Estimating river discharge from rainfall satellite data through simple statistical models

doi:10.21203/rs.3.rs-2456572/v1

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Estimating river discharge from rainfall satellite data through simple statistical models

https://doi.org/10.21203/rs.3.rs-2456572/v1

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published 02 May, 2023

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Quantitative knowledge of river discharge measurements is essential for understanding coastal and estuarine dynamics and salinity variations. However, direct measurements are scarce for a large portion of rivers in Brazil. In this study, five simple models based on remote sensing and local rainfall datasets (MERGE) from the Ribeira de Iguape catchment are used to estimate the Valo Grande Channel (VGC) discharge on the southeastern coast of Brazil. These models use linear, quadratic, exponential and two different multiple linear regression methods. The predicted VGC discharge time series resulting from each model is compared with the estimated time series based on in situ data from the Water and Electric Energy Department (DAEE in Portuguese). The estimated time series presented reasonable results, with skills varying from 0.84 to 0.92 and Nash–Sutcliffe efficiency (NSE) indices varying from 0.54 to 0.75, with the highest values corresponding to the multiple linear regression models. This methodology allowed us to reproduce longer time series at a daily frequency, as well as monthly averages between 2000 and 2020.

discharge

linear model

accumulated rainfall (R)

Ribeira de Iguape watershed

Valo Grande channel

linear regression

The lack of measured river runoff data is evident in Brazil, and this deficiency imposes considerable limitations on the reliability of prediction systems, especially when studying estuarine regions. This kind of information is essential for constructing and calibrating numerical models for oceanic predictions (Marta-Almeida et al. 2021). As an example, an important effort was made by Carvalho et al. (2018) to construct a monthly climatology dataset of river discharges along the Brazilian continental shelf. These authors advised that future studies should focus on individual shelf regions, since near-real-time runoff data are still very rare and, when implemented, do not provide information for long periods of time.

The water volume received by a watershed and, therefore, the river runoff depends on the climatic conditions; soil characteristics; vegetation coverage; human, agricultural and industrial activities; evapotranspiration in the capture region and the interactions between these factors and other factors (Coleman and Wright 1971). When rainfall water does not infiltrate into the soil and runs across the land surface, it results in runoff through streams, rivers, lakes or reservoirs (Perlman 2016). Surface runoff is a leading process in the hydrological cycle that connects precipitation to surface reservoirs (Sitterson et al. 2018). River flows balance the hydrological cycle by returning excess rainfall to the oceans and regulating how much freshwater flows through catchments (Sitterson et al. 2018). Runoff data are relevant for monitoring water resources and solving water quality and quantity problems such as flood forecasting and ecological and biological relationships within watersheds (Kokkonen et al. 2001). River discharge is also the main controller of contaminant dispersion and transport due to excessive nutrients and pesticides from agricultural lands being washed through catchments during rainy periods (Sitterson et al. 2018). The acquisition of runoff data can help water resource managers account for pollution in water resources (Sitterson et al. 2018).

In Brazil, river runoff is usually determined once a day at monitoring stations with linimetric measurements (relative height from sea level), which are converted to discharge amounts using a calibration curve. In rivers that flow into the sea, this monitoring work is carried out upstream of the headwaters of the estuary, where the movement is unidirectional and tides do not interfere with river flow. Other direct measurement methods can also be employed for this purpose, such as the use of current data, the discharge of known effluents in a partial area of the water basin, or the use of semiempirical equations to determine discharge. All these methods have been thoroughly explained in past literature, for instance, in Miranda et al. (2012).

However, due to the lack of discharge data, modeling serves as an important method for predicting river discharge from rainfall data. Accurately representing rainfall both spatially and temporally is important for rainfall-runoff modeling, as rainfall is commonly one of the main model inputs (Faurès et al. 1995). In terms of their spatial structures, catchment processes in rainfall-runoff models can be divided into lumped, semidistributed, and fully distributed processes (Sitterson et al. 2018). Spatial variability is not considered in the outputs of lumped models, while semidistributed models reflect some spatial variability. Fully distributed models process spatial variability and generate runoff for each grid cell (Sitterson et al. 2018). In terms of the model structure, rainfall-runoff models may be classified into physically-based, conceptual, and empirical models (Devia et al. 2015; Sitterson et al. 2018). Physically-based models include explicit physical mechanisms involved in the hydrological cycle but are limited by the meteorological input data, high computational costs and calibration challenges (Wood et al. 2011). These models are also limited by the need for a large number of parameters and are site-specific (Sitterson et al. 2018). Conceptual models are based on simplified equations that represent the water storage in a given catchment and do not consider spatial variability within the catchment (Sitterson et al. 2018). Empirical models usually consider the nonlinear relationships between inputs and outputs within a black-box concept. The best application of these models is in catchments with a lack of data, with runoff being the only required output. Such models can present highly accurate predictions with rapid run times (Sitterson et al. 2018). Some examples of these models include the regression equations and machine learning models involved in artificial and deep neural networks (Devia et al. 2015; Sitterson et al. 2018).

