Improvements of Runoff Models What Way to Go?

1992 ◽  
Vol 23 (5) ◽  
pp. 315-332 ◽  
Author(s):  
Lotta Andersson
Keyword(s):  
Model Performance ◽  
Snow Accumulation ◽  
Model Complexity ◽  
Remote Sensing Images ◽  
Landscape Mosaic ◽  
Distributed Models ◽  
Before And After ◽  
Physically Based ◽  
Model Improvement ◽  
Runoff Model

Model performance before and after the introduction of some alternative routines for calculation of evaporation, snow accumulation and melt with the PULSE/HBV runoff model were compared. The results showed that improvements were, in the best cases, small. Sometimes model fits deteriorated as a result of increased model complexity. On the basis of these, and from other experiences of attempts of model improvements, the success potentials for various efforts of model sophistication are discussed. It is hypothesised that model improvement cannot be achieved by increasing the complexity of some sub-routines, without considering the problems that are linked to spatial resolution of driving variables and the spatial distribution of physiographic parameters. It is suggested that physically based and conceptual schools of modelling can meet in a landscape mosaic context, with development of distributed models, based on information generally available from maps, remote-sensing images and meteorological stations.

scholarly journals Inverse distributed hydrological modelling of Alpine catchments

2006 ◽  
Vol 10 (3) ◽  
pp. 395-412 ◽  
Author(s):  
H. Kunstmann ◽  
J. Krause ◽  
S. Mayr
Keyword(s):  
Parameter Estimation ◽  
Hydrological Model ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Nonlinear Parameter ◽  
Model Complexity ◽  
Distributed Hydrological Model ◽  
Nonlinear Parameter Estimation ◽  
Estimated Parameters ◽  
Physically Based

Abstract. Even in physically based distributed hydrological models, various remaining parameters must be estimated for each sub-catchment. This can involve tremendous effort, especially when the number of sub-catchments is large and the applied hydrological model is computationally expensive. Automatic parameter estimation tools can significantly facilitate the calibration process. Hence, we combined the nonlinear parameter estimation tool PEST with the distributed hydrological model WaSiM. PEST is based on the Gauss-Marquardt-Levenberg method, a gradient-based nonlinear parameter estimation algorithm. WaSiM is a fully distributed hydrological model using physically based algorithms for most of the process descriptions. WaSiM was applied to the alpine/prealpine Ammer River catchment (southern Germany, 710 km2 in a 100×100 m2 horizontal resolution. The catchment is heterogeneous in terms of geology, pedology and land use and shows a complex orography (the difference of elevation is around 1600 m). Using the developed PEST-WaSiM interface, the hydrological model was calibrated by comparing simulated and observed runoff at eight gauges for the hydrologic year 1997 and validated for the hydrologic year 1993. For each sub-catchment four parameters had to be calibrated: the recession constants of direct runoff and interflow, the drainage density, and the hydraulic conductivity of the uppermost aquifer. Additionally, five snowmelt specific parameters were adjusted for the entire catchment. Altogether, 37 parameters had to be calibrated. Additional a priori information (e.g. from flood hydrograph analysis) narrowed the parameter space of the solutions and improved the non-uniqueness of the fitted values. A reasonable quality of fit was achieved. Discrepancies between modelled and observed runoff were also due to the small number of meteorological stations and corresponding interpolation artefacts in the orographically complex terrain. Application of a 2-dimensional numerical groundwater model partly yielded a slight decrease of overall model performance when compared to a simple conceptual groundwater approach. Increased model complexity therefore did not yield in general increased model performance. A detailed covariance analysis was performed allowing to derive confidence bounds for all estimated parameters. The correlation between the estimated parameters was in most cases negligible, showing that parameters were estimated independently from each other.

scholarly journals Comparing model performance of two rainfall-runoff models in the Rhine basin using different atmospheric forcing data sets

2007 ◽  
Vol 4 (6) ◽  
pp. 4325-4360 ◽  
Author(s):  
A. H. te Linde ◽  
J. C. J. H. Aerts ◽  
R. T. W. L. Hurkmans ◽  
M. Eberle
Keyword(s):  
Climate Change ◽  
Model Performance ◽  
Change Scenario ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Meteorological Forcing ◽  
Lumped Model ◽  
Distributed Models ◽  
Rhine Basin ◽  
Physically Based

Abstract. Due to the growing wish and necessity to simulate the possible effects of climate change on the discharge regime on large rivers such as the Rhine in Europe, there is a need for well performing hydrological models that can be applied in climate change scenario studies. There exists large variety in available models and there is an ongoing debate in research on rainfall-runoff modelling on whether or not physically based distributed models better represent observed discharges than conceptual lumped model approaches do. In this paper, the hydrological models HBV and VIC were compared for the Rhine basin by testing their performance in simulating discharge. Overall, the semi-distributed conceptual HBV model performed much better than the distributed physically based VIC model (E=0.62, r2=0.65 vs. E=0.31, r2=0.54 at Lobith). It is argued here that even for a well-documented river basin such as the Rhine, more complex modelling does not automatically lead to better results. Moreover, it is concluded that meteorological forcing data has a considerable influence on model performance, irrespectively to the type of model structure and the need for ground-based meteorological measurements is emphasized.

