scholarly journals Bias-correction schemes for calibrated flow in a conceptual hydrological model

2021 ◽  
Author(s):  
Kue Bum Kim ◽  
Hyun-Han Kwon ◽  
Dawei Han
Keyword(s):  
Bias Correction ◽  
Hydrological Model ◽  
Post Processing ◽  
Rating Curve ◽  
Baseline Model ◽  
Extended Range ◽  
Entire Flow ◽  
Systematic Biases ◽  
High Flows ◽  
Simulated Flow

Abstract We explore post-processing methods that can reduce biases in simulated flow in a hydrological model (HYMOD). Here, three bias-correction methods are compared using a set of calibrated parameters as a baseline (Cases 1 and 5). The proposed bias-correction methods are based on a flow duration curve (Case 2), an autoregressive model based on residuals obtained from simulated flows (Case 3), and a rating curve (Case 4). A clear seasonality representing a more substantial variability in winter than summer was evident in all cases. The extended range of residuals was usually observed in winter, indicating that the HYMOD model may not reproduce high flows appropriately. This study confirmed that bias-corrected flows are more effective than the baseline model in terms of correcting a systematic error in the simulated flow. Moreover, a comparison of root mean square error over different flow regimes demonstrates that Case 3 is the most effective at correcting systematic biases over the entire flow regime. Finally, monthly water balances for all cases are evaluated and compared during both calibration and validation periods. The water balance in Case 3 is also closer to the observed values. The effects of different post-processing approaches on the performance of bias-correction are examined and discussed.

scholarly journals Statistical processing of forecasts for hydrological ensemble prediction: a comparative study of different bias correction strategies

2012 ◽  
Vol 8 (1) ◽  
pp. 135-141 ◽  
Author(s):  
I. Zalachori ◽  
M.-H. Ramos ◽  
R. Garçon ◽  
T. Mathevet ◽  
J. Gailhard
Keyword(s):  
Bias Correction ◽  
Hydrological Model ◽  
Statistical Processing ◽  
Ensemble Prediction ◽  
Post Processing ◽  
Streamflow Forecasts ◽  
The Impact ◽  
Correction Strategies ◽  
Precipitation And Temperature

Abstract. The aim of this paper is to investigate the use of statistical correction techniques in hydrological ensemble prediction. Ensemble weather forecasts (precipitation and temperature) are used as forcing variables to a hydrologic forecasting model for the production of ensemble streamflow forecasts. The impact of different bias correction strategies on the quality of the forecasts is examined. The performance of the system is evaluated when statistical processing is applied: to precipitation and temperature forecasts only (pre-processing from the hydrological model point of view), to flow forecasts (post-processing) and to both. The pre-processing technique combines precipitation ensemble predictions with an analog forecasting approach, while the post-processing is based on past errors of the hydrological model when simulating streamflows. Forecasts from 11 catchments in France are evaluated. Results illustrate the importance of taking into account hydrological uncertainties to improve the quality of operational streamflow forecasts.

scholarly journals Continuous modelling of the Bouregreg watershed (Morocco) using the HEC-HMS model

2021 ◽  
Vol 314 ◽  
pp. 05001
Author(s):  
Oussama Laassilia ◽  
Driss Ouazar ◽  
Ahmed Bouziane ◽  
Moulay Driss Hasnaoui
Keyword(s):  
Annual Variation ◽  
Hydrological Model ◽  
Deep Understanding ◽  
Rainfall Runoff ◽  
Unit Hydrograph ◽  
Soil Moisture Accounting ◽  
Change Impact ◽  
Loss Method ◽  
Acceptable Agreement ◽  
Simulated Flow

