scholarly journals StreamFlow 1.0: An extension to the spatially distributed snow model Alpine3D for hydrological modeling and deterministic stream temperature prediction

2016 ◽  
Author(s):  
Aurélien Gallice ◽  
Mathias Bavay ◽  
Tristan Brauchli ◽  
Francesco Comola ◽  
Michael Lehning ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Source Code ◽  
Stream Temperature ◽  
Object Oriented Programming ◽  
Temperature Prediction ◽  
Uncertainty Estimates ◽  
Physically Based ◽  
Snow Model ◽  
Alpine Catchments

Abstract. Climate change is expected to strongly impact the hydrological and thermal regimes of Alpine rivers within the coming decades. In this context, the development of hydrological models accounting for the specific dynamics of Alpine catchments appears as a one of the promising approaches to reduce our uncertainty on future mountain hydrology. This paper describes the improvements brought to StreamFlow, an existing model for hydrological and stream temperature prediction built as an external extension to the physically-based snow model Alpine3D. StreamFlow's source code has been entirely written anew, taking advantage of object-oriented programming to significantly improve its structure and ease the implementation of future developments. The source code is now publicly available online, along with a complete documentation. A special emphasis has been put on modularity during the re-implementation of StreamFlow, so that many model aspects can be represented using different alternatives. For example, several options are now available to model the advection of water within the stream. This allows for an easy and fast comparison between different approaches and helps in defining more reliable uncertainty estimates of the model forecasts. In particular, a case study in a Swiss Alpine catchment reveals that the stream temperature predictions are particularly sensitive to the approach used to model the temperature of subsurface runoff, a fact which has been poorly reported in the literature to date. Based on the case study, StreamFlow is shown to reproduce hourly mean discharge with a Nash–Sutcliffe efficiency (NSE) of 0.82, and hourly mean temperature with a NSE of 0.78.

scholarly journals StreamFlow 1.0: an extension to the spatially distributed snow model Alpine3D for hydrological modelling and deterministic stream temperature prediction

2016 ◽  
Vol 9 (12) ◽  
pp. 4491-4519 ◽  
Author(s):  
Aurélien Gallice ◽  
Mathias Bavay ◽  
Tristan Brauchli ◽  
Francesco Comola ◽  
Michael Lehning ◽  
...  
Keyword(s):  
Source Code ◽  
Stream Temperature ◽  
Object Oriented Programming ◽  
Temperature Prediction ◽  
Uncertainty Estimates ◽  
Physically Based ◽  
Snow Model ◽  
Alpine Catchments ◽  
Alpine Rivers

Abstract. Climate change is expected to strongly impact the hydrological and thermal regimes of Alpine rivers within the coming decades. In this context, the development of hydrological models accounting for the specific dynamics of Alpine catchments appears as one of the promising approaches to reduce our uncertainty of future mountain hydrology. This paper describes the improvements brought to StreamFlow, an existing model for hydrological and stream temperature prediction built as an external extension to the physically based snow model Alpine3D. StreamFlow's source code has been entirely written anew, taking advantage of object-oriented programming to significantly improve its structure and ease the implementation of future developments. The source code is now publicly available online, along with a complete documentation. A special emphasis has been put on modularity during the re-implementation of StreamFlow, so that many model aspects can be represented using different alternatives. For example, several options are now available to model the advection of water within the stream. This allows for an easy and fast comparison between different approaches and helps in defining more reliable uncertainty estimates of the model forecasts. In particular, a case study in a Swiss Alpine catchment reveals that the stream temperature predictions are particularly sensitive to the approach used to model the temperature of subsurface flow, a fact which has been poorly reported in the literature to date. Based on the case study, StreamFlow is shown to reproduce hourly mean discharge with a Nash–Sutcliffe efficiency (NSE) of 0.82 and hourly mean temperature with a NSE of 0.78.

scholarly journals Improving uncertainty estimation in urban hydrological modeling by statistically describing bias

2013 ◽  
Vol 17 (10) ◽  
pp. 4209-4225 ◽  
Author(s):  
D. Del Giudice ◽  
M. Honti ◽  
A. Scheidegger ◽  
C. Albert ◽  
P. Reichert ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Hydrological Modeling ◽  
Parametric Uncertainty ◽  
Validation Data ◽  
Error Type ◽  
Natural Case ◽  
Uncertainty Estimates ◽  
Optimal Bias ◽  
Parsimonious Model

