A software framework for construction of process-based stochastic spatio-temporal models and data assimilation

Karssenberg, Derek; Schmitz, Oliver; Salamon, Peter; Jong, Kees de; Bierkens, Marc F. P.

doi:10.1016/j.envsoft.2009.10.004

Cited by 177 publications

(180 citation statements)

References 48 publications

(54 reference statements)

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“…It can be applied at 30 arcmin resolution (approximately 55 km × 55 km at the Equator) and at 5 arcmin resolution (approximately 10 km × 10 km at the Equator), which may increase accuracy but also runtime. PCR is entirely coded in PCRaster Python (Karssenberg et al, 2010) and distinguishes between two vertically stacked soil layers, an underlying groundwater layer, and a surface canopy layer. Water can be exchanged vertically, and excess surface water can be routed horizontally along a local drainage direction network employing the kinematic wave approximation.…”

Section: Pcr-globwbmentioning

confidence: 99%

GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling

et al. 2017

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Abstract. We here present GLOFRIM, a globally applicable computational framework for integrated hydrologicalhydrodynamic modelling. GLOFRIM facilitates spatially explicit coupling of hydrodynamic and hydrologic models and caters for an ensemble of models to be coupled. It currently encompasses the global hydrological model PCR-GLOBWB as well as the hydrodynamic models Delft3D Flexible Mesh (DFM; solving the full shallow-water equations and allowing for spatially flexible meshing) and LISFLOOD-FP (LFP; solving the local inertia equations and running on regular grids). The main advantages of the framework are its open and free access, its global applicability, its versatility, and its extensibility with other hydrological or hydrodynamic models. Before applying GLOFRIM to an actual test case, we benchmarked both DFM and LFP for a synthetic test case. Results show that for sub-critical flow conditions, discharge response to the same input signal is near-identical for both models, which agrees with previous studies. We subsequently applied the framework to the Amazon River basin to not only test the framework thoroughly, but also to perform a first-ever benchmark of flexible and regular grids on a large-scale. Both DFM and LFP produce comparable results in terms of simulated discharge with LFP exhibiting slightly higher accuracy as expressed by a Kling-Gupta efficiency of 0.82 compared to 0.76 for DFM. However, benchmarking inundation extent between DFM and LFP over the entire study area, a critical success index of 0.46 was obtained, indicating that the models disagree as often as they agree. Differences between models in both simulated discharge and inundation extent are to a large extent attributable to the gridding techniques employed. In fact, the results show that both the numerical scheme of the inundation model and the gridding technique can contribute to deviations in simulated inundation extent as we control for model forcing and boundary conditions. This study shows that the presented computational framework is robust and widely applicable. GLOFRIM is designed as open access and easily extendable, and thus we hope that other large-scale hydrological and hydrodynamic models will be added. Eventually, more locally relevant processes would be captured and more robust model inter-comparison, benchmarking, and ensemble simulations of flood hazard on a large scale would be allowed for.

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Section: Pcr-globwbmentioning

confidence: 99%

GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling

et al. 2017

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“…In 1996, a comprehensive re-evaluation of the HBV model routines was carried out (Lindström et al, 1997), which resulted in the HBV-96 version. The OpenStreams wflow_hbv model is a variant of this model, programmed in the PCRaster-Python environment (Karssenberg et al, 2009), but using a kinematic wave for hydrological routing. It is publicly available through the OpenStreams project (https: //code.google.com/p/wflow/; last access: 18 January 2015).…”

Section: Hydrological Modellingmentioning

confidence: 99%

Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

Tangdamrongsub

Steele‐Dunne

Gunter

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less wellmonitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35 %. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15 %. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.

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“…The low data requirements and the compaction module make the seNorge snow model suitable for application in this study. The seNorge snow model was rewritten from its original code into the environmental modeling software PCRaster Python (Karssenberg et al, 2010) to allow spatiotemporal modeling of the SWE and runoff within the catchment. The snow is modeled as a single homogeneous layer with a spatial resolution of 100 m and a daily time step.…”

Section: Modified Senorge Modelmentioning

confidence: 99%

Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

et al. 2017

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Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

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A software framework for construction of process-based stochastic spatio-temporal models and data assimilation

Cited by 177 publications

References 48 publications

GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling

GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling

Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

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