFLOW-USG: the New Possibilities in Mine Hydrogeology Modelling (or What is Not Written in the Manuals).
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- Mine Water & the Environment, 2014, v. 33, n. 4, p. 376, doi. 10.1007/s10230-014-0273-9
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- Article
Works matching DE "HYDROLOGIC models"
Results: 5000
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Inferring causal associations in hydrological systems: a comparison of methods.
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- Stochastic Environmental Research & Risk Assessment, 2025, v. 39, n. 6, p. 2427, doi. 10.1007/s00477-025-02977-3
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- Article
3
Effect of different likelihood measures on parameter conditioning and model uncertainty.
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- Stochastic Environmental Research & Risk Assessment, 2025, v. 39, n. 3, p. 1091, doi. 10.1007/s00477-025-02909-1
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- Article
4
Prediction of non-stationary daily streamflow series based on ensemble learning: a case study of the Wei River Basin, China.
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- Stochastic Environmental Research & Risk Assessment, 2025, v. 39, n. 2, p. 509, doi. 10.1007/s00477-024-02877-y
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- Article
5
Application of stochastic storm transposition for hydrologic modeling in the mountainous western US.
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- Stochastic Environmental Research & Risk Assessment, 2025, v. 39, n. 1, p. 109, doi. 10.1007/s00477-024-02853-6
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- Article
6
Generating hourly mean areal precipitation times series with an at-site weather generator in Switzerland.
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- Stochastic Environmental Research & Risk Assessment, 2024, v. 38, n. 10, p. 3737, doi. 10.1007/s00477-024-02757-5
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- Article
7
Enhancing the reliability of hydrological simulations through global weather data assimilation in watersheds with limited data.
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- Stochastic Environmental Research & Risk Assessment, 2024, v. 38, n. 9, p. 3445, doi. 10.1007/s00477-024-02758-4
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- Article
8
Spatial pattern of bias in areal rainfall estimations and its impact on hydrological modeling: a comparative analysis of estimating areal rainfall based on radar and weather station networks in South Korea.
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- Stochastic Environmental Research & Risk Assessment, 2024, v. 38, n. 7, p. 2797, doi. 10.1007/s00477-024-02714-2
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- Article
9
Assessing Fourier and Latin hypercube sampling methods as new multi-model methods for hydrological simulations.
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- Stochastic Environmental Research & Risk Assessment, 2024, v. 38, n. 4, p. 1271, doi. 10.1007/s00477-023-02627-6
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- Article
10
A stochastic deep-learning-based approach for improved streamflow simulation.
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- Stochastic Environmental Research & Risk Assessment, 2024, v. 38, n. 1, p. 107, doi. 10.1007/s00477-023-02567-1
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- Article
11
Assessing the return flow in human-induced rivers using data-driven and hydrologic models case study: Ergene River Basin.
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- Stochastic Environmental Research & Risk Assessment, 2023, v. 37, n. 12, p. 4679, doi. 10.1007/s00477-023-02525-x
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- Article
12
Flood-based critical sub-watershed mapping: comparative application of multi-criteria decision making methods and hydrological modeling approach.
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- Stochastic Environmental Research & Risk Assessment, 2023, v. 37, n. 7, p. 2757, doi. 10.1007/s00477-023-02417-0
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- Article
13
Exploring the use of multi-gene genetic programming in regional models for the simulation of monthly river runoff series.
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- Stochastic Environmental Research & Risk Assessment, 2023, v. 37, n. 5, p. 1917, doi. 10.1007/s00477-022-02373-1
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- Article
14
The future water vulnerability assessment of the Seoul metropolitan area using a hybrid framework composed of physically-based and deep-learning-based hydrologic models.
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- Stochastic Environmental Research & Risk Assessment, 2023, v. 37, n. 5, p. 1777, doi. 10.1007/s00477-022-02366-0
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- Article
15
Bootstrapped ensemble and reliability ensemble averaging approaches for integrated uncertainty analysis of streamflow projections.
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- Stochastic Environmental Research & Risk Assessment, 2023, v. 37, n. 4, p. 1213, doi. 10.1007/s00477-022-02337-5
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- Article
16
Multi-GCM ensemble model for reduction of uncertainty in runoff projections.
