Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

Knoben, Wouter; Freer, Jim; Woods, Ross

doi:10.5194/hess-23-4323-2019

Cited by 834 publications

(515 citation statements)

References 30 publications

(35 reference statements)

Supporting

Mentioning

499

Contrasting

Unclassified

Order By: Relevance

“…KGE is based on a decomposition of NSE into its constitutive components (correlation, bias and variability) in the context of hydrological modelling [104]. While KGE = 1 indicates perfect correspondence between simulations and observations, it has been argued that KGE < 0 indicates that the mean of observations provides better estimates than simulations [106]. Therefore, any positive value of NSE and KGE suggests that the model has some predictive power and higher values indicate better model performance.…”

Section: Surface-subsurface Runoff Routingmentioning

confidence: 99%

Shifting Hydrological Processes in a Canadian Agroforested Catchment due to a Warmer and Wetter Climate

Aygün

Kinnard

Campeau

et al. 2020

Water

View full text Add to dashboard Cite

This study examines the hydrological sensitivity of an agroforested catchment to changes in temperature and precipitation. A physically based hydrological model was created using the Cold Regions Hydrological Modelling platform to simulate the hydrological processes over 23 years in the Acadie River Catchment in southern Québec. The observed air temperature and precipitation were perturbed linearly based on existing climate change projections, with warming of up to 8 • C and an increase in total precipitation up to 20%. The results show that warming causes a decrease in blowing snow transport and sublimation losses from blowing snow, canopy-intercepted snowfall and the snowpack. Decreasing blowing snow transport leads to reduced spatial variability in peak snow water equivalent (SWE) and a more synchronized snow cover depletion across the catchment. A 20% increase in precipitation is not sufficient to counteract the decline in annual peak SWE caused by a 1 • C warming. On the other hand, peak spring streamflow increases by 7% and occurs 20 days earlier with a 1 • C warming and a 20% increase in precipitation. However, when warming exceeds 1.5 • C, the catchment becomes more rainfall dominated and the peak flow and its timing follows the rainfall rather than snowmelt regime. Results from this study can be used for sustainable farming development and planning in regions with hydroclimatic characteristics similar to the Acadie River Catchment, where climate change may have a significant impact on the dominating hydrological processes.Water 2020, 12, 739 2 of 29 controlled by snow processes that are expected to be particularly sensitive to climate change [7][8][9][10][11][12][13][14]. Changes to snow accumulation and melt are expected to modify the timing, duration and magnitude of streamflow in the mid-latitudes of the Northern Hemisphere [15], which could redefine flooding risks as well as hydrological services, such as water supply from snowmelt runoff. The interactions between snow and vegetation play a significant role in snow accumulation [16,17], which can influence runoff volumes and timing. Snowfall intercepted by vegetation can increase sublimation losses, depending on tree species, canopy structure as well as atmospheric conditions [18,19]. Once on the ground, snow can be redistributed by wind, particularly in open and wind-exposed environments, which increases sublimation losses from blowing snow [20,21]. Snow is typically transported from sparsely vegetated and exposed terrains to densely vegetated areas and/or topographic depressions [22,23].The traditional approach to assess climate change impact on hydrology is a "top-down" approach, where one or several hydrological models are forced by climate change scenarios from Global Circulation Models (GCMs) [24]. The spatially coarse outputs of GCMs (approximately 150-300 km) are downscaled to represent local climate conditions required by hydrological models, using either statistical or dynamical downscaling approaches [25]. Statistical downscaling relies on...

show abstract

Section: Surface-subsurface Runoff Routingmentioning

confidence: 99%

Shifting Hydrological Processes in a Canadian Agroforested Catchment due to a Warmer and Wetter Climate

Aygün

Kinnard

Campeau

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…Based on the literature, a streamflow simulation is considered satisfactory if NSE > 0.5, RSR ≤ 0.70, and PBIAS < ± 15%; a sediment simulation is considered satisfactory if NSE > 0.45, RSR ≤ 0.70, and PBIAS < ± 20%; and a nutrient simulation is considered satisfactory if NSE > 0.35, RSR ≤ 0.70, and PBIAS < ± 30%. Considering that the KGE metric is mathematically different from NSE and the threshold cannot be simply compared [56], we used this metric as an informative and multi-criterion diagnostic evaluation of the whole model.…”

Section: Model Calibration and Validationmentioning

confidence: 99%

Predicting Streamflow and Nutrient Loadings in a Semi-Arid Mediterranean Watershed with Ephemeral Streams Using the SWAT Model