As worldwide examples, we can cite some rainfall-runoff models, such as the conceptual and semidistributed TOPMODEL (Topography Based Hydrological Model) (Devia et al. 2015) and HBV (Hydrologiska Byrans Vattenavdelning) (Bergstrom 1976; Devia et al. 2015) models and the complex physically based SWAT (Soil and Water Assessment Tool) and MIKE SHE (Systeme Hydrologique European) models (Devia et al. 2015). The MGB-IPH rainfall-runoff model (Collischonn 2007) has been used for large-scale basins in South America (Allasia et al. 2006). When comparing the model results with the data collected at riverine gauging stations in the Taquari-Antas basin, Collischonn et al. (2007) found Nash–Sutcliffe efficiency (NSE) coefficient values varying from 0.40 to 0.84. The NSE coefficient measures the efficiency E proposed by Nash and Sutcliffe (1970) as one minus the sum of the absolute squared difference between the modeled and observed values normalized by the variance of the observed values during the investigated period. Model simulations can be judged as satisfactory if the NSE value is higher than 0.50 (Moriasi et al., 2017).

In Brazil, the Storm Water Management Model (SWMM) (Rossman et al. 2010) has been applied for the drainage area of Belo Horizonte, Minas Gerais, producing an average Nash-Sutcliffe coefficient of 0.72 (Rosa et al. 2020). The performance of the SWMM was good, but its application required not only rainfall data but also the topographic data and land use map of the region and was thus a much more complex model than the empirical model that uses only rainfall data as the input. Another example is the probability-distributed model (PDM, Moore 2007), which has been applied to 5 basins in the southeastern region of Brazil. The input data required for this model are rainfall and potential evapotranspiration, and the model uses a specific and complex formulation.

The majority of the models exemplified above use additional physical parameters beyond rainfall as inputs and are therefore considered complex, as they take into account the hydrological processes of the studied catchment. Here, we propose the application of regression equations in our models, using rainfall as the single input with the objective of obtaining runoff. We apply these regression models to an important estuarine region with scarce freshwater discharge measurements along the Brazilian coast: the Cananéia-Iguape estuarine lagoon system (CIELS, hereafter).

The CIELS spreads for more than 70 km along the coast of the state of São Paulo, hosts relatively large populations of cetaceans (Santos and Rosso 2007; Geise et al. 1999; Filla et al. 2012) and fishes (Barcellini et al. 2013, Curcho et al. 2009), and is a reproduction area for several other organisms (Barioto et al. 2017; Stanski et al. 2018; Bochini et al. 2019; Galvão et al. 2000). However, considering the ecological importance of this system, the freshwater runoff of the Valo Grande Channel (VGC), the main freshwater source for the CIELS, has been poorly measured over time. As an example of runoff measurements obtained in the CIELS, Bérgamo (2000) presented monthly discharge estimates using the river input data from the drainage basins of the Ribeira de Iguape River and next to Cananéia. The author concluded that the seasonal freshwater variations in the systems of both drainage basins followed the seasonal fluctuations in rainfall in the region. Additionally, in 1955 and 1965, the currents in the VGC and the hourly instantaneous discharge in a complete tidal cycle (12h25) were measured (GEOBRÁS 1966). Estimates of the average daily discharge of the VGC region in a 12-year period (1954 to 1965) were also produced based on discharge data collected from the Três Barras station along Ribeira de Iguape River, located upstream of the VGC (GEOBRAS 1966).

In the present study, rainfall data are applied as the unique fully distributed inputs from the Ribeira de Iguape watershed in empirical and simplified statistical models to estimate the lumped time-series discharge of the VGC. A particular advantage of these statistical models is that limited hydrological data (except rainfall and runoff data) are demanded without considering the other physical variables representing the hydrological process of the studied catchment, such as topography, land use maps, or evaporation.

The scientific question addressed herein is as follows: what is the performance of a simple statistical model based on rainfall in predicting the discharge of a water basin? We hypothesize that simple statistical models that use the rainfall data from MERGE, which combines satellite-derived and local precipitation data, are capable of generating daily discharge time series with a good accuracy. Such detailed runoff outputs would improve the quality of CIELS numerical simulations aiming to reproduce its physical characteristics, such as currents and salinity, and would have application potential for other estuarine systems, as well as supporting other research subjects with this essential information. The model results can also support decision-making in the areas of water resource planning and management. In addition, these results can assist urban planners and managers in undertaking the necessary measures to address extreme high-flow predictions.

