VIC模型参数的地区分布规律及在无资料流域的移用Regional Distribution of the VIC Model Parameters and Application in Ungauged Basins

2012 ◽  
Vol 01 (03) ◽  
pp. 57-64 ◽  
Author(s):  
周 研来
Keyword(s):  
Regional Distribution ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Vic Model

scholarly journals Estimation of baseflow parameters of variable infiltration capacity model with soil and topography properties for predictions in ungauged basins

2011 ◽  
Vol 8 (4) ◽  
pp. 7017-7053 ◽  
Author(s):  
Z. Bao ◽  
J. Liu ◽  
J. Zhang ◽  
G. Fu ◽  
G. Wang ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Model Simulation ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Variable Infiltration Capacity ◽  
Infiltration Capacity ◽  
Ungauged Catchments ◽  
Vic Model ◽  
Predictions In Ungauged Basins

Abstract. Equifinality is unavoidable when transferring model parameters from gauged catchments to ungauged catchments for predictions in ungauged basins (PUB). A framework for estimating the three baseflow parameters of variable infiltration capacity (VIC) model, directly with soil and topography properties is presented. When the new parameters setting methodology is used, the number of parameters needing to be calibrated is reduced from six to three, that leads to a decrease of equifinality and uncertainty. This is validated by Monte Carlo simulations in 24 hydro-climatic catchments in China. Using the new parameters estimation approach, model parameters become more sensitive and the extent of parameters space will be smaller when a threshold of goodness-of-fit is given. That means the parameters uncertainty is reduced with the new parameters setting methodology. In addition, the uncertainty of model simulation is estimated by the generalised likelihood uncertainty estimation (GLUE) methodology. The results indicate that the uncertainty of streamflow simulations, i.e., confidence interval, is lower with the new parameters estimation methodology compared to that used by original calibration methodology. The new baseflow parameters estimation framework could be applied in VIC model and other appropriate models for PUB.

scholarly journals Streamflow prediction in ungauged basins: benchmarking the efficiency of deep learning

2020 ◽  
Vol 163 ◽  
pp. 01001
Author(s):  
Georgy Ayzel ◽  
Liubov Kurochkina ◽  
Eduard Kazakov ◽  
Sergei Zhuravlev
Keyword(s):  
Deep Learning ◽  
Hydrological Model ◽  
State Of The Art ◽  
Flash Flood ◽  
Early Warning Systems ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Streamflow Prediction ◽  
Basin Scale ◽  
The Impact

Streamflow prediction is a vital public service that helps to establish flash-flood early warning systems or assess the impact of projected climate change on water management. However, the availability of streamflow observations limits the utilization of the state-of-the-art streamflow prediction techniques to the basins where hydrometric gauging stations exist. Since the most river basins in the world are ungauged, the development of the specialized techniques for the reliable streamflow prediction in ungauged basins (PUB) is of crucial importance. In recent years, the emerging field of deep learning provides a myriad of new models that can breathe new life into the stagnating PUB methods. In the presented study, we benchmark the streamflow prediction efficiency of Long Short-Term Memory (LSTM) networks against the standard technique of GR4J hydrological model parameters regionalization (HMREG) at 200 basins in Northwest Russia. Results show that the LSTM-based regional hydrological model significantly outperforms the HMREG scheme in terms of median Nash-Sutcliffe efficiency (NSE), which is 0.73 and 0.61 for LSTM and HMREG, respectively. Moreover, LSTM demonstrates the comparable median NSE with that for basin-scale calibration of GR4J (0.75). Therefore, this study underlines the high utilization potential of deep learning for the PUB by demonstrating the new state-of-the-art performance in this field.

scholarly journals Assessing water resources in China using PRECIS projections and a VIC model