A deep understanding of the rainfall-runoff mechanism is essential to estimate the runoff generated in a given basin. In this regard, this paper aims to develop a continuous hydrological model of the Bouregreg watershed. The objective of this modelling is to evaluate the inflow to the Sidi Mohamed Ben Abdellah (SMBA) dam, located at the outlet of this basin. To this end, using the HEC-HMS model, the Soil Moisture Accounting (SMA) Loss Method was used to model infiltration losses. The SCS Unit hydrograph (SCS UH) and the Recession method were chosen as transform model and baseflow model, respectively. As a result, the comparison shows an acceptable agreement between observed and simulated flow in terms of streamflow distribution and peak values (NSE=0.57, R2=0.58). During validation, the model retained its ability to sufficiently reproduce the rainfall-runoff mechanism of the studied basin with a slight overestimation of peaks (NSE=0.61, R2=0.60). This study allows to assess and predict the inter-annual and intra-annual variation of the SMBA dam reservoir’ inflows, and therefore to forecast the climate change impact on this basin.

scholarly journals Estimating uncertainties in hydraulicallymodelled rating curves for discharge time series assessment

2018 ◽  
Vol 40 ◽  
pp. 06013
Author(s):  
Valentin Mansanarez ◽  
Ida K. Westerberg ◽  
Steve W. Lyon ◽  
Norris Lam
Keyword(s):  
Time Series ◽  
Hydraulic Model ◽  
Observation Data ◽  
Rating Curve ◽  
Hydraulic Modelling ◽  
Discharge Time ◽  
High Flows ◽  
Rating Curves ◽  
Hydrometric Station ◽  
The Impact

Establishing a reliable stage-discharge (SD) rating curve for calculating discharge at a hydrological gauging station normally takes years of data collection. Estimation of high flows is particularly difficult as they occur rarely and are often difficult to gauge in practice. At a minimum, hydraulicallymodelled rating curves could be derived with as few as two concurrent SD and water-surface slope measurements at different flow conditions. This means that a reliable rating curve can, potentially, be developed much faster via hydraulic modelling than using a traditional rating curve approach based on numerous stage-discharge gaugings. In this study, we use an uncertainty framework based on Bayesian inference and hydraulic modelling for developing SD rating curves and estimating their uncertainties. The framework incorporates information from both the hydraulic configuration (bed slope, roughness, vegetation) using hydraulic modelling and the information available in the SD observation data (gaugings). Discharge time series are estimated by propagating stage records through the posterior rating curve results. Here we apply this novel framework to a Swedish hydrometric station, accounting for uncertainties in the gaugings and the parameters of the hydraulic model. The aim of this study was to assess the impact of using only three gaugings for calibrating the hydraulic model on resultant uncertainty estimations within our framework. The results were compared to prior knowledge, discharge measurements and official discharge estimations and showed the potential of hydraulically-modelled rating curves for assessing uncertainty at high and medium flows, while uncertainty at low flows remained high. Uncertainty results estimated using only three gaugings for the studied site were smaller than ±15% for medium and high flows and reduced the prior uncertainty by a factor of ten on average and were estimated with only 3 gaugings.

Impact of bias correction techniques on an ensemble of seasonal discharge forecast for the Danube upstream of Vienna

2021 ◽  
Author(s):  
Ignacio Martin Santos ◽  
Mathew Herrnegger ◽  
Hubert Holzmann
Keyword(s):  
Bias Correction ◽  
Hydrological Model ◽  
Systematic Errors ◽  
Seasonal Forecasts ◽  
Scaling Factors ◽  
Quantile Mapping ◽  
Forecasting System ◽  
Mapping Techniques ◽  
Seasonal Discharge

