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

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.

Download Full-text

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-7017-2011 ◽

2011 ◽

Vol 8 (4) ◽

pp. 7017-7053 ◽

Cited By ~ 4

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.

Download Full-text

The Role of the Spatial Distribution of Radar Rainfall on Hydrological Modeling for an Urbanized River Basin in Japan

Water ◽

10.3390/w11081703 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1703 ◽

Cited By ~ 7

Author(s):

Shakti P. C. ◽

Tsuyoshi Nakatani ◽

Ryohei Misumi

Keyword(s):

River Basin ◽

Spatial Resolution ◽

Hydrological Modeling ◽

Distribution Patterns ◽

Rainfall Data ◽

Model Parameters ◽

Radar Rainfall ◽

Rain Events ◽

The Impact ◽

Hydrological Applications

Recently, the use of gridded rainfall data with high spatial resolutions in hydrological applications has greatly increased. Various types of radar rainfall data with varying spatial resolutions are available in different countries worldwide. As a result of the variety in spatial resolutions of available radar rainfall data, the hydrological community faces the challenge of selecting radar rainfall data with an appropriate spatial resolution for hydrological applications. In this study, we consider the impact of the spatial resolution of radar rainfall on simulated river runoff to better understand the impact of radar resolution on hydrological applications. Very high-resolution polarimetric radar rainfall (XRAIN) data are used as input for the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to simulate runoff from the Tsurumi River Basin, Japan. A total of 20 independent rainfall events from 2012–2015 were selected and categorized into isolated/convective and widespread/stratiform events based on their distribution patterns. First, the hydrological model was established with basin and model parameters that were optimized for each individual rainfall event; then, the XRAIN data were rescaled at various spatial resolutions to be used as input for the model. Finally, we conducted a statistical analysis of the simulated results to determine the optimum spatial resolution for radar rainfall data used in hydrological modeling. Our results suggest that the hydrological response was more sensitive to isolated or convective rainfall data than it was to widespread rain events, which are best simulated at ≤1 km and ≤5 km, respectively; these results are applicable in all sub-basins of the Tsurumi River Basin, except at the river outlet.

Download Full-text

High resolution data for semi-distributed hydrological modeling: where should we draw the line?

10.5194/egusphere-egu2020-12045 ◽

2020 ◽

Author(s):

Etienne Foulon ◽

Alain N. Rousseau ◽

Eduardo J. Scarpari Spolidorio ◽

Kian Abbasnezhadi

Keyword(s):

High Resolution ◽

Spatial Resolution ◽

Hydrological Modeling ◽

Hydrological Model ◽

Swat Model ◽

Computational Domain ◽

High Resolution Data ◽

Trade Offs ◽

Resolution Data ◽

Calibration Parameters

High-resolution data are readily available and used more than ever in hydrological modeling, despite few investigations demonstrating the added value. Nonetheless, a few studies have looked into the benefits of using increased spatial resolution data with the widely-used, semi-distributed, SWAT model. Meanwhile, far too little attention has been paid to the physically-based, semi-distributed, hydrological model HYDROTEL which is widely used for hydrological forecasting and hydroclimatic studies in Quebec, Canada. In a preliminary study, we demonstrated that increasing the spatial resolution of the digital elevation model (DEM) had a significant impact on the discretization of a watershed into hillslopes (i.e., computational units of HYDROTEL), and on their topographic attributes (slope, elevation and area). Accordingly, values of the calibration parameters were also substantially affected; whereas model performance was slightly improved for high- and low-flows only. This is why, we hereby propose the systematic assessment of HYDROTEL with respect to the resolution of the spatiotemporal computational domain for a specific physiographic scale. This investigation was conducted for the 350-km2 St. Charles River watershed, Quebec, Canada. The DEM used was derived from LiDAR data and aggregated at 20&#160;m. Due to a lack of accurate precipitation information at time scales less than 24&#160;hr, data from the high resolution deterministic precipitation analysis system, CaPA-HRDPA, were used to generate various time steps (6, 8, 12, and 24 hr) and to control results obtained from observed data. This approach, recently applied to three watersheds in Yukon, proved to be an excellent alternative to calibrate a hydrological model in a region known as a hydometeorological desert (see EGU 2020 presentation of Abbasnezhadi and Rousseau). The number of computational units ranged between 5 to 684 hillslopes, with mean areas ranging from 75 km2 to 0.5 km2. HYDROTEL was automatically calibrated over the 2013-2018 period using PADDS. We combined the Kling Gupta Efficiency and the log-transformed Nash Sutcliffe Efficiency to ensure good seasonal and annual representations of the hydrographs. The 12 most sensitive calibration parameters were adjusted using 150 optimisation trials with 150 repetitions each. Behavioral parameters were used to assess uncertainty and ensuing equifinality. All scenarios were evaluated using flow duration curves, performance indicators (RMSE, % Bias) and hydrograph analyses. In addition, quantitative analyses were done with respect to physiographic features such as: length of river segments, hillslopes, and sub-watershed boundaries for each resolution. We believe this study provides the needed systematic framework to assess trade-offs between spatiotemporal resolutions and modeling performances that can be achieved with HYDROTEL. Moreover, the use of various numbers of CaPA-HRDPA stations for model calibration has allowed us to determine the number of precipitation stations needed to achieve a given performance threshold.