Abstract. Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and relatively efficient methodology (i) to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii) to separate prediction uncertainty into its components. Our approach acknowledges that urban drainage predictions are biased. This is mostly due to input errors and structural deficits of the model. We address this issue by describing model bias in a Bayesian framework. The bias becomes an autoregressive term additional to white measurement noise, the only error type accounted for in traditional uncertainty analysis. To allow for bigger discrepancies during wet weather, we make the variance of bias dependent on the input (rainfall) or/and output (runoff) of the system. Specifically, we present a structured approach to select, among five variants, the optimal bias description for a given urban or natural case study. We tested the methodology in a small monitored stormwater system described with a parsimonious model. Our results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected. Furthermore, our probabilistic predictions can discriminate between three uncertainty contributions: parametric uncertainty, bias, and measurement errors. In our case study, the best performing bias description is the output-dependent bias using a log-sinh transformation of data and model results. The limitations of the framework presented are some ambiguity due to the subjective choice of priors for bias parameters and its inability to address the causes of model discrepancies. Further research should focus on quantifying and reducing the causes of bias by improving the model structure and propagating input uncertainty.

scholarly journals Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile

2021 ◽  
Author(s):  
Annelies Voordendag ◽  
Marion Réveillet ◽  
Shelley MacDonell ◽  
Stef Lhermitte
Keyword(s):  
Model Calibration ◽  
Model Comparison ◽  
Meteorological Forcing ◽  
Local Point ◽  
Physically Based ◽  
Order Of Magnitude ◽  
Snow Model ◽  
Snow Physics ◽  
Semi Arid

Abstract. Physically-based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, issues still arise, especially in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point, over one snow season. Their sensitivity relative i) to physical calibration for albedo and fresh snow density and ii) to forcing perturbation is evaluated based on ensemble approaches. Results indicate larger uncertainty depending on the model calibration than between the two models (even though the significant differences in their physical complexity). We also confirm the importance of albedo parameterization, even though ablation is driven by sublimation. SnowModel is particularly sensitive to this choice as it strongly affects both the sublimation and the melt rates. However, the day of snow-free snow surface is not sensitive to the parameterization as it only varies every eight days. The albedo parameterization of SNOWPACK has stronger consequences on melt at the end of the season leading to a date difference of the end of the season of 41 days. However, despite these differences, the sublimation ratio ranges are in agreement for the two models: 42.7–63.5 % for SnowModel and 51.3 and 64.6 % for SNOWPACK, and are related to the albedo calibration choice for the two models. Finally, the sensitivity of both models to the forcing data was in the same order of magnitude and highly influenced by the precipitation uncertainties.

scholarly journals Improving uncertainty estimation in urban hydrological modeling by statistically describing bias

2013 ◽  
Vol 10 (4) ◽  
pp. 5121-5167 ◽  
Author(s):  
D. Del Giudice ◽  
M. Honti ◽  
A. Scheidegger ◽  
C. Albert ◽  
P. Reichert ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Hydrological Modeling ◽  
Parametric Uncertainty ◽  
Computationally Efficient ◽  
Validation Data ◽  
Error Type ◽  
Natural Case ◽  
Uncertainty Estimates ◽  
Optimal Bias

Abstract. Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and computationally efficient methodology (i) to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii) to separate prediction uncertainty into its components. Our approach acknowledges that urban drainage predictions are biased. This is mostly due to input errors and structural deficits of the model. We address this issue by describing model bias in a Bayesian framework. The bias becomes an autoregressive term additional to white measurement noise, the only error type accounted for in traditional uncertainty analysis in urban hydrology. To allow for bigger discrepancies during wet weather, we make the variance of bias dependent on the input (rainfall) or/and output (runoff) of the system. Specifically, we present a structured approach to select, among five variants, the optimal bias description for a given urban or natural case study. We tested the methodology in a small monitored stormwater system described by means of a parsimonious model. Our results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected. Furthermore, our probabilistic predictions can discriminate between three uncertainty contributions: parametric uncertainty, bias (due to input and structural errors), and measurement errors. In our case study, the best performing bias description was the output-dependent bias using a log-sinh transformation of data and model results. The limitations of the framework presented are some ambiguity due to the subjective choice of priors for bias parameters and its inability to directly reduce the causes of bias. More experience with the application of this framework will lead to a greater prior knowledge for a bias formulation. Furthermore, propagation of input uncertainty and improvement to the model structure are expected to reduce the bias.

