Hydrological Model Diversity Enhances Streamflow Forecast Skill at Short‐ to Medium‐Range Timescales

Sharma, Sanjib; Siddique, Ridwan; Reed, Seann; Ahnert, Peter; Mejía, Alfonso

doi:10.1029/2018wr023197

Cited by 61 publications

(45 citation statements)

References 91 publications

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“…Nevertheless, WRF-Hydro provided a more favorable forecast for Event 4, demonstrating its potential ability to predict flash floods. Overall, however, the accuracy of the WRF-Hydro forecasts was poor, supporting the findings of previous studies by Wang et al [58] and Sharma et al [31]. Figure 12 provides further statistics on the extent to which the three coupled systems contribute to an overall improvement in response to flood events.…”

Section: Results With the Wrf-hydro Modeling Systemsupporting

confidence: 78%

“…Most LSM and hydrological models incorporate the same descriptions of water balance, albeit with different aims [31,32]. LSM evolves from land-atmosphere coupling models with the purpose of solving the surface energy balance equation and providing the necessary lower boundary conditions for the atmosphere [31,33]. Inversely, hydrological models focus less on radiation and more on hydrological changes (i.e., the lateral route of water along land surfaces).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data Assimilation for Rainfall-Runoff Prediction Based on Coupled Atmospheric-Hydrologic Systems with Variable Complexity

Wang

Liu

et al. 2021

Remote Sensing

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The data assimilation technique is an effective method for reducing initial condition errors in numerical weather prediction (NWP) models. This paper evaluated the potential of the weather research and forecasting (WRF) model and its three-dimensional data assimilation (3DVar) module in improving the accuracy of rainfall-runoff prediction through coupled atmospheric-hydrologic systems. The WRF model with the assimilation of radar reflectivity and conventional surface and upper-air observations provided the improved initial and boundary conditions for the hydrological process; subsequently, three atmospheric-hydrological systems with variable complexity were established by coupling WRF with a lumped, a grid-based Hebei model, and the WRF-Hydro modeling system. Four storm events with different spatial and temporal rainfall distribution from mountainous catchments of northern China were chosen as the study objects. The assimilation results showed a general improvement in the accuracy of rainfall accumulation, with low root mean square error and high correlation coefficients compared to the results without assimilation. The coupled atmospheric-hydrologic systems also provide more accurate flood forecasts, which depend upon the complexity of the coupled hydrological models. The grid-based Hebei system provided the most stable forecasts regardless of whether homogeneous or inhomogeneous rainfall was considered. Flood peaks before assimilation were underestimated more in the lumped Hebei model relative to the other coupling systems considered, and the model seems more applicable for homogeneous temporal and spatial events. WRF-Hydro did not exhibit desirable predictions of rapid flood process recession. This may reflect increasing infiltration due to the interaction of atmospheric and land surface hydrology at each integration, resulting in mismatched solutions for local runoff generation and confluence.

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Section: Results With the Wrf-hydro Modeling Systemsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Data Assimilation for Rainfall-Runoff Prediction Based on Coupled Atmospheric-Hydrologic Systems with Variable Complexity

Wang

Liu

et al. 2021

Remote Sensing

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“…The additional benefit derived from using ensembles of models for maximising skill persistence could also be assessed for different lead times and initialisation months. This is a promising avenue, as model diversity has been shown to enhance forecast skill in ensemble experiments (Sharma et al, 2019).…”

Section: Potential For Future Workmentioning

confidence: 99%

Conditioning Ensemble Streamflow Prediction with the North Atlantic Oscillation improves skill at longer lead times

Donegan¹,

Murphy²,

Harrigan³

et al. 2020

Preprint

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Abstract. Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of Ensemble Streamflow Prediction (ESP) for a diverse sample of 46 catchments using the GR4J hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 day to 12 months for each of 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with continuous ranked probability skill scores of 0.8 (1-day), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ = 0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and East, being those where slowly responding, high storage catchments are located. Results also highlight the potential utility of ESP for decision-making, as measured by its ability to forecast low and high flow events. In addition to our benchmarking experiment, we conditioned ESP on the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office's Global Seasonal Forecasting System version 5. We found gains in winter forecast skill of 7–18 % were possible over lead times of 1 to 3 months, and that NAO-conditioned ESP is particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.

