Data assimilation of satellite-based actual evapotranspiration in a distributed hydrological model of a controlled water system

2017 ◽  
Vol 57 ◽  
pp. 123-135 ◽  
Author(s):  
I.M. Hartanto ◽  
J. van der Kwast ◽  
T.K. Alexandridis ◽  
W. Almeida ◽  
Y. Song ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hydrological Model ◽  
Water System ◽  
Actual Evapotranspiration ◽  
Distributed Hydrological Model

scholarly journals Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts

2019 ◽  
Vol 23 (9) ◽  
pp. 3823-3841 ◽  
Author(s):  
Maria Laura Poletti ◽  
Francesco Silvestro ◽  
Silvio Davolio ◽  
Flavio Pignone ◽  
Nicola Rebora
Keyword(s):  
Data Assimilation ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Distributed Hydrological Model ◽  
Streamflow Prediction ◽  
Flood Prediction ◽  
Meteorological Model ◽  
Distributed Approach ◽  
Nwp Model ◽  
Small Catchments

Abstract. Forecasting flash floods some hours in advance is still a challenge, especially in environments made up of many small catchments. Hydrometeorological forecasting systems generally allow for predicting the possibility of having very intense rainfall events on quite large areas with good performances, even with 12–24 h of anticipation. However, they are not able to predict the exact rainfall location if we consider portions of a territory of 10 to 1000 km2 as the order of magnitude. The scope of this work is to exploit both observations and modelling sources to improve the discharge prediction in small catchments with a lead time of 2–8 h. The models used to achieve the goal are essentially (i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations, (ii) a non-hydrostatic high-resolution numerical weather prediction (NWP) model and (iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other. For this purpose observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information as input to the nowcasting technique in terms of a predicted rainfall volume trend; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events that occurred in the Liguria region (Italy) first to produce a standard analysis on predefined basin control sections and then using a distributed approach that exploits the capabilities of the employed hydrological model. The results obtained for these three analysed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is observed less, and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.

scholarly journals Assessment of evapotranspiration simulations in the Malše basin

2010 ◽  
Vol 4 (Special Issue 2) ◽  
pp. S111-S122
Author(s):  
R. Košková ◽  
S. Němečková
Keyword(s):  
Hydrological Model ◽  
Precipitation Amount ◽  
The Other ◽  
Actual Evapotranspiration ◽  
Soil Types ◽  
Distributed Hydrological Model ◽  
The Czech Republic ◽  
Landscape Characteristics ◽  
Other Hand ◽  
Spatially Distributed

The application of the distributed hydrological model brings the benefits of assessment of the spatially distributed quantities which are hard to measure in the field over a larger area, e.g. evapotranspiration. The Malše River basin has been chosen for the evaluation of evapotranspiration simulation by the distributed hydrological model, SWIM (Soil and Water Integrated Model). The primary interest in this analysis was to assess the ability of the hydrological model to simulate the actual evapotranspiration on larger scales and to evaluate its dependence on the landscape characteristics such as the vegetation cover, soil type, and average precipitation amount during the simulation. Annual actual evapotranspiration in each hydrotope was evaluated in the simulation period of 1985–1998. Because of the lack of the data observed (evapotranspiration), the model was calibrated on the discharge time series. The credibility was quantified using Nash Sutcliffe efficiency which was more than 0.7. The main trends of the simulated actual evapotranspiration were evaluated and assessed as satisfactory. The differences in the soil types did not seem significant for the evapotranspiration variation, the monthly average values among soil types differing by ± 10% except histosol. On the other hand the differences in the land-use categories strongly influenced the amount of evapotranspiration (–30; +50%). It appears that the model SWIM overestimates the actual evapotranspiration in the spring and, on the other hand, underestimates that in the autumn according to the comparison with the only data available in the entire Climate Atlas of the Czech Republic.

scholarly journals Data assimilation using the ensemble Kalman filter in a distributed hydrological model on the Tocantins River, Brasil

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Karena Quiroz Jiménez ◽  
Walter Collischonn ◽  
Rodrigo Cauduro Dias de Paiva
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Real Time ◽  
Ensemble Kalman Filter ◽  
Information Transfer ◽  
Hydrological Model ◽  
Distributed Hydrological Model ◽  
Streamflow Forecasting ◽  
Ungauged Sites ◽  
Tocantins River

