Quantifying the space – time variability of water balance components in an agricultural basin using a process-based hydrologic model and the Budyko framework

Qiu, Han; Niu, Jie; Phanikumar, Mantha S.

doi:10.1016/j.scitotenv.2019.04.147

Cited by 15 publications

(9 citation statements)

References 56 publications

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“…For subsurface water, PBHMs can simulate complex hydrological processes in the soil, such as root extraction, infiltration, soil evaporation, and groundwater discharge and recharge in the vadose zone, by solving the Richards equation (Maxwell et al, 2014). However, these complex processes and governing equations embedded in the PBHMs inevitably induce large uncertainties in the modeling predictions (Neuman, 2003;Rojas et al, 2010;Lu et al, 2012;Shen et al, 2014;Gupta, 2015, 2016;Qiu et al, 2019). How to efficiently decrease these large uncertainties becomes an essential problem for modelers.…”

Section: Introductionmentioning

confidence: 99%

“…Coupling PAWS with the CLM (Community Land Model) can enable the model to describe vegetation respiration and evapotranspiration in a physics-based manner (Shen et al, 2014;Niu et al, 2017). The model has been applied extensively in many watersheds, e.g., the large-scale watersheds in Michigan, USA (Shen et al, 2013(Shen et al, , 2014(Shen et al, , 2016Niu et al, 2014Niu et al, , 2017Ji et al, 2015;Qiu et al, 2019), and the watershed in the Amazon basin (Niu et al, 2017), and the model has presented good performances in these watersheds. PAWS can also estimate multiple key variables of hydrological states and fluxes at different spatiotemporal scales.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed

Liu

Dai

Niu

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Sensitivity analysis methods have recently received much attention for identifying important uncertainty sources (or uncertain inputs) and improving model calibrations and predictions for hydrological models. However, it is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models (PBHMs) because of its variant uncertainty sources and high computational cost. Therefore, a global sensitivity analysis method that is capable of simultaneously analyzing multiple uncertainty sources of PBHMs and providing quantitative sensitivity analysis results is still lacking. In an effort to develop a new tool for overcoming these weaknesses, we improved the hierarchical sensitivity analysis method by defining a new set of sensitivity indices for subdivided parameters. A new binning method and Latin hypercube sampling (LHS) were implemented for estimating these new sensitivity indices. For test and demonstration purposes, this improved global sensitivity analysis method was implemented to quantify three different uncertainty sources (parameters, models, and climate scenarios) of a three-dimensional large-scale process-based hydrologic model (Process-based Adaptive Watershed Simulator, PAWS) with an application case in an ∼ 9000 km2 Amazon catchment. The importance of different uncertainty sources was quantified by sensitivity indices for two hydrologic outputs of interest: evapotranspiration (ET) and groundwater contribution to streamflow (QG). The results show that the parameters, especially the vadose zone parameters, are the most important uncertainty contributors for both outputs. In addition, the influence of climate scenarios on ET predictions is also important. Furthermore, the thickness of the aquifers is important for QG predictions, especially in main stream areas. These sensitivity analysis results provide useful information for modelers, and our method is mathematically rigorous and can be applied to other large-scale hydrological models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed

Liu

Dai

Niu

et al. 2020

Hydrol. Earth Syst. Sci.

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“…In addition, the governing equations for subsurface flow are explicitly solved in PBHMs; thus, they can simulate detailed hydrological processes, including root extraction, infiltration, soil evaporation, and groundwater discharge and recharge in the vadose zone (Maxwell et al, 2014). However, the complexities and uncertainties inherent in PBHM structures, heterogeneous model parameters, heterogeneous data sources (e.g., elevation, soil properties, groundwater conductivities), and climate forcing may produce high uncertainties in the modeling outputs (Shen et al, 2014;Qiu et al, 2019). Uncertainty in numerical modeling is inevitable and important (Neuman, 2003;Rojas et al, 2010), especially for PBHMs that represent a high level of physical process complexity.…”

Section: Introductionmentioning

confidence: 99%

“…PAWS has been applied extensively in many watersheds, e.g., the large watersheds in Michigan state, U.S. (Niu et al, 2014(Niu et al, , 2017Ji et al, 2015;Shen et al, 2013Shen et al, , 2014Shen et al, , 2016Qiu et al, 2019), and the watershed in the Amazon basin (Niu et al, 2017), and demonstrates high efficiency and good performance. Here, we applied PAWS to the Amazon because this region includes more than half of the tropical rainforests globally (Morley, 2000) and plays an essential role in the world carbon (Richey et al, 2002;Phillips et al, 2009;Lintner et al, 2017) and water (Fearnside, 2005;Phillips et al, 2009) cycles.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Sensitivity Analysis for Large-scale Process-based Hydrological Modeling with Application in an Amazonian Watershed