In the next section, we describe the main physical characteristics of the CIELS method, followed by a description of the methodology in Section 2. The results and discussion are explained in Sections 3 and 4, respectively, and we present the conclusion in Section 5.

1.1 Study area

The Ribeira de Iguape watershed, the main contributor to the VGC, occupies the southeastern portion of São Paulo state and the eastern portion of Paraná state between the latitudes of 23°50’ and 25°30’ S and longitudes of 46°50’ and 50° 00’ W. Currently, the Ribeira de Iguape basin is an unhindered catchment with no constructed dams (CBH-RB 2008; IBAMA 2016). The catchment covers a total area of 24.980 km² inside São Paulo and Paraná states (DAEE 1998). In the Iguape region, part of the discharge of the Ribeira de Iguape River is diverted to Mar Pequeno through the VGC, which is an artificial connection constructed between 1828 and 1830 (Moraes 1997). The channel originally presented a width of approximately 5 m, but erosion over time has enlarged it to a 250-m width.

The southern coast of the São Paulo climate is defined as tropical and humid with rainfall related to the seasons, with wet summers and dry winters (Ma et al. 2011). This region is influenced by the South Atlantic Convergence Zone (ZCAS), which is a semipermanent feature characterized by a NW‒SE-oriented band of condensation and nebulosity (Satyamurti et al. 1998). This feature is responsible for most of the precipitation in South America during summertime (Ma et al. 2011) and can occur in the Ribeira de Iguape watershed region. The passage of cold front systems also influences the precipitation regime in this area, mostly occurring during wintertime (Stech and Lorenzetti 1995). Frontal systems associated with precipitation lead to the absence of a true dry season in the region, generating relatively well-distributed precipitation throughout the year, with the highest and lowest rainfall measured in summer and winter, respectively (GEOBRÁS 1966). The seasonal rainfall variability in southeastern Brazil can cause negative environmental, social and economic impacts due to anomalous precipitation and water availability (Zhang et al. 2018). During 2014 and 2015, this region suffered one of the most severe droughts since 1960, leading to insufficient hydroelectric power generation throughout the entire country and the depletion of water reservoirs in the metropolitan region of São Paulo (Zhang et al. 2018).

In the Iguape region, relative humidity presents values greater than 70% throughout the year, with an annual mean rainfall of 1555 mm (GEOBRÁS 1966). In the Cananéia-Iguape estuary, the annual average precipitation is 2200 mm, with a maximum monthly average precipitation of 266.9 mm occurring between January and March (Oliveira et al. 2009) and a minimum monthly average precipitation of 95.3 mm occurring in July and August (Oliveira et al. 2009).

The Ribeira de Iguape River is the main tributary discharging freshwater into the VGC (Afonso 2006; Marta-Almeida et al. 2021). The freshwater discharge of this river presented daily mean values between 84 m³.s⁻¹ and 1.601 m³.s⁻¹ from 1954 to 1965 (GEOBRÁS 1966). The monthly mean discharge values in the VGC range from 240 m³.s⁻¹ in August to 550 m³.s⁻¹ in February (Ambrosio 2016).

2.1 A daily precipitation dataset: MERGE

To evaluate the VGC discharge, we used the MERGE precipitation dataset (Rozante et al. 2010). This dataset is produced and distributed in Brazil by the Center for Weather Forecasting and Climate Studies (CPTEC) at the National Institute for Space Research (INPE). MERGE applies a Barnes objective analysis (Barnes 1973), combining data from meteorological stations distributed over the Brazilian territory and satellite precipitation data (Rozante et al. 2010). The daily rainfall data from MERGE used in this study have a spatial resolution of 0.1° in the Ribeira do Iguape watershed (Fig. 2) and span the period from June 2000 to December 2020.

2.2 Valo Grande Channel discharge estimate

The Três Barras mouth (Fig. 1) is the location where the Ribeira de Iguape River bifurcates in the VGC and continues to flow through the other branch. The VGC total discharge results from approximately 75% of the total contribution of the Três Barras mouth (GEOBRÁS 1966), corresponding to approximately 95.6% of the main drainage represented by the discharge of three main affluents, namely, the Jacupiranga, Ribeira do Iguape and Pariquera-Açu rivers (Fig. 1).