Download Full-text

A large-scale, high-resolution hydrological model parameter data set for climate change impact assessment for the conterminous US

Hydrology and Earth System Sciences ◽

10.5194/hess-18-67-2014 ◽

2014 ◽

Vol 18 (1) ◽

pp. 67-84 ◽

Cited By ~ 50

Author(s):

A. A. Oubeidillah ◽

S.-C. Kao ◽

M. Ashfaq ◽

B. S. Naz ◽

G. Tootle

Keyword(s):

Climate Change ◽

High Resolution ◽

Impact Assessment ◽

Spatial Resolution ◽

Climate Impact ◽

Hydrologic Model ◽

Model Parameters ◽

Data Set ◽

Model Parameter ◽

Climate Impact Assessment

Abstract. To extend geographical coverage, refine spatial resolution, and improve modeling efficiency, a computation- and data-intensive effort was conducted to organize a comprehensive hydrologic data set with post-calibrated model parameters for hydro-climate impact assessment. Several key inputs for hydrologic simulation – including meteorologic forcings, soil, land class, vegetation, and elevation – were collected from multiple best-available data sources and organized for 2107 hydrologic subbasins (8-digit hydrologic units, HUC8s) in the conterminous US at refined 1/24° (~4 km) spatial resolution. Using high-performance computing for intensive model calibration, a high-resolution parameter data set was prepared for the macro-scale variable infiltration capacity (VIC) hydrologic model. The VIC simulation was driven by Daymet daily meteorological forcing and was calibrated against US Geological Survey (USGS) WaterWatch monthly runoff observations for each HUC8. The results showed that this new parameter data set may help reasonably simulate runoff at most US HUC8 subbasins. Based on this exhaustive calibration effort, it is now possible to accurately estimate the resources required for further model improvement across the entire conterminous US. We anticipate that through this hydrologic parameter data set, the repeated effort of fundamental data processing can be lessened, so that research efforts can emphasize the more challenging task of assessing climate change impacts. The pre-organized model parameter data set will be provided to interested parties to support further hydro-climate impact assessment.

Download Full-text

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

Hydrology and Earth System Sciences ◽

10.5194/hess-16-231-2012 ◽

2012 ◽

Vol 16 (1) ◽

pp. 231-240 ◽

Cited By ~ 83

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.

Download Full-text

Assessing water resources in China using PRECIS projections and VIC model

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-7293-2011 ◽

2011 ◽

Vol 8 (4) ◽

pp. 7293-7317 ◽

Cited By ~ 3

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 on water resources in China, a Variable Infiltration Capacity (VIC) model with a resolution of 50 × 50 km2 was calibrated using data from 125 well gauged catchments. According to 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, the impact of climate change on runoff under three emissions scenarios of A2, B2 and A1B was studied. 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.

