Ensemble long-term soil moisture forecast using hydrological modeling

Colossi, Bibiana Rodrigues; Tucci, Carlos Eduardo Morelli

doi:10.1590/2318-0331.252020190036

Bibiana Rodrigues Colossi

Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil

http://orcid.org/0000-0002-8964-7024

Carlos Eduardo Morelli Tucci

Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil

Rhama Consultoria Ambiental, Porto Alegre, RS, Brasil

http://orcid.org/0000-0002-5608-0631

E-mails: bibiana.colossi@gmail.com (BRC), cemtucci@uol.com.br (CEMT)

Authors contributions Bibiana Rodrigues Colossi: Performed all stages of the study, as well as the paper redaction. Carlos Eduardo Morelli Tucci: Advisor, guided the study elaboration and revised the manuscript.

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Figure 1
Main framework.

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Figure 2
MGB-IPH water balance. PC [mm] is the precipitation over the canopy. EI [mm] is the evaporation from the intercepted water layer. Wm [mm] is the maximum storage capacity for the soil layer. All other variables have the same meaning as in .

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Figure 3
Ijuí River basin location.

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Figure 4
Ijuí River basin hydrological response units (HRUs).

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Figure 5
Precipitation gauges.

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Figure 6
Fluviometric gauges.

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Figure 7
Precipitation forecast grid points.

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Figure 8
Example of rainfall bias correction effect. Ijuí River basin average precipitation, ensemble mean.

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Figure 6
Fluviometric gauges.

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Figure 7
Precipitation forecast grid points.

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Figure 8
Example of rainfall bias correction effect. Ijuí River basin average precipitation, ensemble mean.

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Figure 9
Flowchart representing the soil moisture forecast and results classification.

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Figure 10
Ijuí River basin average soil moisture for the period between 1980 and 2004, its standard deviation, and the classification of the saturation degree.

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Figure 11
Contingency table for binary events. Source: Wilks (2006)Wilks, D. S. (2006). Statistical methods in the atmospheric sciences (2nd ed). San Diego, California: Academic Press..

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Figure 11
Contingency table for binary events. Source: Wilks (2006)Wilks, D. S. (2006). Statistical methods in the atmospheric sciences (2nd ed). San Diego, California: Academic Press..

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Figure 12
Soil moisture pseudo-observed class and forecast id 1 for a lead time of 1 month. Anomaly class -1 indicates a month dryer than the historical mean, 0 indicates normal soil moisture, and +1 indicates a more humid period.

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Figure 13
Soil moisture pseudo-observed class and forecast id 2 for a lead time of 6 months.

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Figure 14
Soil moisture pseudo-observed class and forecast id 3 for a lead time of 3 months.

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Figure 15
Soil moisture pseudo-observed class and forecast id 4 for a lead time of 1 month.

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Figure 16
Soil moisture pseudo-observed class and forecast id 5 for a lead time of 3 months.

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Figure 17
Soil moisture pseudo-observed class and forecast id 6 for a lead time of 6 months.

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Figure 18
Soil moisture pseudo-observed class and forecast id 7 for a lead time of 3 months.

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Figure 19
Soil moisture pseudo-observed class and forecast id 8 for a lead time of 1 month.

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Figure 20
Soil moisture pseudo-observed class and forecast id 8 for a lead time of 6 months.

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Figure 21
POD and POFD for dry, normal, and humid classes for forecasts. Use as reference for forecasts types and ids.

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Figure 22
False alarm ratio for dry, normal, and humid classes for different forecasts. Use as reference for forecasts types and ids.

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Figure 23
Bias for dry, normal, and humid classes for different forecasts. Use as reference for forecasts types and ids.

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Table 1
Information regarding selected databases.

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Table 2
Mean absolute error of monthly forecasted precipitation comparing to observed precipitation.

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Table 3
Soil moisture forecasts for each lead time.

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Table 4
Soil moisture classes. Sm is the monthly mean observed soil moisture.

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Table 5
Successes and errors of forecasts for 1 month lead time.

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Table 6
Successes and errors of forecasts for 3 months lead time.

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Table 7
Successes and errors of forecasts for 6 months lead time.