To estimate the VGC discharge and to train and evaluate the statistical models (Section 2.3), we applied a series of approximations to represent the best possible estimates without direct measurements. Data from the Water and Electric Energy Department (DAEE in Portuguese) database collected from 2011 to 2019 were used, representing the most recent available measurements of the Jacupiranga (Q_J) and Ribeira do Iguape (Q_R) discharges. Gaps in the time series ranged from 2 to 5 days and were filled using linear interpolation. It is important to mention that Q_R was measured in the city of Registro (Fig. 1), which is located approximately 70 km from the connection to the Três Barras mouth, resulting in a lag of approximately 36 hours between the measurements taken at Registro and at this location (Pisetta 2006; Pisetta 2010). As the discharge data at the fluviometric station are daily means, we assumed that the Ribeira de Iguape River discharge values (Q_R) corresponded to the measurements obtained on the previous day at the Jacupiranga River (Q_J) to calculate the Q_TB. This same method was applied by Pisetta (2006, 2010), thus introducing a 24-hour lag. As an example, for the calculation of Q_TB on 14 April, we used discharge values from 13 April for Q_R and from 14 April for Q_J. Finally, we estimated the Pariquera-Açú River discharge (Q_PA) by adding 10% of the Jacupiranga River discharge, which, on average, corresponded with the measurements well (GEOBRÁS 1966).

The Três Barras discharge (Q_TB) was computed using the following equation, which includes all three discharge measurements:

Q_TB = Q_R + 1.1 Q_J

Notably, Q_PA is embedded in the Q_J coefficient.

Since the VGC discharge is approximately 75% of Q_TB, we estimated the Valo Grande artificial channel discharge (Q_VGC) as follows:

Q_VGC = 0.75 Q_TB

2.3 Models

Five different models were implemented: linear regression, quadratic regression, exponential regression and two distinct multiple linear regression (MLR) models. These models were developed using the Python scikit-learn library (Pedregosa et al. 2011) by applying the ordinary least squares (OLS) linear regression method.

We randomly trained the models using 67% of the rainfall data from MERGE and the VGC discharge data as our training subsets. The other portion (33%) was used as the test subset to validate these models. During the training stage, we shuffled the dataset input to each model n times and selected the moment where the root mean squared error (RMSE) was minimized. In these cases, n ranged from 0 to 2000, and the minimum RMSE was achieved at n = 1506 for the linear, quadratic, and exponential models. This method was utilized to obtain a set containing 67% of the total time series that presented the minimum error possible. After splitting the data into training and validation portions, the data were linearized using the corresponding methods in the quadratic and exponential models. For the linearization of these models, we fitted the 67% training portion of the VGC discharge data into quadratic and exponential functions using the root-mean square and log, respectively. This process generated transformed VGC discharge values as outputs, which were then used for the linear regression.

The RMSE, skill (Willmott 1981), and coefficient of determination (CD) were computed for the models. We used the test subset and observed VGC discharge values to calculate these parameters, and from now on, we will call this process the test subset validation step. The CD is the percentage of variation that is described by the linear regression line. This term represents the proportion of variation in the dependent variable (discharge) that is predictable from the independent variables (rainfall data points from MERGE) and is defined as follows:

CD = 1 - (SE_line/SE_d)

where SE_line is the total squared error between the data points and the line fitted by the linear regression model, representing the distance between the data and regression line, and SE_d is the total variation in the predicted discharge. The CD value varies from 0 to 1, where 1 indicates a perfect fit of the model with the data.

For the linear, quadratic, and exponential regression models, the VGC discharge was estimated as a function of the average rainfall (R) over the area and can be expressed as follows:

Q_VGC = a + b * R (linear)

Q_VGC = a * e^b*R (exponential)

Q_VGC = a + b * (R)² (quadratic)

where a is an offset or a constant linear coefficient and b is the linear, quadratic or exponential coefficient, depending on the model, determined by the least square fitting method mentioned above.

The rainfall (R) values used in the linear, quadratic and exponential models were computed as follows:

R=(Σ²⁶⁰ _n=1 R _n^FT)/n

where n is the number of grid points (260) from MERGE and R _n^FT is the accumulated rainfall over a specific and fixed period of time on each grid point. This time was chosen based on the maximum correlation found between accumulated precipitation and runoff, as shown in Section 3.1.