Liu¹,

Dai²,

Niu³

et al. 2020

Preprint

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Abstract. Sensitivity analysis is an effective tool for identifying important uncertainty sources and improving model calibration and predictions, especially for integrated systems with heterogeneous parameter inputs and complex process coevolution. In this work, an advanced hierarchical global sensitivity analysis framework, which integrates the concept of variance-based global sensitivity analysis with a hierarchical uncertainty framework, was implemented to quantitatively analyse several uncertainties associated with a three-dimensional, process-based hydrologic model (PAWS). The uncertainty sources considered include model parameters (three vadose zone parameters, two groundwater parameters, and one overland flow parameter), model structure (different thicknesses to represent unconfined and confined aquifer layers) and climate scenarios. We apply the approach to an ~ 9,000 km2 Amazon catchment modeled at 1 km resolution to provide a demonstration of multiple uncertainty source quantification using a large-scale process-based hydrologic model. The sensitivity indices are assessed based on two important hydrologic outputs: evapotranspiration (ET) and groundwater contribution to streamflow (QG). It was found that, in general, parameters, especially the vadose zone parameters, are the most important uncertainty contributors for all sensitivity indices. In addition, the influence of climate scenarios on ET predictions is also nonignorable. Furthermore, the thickness of the aquifers along the river grid cells is important for both ET and QG. These results can assist in model calibration and provide modelers with a better understanding of the general sources of uncertainty in predictions associated with complex hydrological systems in Amazonia. We demonstrated a pilot example of comprehensive global sensitivity analysis of large-scale, complex hydrological and environmental models in this study. The hierarchical sensitivity analysis methodology used is mathematically rigorous and capable of being implemented in a variety of large-scale hydrological models with various sources of uncertainty.

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“…PAWS has been applied extensively in many watersheds, e.g., the large watersheds in Michigan state, USA (Niu et al, 2014(Niu et al, , 2017Ji et al, 2015;Shen et al, 2013Shen et al, , 2014Shen et al, , 2016Qiu et al, 2019) and the watershed in Amazon basin (Niu et al, 2017), 80 and demonstrates high efficiency and good performances. We applied PAWS here in the Amazon because it consists of more than half of the tropical rainforests (Morley, 2000) and plays an essential role in the world carbon (Richey et al, 2002;Phillips et al, 2009;Lintner et al, 2017) and water (Fearnside, 2005;Phillips et al, 2009) cycles.…”

mentioning

confidence: 99%

Hierarchical Sensitivity Analysis for Large Scale Process-based Hydrological Modeling with Application in an Amazonian Watershed

Liu

Dai

Niu

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Sensitivity analysis is an effective tool for identifying important uncertainty sources and improving model calibration and predictions, especially for integrated systems with heterogeneous parameter inputs and complex processes coevolution. In this work, an advanced hierarchical global sensitivity analysis framework, which integrates a hierarchical uncertainty framework and a variance-based global sensitivity analysis, was implemented to quantitatively analyze several uncertainties of a three-dimensional, process-based hydrologic model (PAWS). The uncertainty sources considered include model parameters, model structures (with/without overland flow module), and climate forcing. We apply the approach in a ~ 9000 km2 Amazon catchment modeled at 1 km resolution to provide a demonstration of multiple uncertainty source quantification using a large-scale process-based hydrologic model. The sensitivity indices are assessed based on three important hydrologic outputs: evapotranspiration (ET), ground evaporation (EG), and groundwater contribution to streamflow (QG). It is found that, in general, model parameters (especially those within the streamside model grid cells) are the most important uncertainty contributor for all sensitivity indices. In addition, the overland flow module significantly contributes to model predictive uncertainty. These results can assist model calibration and provide modelers a better understanding of the general sources of uncertainty in predictions of complex hydrological systems in Amazonia. We demonstrated a pilot example for comprehensive global sensitivity analysis of large-scale complex hydrological models in this research. The hierarchical sensitivity analysis methodology used is mathematically rigorous and can be applied to a wide range of large-scale hydrological models with various sources of uncertainty.

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Quantifying the space – time variability of water balance components in an agricultural basin using a process-based hydrologic model and the Budyko framework

Cited by 15 publications

References 56 publications

Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed

Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed

Hierarchical Sensitivity Analysis for Large-scale Process-based Hydrological Modeling with Application in an Amazonian Watershed

Hierarchical Sensitivity Analysis for Large Scale Process-based Hydrological Modeling with Application in an Amazonian Watershed

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