<p>The skill of seasonal hydro-meteorological forecasts with a lead time of up to six months is currently limited, since they frequently exhibit random but also systematic errors. Bias correction algorithms can be applied and provide an effective approach in removing historical biases relative to observations. Systematic errors in hydrology model outputs can be consequence of different sources: i) errors in meteorological data used as input data, ii) errors in the hydrological model response to climate forcings, iii) unknown/unobservable internal states and iv) errors in the model parameterizations, also due to unresolved subgrid scale variability.</p><p>Normally, bias correction techniques are used to correct meteorological, e.g. precipitation data, provided by climate models. Only few studies are available applying these techniques to hydrological model outputs. Standard bias correction techniques used in literature can be classified into scaling-, and distributional-based methods. The former consists of using multiplicative or additive scaling factors to correct the modeled simulations, while the later methods are quantile mapping techniques that fit the distribution of the simulation to fit to the observations. In this study, the impact of different bias correction techniques on the seasonal discharge forecasts skill is assessed.</p><p>As a case study, a seasonal discharge forecasting system developed for the Danube basin upstream of Vienna, is used. The studied basin covers an area of around 100 000 km<sup>2</sup> and is subdivided in 65 subbasins, 55 of them gauged with a long historical record of observed discharge. The forecast system uses the calibrated hydrological model, COSERO, which is fed with an ensemble of seasonal temperature and precipitation forecasts. The output of the model provides an ensemble of seasonal discharge forecasts for each of the (gauged) subbasins. Seasonal meteorological forecasts for the past (hindcast), together with historical discharge observations, allow to assess the quality of the seasonal discharge forecasting system, also including the effects of different bias correction methods. The corrections applied to the discharge simulations allow to eliminate potential systematic errors between the modeled and observed values.</p><p>Our findings generally suggest that the quality of the seasonal forecasts improve when applying bias correction. Compared to simpler methods, which use additive or multiplicative scaling factors, quantile mapping techniques tend to be more appropriate in removing errors in the ensemble seasonal forecasts.</p>

scholarly journals Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 823
Author(s):  
Ting Peng ◽  
Xiefei Zhi ◽  
Yan Ji ◽  
Luying Ji ◽  
Ye Tian
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Air Temperature ◽  
Post Processing ◽  
Learning Methods ◽  
Processing Methods ◽  
Machine Learning Methods ◽  
Extended Range ◽  
The Neural Networks ◽  
Maximum Air Temperature

The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances.

scholarly journals Bias correction and post-processing under climate change

2011 ◽  
Vol 18 (6) ◽  
pp. 911-924 ◽  
Author(s):  
S. Vannitsem
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
General Circulation ◽  
Climate Changes ◽  
Climate Projections ◽  
Model Errors ◽  
Post Processing ◽  
Straightforward Application ◽  
Potential Impact ◽  
Temperature Climate

Abstract. The statistical and dynamical properties of bias correction and linear post-processing are investigated when the system under interest is affected by model errors and is experiencing parameter modifications, mimicking the potential impact of climate change. The analysis is first performed for simple typical scalar systems, an Ornstein-Uhlenbeck process (O-U) and a limit point bifurcation. It reveals system's specific (linear or non-linear) dependences of biases and post-processing corrections as a function of parameter modifications. A more realistic system is then investigated, a low-order model of moist general circulation, incorporating several processes of high relevance in the climate dynamics (radiative effects, cloud feedbacks...), but still sufficiently simple to allow for an extensive exploration of its dynamics. In this context, bias or post-processing corrections also display complicate variations when the system experiences temperature climate changes up to a few degrees. This precludes a straightforward application of these corrections from one system's state to another (as usually adopted for climate projections), and increases further the uncertainty in evaluating the amplitudes of climate changes.

scholarly journals Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2126 ◽  
Author(s):  
Georgia Papacharalampous ◽  
Hristos Tyralis ◽  
Andreas Langousis ◽  
Amithirigala W. Jayawardena ◽  
Bellie Sivakumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantile Regression ◽  
Random Forests ◽  
Large Scale ◽  
Hydrological Model ◽  
Model Errors ◽  
Post Processing ◽  
Conditional Quantiles ◽  
Daily Streamflow ◽  
Regression Algorithms