ABSTRACT In this work, the data assimilation method namely ensemble Kalman filter (EnKF) is applied to the Tocantins River basin. This method assimilates streamflow results by using a distributed hydrological model. The performance of the EnKF is also compared with an empirical assimilation method for hourly time intervals, in which two applications based on information transfer from gauged to ungauged sites and real time streamflow forecasting are assessed. In the first application, both assimilation methods are able to transfer streamflow to ungauged sites, obtaining better results when more than one station located upstream or downstream of the basin are gauged. In the second application, integration of a real time forecast model with EnKF is able to absorb errors at the beginning of the forecast. Therefore, a greater efficiency in the Nash-Sutcliffe index for the first 144 hours in advance in relation to its counterpart without assimilation is obtained. Finally, a comparison between both data assimilation methods shows a greater advantage for the EnKF in long lead times.

scholarly journals Study on Applicability of Distributed Hydrological Model under Different Terrain Conditions

2020 ◽  
Vol 12 (22) ◽  
pp. 9684
Author(s):  
Tianxin Li ◽  
Yuxin Duan ◽  
Shanbo Guo ◽  
Linglong Meng ◽  
Matomela Nametso
Keyword(s):  
Hydrological Model ◽  
Water System ◽  
Model Verification ◽  
Distributed Hydrological Model ◽  
Rainfall Runoff ◽  
Model Framework ◽  
Research Areas ◽  
Division Process ◽  
Steep Terrain ◽  
Empty Area

This research aimed to study the applicability and limitations of a distributed hydrological model under discontinuous steep topography and hydrogeological conditions. Based on GIS spatial analysis, typical cases of steep and gentle terrains were selected to construct the distributed hydrological model framework of the research areas (Qinhuangdao and Zhuanghe City, China). The observed runoff was used to test the applicability of the model in different terrain watersheds and to analyze the versatility of the model structure and the relevant parameters of the core modules. The results show that: in the process of using a distributed hydrological model to build models for different regions, problems such as a discontinuous dislocation of the empty area and poor connectivity of the water system will appear in the process of sub-basin division of a steep terrain. By determining the optimal threshold, selecting the best node, discontinuous dislocation, void fusion and other methods, we put forward the corresponding solutions to the problems in the division process and constructed the research area’s distributed hydrological model. The rainfall–runoff process in the study area was simulated accordingly, and the SUFI2 algorithm was used to calibrate the relevant parameters in the model. The relative error (Re), correlation coefficient (R2) and Nash–Sutcliffe efficiency (NSE), which meet the runoff accuracy in the study area, were obtained. The model verification results show that the NSE of steep terrain is 0.90, and R2 is 0.98; the NSE of gentle terrain is 0.91, and R2 is 0.984: the simulation values fit the measured values well, which makes the calibrated model suitable for both steep and gentle terrains. The results can provide a reference for the construction of a distributed hydrological model in watersheds with different terrain.

scholarly journals An Initial Assessment of Radar Data Assimilation on Warm Season Rainfall Forecasts for Use in Hydrologic Models

2015 ◽  
Vol 30 (6) ◽  
pp. 1491-1520 ◽  
Author(s):  
Ben A. Moser ◽  
William A. Gallus ◽  
Ricardo Mantilla
Keyword(s):  
Data Assimilation ◽  
Hydrological Model ◽  
Flash Flood ◽  
Perfect Match ◽  
Stage Iv ◽  
Radar Data ◽  
Initial Assessment ◽  
Distributed Hydrological Model ◽  
Flash Flooding ◽  
Radar Data Assimilation

Abstract The effect of introducing radar data assimilation into the WRF Model to improve high-resolution rainfall forecasts that are used for flash flood forecasting is analyzed. The authors selected 12 heavy rainfall events and performed two WRF 24-h simulations that produced quantitative precipitation forecasts (QPFs) for each, one using the standard configuration in forecast mode (QPF-Cold) and one using radar data assimilated at initialization (QPF-Hot). Simulation outputs are compared with NWS stage IV QPEs for storm placement, area over threshold coverage, and equitable threat scores. The two QPF products and stage IV data are used to force the distributed hydrological model CUENCAS for the same 800 km × 800 km domain centered over Iowa (and to calculate peak flows across the river network). The hydrological model responses to the three products are compared in terms of spatial location and flood intensity. In general, QPF-Hot outperformed QPF-Cold in replicating stage IV QPE statistics. However, QPF-Hot was too wet in the first 2 h of the event, and storms created by the radar-assimilation techniques dissipated quickly, with rainfall forecasts resembling QPF-Cold after 12 h. Flash flooding predicted by CUENCAS using QPF-Hot was more consistent with stage IV in terms of placement and intensity; however, results were not consistent for all events evaluated. The most encouraging result is that expected flash flooding was indeed predicted in all 12 cases using QPF-Hot and not QPF-Cold even though placement and intensity were not a perfect match. The initial results of this study indicate that radar assimilation improves WRF’s ability to capture the character of storms, promising more accurate guidance for flash flood warnings.