The other two models, which are classified as MLR models, predict the value of one dependent variable based on two or more independent variables (Shoaib et al. 2018). In these models, we used the 260 grid points of accumulated rainfall from the MERGE dataset as independent variables. Spatial grid points with missing values were removed. We considered two MLR models for comparison. The first one considered the accumulated rainfall in a fixed period of time for every grid point (R^FT) according to the highest correlation found between the spatial-mean MERGE rainfall value and the VGC discharge. In this case, the equation used to predict the VGC discharge is given as follows:

Q_VGC = a + Σ²⁶⁰_n=1 (b_n * R^FT _n)

Note that, unlike the linear model, which has a single linear coefficient for the whole domain, in this model, each grid point has a specific linear coefficient (b_n). However, the period of time over which the accumulated rainfall was computed was the same for every grid point. This specific time was the same as that applied for the first three models, as presented in Section 3.1.

The second MLR model was very similar to the first but also considered a different period of time to compute the accumulated rainfall at each grid point, also based on the highest correlation obtained between the rainfall and discharge at each grid point. In this case, the equation used to estimate the VGC discharge is given as follows:

Q_VGC = a + Σ²⁶⁰ _n=1 (b_n * R^VT _n)

where R^VT (to avoid confusion with R^FT) is the accumulated rainfall at each grid point computed in different time periods. These specific time periods were chosen according to the maximum correlation between the accumulated rainfall and runoff and are presented in Fig. 6.

The minimum RMSEs were achieved at n values of 736 and 1513 for the MLR models using R^FT and R^VT, respectively. For the multiple regression models, we also applied the statsmodel library (Seabold et al. 2010) to obtain the spatial distributions of the correlation, p value and standard error associated with the OLS method. We used the spatially distributed standard errors and p values derived from these models for the MERGE grid. The standard error represents the average distance that the observed values fall from the regression line. The p value is a measurement of how likely a coefficient is to be calculated through our model by chance (McAleer 2020). For example, a p value of 0.378 indicates that there is a 37.8% chance that the independent variable (rainfall) has no effect on the dependent variable (VGC discharge), and our results are produced by chance.

To quantitatively compare the MERGE rainfall data with the DAEE discharge data and generate the model analysis results, we converted the MERGE data from their original unit, kg.m⁻².day⁻¹, to m³.s⁻¹ (the same unit used for the discharge data). In performing this conversion, we assumed a water density value of ~ 1000 kg.m⁻³.

We obtained the predicted VGC discharge time series from each of the models. Then, we compared these predictions with the time series estimated based on data collected from DAEE using the method proposed by GEOBRÁS (1966) and described in Section 2.2. This process, defined here as the time series validation process, compares the modeled and observed VGC discharge time series by calculating the skill score, RMSE, CD, Pearson's correlation coefficient, and Nash–Sutcliffe efficiency (NSE) index (Nash and Sutcliffe 1970).

3.1 Accumulated rainfall (R)

The accumulated rainfall (R) is defined as the sum of precipitation over a given period and a given area. R⁷ is presented here as the accumulated rainfall over 7 days, as this term presented the maximum correlation with the discharge data in the correlation analysis, as explained above. Henceforth, R⁷ corresponds to the accumulated rainfall fixed on time (R^FT). Fig. 3 shows the time series of discharge and R⁷. This quantity presents a correlation of 0.735 and p value < 0.01 with the discharge data estimated for the Valo Grande Channel using the method described in Section 2.2 (Fig. S1, available in the Electronic Supplementary Material).

The river discharge peaks coincided with the rainfall peaks, indicating the influence of rainfall in modulating discharge (Fig. 3). For instance, intense rainfall occurred at the beginning of 2011, reaching rates over 10000 m³.s⁻¹ before decreasing progressively until June. This same pattern was observed in the VGC discharge, with a peak flux of almost 1200 m³.s⁻¹ at the beginning of 2011 and a decreasing flux until the end of June. Interestingly, all rainfall peaks approximately coincided with the river discharge peaks, showing the coherence between the two datasets.

3.2 Regression models

In this section, we present the model results. We focus on the MLR models, as they were considerably more reliable than the linear, quadratic, and exponential models. Details about the statistical differences among these models are presented in Section 3.3, and the scientific reasons and explanation of these differences are presented in Section 4. The detailed results of the linear, quadratic, and exponential models are available in the Electronic Supplementary Material (Figs. S2 and S3).

3.2.1 Multiple linear regression models

We present two different MLR models. As we found the highest correlation between discharge and accumulated rainfall in 7 days (R⁷), in the first multiple regression model, we used MERGE R⁷. In the second one, we used the accumulated rainfall varying in time (R^VT), depending on the highest correlation for each grid cell of MERGE.

The multiple regression model using R⁷ resulted in minimum, maximum and mean standard errors of 0.003, 0.064 and 0.009, respectively. In 99% of the MERGE grid cells, the standard errors were lower than 0.03, except in the three grid cells in the southern region of the catchment (Fig. S4-a). In general, the errors associated with the regression results were low in almost the entire domain. The p values were lower than 0.05 for 27% of the grid points (69 grid points of 260 in total) (Fig. S4 -b).