scholarly journals Prediction of snowmelt derived streamflow in a wetland dominated prairie basin

2010 ◽  
Vol 14 (6) ◽  
pp. 991-1006 ◽  
Author(s):  
X. Fang ◽  
J. W. Pomeroy ◽  
C. J. Westbrook ◽  
X. Guo ◽  
A. G. Minke ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Model Performance ◽  
Open Water ◽  
Snow Accumulation ◽  
River Channel ◽  
Model Parameters ◽  
Runoff Generation ◽  
Stream Network ◽  
Canadian Prairie ◽  
Physically Based

Abstract. The Cold Regions Hydrological Modelling platform (CRHM) was used to create a prairie hydrological model for Smith Creek Research Basin (~400 km2), east-central Saskatchewan, Canada. Physically based modules were sequentially linked in CRHM to simulate snow processes, frozen soils, variable contributing area and wetland storage and runoff generation. Five "representative basins" (RBs) were defined and each was divided into seven hydrological response units (HRUs): fallow, stubble, grassland, river channel, open water, woodland, and wetland. Model parameters were estimated using field survey data, LiDAR digital elevation model (DEM), SPOT 5 satellite imageries, stream network and wetland inventory GIS data. Model simulations were conducted for 2007/2008 and 2008/2009. No calibration was performed. The model performance in predicting snowpack, soil moisture and streamflow was evaluated against field observations. Root mean square differences (RMSD) between simulation and observations ranged from 1.7 to 25.2 mm and from 4.3 to 22.4 mm for the simulated snow accumulation in 2007/2008 and 2008/2009, respectively, with higher RMSD in grassland, river channel, and open water HRUs. Spring volumetric soil moisture was reasonably predicted compared to a point observation in a grassland area, with RMSD of 0.011 and 0.009 for 2008 and 2009 simulations, respectively. The model was able to capture the timing and magnitude of peak spring basin discharge, but it underestimated the cumulative volume of basin discharge by 32% and 56% in spring 2008 and 2009, respectively. The results suggest prediction of Canadian Prairie basin snow hydrology is possible with no calibration if physically based models are used with physically meaningful model parameters that are derived from high resolution geospatial data.

Predictions in catchment hydrology: an Australian perspective

2001 ◽  
Vol 52 (1) ◽  
pp. 65 ◽  
Author(s):  
B. F. W. Croke ◽  
A. J. Jakeman
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Model Performance ◽  
Model Complexity ◽  
Hydrological Response ◽  
Distributed Models ◽  
General Terms ◽  
Other Information ◽  
Principal Factors ◽  
Concept Of Information

Throughout Australia, there are strong regional differences in hydrological response to landscape and climate; however, in general terms, in Australian catchments the flows are typically peakier, base flows are of lower proportion, runoff coefficients are smaller, and dry periods are longer and more variable, than in European and North American catchments. In this context, this paper assesses the model types available to improve understanding and prediction of catchment flows and transport. Included in this is the concept of information and its influence on appropriate model complexity, as well as a characterization of the principal factors inhibiting model performance. The ability to predict the effects on flows and water quality of anything but major changes in climate and land use is limited. Improvement of understanding and prediction relies on the following: more rigorous testing of models to assess their ability to separate climate and land use effects on hydrological response; the use and improved interpretation of spatial data; more and better monitoring of hydrological response at a range of scales; complementary use of conceptual and distributed models; and integration of modelling with other information such as that from geochemical studies including tracer analysis.

scholarly journals CPT to RVU conversion improves model performance in the prediction of surgical case length

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicholas Garside ◽  
Hamed Zaribafzadeh ◽  
Ricardo Henao ◽  
Royce Chung ◽  
Daniel Buckland
Keyword(s):  
Model Performance ◽  
Individual Case ◽  
Model Complexity ◽  
Actual Case ◽  
Surgical Case ◽  
Relative Value ◽  
Prediction Time ◽  
Boosted Decision Tree ◽  
Tree Models ◽  
Duke University

