Targeted hydrologic model calibration to improve prediction of ecologically-relevant flow metrics

Parker, Sarah R.; Adams, Stephen K.; Lammers, Roderick W.; Stein, Eric D.; Bledsoe, Brian P.

doi:10.1016/j.jhydrol.2019.03.081

Cited by 20 publications

(15 citation statements)

References 42 publications

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“…This list cannot be considered exhaustive, and other metrics not included in the list were used in specific analyses during HM calibration (e.g. the Richard-Bark flashness index proposed by Parker et al, 2019).…”

Section: Metrics As Objective Functions In Hydrologic Model Calibrationmentioning

confidence: 99%

Objective functions used as performance metrics for hydrological models: state-of-the-art and critical analysis

Ferreira

Paz

Bravo

2020

RBRH

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Hydrological models (HMs) can be applied for different purposes, and a key step is model calibration using objective functions (OF) to quantify the agreement between observed and calculated discharges. Fully understanding the OF is important to properly take advantage of model calibration and interpret the results. This study evaluates 36 OF proposed in the literature, considering two watersheds of different hydrological regimes. Daily simulated streamflow time-series, using a distributed hydrological model (MGB-IPH), and ten daily streamflow synthetic time-series, generated from the observed and calculated streamflows, were used in the analysis of each watershed. These synthetic data were used to evaluate how does each metric evaluate hypothetical cases that present isolated very well known error behaviors. Despite of all NSE-derived (Nash-Sutcliffe efficiency) metrics that use the square of the residuals in their formulation have shown higher sensitivity to errors in high flows, the ones that use daily and monthly averages of flow rates in absolute terms were more stringent than the others to assess HMs performance. Low flow errors were better evaluated by metrics that use the flow logarithm. The constant presence of zero flow rates deteriorate them significantly, with the exception of the metrics TRMSE (Transformed root mean square error) did not demonstrate this problem. An observed limitation of the formulations of some metrics was that the errors of overestimation or underestimation are compensated. Our results reassert that each metric should be interpreted specifically thinking about the aspects it has been proposed for, and simultaneously taking into account a set of metrics would lead to a broader evaluation of HM ability (e.g. multiobjective model evaluation). We recommend that the use of synthetic time series as those proposed in this work could be useful as an auxiliary step towards better understanding the evaluation of a calibrated hydrological model for each study case, taking into account model capabilities and observed hydrologic regime characteristics.

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Section: Metrics As Objective Functions In Hydrologic Model Calibrationmentioning

confidence: 99%

Objective functions used as performance metrics for hydrological models: state-of-the-art and critical analysis

Ferreira

Paz

Bravo

2020

RBRH

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“…The coastal area of southern California (So. CA) is experiencing substantial hydrologic alteration (Hawley and Bledsoe, 2011) and associated ecological decline (Stein et al, 2012), which has prompted application of ELOHA Parker et al, 2019;Pyne et al, 2017;Sengupta et al, 2018;Stein et al, 2017). The region is highly heterogenous, spanning an extensive range of geology, stream types, and land uses, which presents unique challenges for implementing ELOHA.…”

Section: Introductionmentioning

confidence: 99%

“…This study was motivated by a desire to improve the science supporting environmental streamflows in So. CA where flow criteria are under development (Mazor et al, 2018;Parker et al, 2019;Sengupta et al, 2018;Stein et al, 2017). In this study, we develop a new method of stream classification that quantifies hydrologic similarity for regionalizing ungaged basins in a heterogeneous region.…”

