scholarly journals Improving WetSpa model to predict streamflows for gaged and ungaged catchments

2013 ◽  
Vol 16 (4) ◽  
pp. 758-771 ◽  
Author(s):  
Alireza Safari ◽  
F. De Smedt
Keyword(s):  
Model Performance ◽  
Flow Equation ◽  
Distributed Model ◽  
Model Parameters ◽  
Second Phase ◽  
Modified Model ◽  
Wetspa Model ◽  
Runoff Modeling ◽  
Intercomparison Project ◽  
High Flows

In the second phase of the Distributed Model Intercomparison Project (DMIP2), the WetSpa model is applied to simulate flows at basin and subbasin scales. Parent basins and their nested subbasins are modeled as gaged and ungaged basins, respectively. Available observations in the subbasins were only used to validate the model predictions. Gaged basins simulation results show that the predictions and observations are in good agreement. However, major peaks are underestimated, as is often the case in runoff modeling. Underestimation of high flows and in particular peak flows indicates that the prediction of actual runoff coefficients in the current WetSpa model needs to be improved. Also, sensitivity analysis of the model parameters reveals that the baseflow recession coefficient is the most sensitive parameter and care should be taken when modeling ungaged basins. Hence, by estimating this parameter for each subbasin separately, the model performance for the subbasins can be improved. To do this, a Boussinesq groundwater flow equation is used to improve the prediction of baseflow recession coefficients in the subbasins. Comparison between the original and the modified WetSpa models shows that the modified model yields relatively higher performances for the subbasins, creating a more accurate model for predicting ungaged subbasins.

Probabilistic climate projections with Minimal CMIP Emulator (MCE)

2021 ◽  
Author(s):  
Junichi Tsutsui
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Model Parameters ◽  
Climate Projections ◽  
Second Phase ◽  
Impulse Response Functions ◽  
Model Intercomparison ◽  
Probabilistic Climate Projections ◽  
Intercomparison Project

<p>One of the key applications of simple climate models is probabilistic climate projections to assess a variety of emission scenarios in terms of their compatibility with global warming mitigation goals. The second phase of the Reduced Complexity Model Intercomparison Project (RCMIP) compares nine participating models for their probabilistic projection methods through scenario experiments, focusing on consistency with given constraints for climate indicators including radiative forcing, carbon budget, warming trends, and climate sensitivity. The MCE is one of the nine models, recently developed by the author, and has produced results that well match the ranges of the constraints. The model is based on impulse response functions and parameterized physics of effective radiative forcing and carbon uptake over ocean and land. Perturbed model parameters are generated from statistical models and constrained with a Metropolis-Hastings independence sampler. A parameter subset associated with CO<sub>2</sub>-induced warming is assured to have a covariance structure as diagnosed from complex climate models of the Coupled Model Intercomparison Project (CMIP). The model's simplicity and the successful results imply that a method with less complicated structures and fewer control parameters has an advantage when building reasonable perturbed ensembles in a transparent way despite less capacity to emulate detailed Earth system components. Experimental results for future scenarios show that the climate sensitivity of CMIP models is overestimated overall, suggesting that probabilistic climate projections need to be constrained with observed warming trends.</p>

scholarly journals Research on Runoff Simulations Using Deep-Learning Methods

2021 ◽  
Vol 13 (3) ◽  
pp. 1336
Author(s):  
Yan Liu ◽  
Ting Zhang ◽  
Aiqing Kang ◽  
Jianzhu Li ◽  
Xiaohui Lei
Keyword(s):  
Research Area ◽  
Data Driven ◽  
Distributed Model ◽  
Model Parameters ◽  
Optimal Parameters ◽  
Output Data ◽  
Runoff Modeling ◽  
Model Component ◽  
Hanjiang River ◽  
Lstm Network

