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Journal of Arid Land
Journal of Arid Land  2021, Vol. 13 Issue (3): 205-223    DOI: 10.1007/s40333-021-0097-x
Research article     
Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil
Alexandre C COSTA1,*(), Alvson B S ESTACIO2, Francisco de A de SOUZA FILHO2, Iran E LIMA NETO2
1Institute of Engineering and Sustainable Development, University of International Integration of the Afro-Brazilian Lusophony, Redenção, CEP 62.790-970, Brazil
2Department of Hydraulic Engineering and Environment, Federal University of Ceará, Fortaleza, CEP 60.451-970, Brazil
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Abstract  

Streamflow forecasting in drylands is challenging. Data is scarce, catchments are highly human-modified and streamflow exhibits strong nonlinear responses to rainfall. The goal of this study was to evaluate the monthly and seasonal streamflow forecasting in two large catchments in the Jaguaribe River Basin in the Brazilian semi-arid area. We adopted four different lead times: one month ahead for monthly scale and two, three and four months ahead for seasonal scale. The gaps of the historic streamflow series were filled up by using rainfall-runoff modelling. Then, time series model techniques were applied, i.e., the locally constant, the locally averaged, the k-nearest-neighbours algorithm (k-NN) and the autoregressive model (AR). The criterion of reliability of the validation results is that the forecast is more skillful than streamflow climatology. Our approach outperformed the streamflow climatology for all monthly streamflows. On average, the former was 25% better than the latter. The seasonal streamflow forecasting (SSF) was also reliable (on average, 20% better than the climatology), failing slightly only for the high flow season of one catchment (6% worse than the climatology). Considering an uncertainty envelope (probabilistic forecasting), which was considerably narrower than the data standard deviation, the streamflow forecasting performance increased by about 50% at both scales. The forecast errors were mainly driven by the streamflow intra-seasonality at monthly scale, while they were by the forecast lead time at seasonal scale. The best-fit and worst-fit time series model were the k-NN approach and the AR model, respectively. The rainfall-runoff modelling outputs played an important role in improving streamflow forecasting for one streamgauge that showed 35% of data gaps. The developed data-driven approach is mathematical and computationally very simple, demands few resources to accomplish its operational implementation and is applicable to other dryland watersheds. Our findings may be part of drought forecasting systems and potentially help allocating water months in advance. Moreover, the developed strategy can serve as a baseline for more complex streamflow forecast systems.

Key words： nonlinear time series analysis      probabilistic streamflow forecasting      reconstructed streamflow data      dryland hydrology      rainfall-runoff modelling      stochastic dynamical systems     
Received: 27 October 2020      Published: 10 March 2021
Corresponding Authors:
About author: * Alexandre C COSTA (E-mail: cunhacos@unilab.edu.br)
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C COSTA Alexandre
B S ESTACIO Alvson
de A de SOUZA FILHO Francisco
E LIMA NETO Iran
Cite this article:

Alexandre C COSTA, Alvson B S ESTACIO, Francisco de A de SOUZA FILHO, Iran E LIMA NETO. Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil. Journal of Arid Land, 2021, 13(3): 205-223.

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http://jal.xjegi.com/10.1007/s40333-021-0097-x     OR     http://jal.xjegi.com/Y2021/V13/I3/205

