Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting

Musuuza, Jude; Crochemore, Louise; Pechlivanidis, Ilias

doi:10.1029/2022wr033655

Cited by 4 publications

(3 citation statements)

References 79 publications

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“…With the acknowledgements above, our study reveals links between hydrological regimes and the skill of forecasted hydrological extremes, implying that basin-specific outcomes can be regionalized to other geographic locations. This finding is in accordance with conclusions from previous studies exploring key drivers of seasonal hydrological forecast skills (Crochemore et al 2020, Girons Lopez et al 2021, Brunner and Fischer 2022b, Musuuza et al 2023. Particularly for European drought forecasting, Sutanto and van Lanen (2022) highlighted the strong link between river memory and seasonal hydrological drought predictability.…”

Section: Understanding the Predictability Of Extremes Across Hydro-cl...supporting

confidence: 91%

Hydrological regimes explain the seasonal predictability of streamflow extremes

Du,

Clemenzi,

Pechlivanidis

2023

Environ. Res. Lett.

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Advances in hydrological modeling and numerical weather forecasting have allowed hydro-climate services to provide accurate impact simulations and skillful forecasts that can drive decisions at the local scale. To enhance early warnings and long-term risk reduction actions, it is imperative to better understand the hydrological extremes and explore the drivers for their predictability. Here, we investigate the seasonal forecast skill of streamflow extremes over the pan-European domain, and further attribute the discrepancy in their predictability to the local river system memory as described by the hydrological regimes. Streamflow forecasts at about 35 400 basins, generated from the E-HYPE hydrological model driven with bias-adjusted ECMWF SEAS5 meteorological forcing input, are explored. Overall the results show adequate predictability for both hydrological extremes over Europe, despite the spatial variability in skill. The skill of high streamflow extreme deteriorates faster as a function of lead time than that of low extreme, with a positive skill persisting up to 12 and 20 weeks ahead for high and low extremes, respectively. A strong link between the predictability of extremes and the underlying local hydrological regime is identified through comparative analysis, indicating that systems of analogous river memory, e.g. fast or slow response to rainfall, can similarly predict the high and low streamflow extremes. The results improve our understanding of the geographical areas and periods, where the seasonal forecasts can timely provide information on very high and low streamflow conditions, including the drivers controlling their predictability. This consequently benefits regional and national organizations to embrace seasonal prediction systems and improve the capacity to act in order to reduce disaster risk and support climate adaptation.

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Section: Understanding the Predictability Of Extremes Across Hydro-cl...supporting

confidence: 91%

Hydrological regimes explain the seasonal predictability of streamflow extremes

Du,

Clemenzi,

Pechlivanidis

2023

Environ. Res. Lett.

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“…The chaotic development at weather scales means that precipitation typically becomes independent after around 5–10 days, although large scale features may sometimes have longer predictability with some impact on precipitation (Kelder et al, 2020) and thus, to some extent, streamflow. Streamflow forecasting is to a large extent determined by the initial hydrological states (Musuuza et al, 2023; Shukla & Lettenmaier, 2011), and can for some catchments and seasons retain traces of the initial state for days, weeks or even months. This implies that the different forecast members will retain a dependence with each other even though the meteorological drivers have become independent.…”

Section: Methodsmentioning

confidence: 99%

Robustness of hydrometeorological extremes in surrogated seasonal forecasts

Klehmet,

Berg,

Bozhinova

et al. 2024

Intl Journal of Climatology

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Water and disaster risk management require accurate information about hydrometeorological extremes. However, estimation of rare events using extreme value analysis is hampered by short observational records, with large resulting uncertainties. Here, we present a surrogate world setup that makes use of data samples from meteorological and hydrological seasonal re‐forecasts to explore extremes for long return periods. The surrogate timeseries allow us to pool the re‐forecasts into 1000‐year‐long timeseries. We can then calculate return values of extremes and explore how they are affected by the size of sub‐samples as method for estimating the uncertainty. The approach relies on the fact that probabilistic seasonal re‐forecasts, initialized with perturbed initial conditions, have limited predictive skill with increasing lead time. At long lead times re‐forecasts will diverge into independent samples. The meteorological seasonal re‐forecasts are taken from the SEAS5 system, and hydrological re‐forecasts are generated with the E‐HYPE process‐based model for the pan‐European domain. Extreme value analysis is applied to annual maxima of precipitation and streamflow for return periods of 100 years. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is somewhat reduced for 100‐year samples, but several 100 years seem to be necessary to have robust estimates. The bootstrap with replacement approach is applied to shorter timeseries, and is shown to well reproduce the uncertainty range of the longer samples. However, the main estimate of the return value can be significantly offset. Although the method is model based, with the associated uncertainties and bias compared to the real world, the surrogate approach is likely useful to explore rare and compounding extremes.

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“…It also holds great importance to use indirect observation data, which are relatively easier to obtain but sparse, to infer the seepage parameters in the case of complex seepage problems. Since the ensemble Kalman filter (EnKF) was proposed by Evensen [6], a data assimilation approach that uses a set of samples has been successfully utilized in various fields, such as meteorology [7], hydrology [8], petroleum [9], remote sensing [10], etc. Derivative algorithms have been developed to address specific issues in these domains.…”

Section: Introductionmentioning

confidence: 99%

A Data Assimilation Methodology to Analyze the Unsaturated Seepage of an Earth–Rockfill Dam Using Physics-Informed Neural Networks Based on Hybrid Constraints

Dai,

Zhou,

et al. 2024

Water

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Data assimilation for unconfined seepage analysis has faced significant challenges due to hybrid causes, such as sparse measurements, heterogeneity of porous media, and computationally expensive forward models. To address these bottlenecks, this paper introduces a physics-informed neural network (PINN) model to resolve the data assimilation problem for seepage analysis of unsaturated earth–rockfill dams. This strategy offers a solution that decreases the reliance on numerical models and enables an accurate and efficient prediction of seepage parameters for complex models in the case of sparse observational data. For the first attempt in this study, the observed values are obtained by random sampling of numerical solutions, which are then contributed to the synchronous constraints in the loss function by informing both the seepage control equations and boundary conditions. To minimize the effects of sharp gradient shifts in seepage parameters within the research domain, a residual adaptive refinement (RAR) constraint is introduced to strategically allocate training points around positions with significant residuals in partial differential equations (PDEs), which could facilitate enhancing the prediction accuracy. The model’s effectiveness and precision are evaluated by analyzing the proposed strategy against the numerical solutions. The results indicate that even with limited sparse data, the PINN model has great potential to predict seepage data and identify complex structures and anomalies inside the dam. By incorporating coupling constraints, the validity of our PINN model could lead to theoretically viable applications of hydrogeophysical inversion or multi-parameter seepage inversion. The results show that the proposed framework can predict the seepage parameters for the entire research domain with only a small amount of observation data. Furthermore, with a small amount of observation data, PINNs are able to obtain more accurate results than purely data-driven DNNs.

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Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting

Cited by 4 publications

References 79 publications

Hydrological regimes explain the seasonal predictability of streamflow extremes

Hydrological regimes explain the seasonal predictability of streamflow extremes

Robustness of hydrometeorological extremes in surrogated seasonal forecasts

A Data Assimilation Methodology to Analyze the Unsaturated Seepage of an Earth–Rockfill Dam Using Physics-Informed Neural Networks Based on Hybrid Constraints

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