Performance dependence of multi-model combination methods on hydrological model calibration strategy and ensemble size

<p>Hydrological models are often calibrated with respect to flow observations at the basin outlet. As a result, flow predictions may seem reliable but this is not necessarily the case for the spatiotemporal variability of system-internal processes, especially in large river basins. Satellite observations contain valuable information not only for poorly gauged basins with limited ground observations and spatiotemporal model calibration, but also for stepwise model development. This study explored the value of satellite observations to improve our understanding of hydrological processes through stepwise model structure adaption and to calibrate models both temporally and spatially. More specifically, satellite-based evaporation and total water storage anomaly observations were used to diagnose model deficiencies and to subsequently improve the hydrological model structure and the selection of feasible parameter sets. A distributed, process based hydrological model was developed for the Luangwa river basin in Zambia and calibrated with respect to discharge as benchmark. This model was modified stepwise by testing five alternative hypotheses related to the process of upwelling groundwater in wetlands, which was assumed to be negligible in the benchmark model, and the spatial discretization of the groundwater reservoir. Each model hypothesis was calibrated with respect to 1) discharge and 2) multiple variables simultaneously including discharge and the spatiotemporal variability in the evaporation and total water storage anomalies. The benchmark model calibrated with respect to discharge reproduced this variable well, as also the basin-averaged evaporation and total water storage anomalies. However, the evaporation in wetland dominated areas and the spatial variability in the evaporation and total water storage anomalies were poorly modelled. The model improved the most when introducing upwelling groundwater flow from a distributed groundwater reservoir and calibrating it with respect to multiple variables simultaneously. This study showed satellite-based evaporation and total water storage anomaly observations provide valuable information for improved understanding of hydrological processes through stepwise model development and spatiotemporal model calibration.</p>

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How good are hydrological models for gap-filling streamflow data?

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4593-2018 ◽

2018 ◽

Vol 22 (8) ◽

pp. 4593-4604 ◽

Cited By ~ 8

Author(s):

Yongqiang Zhang ◽

David Post

Keyword(s):

Model Calibration ◽

Hydrological Model ◽

Hydrological Models ◽

Hydrological Response ◽

Gap Filling ◽

Annual Streamflow ◽

Response Variable ◽

Benchmark Data ◽

Streamflow Data ◽

Streamflow Trend

Abstract. Gap-filling streamflow data is a critical step for most hydrological studies, such as streamflow trend, flood, and drought analysis and hydrological response variable estimates and predictions. However, there is a lack of quantitative evaluation of the gap-filled data accuracy in most hydrological studies. Here we show that when the missing data rate is less than 10 %, the gap-filled streamflow data obtained using calibrated hydrological models perform almost the same as the benchmark data (less than 1 % missing) when estimating annual trends for 217 unregulated catchments widely spread across Australia. Furthermore, the relative streamflow trend bias caused by the gap filling is not very large in very dry catchments where the hydrological model calibration is normally poor. Our results clearly demonstrate that the gap filling using hydrological modelling has little impact on the estimation of annual streamflow and its trends.

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Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

Hydrology and Earth System Sciences ◽

10.5194/hess-18-353-2014 ◽

2014 ◽

Vol 18 (1) ◽

pp. 353-365 ◽

Cited By ~ 35

Author(s):

U. Haberlandt ◽

I. Radtke

Keyword(s):

Time Series ◽

Frequency Analysis ◽

Model Calibration ◽

Hydrological Model ◽

Probability Distributions ◽

Flood Frequency ◽

Rainfall Data ◽

Flood Frequency Analysis ◽

Peak Flows ◽

Hourly Rainfall

Abstract. Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.

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Improving process representation in conceptual hydrological model calibration using climate simulations

Water Resources Research ◽

10.1002/2013wr013857 ◽

2014 ◽

Vol 50 (6) ◽

pp. 5044-5073 ◽

Cited By ~ 23

Author(s):

Marie Minville ◽

Dominique Cartier ◽

Catherine Guay ◽

Louis-Alexandre Leclaire ◽

Charles Audet ◽

...

Keyword(s):

Model Calibration ◽

Hydrological Model ◽

Process Representation ◽

Climate Simulations ◽

Conceptual Hydrological Model

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ỨNG DỤNG DỮ LIỆU MƯA CHIRPS VÀ MÔ HÌNH THỦY VĂN HEC-HMS MÔ PHỎNG DÒNG CHẢY LŨ Ở LƯU VỰC SÔNG LẠI GIANG

Tạp chí Khoa học và công nghệ nông nghiệp, Trường Đại học Nông Lâm Huế ◽

10.46826/huaf-jasat.v5n1y2021.419 ◽

2021 ◽

Vol 5 (1) ◽

pp. 2252-2261

Author(s):

Ngô Anh Tú ◽

Phan Thái Lê ◽

Nguyễn Hữu Xuân ◽

Trần Văn Bình

Keyword(s):

Economic Development ◽

River Basin ◽

Hydrological Model ◽

Flow Data ◽

Model Combination ◽

The North ◽

Socio Economic Development ◽

Flood Peaks ◽

Simulation Results ◽

Flood Flow

Bài báo xác định lưu lượng dòng chảy theo thời đoạn dựa vào mô hình HEC-HMS, số liệu mưa từ ảnh vệ tinh CHIRPS của NASA và Hệ thống thông tin địa lý (GIS) trong mô phỏng dòng chảy lũ tháng 12 năm 2016 tại lưu vực sông Lại Giang, lưu vực lớn thứ hai của tỉnh Bình Định (sau lưu vực sông Kôn) và có vai trò quan trọng về phát triển kinh tế-xã hội ở phía Bắc của tỉnh. Kết quả mô phỏng dòng chảy lũ rất đáng tin cậy, lưu lượng dòng chảy lũ đạt đỉnh 2542,6 m3/s tương ứng với với tần suất lũ 5%. Chỉ số kiểm định mô hình NSE với giá trị là 0,93; hệ số R2 đạt 0,78 sai số PBIAS khoảng 24% và sai số đỉnh lũ PEC = 52,01. ABSTRACT The paper aimed to introduce the application of the HEC-HMS hydrological model combination with the CHIRPS (Climate Hazards Group Infrared Precipitation with Station) and GIS to restore flood flow data in the Lai Giang river basin in 2016. The Lai Giang river basin is the second largest basin of Binh Dinh province (after the Kon river basin), it plays an important role in socio-economic development in the North of Binh Dinh province. The simulation results of flood peaks reached 2542,6 m3.s-1 (P=5%). Model test indices such as NSE = 0.93, the correlation coefficient reached 0,78; the percentage of PBIAS error was about 24%, and peak error (PEC) was 52,01.

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