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- Stochastic Environmental Research & Risk Assessment, 2023, v. 37, n. 3, p. 953, doi. 10.1007/s00477-022-02311-1
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- Article
17
A hybrid time- and signature-domain Bayesian inference framework for calibration of hydrological models: a case study in the Ren River basin in China.
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- Stochastic Environmental Research & Risk Assessment, 2023, v. 37, n. 1, p. 153, doi. 10.1007/s00477-022-02282-3
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- Article
18
Data-driven approaches for runoff prediction using distributed data.
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- Stochastic Environmental Research & Risk Assessment, 2022, v. 36, n. 8, p. 2153, doi. 10.1007/s00477-021-01993-3
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- Article
19
Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea.
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- Stochastic Environmental Research & Risk Assessment, 2022, v. 36, n. 6, p. 1615, doi. 10.1007/s00477-021-02094-x
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- Article
20
Understanding the hierarchical controls of geographical features on hydrological responses in humid mountainous areas through a stepwise clustering scheme.
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- Stochastic Environmental Research & Risk Assessment, 2022, v. 36, n. 6, p. 1521, doi. 10.1007/s00477-021-02080-3
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- Article
21
Impact of rainfall spatiotemporal variability and model structures on flood simulation in semi-arid regions.
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- Stochastic Environmental Research & Risk Assessment, 2022, v. 36, n. 3, p. 785, doi. 10.1007/s00477-021-02050-9
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- Article
22
Model-wise uncertainty decomposition in multi-model ensemble hydrological projections.
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- Stochastic Environmental Research & Risk Assessment, 2021, v. 35, n. 12, p. 2549, doi. 10.1007/s00477-021-02039-4
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- Article
23
Optimization of land cover mapping through improvements in Sentinel-1 and Sentinel-2 image dimensionality and data mining feature selection for hydrological modeling.
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- Stochastic Environmental Research & Risk Assessment, 2021, v. 35, n. 12, p. 2493, doi. 10.1007/s00477-021-02014-z
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- Article
24
Assessing the restoration time of surface water and groundwater systems under groundwater pumping.
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- Stochastic Environmental Research & Risk Assessment, 2018, v. 32, n. 9, p. 2741, doi. 10.1007/s00477-018-1570-9
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- Article
25
River flow modelling: comparison of performance and evaluation of uncertainty using data-driven models and conceptual hydrological model.
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- Stochastic Environmental Research & Risk Assessment, 2018, v. 32, n. 9, p. 2667, doi. 10.1007/s00477-018-1536-y
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- Article
26
Effect of single and multi-site calibration techniques on hydrological model performance, parameter estimation and predictive uncertainty: a case study in the Logone catchment, Lake Chad basin.
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- Stochastic Environmental Research & Risk Assessment, 2018, v. 32, n. 6, p. 1665, doi. 10.1007/s00477-017-1466-0
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- Article
27
Estimating hydrologic model uncertainty in the presence of complex residual error structures.
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- Stochastic Environmental Research & Risk Assessment, 2018, v. 32, n. 5, p. 1259, doi. 10.1007/s00477-017-1489-6
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28
Using maximum likelihood to derive various distance-based goodness-of-fit indicators for hydrologic modeling assessment.
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- Stochastic Environmental Research & Risk Assessment, 2018, v. 32, n. 4, p. 949, doi. 10.1007/s00477-017-1507-8
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29
Quantifying differences between reservoir inflows and dam site floods using frequency and risk analysis methods.
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- Stochastic Environmental Research & Risk Assessment, 2018, v. 32, n. 2, p. 419, doi. 10.1007/s00477-017-1401-4
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- Article
30
A wavelet based approach for combining the outputs of different rainfall-runoff models.
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- Stochastic Environmental Research & Risk Assessment, 2018, v. 32, n. 1, p. 155, doi. 10.1007/s00477-016-1364-x
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31
Effect of projected climate change on the hydrological regime of the Yangtze River Basin, China.
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- Stochastic Environmental Research & Risk Assessment, 2018, v. 32, n. 1, p. 1, doi. 10.1007/s00477-017-1391-2
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32
Multisource hydrologic modeling uncertainty analysis using the IBUNE framework in a humid catchment.
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- Stochastic Environmental Research & Risk Assessment, 2018, v. 32, n. 1, p. 37, doi. 10.1007/s00477-017-1424-x
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33
Complexity as a streamflow metric of hydrologic alteration.