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Model	Description	Spatial resolution	Members
CCM3v6-SSST	CCM3.6 is a version of NCAR’S Community Climate Model. See Hack et al. (1998)Hack, J. J., Kiehl, J. T., & Hurrell, J. W. (1998). The hydrologic and thermodynamic characteristics of the NCAR CCM3. Journal of Climate, 11(6), 1179-1206. http://dx.doi.org/10.1175/1520-0442(1998)011<1179:THATCO>2.0.CO;2. http://dx.doi.org/10.1175/1520-0442(1998... , Hurrell et al. (1998)Hurrell, J. W., Hack, J. J., Boville, B. A., Williamson, D. L., & Kiehl, J. T. (1998). The dynamical simulation of the NCAR Community Climate Model version 3 (CCM3). Journal of Climate, 11, 1207-1236., and Kiehl et al. (1998)Kiehl, J. T., Hack, J. J., Bonan, G. B., Boville, B. A., Williamson, D. L., & Rasch, P. J. (1998). The National Center for Atmospheric Research Community Climate Model. Journal of Climate, 11(6), 1131-1149. http://dx.doi.org/10.1175/1520-0442(1998)011<1131:TNCFAR>2.0.CO;2. http://dx.doi.org/10.1175/1520-0442(1998... .	2.8125° × 2.789327° (T42)	24 members
ECPC-SSST	This is the model of the Experimental Climate Prediction Center, developed and run at the Scripps Institution of Oceanography, University of California, USA (International Research Institute for Climate and Society, 2014International Research Institute for Climate and Society – IRI. (2014). The science and practice of seasonal climate forecasting at the IRI. New York: Earth Institute, Columbia University. Retrieved in 2014, March 29, from http://iri.columbia.edu/climate/forecast/tutorial2/ http://iri.columbia.edu/climate/forecast... ).	1.875° × 1.904128°	12 members
GFDL-SSST	Forecasts from the AM2 model of the Geophysical Fluid Dynamics Laboratory from Princeton University were employed. See Delworth et al. (2006)Delworth, T. L., Broccoli, A. J., Rosati, A., Stouffer, R. J., Balaji, V., Beesley, J. A., Cooke, W. F., Dixon, K. W., Dunne, J., Dunne, K. A., Durachta, J. W., Findell, K. L., Ginoux, P., Gnanadesikan, A., Gordon, C. T., Griffies, S. M., Gudgel, R., Harrison, M. J., Held, I. M., Hemler, R. S., Horowitz, L. W., Klein, S. A., Knutson, T. R., Kushner, P. J., Langenhorst, A. R., Lee, H.-C., Lin, S.-J., Lu, J., Malyshev, S. L., Milly, P. C. D., Ramaswamy, V., Russell, J., Schwarzkopf, M. D., Shevliakova, E., Sirutis, J. J., Spelman, M. J., Stern, W. F., Winton, M., Wittenberg, A. T., Wyman, B., Zeng, F., & Zhang, R. (2006). GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. Journal of Climate, 19(5), 643-674. http://dx.doi.org/10.1175/JCLI3629.1. http://dx.doi.org/10.1175/JCLI3629.1... and GFDL Global Atmospheric Model Development Team (2004)Global Atmospheric Model Development Team – GFDL. (2004). The new GFDL global atmosphere and land model AM2/LM2: evaluation with prescribed SST simulations. Journal of Climate, 17(24), 4641-4673. http://dx.doi.org/10.1175/JCLI-3223.1. http://dx.doi.org/10.1175/JCLI-3223.1... .	2.5° × 2.0°	30 members

Model	Lead time (months)	Mean error WITHOUT bias correction (mm)	Mean error WITH bias correction (mm)	Error reduction
CCM3v6	1	-72.6	-3.2	-96%
	3	-71.0	1.0	-99%
	6	-69.8	6.7	-90%
ECPC	1	-27.1	13.8	-49%
	3	-21.5	16.6	-23%
	6	-19.1	18.0	-6%
GFDL	1	-85.4	-16.6	-81%
	3	-82.3	-6.4	-92%
	6	-82.4	-12.7	-85%