Pulighe¹,

Bonati²,

Colangeli

et al. 2019

Agronomy

View full text Add to dashboard Cite

Predicting the availability and quality of freshwater resources is a pressing concern in the Mediterranean area, where a number of agricultural systems depend solely on precipitation. This study aims at predicting streamflow and nonpoint pollutant loads in a temporary river system in the Mediterranean basin (Sulcis area, Sardinia, Italy). Monthly discharge, suspended sediment, nitrate nitrogen, total nitrogen, mineral phosphorus, and dissolved oxygen in-stream monitoring data from gauge stations were used to calibrate and validate the Soil and Water Assessment Tool model for the period 1979–2009. A Sequential Uncertainty Fitting procedure was used to auto-calibrate parameter uncertainties and model evaluation. Monthly simulation during the validation period showed a positive model performance for streamflow with Nash–Sutcliffe efficiency and percent bias values of 0.7% and 18.7%, respectively. The simulation results at a watershed level indicate that the sediment load was 1.13 t ha−1 year−1, while for total nitrogen and total phosphorus, the simulated values were 4.8 and 1.18 kg ha−1 year−1, respectively. These results were consistent with the values of soil and nutrient losses observed in the Mediterranean area, although hot-spot areas with high nutrient loadings were identified. The calibrated model could be used to assess long-term impacts on water quality associated with the simulated land use scenarios.

show abstract

“…Kling‐Gupta Efficiencies (KGE; Gupta, Kling, Yilmaz, & Martinez, ) using 5‐min (hourly) rainfall are 0.46 (0.54) for the site in Indiana (Figure a) and 0.66 (0.70) for the site in Kansas (Figure b). These are well above the KGE threshold of zero‐skill, which is approximately −0.41 (see Knoben, Freer, & Woods, ). The slight improvement of KGE using hourly rainfall should not be interpreted as evidence of superiority of the hourly rainfall (which is identical to the 5‐min rainfall in terms of total volume).…”

Section: Resultsmentioning

confidence: 84%

The Role of Rainfall Temporal and Spatial Averaging in Seasonal Simulations of the Terrestrial Water Balance

Sampson

Wright

Stewart

et al. 2020

Hydrological Processes

View full text Add to dashboard Cite

The partitioning of rainfall into surface runoff and infiltration influences many other aspects of the hydrologic cycle including evapotranspiration, deep drainage and soil moisture. This partitioning is an instantaneous non-linear process that is strongly dependent on rainfall rate, soil moisture and soil hydraulic properties. Though all rainfall datasets involve some degree of spatial or temporal averaging, it is not understood how this averaging affects simulated partitioning and the land surface water balance across a wide range of soil and climate types. We used a one-dimensional physics-based model of the near-surface unsaturated zone to compare the effects of different rainfall discretization (5-min point-scale; hourly point-scale; hourly 0.125 gridded) on the simulated partitioning of rainfall for many locations across the United States. Coarser temporal resolution rainfall data underpredicted seasonal surface runoff for all soil types except those with very high infiltration capacities (i.e., sand, loamy sand). Soils with intermediate infiltration capacities (i.e., loam, sandy loam)were the most affected, with less than half of the expected surface runoff produced in most soil types when the gridded rainfall dataset was used as input. The impact of averaging on the water balance was less extreme but non-negligible, with the hourly point-scale predictions exhibiting median evapotranspiration, drainage and soil moisture values within 10% of those predicted using the higher resolution 5-min rainfall.Water balance impacts were greater using the gridded hourly dataset, with average underpredictions of ET up to 27% in fine-grained soils. The results suggest that "hyperresolution" modelling at continental to global scales may produce inaccurate predictions if there is not parallel effort to produce higher resolution precipitation inputs or sub-grid precipitation parameterizations. K E Y W O R D S error, infiltration, modeling, rainfall, runoff, water balance

show abstract

Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

Cited by 834 publications

References 30 publications

Shifting Hydrological Processes in a Canadian Agroforested Catchment due to a Warmer and Wetter Climate

Shifting Hydrological Processes in a Canadian Agroforested Catchment due to a Warmer and Wetter Climate

Predicting Streamflow and Nutrient Loadings in a Semi-Arid Mediterranean Watershed with Ephemeral Streams Using the SWAT Model

The Role of Rainfall Temporal and Spatial Averaging in Seasonal Simulations of the Terrestrial Water Balance

Contact Info

Product

Resources

About