Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture

Chen, Fan; Crow, Wade T.; Starks, Patrick J.; Moriasi, Daniel N.

doi:10.1016/j.advwatres.2011.01.011

Cited by 170 publications

(162 citation statements)

References 29 publications

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“…The WFD were used to determine the bias correction required for rainfall, snowfall, minimum, mean and maximum air temperature for the control period. The procedure is described in more detail by Piani et al (2010a, b), Chen et al (2011), andHaerter et al (2011). More detailed information on the GCMs can be found in Table 1.…”

Section: General Circulation Modelsmentioning

confidence: 99%

“…The data from the GCMs were used as meteorological input data for the synthetic hydrological modelling approach to produce groundwater discharge time series and associated drought characteristics for: (i) the control period , and (ii) the periods 2021-2050 and 2071-2100 to intercompare obtained drought characteristics against those derived from the reference model . The advantages of this mini-ensemble is that the bias correction was performed by experts in the field both for the control period (Piani et al, 2010a, b;Haerter et al, 2011) using the WFD data set (Weedon et al, 2010 to correct the models and for the future Chen et al, 2011). This resulted in consistent downscaled and bias-corrected GCM data for 1963-2100.…”

Section: General Circulation Modelsmentioning

confidence: 99%

“…The groundwater discharge drought characteristics of each GCM forced hydrological model simulation over the control period were compared against the characteristics derived from the model forced with the WFD reference model for the same period to explore uncertainty due to GCM forcing. Ideally, there should be only minor differences in drought characteristics between the characteristics derived from groundwater discharge simulated with the GCM forcing and the simulation with the WFD, since the control periods of each GCM are bias corrected to match the WFD (Piani et al, 2010a, b;Haerter et al, 2011;Chen et al, 2011;Hagemann et al, 2011). The changes in future drought characteristics were evaluated for the period 2021-2050 and 2071-2100 by comparing against the control period (1971-2000) of each GCM forced hydrological model simulation.…”

Section: Impact Assessment Of Climate Changementioning

confidence: 99%

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Future discharge drought across climate regions around the world modelled with a synthetic hydrological modelling approach forced by three general circulation models

Wanders

Lanen

2015

Nat. Hazards Earth Syst. Sci.

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Abstract. Hydrological drought characteristics (drought in groundwater and streamflow) likely will change in the 21st century as a result of climate change. The magnitude and directionality of these changes and their dependency on climatology and catchment characteristics, however, is uncertain. In this study a conceptual hydrological model was forced by downscaled and bias-corrected outcome from three general circulation models for the SRES A2 emission scenario (GCM forced models), and the WATCH Forcing Data set (reference model). The threshold level method was applied to investigate drought occurrence, duration and severity. Results for the control period show that the drought characteristics of each GCM forced model reasonably agree with the reference model for most of the climate types, suggesting that the climate models' results after post-processing produce realistic outcomes for global drought analyses. For the near future (2021-2050) and far future (2071-2100) the GCM forced models show a decrease in drought occurrence for all major climates around the world and increase of both average drought duration and deficit volume of the remaining drought events. The largest decrease in hydrological drought occurrence is expected in cold (D) climates where global warming results in a decreased length of the snow season and an increased precipitation. In the dry (B) climates the smallest decrease in drought occurrence is expected to occur, which probably will lead to even more severe water scarcity. However, in the extreme climate regions (desert and polar), the drought analysis for the control period showed that projections of hydrological drought characteristics are most uncertain. On a global scale the increase in hydrological drought duration and severity in multiple regions will lead to a higher impact of drought events, which should motivate water resource managers to timely anticipate the increased risk of more severe drought in groundwater and streamflow and to design pro-active measures.

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Section: General Circulation Modelsmentioning

confidence: 99%

Section: General Circulation Modelsmentioning

confidence: 99%

Section: Impact Assessment Of Climate Changementioning

confidence: 99%

See 1 more Smart Citation

Future discharge drought across climate regions around the world modelled with a synthetic hydrological modelling approach forced by three general circulation models

Wanders

Lanen

2015

Nat. Hazards Earth Syst. Sci.