Forecast id	Climatic model	Forecast criteria group	Precipitation forecasts	Soil moisture simulations	Characteristic of the soil moisture forecast
1	CCMv6	A	24, from the 24 members of the model	24	Mode of 24 members
2	ECPC	A	12, from the 12 members of the model	12	Mode of 12 members
3	GFDL	A	30, from the 30 members of the model	30	Mode of 30 members
4	CCMv6	B	1, the mean precipitation from the 24 ensemble members of CCM3v6	1, ran with the ensemble mean precipitation	Mean of 24 members
5	ECPC	B	1, the mean precipitation from the 12 ensemble members of ECPC	1, ran with the ensemble mean precipitation	Mean of 12 members
6	GFDL	B	1, the mean precipitation from the 30 ensemble members of GFDL	1, ran with the ensemble mean precipitation	Mean of 30 members
7	All three models	C	66, all members of the three models (24 from CCM3v6, 12 from ECPC, and 30 from GFDL)	66. Each ensemble member of each model ran the hydrological model	Mode of 66 members
8	All three models	D	3, the ensemble mean precipitation of each model	3, ran with the ensemble mean precipitation of each model	Mean of the mean of three models

Soil moisture class	Soil moisture class code	Lower limit	Upper limit
Humid	+1	Sm + (standard deviation /2)
Normal	0	Sm - (standard deviation /2)	Sm + (standard deviation /2)
Dry	-1		Sm - (standard deviation /2)

Forecast id	1	2	3	7	4	5	6	8
Error type	Ensemble			Super-ensemble	Mean			Multi-model
Error type	CCM3v6	ECPC	GFDL	Super-ensemble	CCM3v6	ECPC	GFDL	Multi-model
Error= 0	36%	27%	25%	28%	37%	21%	37%	27%
+1	9%	19%	13%	9%	34%	38%	22%	38%
-1	33%	33%	38%	40%	14%	14%	17%	19%
+2	5%	8%	6%	1%	14%	22%	17%	15%
-2	17%	13%	18%	22%	2%	5%	8%	1%

Successes: Error= 0	36%	27%	25%	28%	37%	21%	37%	27%
Positive errors	14%	27%	18%	10%	48%	60%	39%	52%
Negative errors	50%	46%	56%	62%	16%	18%	24%	20%

Forecast id	1	2	3	7	4	5	6	8
Error type	Ensemble			Super-ensemble	Mean			Multi-model
Error type	CCM3v6	ECPC	GFDL	Super-ensemble	CCM3v6	ECPC	GFDL	Multi-model
Error= 0	31%	31%	31%	31%	32%	28%	35%	33%
+1	16%	23%	14%	11%	36%	38%	29%	38%
-1	32%	23%	32%	40%	16%	9%	16%	12%
+2	7%	12%	7%	0%	16%	25%	15%	17%
-2	15%	11%	17%	18%	1%	0%	6%	0%

Successes: Error= 0	31%	31%	31%	31%	32%	28%	35%	33%
Positive errors	22%	35%	20%	11%	51%	63%	44%	55%
Negative errors	47%	34%	49%	58%	17%	9%	21%	12%

Forecast id	1	2	3	7	4	5	6	8
Error type	Ensemble			Super-ensemble	Mean			Multi-model
Error type	CCM3v6	ECPC	GFDL	Super-ensemble	CCM3v6	ECPC	GFDL	Multi-model
Error= 0	32%	26%	25%	26%	30%	28%	30%	32%
+1	16%	24%	33%	21%	37%	34%	37%	33%
-1	33%	20%	15%	33%	12%	10%	6%	7%
+2	7%	15%	17%	2%	20%	28%	27%	28%
-2	13%	15%	11%	17%	1%	0%	0%	0%

Successes: Error= 0	32%	26%	25%	26%	30%	28%	30%	32%
Positive errors	22%	39%	50%	23%	57%	62%	64%	61%
Negative errors	46%	35%	25%	50%	13%	10%	6%	7%

S_{i}^{t} = \frac{W_{i}^{t}}{W m_{i}^{}} = \frac{\sum_{j = 1}^{j m a x} (W_{i, j}^{t} . \frac{A_{i, j}}{A_{i}})}{\sum_{j = 1}^{j m a x} (W m_{i, j}^{} . \frac{A_{i, j}}{A_{i}})}

[1] E-mails: bibiana.colossi@gmail.com (BRC), cemtucci@uol.com.br (CEMT)

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Ensemble long-term soil moisture forecast using hydrological modeling

Previsão por conjuntos de umidade do solo para longo prazo usando simulação hidrológica

ABSTRACT