Water Balance Coefficient Research Articles

A physically based distributed karst hydrological model (QMG model-V1.0) for flood simulations

Abstract. Karst trough and valley landforms are prone to flooding, primarily because of the unique hydrogeological features of karst landforms, which are conducive to the spread of rapid runoff. Hydrological models that represent the complicated hydrological processes in karst regions are effective for predicting karst flooding, but their application has been hampered by their complex model structures and associated parameter set, especially for distributed hydrological models, which require large amounts of hydrogeological data. Distributed hydrological models for predicting flooding are highly dependent on distributed modelling, complicated boundary parameter settings and extensive hydrogeological data processing, which consumes large amounts of both time and computational power. Proposed here is a distributed physically based karst hydrological model known as the QMG (Qingmuguan) model. The structural design of this model is relatively simple, and it is generally divided into surface and underground double-layered structures. The parameters that represent the structural functions of each layer have clear physical meanings, and fewer parameters are included in this model than in the current distributed models. This allows karst areas to be modelled with only a small amount of necessary hydrogeological data. Eighteen flood processes across the karst underground river in the Qingmuguan karst trough valley are simulated by the QMG model, and the simulated values agree well with observations: the average values of the Nash–Sutcliffe coefficient and the water balance coefficient are both 0.92, while the average relative flow process error is 10 % and the flood peak error is 11 %. A sensitivity analysis shows that the infiltration coefficient, permeability coefficient and rock porosity are the parameters that require the most attention in model calibration and optimization. The improved predictability of karst flooding enabled by the proposed QMG model promotes a better mechanistic depiction of runoff generation and confluence in karst trough valleys.

Simulation of Karst Floods with a Hydrological Model Improved by Meteorological Model Coupling

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst areas in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting (WRF) Model and quantitative precipitation estimation (QPE) from the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst–Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 h, respectively. Coupling the WRF Model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 h, which is important for flood warning and control. Significance Statement The WRF Model and PERSIANN-CCS can provide precipitation data for mountainous karst areas lacking rainfall gauges, and their rainfall results are forecasted effectively to reduce the uncertainty of input precipitation data. Then, the PERSIANN-CCS QPEs and WRF QPF are coupled with the improved KL model for karst flood simulation and prediction. This coupled model worked well in karst basins.

Enhanced Water Management and Fuel Efficiency of a Fully Passive Direct Methanol Fuel Cell With Super-Hydrophilic/ -Hydrophobic Cathode Porous Flow-Field

Water management is a critical issue for a direct methanol fuel cell (DMFC). This study focuses primarily on the use of a super-hydrophilic or super-hydrophobic cathode porous flow field to improve the water management of a passive air-breathing DMFC. The flow field layer was made of an in-house copper-fiber sintered felt (CFSF) which owns good stability and conductivity. Results indicate that the super-hydrophilic flow field performs better at a lower methanol concentration since it facilitates water removal when the water balance coefficient (WBC) is high. In the case of high-concentration operation, the use of a super-hydrophobic pattern is more able to reduce methanol crossover (MCO) and increase fuel efficiency since it helps maintain a lower WBC due to its ability in enhancing water back flow from the cathode to the anode. The effects of methanol concentration and the porosity of the CFSF are also discussed in this work. The cell based on the super-hydrophobic pattern with a porosity of 60% attains the best performance with a maximum power density of 18.4 mW cm−2 and a maximum limiting current density of 140 mA cm−2 at 4 M.

Sensitivity and Interaction Analysis Based on Sobol’ Method and Its Application in a Distributed Flood Forecasting Model