2012 ◽  
Vol 16 (1) ◽  
pp. 231-240 ◽  
Author(s):  
G. Q. Wang ◽  
J. Y. Zhang ◽  
J. L. Jin ◽  
T. C. Pagano ◽  
R. Calow ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Temporal Distribution ◽  
Annual Runoff ◽  
Model Parameters ◽  
Infiltration Capacity ◽  
Climate Conditions ◽  
Vic Model ◽  
Sustainable Water Resources Management ◽  
The Impact

Abstract. Climate change is now a major environmental and developmental issue, and one that will increase the challenge of sustainable water resources management. In order to assess the implications of climate change for water resources in China, we calibrated a Variable Infiltration Capacity (VIC) model with a resolution of 50×50 km2 using data from 125 well-gauged catchments. Based on similarities in climate conditions, soil texture and other variables, model parameters were transferred to other areas not covered by the calibrated catchments. Taking runoff in the period 1961–1990 as a baseline, we studied the impact of climate change on runoff under three emissions scenarios, A2, B2 and A1B. Model findings indicate that annual runoff over China as a whole will probably increase by approximately 3–10% by 2050, but with quite uneven spatial and temporal distribution. The prevailing pattern of "north dry and south wet" in China is likely to be exacerbated under global warming.

scholarly journals Toward high-spatial resolution hydrological modeling for China: Calibrating the VIC model

2019 ◽  
Author(s):  
Bowen Zhu ◽  
Xianhong Xie ◽  
Chuiyu Lu ◽  
Shanshan Meng ◽  
Yi Yao ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Hydrological Modeling ◽  
Model Parameters ◽  
Data Simulation ◽  
Infiltration Capacity ◽  
Vic Model ◽  
Spatial And Temporal Distributions ◽  
Modeling Studies ◽  
Estimation Scheme

Abstract. High-resolution hydrological modeling is important for understanding fundamental terrestrial processes associated with the effects of climate variability and human activities on water resources availability. However, the spatial resolution of current hydrological modeling studies is mostly constrained to a relative coarse resolution (~ 10–100 km) and they are therefore unable to address many of the water-related issues facing society. In this study, a high resolution (0.0625º, ~ 6 km) hydrological modeling for China was developed based on the Variable Infiltration Capacity (VIC) model, spanning the period from January of 1970 to June of 2016. Distinct from other modeling studies, the parameters in the VIC model were updated using newly developed soil and vegetation datasets, and an effective parameter estimation scheme was used to transfer parameters from gauged to ungauged basins. Simulated runoff, evapotranspiration (ET), and soil moisture (SM) were extensively evaluated using in-situ observations, which indicated that there was a great improvement due to the updated model parameters. The spatial and temporal distributions of simulated ET and SM were also consistent with remote sensing retrievals. Moreover, this high-resolution modeling is capable of capturing flood and drought events with respect to their timing, duration, and spatial extent. This study shows that the hydrological datasets produced from this high-resolution modeling are useful for understanding long-term climate change and water resource security. It also has great potential for coupling with the China Land Data Simulation System to achieve real-time hydrological forecasts across China.

Model adequacy tests for improving predictions in ungauged basins

2020 ◽  
Author(s):  
Cristina Prieto ◽  
Nataliya Le Vine ◽  
Dmitri Kavetski ◽  
César Álvarez ◽  
Raúl Medina
Keyword(s):  
Time Series ◽  
Hydrological Model ◽  
Flow Time ◽  
Flow Index ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Ungauged Catchments ◽  
Subsequent Work ◽  
Study Results