The black circles in Fig. 4 represent each value included in the test subset validation of the multiple regression models using R⁷ (Fig. 4-a) and R^VT(Fig. 4-b). The multiple regression model using R⁷ presented a skill of 0.91, R² score equal to 0.71 and RMSE of 107.05 m³.s⁻¹ (Fig. 4-a). The multiple regression model with R^VT presented a skill of 0.91, CD of 0.70 and RMSE of 104.34 m³.s⁻¹ (Fig. 4-b). The validation of the models showed that both R⁷ and R^VTwere able to reproduce the discharge data considerably well for both low and high values(Fig. 4-a and b, respectively).

The time-series validation of the multiple regression model using R⁷ showed that the model represented the discharge reasonably well (Fig. 5), presenting a skill of 0.92, CD of 0.64 and RMSE of 108.68 m³.s⁻¹. The modeled time series reflected the main discharge patterns, including the peaks observed in January and June, as well as the lowest values present in the observations. Notably, the seasonal patterns were also present in the reconstructed time series (Fig. 5).

The best model results were produced by the R^VT-considering model, using the highest correlation coefficient between each rainfall grid cell of MERGE and the discharge time series. The maximum correlation coefficients (Fig. 6-a) were found between 6 and 9 days of accumulated rainfall (called R⁶ and R⁹, respectively) (Fig. 6-b).

The average standard error of R^VT was 0.009. For all MERGE grid cells, the standard error was lower than 0.03, with the exception of two grid cells in the southern region of the watershed, showing that the errors associated with the regression were low (Fig. S5-a). The p values were lower than 0.05 for 30% of the grid points, or 79 grid points (Fig. S5-b).

For the time-series validation, we found a skill of 0.92, CD of 0.67 and RMSE of 106.35 m³.s⁻¹ (Fig. 7). The modeled time series reflected both the main patterns of and seasonal variability in the data (Fig. 7), similar to the previously tested multiple regression model (Fig. 5).

The results obtained for both multiple regression models were similar. The modeled VGC discharge was better represented in the multiple regression estimates (Fig. 4) than in the linear, quadratic and exponential simulations (Figs. S2 and S3). All models underestimated discharge values higher than 1000 m³.s⁻¹, with the best results provided by the MLR models.

3.3 Model comparison

In this section, we evaluate all model results using the test subset and time series validations. Table 1 shows the skill, coefficient of determination (CD) and RMSE values derived in the test subset validation. Table 2 shows the results of the time-series validation for all models, including the skill, coefficient of determination, RMSE, Pearson’s correlation coefficient and the Nash–Sutcliffe efficiency (NSE) values.

Table 1 Skill, coefficient of determination (CD), and RMSE (m³.s⁻¹) values derived in the test subset validations of the linear, quadratic and exponential models.

Model	Skill	CD	RMSE (m³.s⁻¹)
Linear	0.83	0.51	125.26
Exponential	0.81	0.52	124.26
Quadratic	0.82	0.53	122.66
Multiple linear regression (R⁷)	0.91	0.71	107.05
Multiple linear regression (R^VT)	0.91	0.70	104.34

Table 2 Results of the time-series validation. The skill score, coefficient of determination (CD), RMSE (m³.s⁻¹) and Pearson’s correlation coefficient values between each model result and the discharge time series estimated for the Valo Grande using the method proposed by GEOBRÁS (1966) from 2011 to 2019 are shown.

Model	Skill	CD	RMSE (m³.s⁻¹)	Pearson’s correlation and p value	Nash–Sutcliffe efficiency (NSE) index
Linear	0.84	0.54	141.61	0.73 - <0.01	0.54
Exponential	0.84	0.57	136.38	0.77 - <0.01	0.57
Quadratic	0.83	0.56	137.63	0.76 - <0.01	0.56
Multiple linear regression (R⁷)	0.92	0.64	108.68	0.85 - <0.01	0.74
Multiple linear regression (R^VT)	0.92	0.67	106.35	0.86 - <0.01	0.75

In the test subset validation, the skill values were above 0.8 in all cases, with the highest values corresponding to 0.91 for the MLR models (Table 1). The CDs of the linear, quadratic and exponential models showed values of approximately 0.5, and the RMSE values were between approximately 122 m³.s⁻¹ and 125 m³.s⁻¹ (Table 1). For the test subset validation of the MLR models, the CD values were equal to 0.71 and 0.70, and the RMSE values were equal to 107.05 m³.s⁻¹ and 104.34 m³.s⁻¹ for the R⁷ and R^VT models, respectively (Table 1).