Fig. 1 Location of the Brazilian semi-arid area, the State of Ceará (Ceará) and the Jaguaribe River Basin (a), with the main reservoirs and streamgauges (b) used in this study
Combination Training set Validation set
I 1951-1979; 1990-2015 1980-1989
II 1951-1989; 2000-2015 1990-1999
III 1951-1999; 2010-2015 2000-2009
Table 1 Period combinations for training and validation sets, which were used for the applied cross-validation approach, given 65-a (1951-2015) streamflow time series at both IS and SS
Predicand Predictor
March Jan, Feb - - -
April Jan, Feb, Mar Jan, Feb - -
May Jan, Feb, Mar, Apr - Jan, Feb -
June Jan, Feb, Mar, Apr, May - - Jan, Feb
Lead time (month) 1 2 3 4
Table 2 Developed streamflow forecasting in the Jaguaribe River Basin after the combination of predicands, predictors and lead times.
Fig. 2 Average monthly hydrograph (1951-2015) of the Iguatu streamgauge (IS), with 3.7% of gap-filled streamflow data (29 months) using a rainfall-runoff model
Fig. 3 Average monthly hydrograph (1951-2015) of the Salgado streamgauge (SS), with 35.1% of gap-filled streamflow data (278 months) using a rainfall-runoff model
Fig. 4 SRMSE (standardized root mean square error) of the applied time series models in the validation set (1980-2010) for the monthly streamflow forecasting at the IS and SS
Model performance Mar_IS Mar_SS Apr_IS Apr_SS May_IS May_SS Jun_IS Jun_SS
Best-fit model 3-NN 4-NN 7-NN LC 2-NN 2-NN 5-NN 7-NN
SRMSE Validation 0.71 0.67 0.90 0.86 0.72 0.77 0.65 0.71
SRMSE Training 0.93 0.72 0.89 0.76 0.89 0.80 0.73 0.83
Table 3 SRMSE (standardized root mean square error) of the best-fit models in the validation set for the monthly streamflow forecasting at the Iguatu streamgauge (IS) and Salgado streamgauge (SS)
Fig. 5 Monthly deterministic forecasting in the validation set. The predicand is the streamflow in March at the SS. The predictors are the streamflow in January and February. The time series model, which is the best-fit one, is 4-NN. SRMSE (forecast error) in the validation set is 0.67, which means the 4-NN model is 33% better than the mean predictor (climatology). Here, SRMSE is the rms error divided by the SD (standard deviation) of the validation set.
Confidence interval March (92)* April (204)* May (79)* June (12)*
Length SRMSE Length SRMSE Length SRMSE Length SRMSE
33% 39 0.54 49 0.88 12 0.70 2 0.62
50% 72 0.43 93 0.84 31 0.66 3 0.59
66% 128 0.26 195 0.78 55 0.62 6 0.53
Table 4a Monthly probabilistic forecasting of the validation set at the IS
Confidence interval March (85)* April (123)* May (68)* June (14)*
Length SRMSE Length SRMSE Length SRMSE Length SRMSE
33% 26 0.58 32 0.78 12 0.73 4 0.66
50% 60 0.47 58 0.72 31 0.68 7 0.64
66% 89 0.40 101 0.67 63 0.60 13 0.59
Table 4b Monthly probabilistic forecasting of the validation set at the IS
Fig. 6 Monthly probabilistic forecasting in the validation set (30 a) with a confidence interval of 50%, which defines the upper and lower bound forecast. The predicand is the streamflow in May at the IS. The predictors are the streamflow in January, February, March and April. The time series model is 2-NN. SRMSE in the validation set is 0.66, which means the stochastic approach is comparatively 34% better than the mean predictor (climatology).
Fig. 7 SRMSE of the applied time series models in the validation set (1980-2010) for the seasonal streamflow forecasting (SSF) in April (Apr), May and June (Jun) at the IS and SS
Model performance Apr_IS Apr_SS May_IS May_SS Jun_IS Jun_SS
Best-fit model 6-NN 7-NN LA 7-NN 7-NN 6-NN
SRMSE validation 0.78 1.05 0.80 1.06 0.87 0.94
SRMSE trainning 0.95 1.03 1.60 1.03 1.04 1.00
Table 5 SRMSE of the best-fit models in the validation set for the seasonal streamflow forecasting (SSF) in April, May and June at the IS and SS
Fig. 8 Seasonal deterministic forecasting in the validation set (30 a). The predicand is the streamflow in April at the IS. The predictors are the streamflow in January and February. The time series model, which is the best-fit one, is 6-NN. SRMSE in the validation set is 0.78, which means the 6-NN model is 22% better than the mean predictor (climatology).
Confidence interval April (204)* May (79)* June (12)*
Length SRMSE Length SRMSE Length SRMSE
33% 81 0.67 40 0.56 5 0.78
50% 139 0.60 73 0.46 10 0.71
66% 235 0.48 98 0.38 16 0.60
Table 6a Seasonal probabilistic forecasting of the validation set at the IS
Confidence interval April (123)* May (68)* June (14)*
Length SRMSE Length SRMSE Length SRMSE
33% 54 0.95 24 0.93 5 0.86
50% 110 0.80 37 0.89 11 0.78
66% 164 0.67 58 0.83 18 0.71
Table 6b Seasonal probabilistic forecasting of the validation set at the SS
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