Download Full-text

Large-watershed flood forecasting with high-resolution distributed hydrological model

Hydrology and Earth System Sciences ◽

10.5194/hess-21-735-2017 ◽

2017 ◽

Vol 21 (2) ◽

pp. 735-749 ◽

Cited By ~ 15

Author(s):

Yangbo Chen ◽

Ji Li ◽

Huanyu Wang ◽

Jianming Qin ◽

Liming Dong

Keyword(s):

High Resolution ◽

River Basin ◽

Spatial Resolution ◽

Hydrological Model ◽

Threshold Model ◽

Grid Cell ◽

Flood Forecasting ◽

Model Parameters ◽

Distributed Hydrological Model ◽

Computation Efficiency

Abstract. A distributed hydrological model has been successfully used in small-watershed flood forecasting, but there are still challenges for the application in a large watershed, one of them being the model's spatial resolution effect. To cope with this challenge, two efforts could be made; one is to improve the model's computation efficiency in a large watershed, the other is implementing the model on a high-performance supercomputer. This study sets up a physically based distributed hydrological model for flood forecasting of the Liujiang River basin in south China. Terrain data digital elevation model (DEM), soil and land use are downloaded from the website freely, and the model structure with a high resolution of 200 m  ×  200 m grid cell is set up. The initial model parameters are derived from the terrain property data, and then optimized by using the Particle Swarm Optimization (PSO) algorithm; the model is used to simulate 29 observed flood events. It has been found that by dividing the river channels into virtual channel sections and assuming the cross section shapes as trapezoid, the Liuxihe model largely increases computation efficiency while keeping good model performance, thus making it applicable in larger watersheds. This study also finds that parameter uncertainty exists for physically deriving model parameters, and parameter optimization could reduce this uncertainty, and is highly recommended. Computation time needed for running a distributed hydrological model increases exponentially at a power of 2, not linearly with the increasing of model spatial resolution, and the 200 m  ×  200 m model resolution is proposed for modeling the Liujiang River basin flood with the Liuxihe model in this study. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling the Liujiang River basin flood is a 500 m  ×  500 m grid cell, but the model spatial resolution with a 200 m  ×  200 m grid cell is recommended in this study to keep the model at a better performance.

Download Full-text

Assessing the effects of uncertainty and climate change on hydrological simulations across a permafrost gradient in North-Central Canada

10.24124/2020/59110 ◽

2020 ◽

Author(s):

◽

Rajtantra Lilhare

Keyword(s):

Climate Change ◽

Soil Depth ◽

Climate Change Impacts ◽

Future Climate Change ◽

Model Parameters ◽

Hudson Bay ◽

Infrastructure Development ◽

Infiltration Capacity ◽

Vic Model ◽

Future Warming

Hudson Bay, a vast inland sea in northern Canada, receives the highest average annual freshwater from the Nelson River system among all other contributing rivers. A rapidly changing climate and flow regulation from hydroelectric developments alter Nelson River streamflows timing and magnitude, affecting Hudson Bay’s physical, biological, and biogeochemical state. Despite recent developments and advances in climate datasets, hydrological models, and computational power, modelling the Hudson Bay system remains particularly challenging. Therefore, this dissertation addresses crucial research questions from the Hudson Bay System (BaySys) project by informing how climate change impacts variability and trends of freshwater-marine coupling in Hudson Bay. To that end, I present a comprehensive intercomparison of available climate datasets, their performance, and application within the macroscale Variable Infiltration Capacity (VIC) model, over the Lower Nelson River Basin (LNRB). This work aims to identify the VIC parameters sensitivity and uncertainty in water balance estimations and investigates future warming impacts on soil thermal regimes and hydrology in the LNRB. An intercomparison of six climate datasets and their equally weighted mean reveals generally consistent air temperature climatologies and trends (1981–2010) but with a prominent disagreement in annual precipitation trends with exceptional wetting trends in reanalysis products. VIC simulations forced by these datasets are utilized to examine parameter sensitivity and uncertainties due to input data and model parameters. Findings suggest that infiltration and prescribed soil depth parameters show prevailing seasonal and annual impacts, among other VIC parameters across the LNRB. Further, VIC simulations (1981–2070) reveal historical and possible future climate change impacts on cold regions hydrology and soil thermal conditions across the study domain. Results suggest that, in the projected climate, soil temperature warming induces increasing baseflows as future warming may intensify infiltration processes across the LNRB. This dissertation reports essential findings in the application of state-of-the-art climate data and the VIC model to explore potential changes in hydrology across the LNRB’s permafrost gradient with industrial relevance of future water management, hydroelectric generation, infrastructure development, operations, optimization, and implementation of adaptation measures for current and future developments.