Can assimilating snow monitoring information offset the adverse effects of precipitation data scarcity in hydrological modelling applications?

2020 ◽  
Author(s):  
Kian Abbasnezhadi ◽  
Alain N. Rousseau
Keyword(s):  
Data Assimilation ◽  
Adverse Effects ◽  
Hydrological Model ◽  
Hydrological Modelling ◽  
Added Value ◽  
Distributed Hydrological Model ◽  
Forecasting System ◽  
Precipitation Analysis ◽  
Gauged Basins ◽  
Monitoring Information

<p>The applicability of the Canadian Precipitation Analysis products known as the Regional Deterministic Precipitation Analysis (CaPA-RDPA) for hydrological modelling in boreal watersheds in Canada, which are constrained with shortage of precipitation information, has been the subject of a number of recent studies. The northern and mid-cordilleran alpine, sub-alpine, and boreal watersheds in Yukon, Canada, are prime examples of such Nordic regions where any hydrological modelling application is greatly scrambled due to lack of accurate precipitation information. In the course of the past few years, proper advancements were tailored to resolve these challenges and a forecasting system was designed at the operational level for short- to medium-range flow and inflow forecasting in major watersheds of interest to Yukon Energy. This forecasting system merges the precipitation products from the North American Ensemble forecasting System (NAEFS) and recorded flows or reconstructed reservoir inflows into the HYDROTEL distributed hydrological model, using the Ensemble Kalman Filtering (EnKF) data assimilation technique. In order to alleviate the adverse effects of scarce precipitation information, the forecasting system also enjoys a snow data assimilation routine in which simulated snowpack water content is updated through a distributed snow correction scheme. Together, both data assimilation schemes offer the system with a framework to accurately estimate flow magnitudes. This robust system not only mitigates the adverse effects of meteorological data constrains in Yukon, but also offers an opportunity to investigate the hydrological footprint of CaPA-RDPA products in Yukon, which is exactly the motivation behind this presentation. However, our overall goal is much more comprehensive as we are trying to elucidate whether assimilating snow monitoring information in a distributed hydrological model could meet the flow estimation accuracy in sparsely gauged basins to the same extent that would be achieved through either (i) the application of precipitation analysis products, or (ii) expanding the meteorological network. A proper answer to this question would provide us with valuable information with respect to the robustness of the snow data assimilation routine in HYDROTEL and the intrinsic added-value of using CaPA-RDPA products in sparsely gauged basins of Yukon.</p>

Hydrological data assimilation using the particle filter in a semi-distributed model MORDOR-SD

2020 ◽  
Author(s):  
Imane Farouk ◽  
Emmanuel Cosme ◽  
Sammy Metref ◽  
Joel Gailhard ◽  
Matthieu Le-Lay
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Hydrological Model ◽  
Ensemble Methods ◽  
Electricity Production ◽  
Distributed Model ◽  
Observation Error ◽  
Distributed Hydrological Model ◽  
In Situ Data

<p>A large number of hydrological forecasts are carried out daily by the hydro-meteorologists of the french electricity production agency (EDF). These forecasts are based on a MORDOR hydrological model [Boy, 1996]. Since its development, this model has been noted for its performance [Mathevet, 2005], and a new more advanced version proposing a semi-distributed (or SD) structure improves the quality of the simulations [Garavaglia et al., 2017].</p><p>However, many uncertainties such as calibration errors, unavailable observations, and the uncertainties linked to the data used as forcing for the model can have a very significant impact on the quality of the results. Data assimilation is a relevant method for reducing the uncertainties of forcings and then obtain better quality simulations. Previous studies show a gain in the contribution of a variational assimilation to initialize a semi-distributed hydrological model [Lee et al., 2011], but the variational methods are less effective with non-linear behaviors. Therefore the ensemble methods are more widely adopted, as the ensemble Kalman filter (or EnKF) assimilation method which can be found in various studies ([Han et al., 2012], [Clark et al., 2008], [Xie and Zhang, 2010], [Slater and Clark, 2006], [Chen et al., 2011], [Alvarez-Garreton et al., 2015]).</p><p>As part of our study, a particle filter has been implemented as an assimilation scheme in the semi-distributed hydrological model MORDOR-SD. Several types of observations, such as the flow at the outlet of the watershed or the snow stock, were used in this assimilation system. Some sensitivities experiments on the various parameters specific to the system as well as on the choice of the observations to be taken into account were carried out. This study will show the benefits obtained from the assimilation of in situ data on the quality of the simulations as well as on the forecasts. Performed in many different areas (the study covers several watersheds), the analysis of observation errors and the construction of a specific observation error model brings an additional benefit in the quality of the results.</p><p> </p>

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