A methodology for monitoring and modeling of high altitude Alpine catchments

2017 ◽  
Vol 41 (4) ◽  
pp. 393-420 ◽  
Author(s):  
A Soncini ◽  
D Bocchiola ◽  
RS Azzoni ◽  
G Diolaiuti
Keyword(s):  
High Altitude ◽  
Hydrological Modeling ◽  
Hydrological Cycle ◽  
Climate Data ◽  
Full Account ◽  
Physically Based ◽  
The Alps ◽  
Field Campaigns ◽  
Alpine Catchments

Hydrological monitoring and modeling of high altitude Alpine catchments is of paramount importance. This is difficult, however, given the complex logistics of field campaigns and the need for long-term data. Here, we present a method for long term monitoring of high altitude catchments, which we tested within the Alps of Italy. This includes i) extensive gathering of climate data and hydrological fluxes, ii) high altitude field campaigns, and iii) robust physically based glacio-hydrological modeling, providing full account of ice flow, ice and snow ablation, and stream flows. We present an application of this method based on six years (2009–2014) of field monitoring in the Dosdè catchment, in the Italian Alps (17 km2, average altitude 2858 masl, outlet 2133 masl), nesting 1.90 km2 of glaciers. We demonstrate that i) high altitude Alpine catchments can be monitored in spite of geographical complexity, and ii) a data based approach delivers accurate stream flow estimates and improves our knowledge of flow components in the high altitudes. We then provide some estimates of the recent glaciers’ dynamics, and water resources from this high-altitude catchment, paradigmatic of the recent cryospheric evolution in the Alps of Italy. We estimated an average ice mass loss nearby −1.76E8 m3yr−1, i.e. −20% of the ice mass in 2009, possibly pointing to accelerated glaciers’ down wasting. Instream discharges increased (+0.12 m3s−1y−1); however, this requires further monitoring. We then benchmark our findings against recent studies in the Alps, and other glacierized areas worldwide, displaying similarities in present glaciers’ dynamics. We suggest that our robust, yet flexible approach can be used for glacio-hydrological investigation in Alpine (and generally mountain) rivers, and for conjectures of potential future hydrological cycle under climate scenarios.

scholarly journals Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile

2021 ◽  
Vol 15 (9) ◽  
pp. 4241-4259
Author(s):  
Annelies Voordendag ◽  
Marion Réveillet ◽  
Shelley MacDonell ◽  
Stef Lhermitte
Keyword(s):  
Model Comparison ◽  
Roughness Length ◽  
Meteorological Forcing ◽  
Local Point ◽  
Physically Based ◽  
Order Of Magnitude ◽  
Snow Model ◽  
Snow Physics ◽  
Semi Arid

Abstract. Physically based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, no snow model comparison has been done in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point over one snow season and to evaluate their sensitivity relative to parameterisation and forcing. For that purpose, the two models are forced with (i) the most ideal set of input parameters, (ii) an ensemble of different physical parameterisations, and (iii) an ensemble of biased forcing. Results indicate large uncertainties depending on forcing, the snow roughness length z0, albedo parameterisation, and fresh snow density parameterisation. The uncertainty caused by the forcing is directly related to the bias chosen. Even though the models show significant differences in their physical complexity, the snow model choice is of least importance, as the sensitivity of both models to the forcing data was on the same order of magnitude and highly influenced by the precipitation uncertainties. The sublimation ratio ranges are in agreement for the two models: 36.4 % to 80.7 % for SnowModel and 36.3 % to 86.0 % for SNOWPACK, and are related to the albedo parameterisation and snow roughness length choice for the two models.

scholarly journals Decision Support for the (Inter-)Basin Management of Water Resources Using Integrated Hydro-Economic Modeling

Hydrology ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Gerald Norbert Souza da Silva ◽  
Márcia Maria Guedes Alcoforado de Moraes
Keyword(s):  
Decision Support ◽  
Water Resources ◽  
Water Allocation ◽  
Hydrological Modeling ◽  
Well Being ◽  
Water Transfer ◽  
Economic Modeling ◽  
Trade Offs ◽  
Study Results

The development of adequate modeling at the basin level to establish public policies has an important role in managing water resources. Hydro-economic models can measure the economic effects of structural and non-structural measures, land and water management, ecosystem services and development needs. Motivated by the need of improving water allocation using economic criteria, in this study, a Spatial Decision Support System (SDSS) with a hydro-economic optimization model (HEAL system) was developed and used for the identification and analysis of an optimal economic allocation of water resources in a case study: the sub-middle basin of the São Francisco River in Brazil. The developed SDSS (HEAL system) made the economically optimum allocation available to analyze water allocation conflicts and trade-offs. With the aim of providing a tool for integrated economic-hydrological modeling, not only for researchers but also for decision-makers and stakeholders, the HEAL system can support decision-making on the design of regulatory and economic management instruments in practice. The case study results showed, for example, that the marginal benefit function obtained for inter-basin water transfer, can contribute for supporting the design of water pricing and water transfer decisions, during periods of water scarcity, for the well-being in both basins.

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