AbstractMethods used to predict surgical case time often rely upon the current procedural terminology (CPT) code as a nominal variable to train machine-learned models, however this limits the ability of the model to incorporate new procedures and adds complexity as the number of unique procedures increases. The relative value unit (RVU, a consensus-derived billing indicator) can serve as a proxy for procedure workload and could replace the CPT code as a primary feature for models that predict surgical case length. Using 11,696 surgical cases from Duke University Health System electronic health records data, we compared boosted decision tree models that predict individual case length, changing the method by which the model coded procedure type; CPT, RVU, and CPT–RVU combined. Performance of each model was assessed by inference time, MAE, and RMSE compared to the actual case length on a test set. Models were compared to each other and to the manual scheduler method that currently exists. RMSE for the RVU model (60.8 min) was similar to the CPT model (61.9 min), both of which were lower than scheduler (90.2 min). 65.2% of our RVU model’s predictions (compared to 43.2% from the current human scheduler method) fell within 20% of actual case time. Using RVUs reduced model prediction time by ninefold and reduced the number of training features from 485 to 44. Replacing pre-operative CPT codes with RVUs maintains model performance while decreasing overall model complexity in the prediction of surgical case length.

Multi-Criteria and Time Dependent Sensitivity Analysis of an Event-Oriented and Physically-Based Distributed Sediment and Runoff Model

2021 ◽  
pp. 126268
Author(s):  
Menberu B. Meles ◽  
Dave C. Goodrich ◽  
Hoshin V. Gupta ◽  
I. Shea Burns ◽  
Carl L. Unkrich ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Time Dependent ◽  
Physically Based ◽  
Runoff Model

scholarly journals Sensitivity and Performance Analyses of the Distributed Hydrology–Soil–Vegetation Model Using Geomorphons for Landform Mapping

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2032
Author(s):  
Pâmela A. Melo ◽  
Lívia A. Alvarenga ◽  
Javier Tomasella ◽  
Carlos R. Mello ◽  
Minella A. Martins ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Overland Flow ◽  
Hydrological Model ◽  
Model Performance ◽  
Soil Parameters ◽  
Vegetation Model ◽  
Landform Classification ◽  
Physically Based ◽  
And Performance ◽  
Streamflow Simulation

Landform classification is important for representing soil physical properties varying continuously across the landscape and for understanding many hydrological processes in watersheds. Considering it, this study aims to use a geomorphology map (Geomorphons) as an input to a physically based hydrological model (Distributed Hydrology Soil Vegetation Model (DHSVM)) in a mountainous headwater watershed. A sensitivity analysis of five soil parameters was evaluated for streamflow simulation in each Geomorphons feature. As infiltration and saturation excess overland flow are important mechanisms for streamflow generation in complex terrain watersheds, the model’s input soil parameters were most sensitive in the “slope”, “hollow”, and “valley” features. Thus, the simulated streamflow was compared with observed data for calibration and validation. The model performance was satisfactory and equivalent to previous simulations in the same watershed using pedological survey and moisture zone maps. Therefore, the results from this study indicate that a geomorphologically based map is applicable and representative for spatially distributing hydrological parameters in the DHSVM.

scholarly journals ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1339 ◽  
Author(s):  
Shuo Liu ◽  
Wenrui Ding ◽  
Chunhui Liu ◽  
Yu Liu ◽  
Yufeng Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Qualitative Evaluation ◽  
Semantic Segmentation ◽  
Model Complexity ◽  
Semantic Ambiguity ◽  
Remote Sensing Images ◽  
Future Networks ◽  
Spatial Boundary ◽  
Boundary Information

The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.

scholarly journals Inundation mapping based on reach-scale effective geometry

2018 ◽  
Vol 22 (11) ◽  
pp. 5967-5985 ◽  
Author(s):  
Cédric Rebolho ◽  
Vazken Andréassian ◽  
Nicolas Le Moine
Keyword(s):  
Model Performance ◽  
Model Complexity ◽  
Hydraulic Geometry ◽  
Future Event ◽  
New Approach ◽  
Flow Equations ◽  
Geometrical Properties ◽  
Inundation Maps ◽  
Extreme Flood ◽  
Inundation Mapping

Abstract. The production of spatially accurate representations of potential inundation is often limited by the lack of available data as well as model complexity. We present in this paper a new approach for rapid inundation mapping, MHYST, which is well adapted for data-scarce areas; it combines hydraulic geometry concepts for channels and DEM data for floodplains. Its originality lies in the fact that it does not work at the cross section scale but computes effective geometrical properties to describe the reach scale. Combining reach-scale geometrical properties with 1-D steady-state flow equations, MHYST computes a topographically coherent relation between the “height above nearest drainage” and streamflow. This relation can then be used on a past or future event to produce inundation maps. The MHYST approach is tested here on an extreme flood event that occurred in France in May–June 2016. The results indicate that it has a tendency to slightly underestimate inundation extents, although efficiency criteria values are clearly encouraging. The spatial distribution of model performance is discussed and it shows that the model can perform very well on most reaches, but has difficulties modelling the more complex, urbanised reaches. MHYST should not be seen as a rival to detailed inundation studies, but as a first approximation able to rapidly provide inundation maps in data-scarce areas.

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