Sensitivity analysis is a fundamental approach to identify the most significant and sensitive parameters, helping us to understand complex hydrological models, particularly for time-consuming distributed flood forecasting models based on complicated theory with numerous parameters. Based on Sobol’ method, this study compared the sensitivity and interactions of distributed flood forecasting model parameters with and without accounting for correlation. Four objective functions: (1) Nash–Sutcliffe efficiency (ENS); (2) water balance coefficient (WB); (3) peak discharge efficiency (EP); and (4) time to peak efficiency (ETP) were implemented to the Liuxihe model with hourly rainfall-runoff data collected in the Nanhua Creek catchment, Pearl River, China. Contrastive results for the sensitivity and interaction analysis were also illustrated among small, medium, and large flood magnitudes. Results demonstrated that the choice of objective functions had no effect on the sensitivity classification, while it had great influence on the sensitivity ranking for both uncorrelated and correlated cases. The Liuxihe model behaved and responded uniquely to various flood conditions. The results also indicated that the pairwise parameters interactions revealed a non-ignorable contribution to the model output variance. Parameters with high first or total order sensitivity indices presented a corresponding high second order sensitivity indices and correlation coefficients with other parameters. Without considering parameter correlations, the variance contributions of highly sensitive parameters might be underestimated and those of normally sensitive parameters might be overestimated. This research laid a basic foundation to improve the understanding of complex model behavior.

Modelling Snowmelt Runoff under Climate Change Scenarios in an Ungauged Mountainous Watershed, Northwest China

An integrated modeling system has been developed for analyzing the impact of climate change on snowmelt runoff in Kaidu Watershed, Northwest China. The system couples Hadley Centre Coupled Model version 3 (HadCM3) outputs with Snowmelt Runoff Model (SRM). The SRM was verified against observed discharge for outlet hydrological station of the watershed during the period from April to September in 2001 and generally performed well for Nash-Sutcliffe coefficient (EF) and water balance coefficient (RE). The EF is approximately over 0.8, and the water balance error is lower than ± 10%, indicating reasonable prediction accuracy. The Statistical Downscaling Model (SDSM) was used to downscale coarse outputs of HadCM3, and then the downscaled future climate data were used as inputs of the SRM. Four scenarios were considered for analyzing the climate change impact on snowmelt flow in the Kaidu Watershed. And the results indicated that watershed hydrology would alter under different climate change scenarios. The stream flow in spring is likely to increase with the increased mean temperature; the discharge and peck flow in summer decrease with the decreased precipitation under Scenarios 1 and 2. Moreover, the consideration of the change in cryosphere area would intensify the variability of stream flow under Scenarios 3 and 4. The modeling results provide useful decision support for water resources management.

An efficient integrated approach for global sensitivity analysis of hydrological model parameters

Efficient sensitivity analysis, particularly for the global sensitivity analysis (GSA) to identify the most important or sensitive parameters, is crucial for understanding complex hydrological models, e.g., distributed hydrological models. In this paper, we propose an efficient integrated approach that integrates a qualitative screening method (the Morris method) with a quantitative analysis method based on the statistical emulator (variance-based method with the response surface method, named the RSMSobol' method) to reduce the computational burden of GSA for time-consuming models. Using the Huaihe River Basin of China as a case study, the proposed approach is used to analyze the parameter sensitivity of distributed time-variant gain model (DTVGM). First, the Morris screening method is used to qualitatively identify the parameter sensitivity. Subsequently, the statistical emulator using the multivariate adaptive regression spline (MARS) method is chosen as an appropriate surrogate model to quantify the sensitivity indices of the DTVGM. The results reveal that the soil moisture parameter WM is the most sensitive of all the responses of interest. The parameters Kaw and g1 are relatively important for the water balance coefficient (WB) and Nash–Sutcliffe coefficient (NS), while the routing parameter RoughRss is very sensitive for the Nash–Sutcliffe coefficient (NS) and correlation coefficient (RC) response of interest. The results also demonstrate that the proposed approach is much faster than the brute-force approach and is an effective and efficient method due to its low CPU cost and adequate degree of accuracy.