<p>Flow prediction in ungauged catchments is a major unresolved challenge in scientific and engineering hydrology. Meeting this challenge is made difficult by the uncertainty in the “regionalization” model used to transpose hydrological data (e.g., flow indices) from gauged to ungauged basins, and by the uncertainty in the hydrological model used to predict streamflow in the ungauged basin. This study combines recent advances in flow index selection, regionalization via machine learning methods, and a Bayesian inference framework. In addition, it proposes two new statistical metrics, “DistanceTest” and “InfoTest”, to assess the adequacy of a model before estimating its parameters. “DistanceTest” quantifies whether a model (hydrological or regionalization) is likely to reproduce the available hydrological information in a catchment. “InfoTest” is based on Bayes Factors and quantifies the information added by a model (hydrological or regionalization) over prior knowledge about the available hydrological information in a catchment). The proposed adequacy tests can be seen as a prerequisite for a model (hydrological or regionalization) being considered capable of providing meaningful and high quality flow time series predictions in ungauged catchments. If a model is found inadequate a priori and rejected, the modeler is spared the effort in estimating the model parameters, which can be a substantial saving.</p><p>The proposed regionalization approach is applied to 92 northern Spain catchments, with 16 catchments treated as ungauged. It is found that (1) a small number of PCs capture approximately 87% of variability in the flow indices, and (2) adequacy tests with respect to regionalized information are indicative of (but do not guarantee) the ability of a hydrological model to predict flow time series. The adequacy tests identify the regionalization of flow index PCs as adequate in 12 of 16 catchments but the hydrological model as adequate in only 1 of 16 catchments. In addition, the case study results suggest that the hydrological model is the main source of uncertainty in comparison to the regionalization model, and hence should receive the main priority in subsequent work at the case study catchments.</p>

scholarly journals Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 528 ◽  
Author(s):  
Santiago Narbondo ◽  
Angela Gorgoglione ◽  
Magdalena Crisci ◽  
Christian Chreties
Keyword(s):  
Weather Conditions ◽  
Climatic Conditions ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Physical Similarity ◽  
Tropical Regions ◽  
Runoff Prediction ◽  
Tropical Areas ◽  
Rainfall Runoff Model ◽  
Predictions In Ungauged Basins

Regionalization techniques have been comprehensively discussed as the solution for runoff predictions in ungauged basins (PUB). Several types of regionalization approach have been proposed during the years. Among these, the physical similarity one was demonstrated to be one of the most robust. However, this method cannot be applied in large regions characterized by highly variable climatic conditions, such as sub-tropical areas. Therefore, this study aims to develop a new regionalization approach based on an enhanced concept of physical similarity to improve the runoff prediction of ungauged basins at country scale, under highly variable-weather conditions. A clustering method assured that watersheds with different hydrologic and physical characteristics were considered. The novelty of the proposed approach is based on the relationships found between rainfall-runoff model parameters and watershed-physiographic factors. These relationships were successively exported and validated at the ungauged basins. From the overall results, it can be concluded that the runoff prediction in the ungauged basins was very satisfactory. Therefore, the proposed approach can be adopted as an alternative method for runoff prediction in ungauged basins characterized by highly variable-climatic conditions.

scholarly journals A simple model of runoff from ungauged basins in West Greenland

1982 ◽  
Vol 111 ◽  
pp. 1-26
Author(s):  
R.J Braithwaite
Keyword(s):  
Simple Model ◽  
Model Parameters ◽  
Precipitation Series ◽  
Ungauged Basins ◽  
West Greenland ◽  
Independent Variables ◽  
The World ◽  
Order Of Magnitude ◽  
Design Yield ◽  
Systematic Field

A simple model of runoff in West Greenland is proposed for the case that glaciers are in a state of balance. The independent variables in the model are estimated annual precipitation and evaporation while model parameters are evaluated by analyses of precipitation series from Greenland, and mass balance series from other parts of the world. The model ean be applied to ungauged basins for preliminary calculations of mean mnoff, reservoir capacities and relative error in mean runoff. Application of the model to sixteen proposed hydropower projects in West Greenland shows an order-of-magnitude agreement with earlier estimates. However, a more detailed analysis of runoff from Johan Dahl Land confirms that the mnoff there is less than originally estimated. The proposed reservoir appears to be adequate for 30-year storage but the design yield of the project must be reduced for safety. The shortcomings of the model should be obvious. They ean be best overcorne by an improved knowledge of hydrological conditions in the country resulting from systematic field observations.

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