From the time-series validation results, we found a skill of approximately 0.83, CDs between 0.54 and 0.57, RMSEs from 136 m³.s⁻¹ to 142 m³.s⁻¹, and Pearson’s correlation coefficients between 0.73 and 0.77 for the linear, quadratic and exponential models (Table 2). For both MLR models, we found a skill of 0.92 for the time-series validation (Table). In this validation, the CD values were equal to 0.64 and 0.67, the RMSE values were 108.68 m³.s⁻¹ and 106.35 m³.s⁻¹, and the Pearson’s correlation coefficients were 0.85 and 0.86 for the MLR models using R⁷ and R^VT, respectively (Table 2). The Pearson’s correlation coefficients of the 5 models presented p values lower than 0.01.

The NSE index presented an improvement of up to 28% for the multiple regression models when compared to the linear, quadratic and exponential models. The highest NSE index was equal to 0.75, which was found for the multiple regression model using R^VT (Table 2). The multiple regression model using R⁷ presented an NSE index of 0.74, while the linear, quadratic, and exponential models presented NSE indices equal to 0.54, 0.56, and 0.57, respectively (Table 2).

Comparing the QQplot derived for each model, the data quantiles were better reproduced by the MLR models considering R⁷ and R^VT(Fig. 8-d and e, respectively) than by the linear, quadratic and exponential models (Fig. 8 a, b, and c, respectively). The linear, quadratic and exponential models were able to effectively represent VGC discharge values between 250 and 600 m³.s⁻¹, with the model results closely coinciding with the perfect-fit lines (in red) in the QQ plots (Fig. 8 (a, b, and c)). Increased river discharge (>600 m³.s⁻¹) is associated with a degradation of the results of these models, as the models tended to underestimate peak values, presenting RMSEs varying from 136 m³.s⁻¹ to 141 m³.s⁻¹ at discharge values above 600 m³.s⁻¹. This limitation was more evident in the comparison between the modeled and observed VGC discharge time series (Figs. S3) but was reduced in the multiple regression models (Fig. 8 (d and e)), which exhibited better agreement between the model results and data during high-flow events above 600 m³.s⁻¹, with RMSE values of 106.30 m³.s⁻¹ and 103.70 m³.s⁻¹ for the MLR models considering R⁷ and R^VT, respectively. These results indicate improvements between 22% and 27% for the MLR models compared to the previous ones. The multiple regression models fit the data well at discharge values between 240 m³.s⁻¹ and 900 m³.s⁻¹ (Fig. 8-d and -e). In these cases, values lower than 240 m³.s⁻¹ and above 900 m³.s⁻¹ were overestimated and underestimated, respectively, and generally increased with the discharge amount. Nevertheless, these underestimations detected in the multiple regression models were lower than those in the linear, quadratic and exponential models, showing the clear improvement of the multiple regression models compared to the other cases. A specific period of the VGC discharge time series containing high and low peaks predicted by the models (Fig. S8-a and S8-b, respectively) indicated that the multiple regression model using R^VT better reproduced the discharge data than the other models. Both MLR models presented satisfactory results and could thus be used to predict good VGC discharge estimates.

3.4 Reconstruction of complete time series and seasonal comparisons (2000-2020)

Based on the results of the previous sections, the MLR model considering R^VT presented the most accurate predictions. Thus, we applied this model to predict a complete time series of VGC discharges (Fig. S6). Since the MERGE data start in June 2000, the time series spans from this month until December 2020 (Fig. S6).

The seasonal precipitation patterns in the Ribeira de Iguape watershed and this reconstructed VGC discharge series were evaluated by the monthly averages considering a 95% confidence level (Fig. 9). The monthly mean rainfall from the MERGE dataset presented the highest values from October to March (Fig. 9-a), varying from 130 mm (in November), with a confidence interval from 111 mm to 149 mm, to 241 mm (in January), with a confidence interval from approximately 74 mm to 133 mm. The highest mean rainfall and discharge values occurred in January (241 mm and 582 m³.s⁻¹, respectively), and the lowest values occurred in August (66 mm and 325 m³.s⁻¹, respectively), considering the period from 2000 to 2020 (Fig. 9). From April to July, the rainfall and VGC discharge values presented relatively small variations, oscillating between 88 and 93 mm and between 342 and 368 m³.s⁻¹, respectively (Fig. 9). These values began increasing in September, with monthly mean values of 100 mm and 380 m³.s⁻¹, within confidence intervals of approximately 74 mm to 133 mm and approximately 370 to 390 m³.s⁻¹, respectively for rainfall and VGC discharge, respectively, achieving their maximum peaks in January.

