Discretization approach for large-scale sediment modeling: calibration strategies based on hydro-sediment variability at a range of spatial scales

Rossoni, Renata Barão; Fan, Fernando Mainardi

doi:10.1590/2318-0331.282320220088

Renata Barão Rossoni

conceptualization

methodology

software

validation

formal analysis

investigation

resources

data curation

writing

original draft

writing

review and editing

visualization

project administration

Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil

http://orcid.org/0000-0001-8544-7167

Fernando Mainardi Fan

conceptualization

methodology

software

resources

data curation

writing

review and editing

supervision

project administration

funding acquisition

Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil

http://orcid.org/0000-0003-0371-7851

E-mails: renata.rossoni@ufrgs.br (RBR), fernando.fan@ufrgs.br (FMF)

Authors contributions

Renata Barão Rossoni: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing – Original Draft, Writing – Review and Editing, Visualization, Project administration.

Fernando Mainardi Fan: Conceptualization, Methodology, Software, Resources, Data Curation, Writing – Review and Editing, Supervision, Project administration, Funding acquisition.

Editor-in-Chief: Adilson Pinheiro

Associated Editor: Iran Lima Neto

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Figure 1
Flowchart of the methodology showing the process. The numbers correspond to the goals of the study, which were as follows: (1) the creation of a large-scale HRUSed input map for hydro-sedimentological modeling; (2) the hydrological modeling experiment between HRU (Fan et al., 2015Fan, F. M., Buarque, D. C., Pontes, P. R. M., & Collischonn, W. (2015, November 22-27). Um mapa de Unidades de Resposta Hidrológica para a América do Sul. In Associação Brasileira de Recursos Hídricos (Org.), XXI Simpósio Brasileiro de Recursos Hídricos (pp. 1-8). Porto Alegre, Brazil: ABRH.) and HRUSed; (3) the experiment for each scale using SSC versus SSD as variable to calibrate the model; (4) the experiment for each scale using HRU versus HRUSed as discretization approach to calibrate the model; and (5) the trade-offs between discretization model approaches. The study area is the red rectangle.

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Figure 2
HRUSed map for South America and the Rio Grande do Sul hydrological (area for modeling study with more details in the study area section). The reference map is for the year 2009. The photos were taken in the Patos Lagoon basin and Uruguai River basin in January 2019 and 2020. (1) Caí River valley; (2) Relief in Sinos River basin; (3) Relief in Pelotas River basin; (4) Land uses in Canoas River basin; (5) Clay soil and flooded areas in Uruguai River basin; (6) Relief and land use in Uruguai River basin.

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Figure 3
Localization of Rio Grande do Sul state (RS), southernmost Brazil. The red line is the limit of RS, and the grey line is the limit of the hydrological region of RS (RSH). Blue lines represent the rivers.

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Figure 4
Data set for hydrological and hydrosedimentological models.

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Figure 5
Basin discretization of study area in each experiment. Regional scale, basin scale and sub-basin scale, respectively.

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Figure 2
HRUSed map for South America and the Rio Grande do Sul hydrological (area for modeling study with more details in the study area section). The reference map is for the year 2009. The photos were taken in the Patos Lagoon basin and Uruguai River basin in January 2019 and 2020. (1) Caí River valley; (2) Relief in Sinos River basin; (3) Relief in Pelotas River basin; (4) Land uses in Canoas River basin; (5) Clay soil and flooded areas in Uruguai River basin; (6) Relief and land use in Uruguai River basin.

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Figure 3
Localization of Rio Grande do Sul state (RS), southernmost Brazil. The red line is the limit of RS, and the grey line is the limit of the hydrological region of RS (RSH). Blue lines represent the rivers.

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Figure 4
Data set for hydrological and hydrosedimentological models.

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Figure 5
Basin discretization of study area in each experiment. Regional scale, basin scale and sub-basin scale, respectively.

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Figure 6
Hydrological model assessment for HRU and HRUSed maps calibration period. In spite of the fact that certain stations provded values < 0 for NSE, and > 50 and < -50 for BIAS, the scale was displayed up to these levels for improved readability. Stations with a brown hue indicated an NSE < 0 or BIAS > 50 or < – 50. The numerals 1, 2 and 3 relate to hydrographs illustrated in . (1) Gauge station 77150000, located downstream in the Uruguai River; (2) Gauge station 85900000, located downstream in the Jacuí River, and (3) Gauge station 75500000, located in Ijuí River.