Download Full-text

Large watershed flood forecasting with high resolution distributed hydrological model

10.5194/hess-2016-489 ◽

2016 ◽

Cited By ~ 1

Author(s):

Yangbo Chen ◽

Ji Li ◽

Huanyu Wang ◽

Jianming Qin ◽

Liming Dong

Keyword(s):

High Resolution ◽

River Basin ◽

Spatial Resolution ◽

Hydrological Model ◽

Threshold Model ◽

Grid Cell ◽

Flood Forecasting ◽

Model Parameters ◽

Distributed Hydrological Model ◽

Computation Efficiency

Abstract. Flooding is one of the most devastating natural disasters in the world with huge damages, and flood forecasting is one of the flood mitigation measurements. Watershed hydrological model is the major tool for flood forecasting, although the lumped watershed hydrological model is still the most widely used model, the distributed hydrological model has the potential to improve watershed flood forecasting capability. Distributed hydrological model has been successfully used in small watershed flood forecasting, but there are still challenges for the application in large watershed, one of them is the model’s spatial resolution effect. To cope with this challenge, two efforts could be made, one is to improve the model's computation efficiency in large watershed, another is implementing the model on high performance supercomputer. By employing Liuxihe Model, a physically based distributed hydrological model, this study sets up a distributed hydrological model for the flood forecasting of Liujiang River Basin in southern China that is a large watershed. Terrain data including DEM, soil type and land use type are downloaded from the website freely, and the model structure with a high resolution of 200 m * 200 m grid cell is set up. The initial model parameters are derived from the terrain property data, and then optimized by using the PSO algorithm, the model is used to simulate 29 observed flood events. It has been found that by dividing the river channels into virtual channel sections and assuming the cross section shapes as trapezoid, the Liuxihe Model largely increases computation efficiency while keeping good model performance, thus making it applicable in larger watersheds. This study also finds that parameter uncertainty exists for physically deriving model parameters, and parameter optimization could reduce this uncertainty, and is highly recommended. Computation time needed for running a distributed hydrological model increases exponentially at a power of 2, not linearly with the increasing of model spatial resolution, and the 200 m * 200 m model resolution is proposed for modeling Liujiang River Basin flood with Liuxihe Model in this study. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling Liujiang River Basin flood is 500 m * 500 m grid cell, but the model spatial resolution at 200 m * 200 m grid cell is recommended in this study to keep the model a better performance.

Download Full-text

Revisiting parameter sensitivities in the Variable Infiltration Capacity model

10.5194/hess-2021-550 ◽

2021 ◽

Author(s):

Ulises Sepúlveda ◽

Pablo A. Mendoza ◽

Naoki Mizukami ◽

Andrew J. Newman

Keyword(s):

Aridity Index ◽

Mean Annual Precipitation ◽

Model Parameters ◽

Variable Infiltration Capacity ◽

Infiltration Capacity ◽

Climate Type ◽

Area Index ◽

Vic Model ◽

Variable Infiltration Capacity Model ◽

Capacity Model

Abstract. Despite the Variable Infiltration Capacity (VIC) model being used for decades in the hydrology community, there are still model parameters whose sensitivities remain unknown. Additionally, understanding the factors that control spatial variations in parameter sensitivities is crucial given the increasing interest to obtain spatially coherent parameter fields over large domains. In this study, we investigate the sensitivities of 43 soil, vegetation and snow parameters in the VIC model for 101 catchments spanning the diverse hydroclimates of continental Chile. We implement a hybrid local-global sensitivity analysis approach, using eight model evaluation metrics to quantify sensitivities, with four of them formulated from runoff time series; two characterizing snow processes, and the remaining two based on evaporation processes. Our results confirm an over-parameterization for the processes analysed here, with only 12 (i.e., 28 %) parameters found as sensitive, distributed among soil (7), vegetation (2) and snow (3) model components. Correlation analyses show that climate variables – in particular, mean annual precipitation and aridity index – are the main controls on parameter sensitivities. Additionally, our results highlight the influence of the leaf area index on simulated hydrologic processes – regardless on the dominant climate types – and the relevance of hard-coded snow parameters. Based on correlation results and the interpretation of spatial sensitivity patterns, we provide guidance on the most relevant parameters for model calibration according to the target processes and the prevailing climate type. Overall, the results presented here contribute to improved understanding of model behaviour across watersheds with diverse physical characteristics that encompass a wide hydroclimatic gradient from hyper-arid to humid systems.

Download Full-text