We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing “at scale” within operational contexts. The experiment is set up using 34-year-long daily time series of precipitation, temperature, evapotranspiration and streamflow for 511 catchments over the contiguous United States. Point hydrological predictions are obtained using the Génie Rural à 4 paramètres Journalier (GR4J) hydrological model and exploited as predictor variables within quantile regression settings. Six machine-learning quantile regression algorithms and their equal-weight combiner are applied to predict conditional quantiles of the hydrological model errors. The individual algorithms are quantile regression, generalized random forests for quantile regression, generalized random forests for quantile regression emulating quantile regression forests, gradient boosting machine, model-based boosting with linear models as base learners and quantile regression neural networks. The conditional quantiles of the hydrological model errors are transformed to conditional quantiles of daily streamflow, which are finally assessed using proper performance scores and benchmarking. The assessment concerns various levels of predictive quantiles and central prediction intervals, while it is made both independently of the flow magnitude and conditional upon this magnitude. Key aspects of the developed methodological framework are highlighted, and practical recommendations are formulated. In technical hydro-meteorological applications, the algorithms should be applied preferably in a way that maximizes the benefits and reduces the risks from their use. This can be achieved by (i) combining algorithms (e.g., by averaging their predictions) and (ii) integrating algorithms within systematic frameworks (i.e., by using the algorithms according to their identified skills), as our large-scale results point out.

scholarly journals A new region-aware bias-correction method for simulated precipitation in areas of complex orography

2018 ◽  
Vol 11 (6) ◽  
pp. 2231-2247 ◽  
Author(s):  
Juan José Gómez-Navarro ◽  
Christoph C. Raible ◽  
Denica Bozhinova ◽  
Olivia Martius ◽  
Juan Andrés García Valero ◽  
...  
Keyword(s):  
Bias Correction ◽  
Hydrological Cycle ◽  
Regional Climate ◽  
Simulated Data ◽  
Correction Method ◽  
Climate Impact ◽  
Climate Modelling ◽  
Correction Technique ◽  
Simulated Precipitation ◽  
Systematic Biases

Abstract. Regional climate modelling is used to simulate the hydrological cycle, which is fundamental for climate impact investigations. However, the output of these models is affected by biases that hamper its direct use in impact modelling. Here, we present two high-resolution (2 km) climate simulations of precipitation in the Alpine region, evaluate their performance over Switzerland and develop a new bias-correction technique for precipitation suitable for complex topography. The latter is based on quantile mapping, which is applied separately across a number of non-overlapping regions defined through cluster analysis. This technique allows removing prominent biases while it aims at minimising the disturbances to the physical consistency inherent in all statistical corrections of simulated data. The simulations span the period 1979–2005 and are carried out with the Weather Research and Forecasting model (WRF), driven by the ERA-Interim reanalysis (hereafter WRF-ERA), and the Community Earth System Model (hereafter WRF-CESM). The simulated precipitation is in both cases validated against observations in Switzerland. In a first step, the area is classified into regions of similar temporal variability of precipitation. Similar spatial patterns emerge in all datasets, with a clear northwest–southeast separation following the main orographic features of this region. The daily evolution and the annual cycle of precipitation in WRF-ERA closely reproduces the observations. Conversely, WRF-CESM shows a different seasonality with peak precipitation in winter and not in summer as in the observations or in WRF-ERA. The application of the new bias-correction technique minimises systematic biases in the WRF-CESM simulation and substantially improves the seasonality, while the temporal and physical consistency of simulated precipitation is greatly preserved.

scholarly journals Skill assessment of sub-seasonal 2-m temperature forecasts for Helsinki, Finland

2021 ◽  
Author(s):  
Mika Rantanen ◽  
Matti Kämäräinen ◽  
Otto Hyvärinen ◽  
Andrea Vajda
Keyword(s):  
Bias Correction ◽  
Mapping Method ◽  
Early Summer ◽  
Skill Assessment ◽  
Added Value ◽  
Cold Bias ◽  
Seasonal Temperature ◽  
Seasonal Forecasts ◽  
Bias Adjustment ◽  
Extended Range