Palmer 가뭄지수의 매개변수 안정화 연구

가뭄은 비정상적인 수분부족 현상이 지속되어 나타나는 수문학적 현상으로 광범위한 지역에서 오랜 시간동안 지속되는 자연재해라 할 수 있다. 연속적인 가뭄의 모니터링은 가뭄으로 인한 피해를 예방하는 차원에서 매우 중요한 요소이다. 가뭄의 모니터링을 위해서는 일반적으로 가뭄지수를 이용하고 있으며, PDSI는 전 세계적으로 많이 이용되고 있는 가뭄지수 중 하나이다. 본 연구에서는 PDSI 산정과정에 포함되어 있는 3가지 매개변수(수분평형계수, 기후특성인자, 지속기간인자)에 대한 자료기간의 영향을 검토하기 위한 연구를 수행하였다. 분석결과, 3가지 매개변수 중 기후특성인자에 대해서는 자료기간에 따른 영향이 상대적으로 뚜렷하게 나타나고 있음을 확인할 수 있었으며, 다른 인자의 경우에도 일부 지점에 따라 유사한 결과를 확인할 수 있었다. 이를 통해 우리나라에서 PDSI를 효과적으로 적용하기 위해서는 우리나라에서 관측된 자료를 이용하여 매개변수 재산정이 필요함을 확인할 수 있었다. 또한 최소 40년 이상의 관측자료를 확보하여 PDSI를 산정하는 것이 매개변수 안정화를 통해 보다 정확한 가뭄특성 반영이 가능할 것으로 판단된다. Drought is a hydrologic phenomenon due to the abnormal water deficiency and a natural disaster lasting for a long time through a large area. Continuous drought monitoring is very important in minimizing damage caused by severe drought. Generally, drought index is used for drought monitoring and PDSI is one of popular indices in the world. In this study, the effect of data duration is analyzed on 3 parameters (water-balance coefficient, climatic characteristic factor, and duration factor) in PDSI. The analysis shows that the effect of data duration on the climatic characteristic factor among 3 parameters is relatively clear and similar patterns are found in the other parameters at some weather stations. Therefore, PDSI parameters need to be recalculated using the observation data in Korea for the correct application of PDSI. In addition, the parameter stabilization of PDSI requires observation data with period longer than 40 years.

Integration of a statistical emulator approach with the SCE-UA method for parameter optimization of a hydrological model

Parameter optimization of a hydrological model is an indispensable process within model development and application. The lack of knowledge regarding the efficient optimization of model parameters often results in a bottle-neck within the modeling process, resulting in the effective calibration and validation of distributed hydrological models being more difficult to achieve. The classical approaches to global parameter optimization are usually characterized by being time consuming, and having a high computation cost. For this reason, an integrated approach coupling a meta-modeling approach with the SCE-UA method was proposed, and applied within this study to optimize hydrological model parameter estimation. Meta-modeling was used to determine the optimization range for all parameters, following which the SCE-UA method was applied to achieve global parameter optimization. The multivariate regression adaptive splines method was used to construct the response surface as a surrogate model to a complex hydrological model. In this study, the daily distributed time-variant gain model (DTVGM) applied to the Huaihe River Basin, China, was chosen as a case study. The integrated objective function based on the water balance coefficient and the Nash-Sutcliffe coefficient was used to evaluate the model performance. The case study shows that the integrated method can efficiently complete the multi-parameter optimization process, and also demonstrates that the method is a powerful tool for efficient parameter optimization.

An efficient global sensitivity analysis approach for distributed hydrological model

Sensitivity analysis of hydrological model is the key for model uncertainty quantification. However, how to effectively validate model and identify the dominant parameters for distributed hydrological models is a bottle-neck to achieve parameters optimization. For this reason, a new approach was proposed in this paper, in which the support vector machine was used to construct the response surface at first. Then it integrates the SVM-based response surface with the Sobol’ method, i.e. the RSMSobol’ method, to quantify the parameter sensitivities. In this work, the distributed time-variant gain model (DTVGM) was applied to the Huaihe River Basin, which was used as a case to verify its validity and feasibility. We selected three objective functions (i.e. water balance coefficient WB, Nash-Sutcliffe efficiency coefficient NS, and correlation coefficient RC) to assess the model performance as the output responses for sensitivity analysis. The results show that the parameters g1 and g2 are most important for all the objective functions, and they are almost the same to that of the classical approach. Furthermore, the RSMSobol method can not only achieve the quantification of the sensitivity, and also reduce the computational cost, with good accuracy compared to the classical approach. And this approach will be effective and reliable in the global sensitivity analysis for a complex modelling system.

