Evaluation of watershed-level runoff against river gauge data

Miquel De Cáceres / María González

2024-11-08

Introduction

Goal

The aim of this article is to provide an assessment of the performance of spwb_land() for the prediction of watershed outflow. To this aim, we simulate hydrological processes in a set of benchmark watersheds and compare the model predictions of watershed outflow against measurements obtained using river gauges at watershed outlets.

Simulation procedure

For each watershed, the following procedure has been conducted:

1. Initial warm-up simulation for a specified number of years
2. Simulation for the period with observed data before calibrating watershed parameters
3. Manual calibration of watershed parameters (to be replaced with automatized calibration)
4. Final simulation for the period with observed data after calibrating watershed parameters.

Goodness-of-fit statistics

The following goodness of fit statistics are calculated using package hydroGOF:

• Nash-Sutcliffe Efficiency (NSE): This coefficient is sensitive to extreme values and might yield sub-optimal results when the dataset contains large outliers.
• Kling–Gupta efficiency (KGE): Provides a decomposition of the Nash-Sutcliffe efficiency, which facilitates the analysis of the importance of different components (bias, correlation and variability).
• Index of agreement (d): Initially proposed by Willmott (1981) to overcome the drawbacks of the R2, such as the differences in observed and predicted means and variances (Legates and McCabe, 1999). d is also dimensionless and bounded between 0 and 1 and can be interpreted similarly to R2.
• Volumetric efficiency index (VE): Originally proposed by Criss and Winston (2008) to circumvent some of the NSE flaws. VE values are also bounded [0, 1] and represent the fraction of water delivered at the proper time.
• Root mean squared error (RMSE): The usual estimation of average model error (i.e. the square root of mean squared errors).

Hydrological analysis

Watershed (TETIS) parameters

The following table contains the set of TETIS parameters employed in spwb_land() simulations on all watersheds, before and after calibration:

medfateland	medfate	model	watershed	Calibration	R_localflow	R_interflow	R_baseflow	n_interflow	n_baseflow	num_daily_substeps	rock_max_infiltration	deep_aquifer_loss
2.4.7	4.7.0	tetis	aiguadora	before	1	50	5	1.0	1.0	4	10	0
2.4.7	4.7.0	tetis	aiguadora	after	1	8	1	0.5	0.7	4	10	5
2.5.0	4.8.0	tetis	aiguadora	before	1	50	5	1.0	1.0	4	10	0
2.5.0	4.8.0	tetis	aiguadora	after	1	8	1	0.5	0.7	4	10	5

Evaluation results

AIGUADORA watershed with TETIS and version 2.4.7

Graphical evaluation

Daily

Monthly

Goodness-of-fit

Scale	Calibration	NSE	KGE	d	VE	RMSE
Daily	Before	-8.095	-1.811	0.568	-3.074	2.310
Daily	After	-0.174	0.334	0.435	0.006	0.830
Monthly	Before	-17.724	-3.194	0.613	-3.017	2.191
Monthly	After	-0.560	0.052	0.463	0.166	0.633

Hydrological analysis

Density distribution

Percentiles

	Observed	Uncalibrated	Calibrated
1%	0.04	0.976	0.050
5%	0.06	1.119	0.091
10%	0.07	1.241	0.131
15%	0.08	1.361	0.164
25%	0.12	1.555	0.243
50%	0.26	2.175	0.573
75%	0.56	3.042	1.244
85%	0.89	3.665	1.717
90%	1.22	4.181	2.054
95%	1.82	5.015	2.594
99%	3.53	7.000	3.797

AIGUADORA watershed with TETIS and version 2.5.0

Graphical evaluation

Daily

Monthly

Goodness-of-fit

Scale	Calibration	NSE	KGE	d	VE	RMSE
Daily	Before	-6.645	-1.553	0.525	-2.701	2.118
Daily	After	0.060	0.484	0.535	0.181	0.742
Monthly	Before	-14.575	-2.811	0.573	-2.637	1.999
Monthly	After	-0.062	0.326	0.587	0.359	0.522

Hydrological analysis

Density distribution

Percentiles

	Observed	Uncalibrated	Calibrated
1%	0.04	0.861	0.021
5%	0.06	0.994	0.057
10%	0.07	1.109	0.088
15%	0.08	1.223	0.114
25%	0.12	1.398	0.174
50%	0.26	1.975	0.444
75%	0.56	2.833	1.068
85%	0.89	3.420	1.487
90%	1.22	3.923	1.819
95%	1.82	4.669	2.315
99%	3.53	6.525	3.488