<p>Sub-seasonal to seasonal scale forecasts provide useful information for city authorities for operational planning, preparedness and maintenance costs optimization. In the EU H2020 E-SHAPE project the Finnish Meteorological Institute aims at developing an operational service providing user-oriented sub-seasonal and seasonal forecast products for the City of Helsinki tailored for winter maintenance activities. To be able to provide skilful sub-seasonal to seasonal forecasts products, bias adjustment and evaluation of the used weather parameters, i.e. temperature and snow is crucial. </p><p>In this study, we focus on the skill assessment of sub-seasonal temperature forecasts in Helsinki, Finland, experimenting with various methods to adjust the bias from the raw temperature forecasts. Due to its coastal location, skilful forecasting of temperatures for Helsinki is challenging. The temperature gradient on the coastline is especially strong during spring when inland areas warm considerably faster than the coastline. Therefore, raw point forecasts for Helsinki suffer from cold bias during the March-July period.</p><p>We use the 2 m temperature extended-range reforecasts obtained from the ECMWF S2S database and apply two bias adjustment techniques: removing the mean bias and the quantile mapping method. Reforecasts for a 20-years period, 2000-2019 with 10 ensemble members, run twice a week for 46 days ahead were calibrated and evaluated. Two datasets are used as reference, observations from Helsinki Kaisaniemi weather station and gridded ERA5 reanalysis data. Thus, these combinations yield in total five sets of forecasts which are evaluated against the observations.</p><p>The results of the experiments and the potential added value of bias correction will be presented for discussion. Based on the preliminary results, especially the cold bias in spring and early summer can be improved with the bias-correction methods. The bias-adjusted extended-range temperature forecasts are used in the development of sub-seasonal winter forecast products tailored for the needs of city maintenance.</p>

scholarly journals High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation in Morocco

2013 ◽  
Vol 10 (5) ◽  
pp. 5687-5737 ◽  
Author(s):  
Y. Tramblay ◽  
D. Ruelland ◽  
S. Somot ◽  
R. Bouaicha ◽  
E. Servat
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Regional Climate Model ◽  
Bias Correction ◽  
Hydrological Model ◽  
Climate Model ◽  
Regional Climate ◽  
Impact Studies ◽  
Hydrological Impact ◽  
Split Sample

Abstract. In the framework of the international CORDEX program, new regional climate model (RCM) simulations at high spatial resolutions are becoming available for the Mediterranean region (Med-CORDEX initiative). This study provides the first evaluation for hydrological impact studies of these high-resolution simulations. Different approaches are compared to analyze the climate change impacts on the hydrology of a catchment located in North Morocco, using a high-resolution RCM (ALADIN-Climate) from the Med-CORDEX initiative at two different spatial resolutions (50 km and 12 km) and for two different Radiative Concentration Pathway scenarios (RCP4.5 and RCP8.5). The main issues addressed in the present study are: (i) what is the impact of increased RCM resolution on present-climate hydrological simulations and on future projections? (ii) Are the bias-correction of the RCM model and the parameters of the hydrological model stationary and transferable to different climatic conditions? (iii) What is the climate and hydrological change signal based on the new Radiative Concentration Pathways scenarios (RCP4.5 and RCP8.5)? Results indicate that high resolution simulations at 12 km better reproduce the seasonal patterns, the seasonal distributions and the extreme events of precipitation. The parameters of the hydrological model, calibrated to reproduce runoff at the monthly time step over the 1984–2010 period, do not show a strong variability between dry and wet calibration periods in a differential split-sample test. However the bias correction of precipitation by quantile-matching does not give satisfactory results in validation using the same differential split-sample testing method. Therefore a quantile-perturbation method that does not rely on any stationarity assumption and produces ensembles of perturbed series of precipitation was introduced. The climate change signal under scenarios 4.5 and 8.5 indicates a decrease of respectively −30% to −57% in surface runoff for the mid-term (2041–2062), when for the same period the projections for precipitation are ranging between −15% and −19% and for temperature between +1.28°C and +1.87°C.

Sign in / Sign up

Export Citation Format

Share Document