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Figure 7
Hydrographs during the calibration period for observed data (green line), HRU model-simulated data (light blue line) and HRUSed model-simulated data (light brown dashed line). GS is the number of ANA gauge station and drainage area in km². The gauge station 77150000 is located downstream in the Uruguai River, the gauge station 85900000 is located downstream in the Jacuí River, and gauge station 77500000 is located downstream in the Ijuí River.

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Figure 8
KGE values for SSC parameter throughout the regional test’s period of calibration (2000 – 2010). Using (a) HRU and SSC, (b) HRUSed and SSC, (c) HRU and SSD, and (d) HRUSed and SSD for calibration. Limits between -0.41 to 1.

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Figure 9
KGE values for SSC parameter for test at basin scale during calibration period (2000 – 2010). Using (a) HRU and SSC, (b) HRUSed and SSC, (c) HRU and SSD, and (d) HRUSed and SSD for calibration. Limits between -0.41 to 1.

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Figure 10
KGE values for SSC parameter for test at sub-basin scale during calibration period (2000 – 2010). Using (a) HRU and SSC, (b) HRUSed and SSC, (c) HRU and SSD, and (d) HRUSed and SSD for calibration. Limits between -0.41 to 1.

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Figure 11
The cumulative distribution function (cdf) of KGE for SSC and SSD parameters is derived using the MGB-SED model for the calibration period, utilizing HRU and HRUSed and calibrating with SSC and SSD at a regional scale. Each line indicates an experiments: HRUwc (HRU discretization without calibration), HRUSEDwc (HRUSed discretization without calibration), HRUssc (HRU calibrating with SSC), HRUSEDssc (HRUSed calibrating with SSC), HRUssd (HRU calibrating with SSD) and HRUSEDssd (HRUSed calibrating with SSD).

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Figure 12
Cumulative distribution function (cdf) of KGE for SSC and SSD results from MGB-SED model for calibration period, using HRU and HRUSed, and calibrating with SSC and SSD for basin scale. Each line indicates an experiment: HRUwc (HRU discretization without calibration), HRUSEDwc (HRUSed discretization without calibration), HRUssc (HRU calibrating with SSC), HRUSEDssc (HRUSed calibrating with SSC), HRUssd (HRU calibrating with SSD) and HRUSEDssd (HRUSed calibrating with SSD).

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Figure 13
Cumulative distribution function (cdf) of KGE for SSC and SSD results from MGB-SED model for calibration period, using HRU and HRUSed, and calibrating with SSC and SSD for sub-basin scale. Each line represents the experiments: HRUwc (HRU discretization without calibration), HRUSEDwc (HRUSed discretization without calibration), HRUssc (HRU calibrating with SSC), HRUSEDssc (HRUSed calibrating with SSC), HRUssd (HRU calibrating with SSD) and HRUSEDssd (HRUSed calibrating with SSD).

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Figure 14
Integrated study of scales for calibration period. Box plots indicate KGE values for each experiment. Results for suspended sediment concentration (SSC) are on top, while suspended sediment discharge (SSD) is at the bottom. The lines with dots represent the values 0 and -0.41. Calibration with: HRU and SSC (light blue), HRUSed and SSC (orange), HRU and SSD (dark greenish-blue), HRUSed and SSD (brown). Experiments on scales: Reg (regional), Bas (basin) and Sub (sub-basin).

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Table 1
Metrics used for statistical evaluation of the model. The benchmark column contains the value when the model is considered superior to the observed data mean. NSE is the Nash-Sutcliffe Efficiency, PBIAS is the percentage of volumetric inaccuracy, and KGE is the Kling-Gupta Efficiency.

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Table 2
BIAS values for each gauge station for the calibration period. We presented the values for each experiment using the HRU map and the HRUSed map.