Integrated Modeling System for Water Resources Management of Tarim River Basin

An integrated modeling system has been developed for water resources management of the Tarim River Basin, China. The system coupled remote sensing (RS)/geography information system (GIS) technique with distributed hydrological model to simulate the rainfall runoff, snow melting, and evapotranspiration process of the hydrological cycle. A case study was carried out in the Kaidu watershed. RS/GIS technique was used for effectively accessing, processing, and managing spatial data, such as land use, vegetative cover, soil, topography, precipitation, and evapotranspiration. The model was calibrated and validated against observed discharge for two hydrological stations during the period 1998–2001, and it generally performed well for Nash-Sutcliffe coefficient, water balance coefficient, and correlation coefficient. The Nash-Sutcliffe coefficient was approximately over 0.7 and the water balance error was lower than ± 5%, indicating reasonable prediction accuracy. A comparison between the conventional and RS-based hydrological models was conducted. Although the two models exhibit similar performances on runoff and snow melt simulation, the RS-based hydrological model had better performance in the simulation of actual evapotranspiration. Modeling results provide useful decision support for water resources management.

Optimization of water and air management systems for a passive direct methanol fuel cell

A multi-phase, multi-component, thermal and transient model is applied to simulate the operation of a passive direct methanol fuel cell and optimize the design. The model takes into consideration the thermal effects and the variation of methanol concentration at the feeding reservoir above the fuel cell. Polarization and constant current cases are numerically simulated and compared with experiments for liquid feed concentration, membrane thickness, water management and air management systems. Parameters considered when determining an optimal design include power density, fuel utilization and energy efficiencies and water balance coefficients. An optimal liquid feed concentration is determined to be 2.0 mol kg −1, which achieved a maximum power density of 21 mW cm −2 and a fuel utilization efficiency of 63.0%. An optimal design of a cell uses a thick membrane (Nafion 117) to reduce methanol crossover and two additional cathode GDLs to improve the water balance coefficient and efficiency of the cell. This combination results in a power density of 23.8 mW cm −2 and a water balance coefficient of −1.71. An air filter may also be added to improve the efficiency and water balance coefficient of the cell, however, a small loss in power density will also occur. Using an Oil Sorbents air filter the water balance coefficient is increased to −0.85, the fuel utilization efficiency is improved by 27.35% and the maximum power density decreased to 21.6 mW cm −2.

Water and air management systems for a passive direct methanol fuel cell

In this paper water and air management systems were developed for a miniature, passive direct methanol fuel cell (DMFC). The membrane thickness, water management system, air management system and gas diffusion electrodes (GDE) were examined to find their effects on the water balance coefficient, fuel utilization efficiency, energy efficiency and power density. Two membranes were used, Nafion ® 112 and Nafion ® 117. Nafion ® 117 cells had greater water balance coefficients, higher fuel utilization efficiency and greater energy efficiency. A passive water management system which utilizes additional cathode gas diffusion layers (GDL) and a passive air management system which makes use of air filters was developed and tested. Water management was improved with the addition of two additional cathode GDLs. The water balance coefficients were increased from −1.930 to 1.021 for a cell using a 3.0 mol kg −1 solution at a current density of 33 mA cm −2. The addition of an air filter further increased the water balance coefficient to 1.131. Maximum power density was improved from 20 mW cm −2 to 25 mW cm −2 for 3.0 mol kg −1 solutions by upgrading from second to third generation GDEs, obtained from E-TEK. There was no significant difference in water management found between second and third generation GDEs. A fuel utilization efficiency of 63% and energy efficiency of 16% was achieved for a 3.0 mol kg −1 solution with a current density of 66 mA cm −2 for third generation GDEs.

Comparing global models of terrestrial net primary productivity (NPP): the importance of water availability

SummaryGiven that neither absolute measures nor direct model validations of global terrestrial net primary productivity (NPP) are feasible, intercomparison of global NPP models provides an effective tool to check model consistency. For this study, we tested the assumption that water availability is the primary limiting factor of NPP in global terrestrial biospheric models. We compared a water balance coefficient (WBC), calculated as the difference of mean annual precipitation and potential evapotranspiration to NPP for each grid cell (0.5° × 0.5° longitude/latitude) in each of 14 models. We also evaluated different approaches used for introducing water budget limitations on NPP: (1) direct physiological control on evapotranspiration through canopy conductance; (2) climatological computation of constraints from supply/demand for ecosystem productivity; and (3) water limitation inferred from satellite data alone. Plots of NPP vs. WBC showed comparable patterns for the models using the same method for water balance limitation on NPP. While correlation plots revealed similar patterns for most global models, other environmental controls on NPP introduced substantial variability.