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“…In their study, the dimensionality and efficiency of DDS, for example, was tested, and the authors concluded that DDS provided better results than SCE both with low-and high-dimensional problems, and is more efficient. DDS has been used to calibrate several hydrological models from simple lumped to medium level distributed models (e.g., SWAT [61,62], MESH [63], CRHM-AHM [64]) to very complicated land-surface based models (e.g., WRF-Hydro [65,66]).…”

Section: Calibration and Validationmentioning

confidence: 99%

Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed

Awol

Coulibaly

Tsanis

et al. 2019

Water

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Accurate and reliable flow forecasting in complex Canadian prairie watersheds has been one of the major challenges faced by hydrologists. In an attempt to improve the accuracy and reliability of a reservoir inflow forecast, this study investigates structurally different hydrological models along with ensemble precipitation forecasts to identify the most skillful and reliable model. The key goal is to assess whether short-and medium-range ensemble flood forecasting in large complex basins can be accurately achieved by simple conceptual lumped models (e.g., SACSMA with SNOW17 and MACHBV with SNOW17) or it requires a medium level distributed model (e.g., WATFLOOD) or an advanced macroscale land-surface based model (VIC coupled with routing module (RVIC)). Eleven (11)-member precipitation forecasts from second-generation Global Ensemble Forecast System reforecast (GEFSv2) were used as inputs. Each of the ensemble members was bias-corrected by Empirical Quantile Mapping method using the Canadian Precipitation Analysis (CaPA) as a training/verification dataset. Forecast evaluation is performed for 1-day up to 8-days forecast lead times in a 6-month hindcast period. Results indicate that bias-correcting precipitation forecasts using verifying datasets (such as CaPA) for a training period of at least two years before the forecast time, produces skillful ensemble hydrological forecasts. A comparison of models in forecast mode shows that the two lumped models (SACSMA and MACHBV) can provide better overall forecast performance than the benchmark WATFLOOD and the macroscale Variable Infiltration Capacity (VIC) model. However, for shorter lead-times, particularly up to day 3, the benchmark distributed model provides competitive reliability, as compared to the lumped models. In general, the SACSMA model provided better forecast quality, reliability and differentiation skill than other considered models at all lead times.2 of 27 soil moisture, and evaporation from summer to fall [1]. Relevant methodologies were proposed to assess several aspects of the hydrological cycle such as snowpack, spring melt, soil moisture, rainfall frequency, and evaporation, in the Canadian Prairie regions [2][3][4][5]. The effect of climate, land use, and ecosystem change on the hydrological processes of cold and wetland regions were also studied [6][7][8]. Even though some efforts were made to formulate the realistic representation of wetland processes in hydrological models [9][10][11][12][13], challenges of hydrological forecasting and flood predictions in such complex watersheds remain at large.Several important works have already been performed for enhancing flood prediction in several watersheds: for example, using single or multiple hydrological models [14-22], or feeding ensemble numerical weather products to models [23][24][25][26][27][28][29]. Velázquez et al. [18], for example, analyzed 16 lumped hydrological models with 50-member ensemble weather inputs. They detected that the multi-model approach of a grand member ensemble provided more...

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Hydrological Model Diversity Enhances Streamflow Forecast Skill at Short‐ to Medium‐Range Timescales

Cited by 61 publications

References 91 publications

Data Assimilation for Rainfall-Runoff Prediction Based on Coupled Atmospheric-Hydrologic Systems with Variable Complexity

Data Assimilation for Rainfall-Runoff Prediction Based on Coupled Atmospheric-Hydrologic Systems with Variable Complexity

Conditioning Ensemble Streamflow Prediction with the North Atlantic Oscillation improves skill at longer lead times

Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed

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