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(1)

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(2)

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(3)

Statistical Metric	Unit	Equation	Optimal Value	Benchmark
NSE	-	$1 - \frac{\sum_{i = 1}^{n} {(O_{i} - P_{i})}^{2}}{\sum_{i = 1}^{n} {(O_{i} - \bar{O})}^{2}}$	1	0	(4)
PBIAS	%	$\frac{\sum_{i = 1}^{n} (O_{i} - P_{i})}{\sum_{i = 1}^{n} O_{i}} \cdot 100$	0	-	(5)
KGE	-	$1 - \sqrt{{(r - 1)}^{2} + {(a - 1)}^{2} + {(b - 1)}^{2}}$	1	-0.41	(6)

Gauge Station	HRU	HRUSed
70100000	-25.8	-17.5
70200000	-9.9	-2.6
70300000	-14.3	-6.8
70700000	3.1	8.9
71200000	-26.6	-24.6
71250000	-10.8	-8.4
71300000	-11.0	-8.5
71490000	-19.3	-30.3
71498000	-10.1	-8.6
71550000	-10.9	-8.6
72300000	4.2	4.9
72430000	2.4	7.4
72630000	-0.5	2.7
72680000	-8.6	-6.0
72715000	9.6	12.7
72810000	26.2	26.9
72849000	0.9	3.7
72870000	-0.2	2.6
73300000	72.7	80.2
73350000	-7.0	-6.5
73550000	6.0	9.9
73581000	-24.5	-23.8
73600000	-23.2	-23.0
73690001	-7.0	-2.6
73693000	-13.1	-9.7
73765000	-0.5	-1.4
73770000	-6.9	-5.5
73780000	2.6	-1.5
73820000	13.7	9.8
73900000	17.4	14.3
73960000	0.6	1.1
74100000	-2.1	1.0
74205000	-3.9	2.9
74270000	-24.1	-21.1
74295000	0.0	-0.9
74300000	-6.9	-6.8
74320000	7.3	5.6
74370000	-24.2	-20.2
74422000	-11.7	-11.7
74424500	4.9	6.6
74470000	-2.7	0.4
74610000	5.2	4.1
74700000	-9.8	-9.9
74750000	8.1	5.6
74880000	1.8	1.4
74900000	-17.5	-18.9
75155000	-13.6	-7.8
75186000	-14.1	-7.7
75200000	-6.7	0.7
75205000	-7.2	-0.5
75230000	-16.4	-11.8
75295000	-11.7	-8.2
75320000	-20.1	-17.9
75400000	-21.7	-22.1
75500000	6.1	1.6
75550000	4.9	6.9
75600000	-46.9	-48.3
75700000	6.6	-2.7
75780000	-4.1	-2.8
75900000	-2.1	0.0
76085000	3.8	5.2
76100000	13.7	12.6
76251000	-80.2	-81.2
76300000	13.1	3.8
76310000	18.4	10.4
76395000	32.2	34.0
76440000	-1.5	-0.1
76460000	0.7	-0.1
76500000	11.3	6.2
76742000	-3.3	-12.9
76750000	-1.0	-8.8
76800000	-3.0	-8.2
77150000	2.5	1.3
77500000	1.2	-2.9
79400000	27.1	21.2
84580000	-5.1	-7.6
84949800	-12.2	-13.9
84950000	-18.5	-19.6
84970000	-39.6	-37.1
85400000	-13.0	-8.8
85438000	-11.9	-14.1
85470000	17.4	12.1
85480000	22.0	16.6
85600000	28.5	24.1
85610000	21.4	15.8
85623000	19.1	14.3
85642000	2.3	3.1
85735000	3.8	6.1
85740000	15.7	13.4
85830000	20.3	21.4
85900000	7.9	7.5
86100000	-33.1	-27.6
86160000	-4.7	-1.2
86410000	-2.1	1.7
86420000	3.4	6.8
86440000	-3.0	0.6
86470000	3.8	7.4
86480000	11.9	16.6
86500000	4.4	8.6
86510000	-7.8	-4.6
86560000	-3.3	0.7
86580000	-16.4	-12.8
86700000	11.5	10.1
86720000	-8.3	-5.2
87160000	22.7	27.6
87170000	52.3	56.1
87317030	2.6	6.6
87374000	-28.9	-27.7
87380000	-21.9	-21.8
87382000	-32.2	-32.1
87450100	-39.9	-46.1
87590000	-0.8	-8.7
87905000	-5.8	-9.7
88575000	-47.2	-49.6
88641000	13.0	9.7
88750000	2.0	1.0
88850000	6.7	3.9

[1] E-mails: renata.rossoni@ufrgs.br (RBR), fernando.fan@ufrgs.br (FMF)

Brasil

Brasil

Discretization approach for large-scale sediment modeling: calibration strategies based on hydro-sediment variability at a range of spatial scales

Abordagem de discretização para modelagem de sedimentos em grande escala: estratégias de calibração baseadas na variabilidade hidrossedimentológica em múltiplas escalas espaciais

ABSTRACT