Runoff simulations are of great significance to the planning management of water resources. Here, we discussed the influence of the model component, model parameters and model input on runoff modeling, taking Hanjiang River Basin as the research area. Convolution kernel and attention mechanism were introduced into an LSTM network, and a new data-driven model Conv-TALSTM was developed. The model parameters were analyzed based on the Conv-TALSTM, and the results suggested that the optimal parameters were greatly affected by the correlation between the input data and output data. We compared the performance of Conv-TALSTM and variant models (TALSTM, Conv-LSTM, LSTM), and found that Conv-TALSTM can reproduce high flow more accurately. Moreover, the results were comparable when the model was trained with meteorological or hydrological variables, whereas the peak values with hydrological data were closer to the observations. When the two datasets were combined, the performance of the model was better. Additionally, Conv-TALSTM was also compared with an ANN (artificial neural network) and Wetspa (a distributed model for Water and Energy Transfer between Soil, Plants and Atmosphere), which verified the advantages of Conv-TALSTM in peak simulations. This study provides a direction for improving the accuracy, simplifying model structure and shortening calculation time in runoff simulations.

scholarly journals Estimation of the Effect of Changes in Forest Associations on Runoff Processes in Basins: Case Study in the Hron and Topľa River Basins

2016 ◽  
Vol 24 (3) ◽  
pp. 1-7 ◽  
Author(s):  
Peter Rončák ◽  
Kamila Hlavčová ◽  
Tamara Látková
Keyword(s):  
Land Use ◽  
Global Climate ◽  
River Basins ◽  
Distributed Model ◽  
Model Parameters ◽  
Climate Data ◽  
Global Climate Changes ◽  
Wetspa Model ◽  
Runoff Regime ◽  
The Impact

Abstract Distributed rainfall-runoff model simulations are often used to evaluate the impact of changes on the generation of runoff. These models have the advantage of reflecting the effects of land use on spatially distributed model parameters. The article deals with changes in forest associations as a result of global climate changes. In this article the WetSpa model was used for estimating the impact of forest changes on the runoff regime in the Hron and Topla river basins, with an emphasis on the parameterization of the land cover properties in the runoff simulations. The parameters of the model were estimated using climate data and three digital map layers: a land-use map, soil map and digital elevation model. This work contains two land use change scenarios of forest associations and also two scenarios of global climate change. Both types of scenarios of changes were prepared, and the runoff under the new conditions was simulated.

scholarly journals Evaluation and verification of the WetSpa model based on selected rural catchments in Poland

2010 ◽  
Vol 14 (1) ◽  
pp. 115-133 ◽  
Author(s):  
Laura Porretta-Brandyk ◽  
Jarosław Chormański ◽  
Stefan Ignar ◽  
Tomasz Okruszko ◽  
Andrzej Brandyk ◽  
...  
Keyword(s):  
Soil Cover ◽  
Capillary Rise ◽  
Spatial Diversity ◽  
Distributed Model ◽  
Model Parameters ◽  
Model Quality ◽  
Model Based ◽  
Wetspa Model ◽  
Distributed Parameters

Evaluation and verification of the WetSpa model based on selected rural catchments in Poland The paper presents results of calibration and verification of the WetSpa model, which enables the modelling of rainfall-runoff process based on mass and energy balance in the soil-plantatmosphere system in the catchment. It is a model with distributed parameters, using the structure of raster GIS model to determine the spatial diversity of the catchment environment. This enables simulation of runoff from the catchment, including: precipitation, evapotranspiration, interception of plant surface and soil cover, infiltration and capillary rise in soil and groundwater runoff. Simulated processes depend on the required non-distributed parameters, which were calibrated based on hydrometeorological data from the three rural catchments with different physical and geographical characteristics: Mławka, located in the Wkra basin in Central Poland and the rivers Kamienna and Sidra, which are tributaries of the upper Biebrza in north-eastern part of the country. Distributed catchment parameters were specified on the basis of digital soil maps, land use maps and digital elevation model using GIS techniques. Non-distributed model parameters were calibrated for the three catchments using automatic techniques based on the PEST algorithm. The obtained values of these parameters were scrutinized in order to analyse differences resulting from various characteristics of the study areas. The quality of the model was verified upon dependent and independent data. Appropriate quality measures, including Nash-Sutcliffe efficiency measure, were used to assess model quality. For two catchments (the Sidra and Kamienna) the model showed a satisfactory quality for modelling high flows, it was, however, not satisfactory for low flows. The values for the Mławka catchment justified the assessment of the model quality measurements as very good and good. The factors most affecting the process of river outflow formation were determined using the analysis of model sensitivity to relative changes in parameter values. It was found that the evaluation of the model quality depended largely on the quality of meteorological data and proper parameterization of the soil cover.