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“…The choice of the data assimilation algorithm turned to be more consistent in this period. The majority of studies chose the ensemble Kalman filter (EnKF) [102][103][104]180,182,183,185,191,196,198], while just a few used nudging [186], hard updating [192] and EKF [197]. There were also few particle filter (PF) applications.…”

Section: The Development Of Flood-orientated Rs-sm Assimilation Studiesmentioning

confidence: 99%

“…Beside a couple of tests using synthetic RS-SM data [104,185,196], ASCAT [102,103,182,184,186,191,192,197], AMSR-E [103,180,182,192] and SMOS [102,103,182,183,198] soil moisture products were widely implemented during this period. Simultaneously assimilating multi-source RS-SM products has also been tested recently [102,103,182,192].…”

Section: The Development Of Flood-orientated Rs-sm Assimilation Studiesmentioning

confidence: 99%

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Grimaldi

Walker

et al. 2016

Remote Sensing

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Abstract:Fluvial flooding is one of the most catastrophic natural disasters threatening people's lives and possessions. Flood forecasting systems, which simulate runoff generation and propagation processes, provide information to support flood warning delivery and emergency response. The forecasting models need to be driven by input data and further constrained by historical and real-time observations using batch calibration and/or data assimilation techniques so as to produce relatively accurate and reliable flow forecasts. Traditionally, flood forecasting models are forced, calibrated and updated using in-situ measurements, e.g., gauged precipitation and discharge. The rapid development of hydrologic remote sensing offers a potential to provide additional/alternative forcing and constraint to facilitate timely and reliable forecasts. This has brought increasing interest to exploring the use of remote sensing data for flood forecasting. This paper reviews the recent advances on integration of remotely sensed precipitation and soil moisture with rainfall-runoff models for rainfall-driven flood forecasting. Scientific and operational challenges on the effective and optimal integration of remote sensing data into forecasting models are discussed.

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Assimilating MODIS-based albedo and snow cover fraction into the Common Land Model to improve snow depth simulation with direct insertion and deterministic ensemble Kalman filter methods

Hong

2014

J. Geophys. Res. Atmos.

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This study assesses the analysis performance of assimilating the Moderate Resolution Imaging Spectroradiometer (MODIS)-based albedo and snow cover fraction (SCF) separately or jointly into the physically based Common Land Model (CoLM). A direct insertion method (DI) is proposed to assimilate the black and white-sky albedos into the CoLM. The MODIS-based albedo is calculated with the MODIS bidirectional reflectance distribution function (BRDF) model parameters product (MCD43B1) and the solar zenith angle as estimated in the CoLM for each time step. Meanwhile, the MODIS SCF (MOD10A1) is assimilated into the CoLM using the deterministic ensemble Kalman filter (DEnKF) method. A new DEnKF-albedo assimilation scheme for integrating the DI and DEnKF assimilation schemes is proposed. Our assimilation results are validated against in situ snow depth observations from November 2008 to March 2009 at five sites in the Altay region of China. The experimental results show that all three data assimilation schemes can improve snow depth simulations. But overall, the DEnKF-albedo assimilation shows the best analysis performance as it significantly reduces the bias and root-mean-square error (RMSE) during the snow accumulation and ablation periods at all sites except for the Fuyun site. The SCF assimilation via DEnKF produces better results than the albedo assimilation via DI, implying that the albedo assimilation that indirectly updates the snow depth state variable is less efficient than the direct SCF assimilation. For the Fuyun site, the DEnKF-albedo scheme tends to overestimate the snow depth accumulation with the maximum bias and RMSE values because of the large positive innovation (observation minus forecast).

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Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture

Cited by 170 publications

References 29 publications

Future discharge drought across climate regions around the world modelled with a synthetic hydrological modelling approach forced by three general circulation models

Future discharge drought across climate regions around the world modelled with a synthetic hydrological modelling approach forced by three general circulation models

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Assimilating MODIS-based albedo and snow cover fraction into the Common Land Model to improve snow depth simulation with direct insertion and deterministic ensemble Kalman filter methods

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