Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models

Mani, Sharannya Thalli; Kolluru, Venkatesh; Mahesha, A.; Acharya, Tri Dev

doi:10.1016/j.ejrh.2022.101190

Cited by 4 publications

(2 citation statements)

References 52 publications

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“…For post-processing, this approach was used to combine outputs from multiple simulations or models with different data sources. For example, Thalli Mani et al [110] built a SWAT model with diverse metrological data and used data-driven models to assemble streamflow data. Li et al [111] processed SWAT, VIC, and TOPMODEL with Muskingum-Cunge routing (BTOPMC) [112] outputs using an ANN to obtain an enhanced prediction.…”

Section: Simulated Streamflow As Inputmentioning

confidence: 99%

Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review

Yifru,

Lim,

Lee

2024

Sustainability

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Streamflow prediction (SFP) constitutes a fundamental basis for reliable drought and flood forecasting, optimal reservoir management, and equitable water allocation. Despite significant advancements in the field, accurately predicting extreme events continues to be a persistent challenge due to complex surface and subsurface watershed processes. Therefore, in addition to the fundamental framework, numerous techniques have been used to enhance prediction accuracy and physical consistency. This work provides a well-organized review of more than two decades of efforts to enhance SFP in a physically consistent way using process modeling and flow domain knowledge. This review covers hydrograph analysis, baseflow separation, and process-based modeling (PBM) approaches. This paper provides an in-depth analysis of each technique and a discussion of their applications. Additionally, the existing techniques are categorized, revealing research gaps and promising avenues for future research. Overall, this review paper offers valuable insights into the current state of enhanced SFP within a physically consistent, domain knowledge-informed data-driven modeling framework.

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Section: Simulated Streamflow As Inputmentioning

confidence: 99%

Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review

Yifru,

Lim,

Lee

2024

Sustainability

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“…Some investigations have proposed that a combination of conceptual prediction models with machine learning methods will improve flood simulation and flood prediction, thereby enhancing the identification of areas at high risk from floods [17][18][19][20]. The snow module as a conceptual model was combined with machine learning modeling approaches, such as wavelet-based support vector regression (WSVR) and wavelet-based multivariate adaptive regression spline (WMARS), to model daily stormwater runoff in a Karst Ljubljanica catchment in Slovenia [17]. They reported that the proposed machine learning models provided unexpected results compared to the conceptual model.…”

Section: Introductionmentioning

confidence: 99%

Flash Flood Risk Assessment in the Asir Region, Southwestern Saudi Arabia, Using a Physically-Based Distributed Hydrological Model and GPM IMERG Satellite Rainfall Data

Salih,

Hassablla

2024

Atmosphere

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Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC–HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC–HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m3/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: ‘low risk’, ‘moderate risk’, ‘high risk’, and ‘very high risk’. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5–11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m3/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness.

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Overcoming the challenges of flow forecasting in a data poor region

Kouwen

Langmuir

Ramanathan

et al. 2023

Canadian Water Resources Journal / Revue canadienne des ressour

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Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models

Cited by 4 publications

References 52 publications

Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review

Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review

Flash Flood Risk Assessment in the Asir Region, Southwestern Saudi Arabia, Using a Physically-Based Distributed Hydrological Model and GPM IMERG Satellite Rainfall Data

Overcoming the challenges of flow forecasting in a data poor region

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