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

Development of an Inverse Plume Model for Mass Eruption Rate in Unsteady Conditions

2021 ◽  
Author(s):  
Stephen A Solovitz
Keyword(s):  
Laboratory Data ◽  
Model Performance ◽  
Volcanic Eruptions ◽  
Reynolds Numbers ◽  
Model Parameters ◽  
Plume Height ◽  
Eruption Rate ◽  
Mass Eruption Rate ◽  
Downstream Effects ◽  
Order Of Magnitude

Abstract Following volcanic eruptions, forecasters need accurate estimates of mass eruption rate (MER) to appropriately predict the downstream effects. Most analyses use simple correlations or models based on large eruptions at steady conditions, even though many volcanoes feature significant unsteadiness. To address this, a superposition model is developed based on a technique used for spray injection applications, which predicts plume height as a function of the time-varying exit velocity. This model can be inverted, providing estimates of MER using field observations of a plume. The model parameters are optimized using laboratory data for plumes with physically-relevant exit profiles and Reynolds numbers, resulting in predictions that agree to within 10% of measured exit velocities. The model performance is examined using a historic eruption from Stromboli with well-documented unsteadiness, again providing MER estimates of the correct order of magnitude. This method can provide a rapid alternative for real-time forecasting of small, unsteady eruptions.

scholarly journals Conditioning rainfall-runoff model parameters for ungauged catchments and land management impacts analysis

2009 ◽  
Vol 13 (6) ◽  
pp. 893-904 ◽  
Author(s):  
N. Bulygina ◽  
N. McIntyre ◽  
H. Wheater
Keyword(s):  
Parameter Space ◽  
Land Management ◽  
Base Flow ◽  
Distributed Model ◽  
Flow Index ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Management Interventions ◽  
Simulated Event ◽  
Base Flow Index

Abstract. Data scarcity and model over-parameterisation, leading to model equifinality and large prediction uncertainty, are common barriers to effective hydrological modelling. The problem can be alleviated by constraining the prior parameter space using parameter regionalisation. A common basis for regionalisation in the UK is the HOST database which provides estimates of hydrological indices for different soil classifications. In our study, Base Flow Index is estimated from the HOST database and the power of this index for constraining the parameter space is explored. The method is applied to a highly discretised distributed model of a 12.5 km2 upland catchment in Wales. To assess probabilistic predictions against flow observations, a probabilistic version of the Nash-Sutcliffe efficiency is derived. For six flow gauges with reliable data, this efficiency ranged between 0.70 and 0.81, and inspection of the results shows that the model explains the data well. Knowledge of how Base Flow Index and interception losses may change under future land use management interventions was then used to further condition the model. Two interventions are considered: afforestation of grazed areas, and soil degradation associated with increased grazing intensity. Afforestation leads to median reduction in modelled runoff volume of 24% over the simulated 3 month period; and a median peak flow reduction ranging from 12 to 15% over the six gauges for the largest simulated event. Uncertainty in all results is low compared to prior uncertainty and it is concluded that using Base Flow Index estimated from HOST is a simple and potentially powerful method of conditioning the parameter space under current and future land management.

scholarly journals Incremental model breakdown to assess the multi-hypotheses problem

2018 ◽  
Vol 22 (8) ◽  
pp. 4565-4581 ◽  
Author(s):  
Florian U. Jehn ◽  
Lutz Breuer ◽  
Tobias Houska ◽  
Konrad Bestian ◽  
Philipp Kraft
Keyword(s):  
Hydrological Modeling ◽  
Model Performance ◽  
Complex Model ◽  
Model Parameters ◽  
Objective Functions ◽  
Final Model ◽  
Incremental Model ◽  
Model Efficiency ◽  
Improved Model ◽  
Model Structures

Abstract. The ambiguous representation of hydrological processes has led to the formulation of the multiple hypotheses approach in hydrological modeling, which requires new ways of model construction. However, most recent studies focus only on the comparison of predefined model structures or building a model step by step. This study tackles the problem the other way around: we start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash–Sutcliffe; percentage bias, PBIAS; and the ratio between the root mean square error and the standard deviation of the measured data). Through this incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 and the final Model 15 with HBV Light. The final model is not able to outperform HBV Light, but we find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and fewer model parameters.

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