Section: Introductionmentioning

confidence: 99%

Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California

Adams

Bledsoe

Stein

2022

Preprint

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Abstract. Environmental streamflow management can improve the ecological health of streams by returning modified flows to more natural conditions. The Ecological Limits of Hydrologic Alteration (ELOHA) framework for developing regional environmental flow criteria has been implemented to reverse hydromodification across the heterogenous region of coastal southern California (So. CA) by focusing on two elements of the flow regime: streamflow permanence and flashiness. Within ELOHA, classification groups streams by hydrologic and geomorphic similarity to stratify flow-ecology relationships. Analogous grouping techniques are used by hydrologic modelers to facilitate streamflow prediction in ungaged basins (PUB) through regionalization. Most watersheds, including those needed for stream classification and environmental flow development, are ungaged. Furthermore, So. CA is a highly heterogeneous region spanning a gradient of urbanization, which presents a challenge for regionalizing ungaged basins. In this study, we develop a novel classification technique for PUB modeling that uses an inductive approach to group regional streams by modeled hydrologic similarity followed by deductively determining class membership with hydrologic model errors and watershed metrics. As a new type of classification, this “Hydrologic Model-based Classification” (HMC) prioritizes modeling accuracy, which in turn provides a means to improve model predictions in ungaged basins, while complementing traditional classifications and improving environmental flow management. HMC is developed by calibrating a regional catalog of process-based rainfall-runoff models, quantifying the hydrologic reciprocity of calibrated parameters that would be unknown in ungaged basins, and grouping sites according to hydrologic and physical similarity. HMC was applied to 25 USGS streamflow gages in the south coast region of California and was compared to other hybrid PUB approaches combining inductive and deductive classification. Using an Average Cluster Error metric, results show HMC provided the most hydrologically similar groups according to calibrated parameter reciprocity. Hydrologic Model-based Classification is relatively complex and time-consuming to implement, but it shows potential for advancing ungaged basin management. This study demonstrates the benefits of thorough stream classification using multiple approaches, and suggests that Hydrologic Model-based Classification has advantages for PUB and building the hydrologic foundation for environmental flow management.

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“…Water 2021, 13, 1709 2 of 16 For analysis of climate-induced streamflow extremes and water balance changes, it is necessary to simulate the spatial and temporal variability in the surface and subsurface runoff using the distributed hydrological modelling that can reflect topographic features as well as respond to meteorological forcings from the coupled or uncoupled climate application system. As for the context of the perspectives and advances in the Land Surface Models (LSMs) [10,11], the LSMs can be a useful tool to simulate the partitioning of precipitation into evapotranspiration and runoff by integrating meteorological factors and the physical geology of the land [12][13][14][15]. As the LSMs coupled to global or regional climate models simulate the water and energy exchanges between the land surface and the atmosphere, robust LSMs are required to provide a comprehensive assessment of hydrological responses to climate change at both regional and global scales [16,17].…”

Section: Introductionmentioning

confidence: 99%

Improved Streamflow Calibration of a Land Surface Model by the Choice of Objective Functions—A Case Study of the Nakdong River Watershed in the Korean Peninsula

Lee

Choi

2021

Water

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Long-term streamflow simulations of the Land Surface Models (LSMs) are necessary for the comprehensive evaluation of hydrological responses to climate change. The high complexity and uncertainty in the LSM modelling require the model calibration to improve the simulation performance and stability. Objective functions are commonly used in the calibration process, and the choice of objective functions plays a crucial role in model performance identification. The Kling and Gupta Efficiency (KGE) has been widely used in the hydrological model calibration by the measure of the three components (variability, bias, and correlation) decomposed from the Nash and Sutcliffe Efficiency (NSE). However, there is a clear tendency of systematic errors in the peak flow and/or water balance of streamflow time series optimized by the KGE calibration when the correlation between simulations and observations is relatively low. For a more balanced optimal solution of the KGE, this study has proposed the adjusted KGE (aKGE) by substituting the normalized correlation score in the KGE. The proposed aKGE was assessed by long-term daily streamflow simulation results from the Common Land Model (CoLM) for the calibration (2000–2009) and validation (2010–2019) periods in the Nakdong River Watershed, Korea. The case study demonstrated that the aKGE calibration can improve the simulation performance of high flow and annual average flow with a slightly inferior correlation of flows compared with the KGE and NSE criteria.

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Targeted hydrologic model calibration to improve prediction of ecologically-relevant flow metrics

Cited by 20 publications

References 42 publications

Objective functions used as performance metrics for hydrological models: state-of-the-art and critical analysis

Objective functions used as performance metrics for hydrological models: state-of-the-art and critical analysis

Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California

Improved Streamflow Calibration of a Land Surface Model by the Choice of Objective Functions—A Case Study of the Nakdong River Watershed in the Korean Peninsula

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