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- Stochastic Environmental Research & Risk Assessment, 2017, v. 31, n. 8, p. 2107, doi. 10.1007/s00477-016-1315-6
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34
An assessment of using subsampling method in selection of a flood frequency distribution.
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- Stochastic Environmental Research & Risk Assessment, 2017, v. 31, n. 8, p. 2033, doi. 10.1007/s00477-016-1318-3
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35
Quantifying anthropogenic and climatic impacts on sediment load in the sediment-rich region of the Chinese Loess Plateau by coupling a hydrological model and ANN.
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- Stochastic Environmental Research & Risk Assessment, 2017, v. 31, n. 8, p. 2057, doi. 10.1007/s00477-017-1381-4
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- Article
36
Assessment of the climate change impacts on flood frequency (case study: Bazoft Basin, Iran).
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- Stochastic Environmental Research & Risk Assessment, 2017, v. 31, n. 5, p. 1171, doi. 10.1007/s00477-016-1263-1
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- Article
37
Comparison of parameter uncertainty analysis techniques for a TOPMODEL application.
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- Stochastic Environmental Research & Risk Assessment, 2017, v. 31, n. 5, p. 1045, doi. 10.1007/s00477-016-1319-2
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- Article
38
Use of the Beta-Dagum and Beta-Singh-Maddala distributions for modeling hydrologic data.
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- Stochastic Environmental Research & Risk Assessment, 2017, v. 31, n. 3, p. 799, doi. 10.1007/s00477-016-1226-6
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- Article
39
Erratum to: Spatial analysis and temporal trends of daily precipitation concentration in the Mantaro River basin: central Andes of Peru.
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- 2017
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- Publication type:
- Erratum
40
Fast and direct nonparametric procedures in the L-moment homogeneity test.
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- Stochastic Environmental Research & Risk Assessment, 2017, v. 31, n. 2, p. 509, doi. 10.1007/s00477-016-1248-0
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- Article
41
Simulating low and high streamflow driven by snowmelt in an insufficiently gauged alpine basin.
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- Stochastic Environmental Research & Risk Assessment, 2016, v. 30, n. 1, p. 59, doi. 10.1007/s00477-015-1028-2
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42
Evaluation of reanalysis and satellite-based precipitation datasets in driving hydrological models in a humid region of Southern China.
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- Stochastic Environmental Research & Risk Assessment, 2015, v. 29, n. 8, p. 2003, doi. 10.1007/s00477-014-1007-z
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43
Assessing the impact of climate change on flood in an alpine catchment using multiple hydrological models.
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- Stochastic Environmental Research & Risk Assessment, 2015, v. 29, n. 8, p. 2143, doi. 10.1007/s00477-015-1062-0
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- Article
44
A PCM-based stochastic hydrological model for uncertainty quantification in watershed systems.
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- Stochastic Environmental Research & Risk Assessment, 2015, v. 29, n. 3, p. 915, doi. 10.1007/s00477-014-0954-8
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- Article
45
Impact of climate change on hydrological extremes in the Yangtze River Basin, China.
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- Stochastic Environmental Research & Risk Assessment, 2015, v. 29, n. 3, p. 693, doi. 10.1007/s00477-014-0957-5
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- Article
46
A method to reduce the computational requirement while assessing uncertainty of complex hydrological models.
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- Stochastic Environmental Research & Risk Assessment, 2015, v. 29, n. 3, p. 847, doi. 10.1007/s00477-014-0958-4
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47
On peaks-over-threshold modeling of floods with zero-inflated Poisson arrivals under stationarity and nonstationarity.
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- Stochastic Environmental Research & Risk Assessment, 2014, v. 28, n. 6, p. 1587, doi. 10.1007/s00477-013-0813-z
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48
Inverse distance interpolation for facies modeling.
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- Stochastic Environmental Research & Risk Assessment, 2014, v. 28, n. 6, p. 1373, doi. 10.1007/s00477-013-0833-8
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49
LH-moment estimation of a four parameter kappa distribution with hydrologic applications.
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- Stochastic Environmental Research & Risk Assessment, 2014, v. 28, n. 2, p. 253, doi. 10.1007/s00477-013-0746-6
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50
Flood frequency under changing climate in the upper Kafue River basin, southern Africa: a large scale hydrological model application.
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- Stochastic Environmental Research & Risk Assessment, 2013, v. 27, n. 8, p. 1883, doi. 10.1007/s00477-013-0724-z
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- Article