Basically, the seasonal variability present in rainfall modulated its variability in the VGC discharge results. Both the rainfall and VGC discharge series presented seasonal variabilities, with the highest values found in summer (December, January, and February) and in March, with values greater than 150 mm and 470 m³.s⁻¹, respectively. The lowest values were found in winter (June, July, and August), at 89 mm and 365 m³.s⁻¹, with the lowest values found in August, at 66 mm and 325 m³.s⁻¹ for rainfall and discharge, respectively (Fig. 3 and Fig. 9).

The highest variabilities were observed in September, January and July, with confidence intervals ranging from 74 mm to 133 mm, from 211 mm to 270 mm, and from 61 mm to 117 mm, respectively (Fig. 9 -a). For the monthly mean VGC discharge values (Fig. 9 -b), the confidence intervals ranged from 111 m³.s⁻¹ to 149 m³.s⁻¹ in October and from 560 m³.s⁻¹ to 603 m³.s⁻¹ in January. From March to November, the monthly mean VGC discharge values presented lower variabilities than those in December, January and February, showing confidence interval range values in October from 383 m³.s⁻¹ and 405 m³.s⁻¹ and in June from 350 m³.s⁻¹ to 380 m³.s⁻¹. During December, January and February, the confidence intervals ranged from 420 m³.s⁻¹ to 453 m³.s⁻¹, 560 m³.s⁻¹ to 603 m³.s⁻¹, and from 481 m³.s⁻¹ to 520 m³.s⁻¹, respectively (Fig. 9 -b).

The climatology of the VGC discharge was computed using the daily discharge estimates obtained with the multiple regression model considering R^VT. These results were compared to the monthly mean VGC discharge data obtained from the daily discharge data available from 2011 and 2019 (Fig. 10). For this period, when comparing the monthly mean VGC discharges between the multiple regression model considering R^VTand the data, we found a skill of 0.96, RMSE of 51.61 m³.s⁻¹ and Pearson’s correlation coefficient of 0.95 (with a p value smaller than 0.01). Therefore, the VGC discharge monthly means were well correlated with the monthly means obtained with the multiple regression model considering R^VT(Fig. 10), allowing us to extend the climatological period to the period in which MERGE data are available. The highest monthly mean values were found during January in both data and modelled series, at 570 m³.s⁻¹ and 530 m³.s⁻¹, respectively (Fig. 10). The lowest values were detected during September and reached 280 m³.s⁻¹ and 300 m³.s⁻¹ in the data and model series, respectively (Fig. 10). The best fit between the data and modelled monthly means occurred in November, followed by in June and August (Fig. 10). From January to April, and also in July, the model predictions showed smaller values than the data, while during May, August, September, October and December, the model overestimated the discharge values compared to the VGC data (Fig. 10). The standard deviation values were similar between the data and multiple regression model considering R^VT (Fig. 10). The highest standard deviations were found in January (from approximately 250 m³.s⁻¹ to 850 m³.s⁻¹) and June (from approximately 180 m³.s⁻¹ to 750 m³.s⁻¹), followed by in February and August, which presented values between 260 m³.s⁻¹ and 720 m³.s⁻¹ and between 90 m³.s⁻¹ to 550 m³.s⁻¹, respectively (Fig. 10).

High anomalous values were detected from 2011 to 2019 in June (Fig. 10). Curiously, during this period, June presented higher monthly mean values (approximately 450 m³.s⁻¹) than May and July (approximately 340 m³.s⁻¹ and 320 m³.s⁻¹, respectively) (Fig. 10). In addition, this pattern contrasted the observed monthly mean June discharge from 2000 to 2010 and in 2020 (Fig. 11). This was the result of the high daily variability (Fig. S7) and the presence of discharge peaks in the VGC discharge during June in 6 of the 9 years from 2011 to 2019 (Fig. S7). These anomalous discharge values in June were caused by the high precipitation in the same period, which followed a consistent pattern starting in 2012 and followed by 2013, 2014, 2016, 2017, and 2019 (Fig. S7). These June anomalies were also found by Marta-Almeida et al. (2021) but were not investigated further in this work, as the topic was out of the scope of this study.

In addition, we presented the monthly mean values from 2000 to 2020 in the predicted time series using daily estimates from the multiple regression model considering R^VT and from 2011 to 2019 compared to the observed VGC discharges from the DAEE (Fig. 11). The VGC discharge values estimated from the DAEE data from 2011 to 2019 (orange bars in Fig. 11 (l to t)) were effectively represented by the multiple regression model considering R^VT (blue bars in Fig. 11 (l to t)). Twelve of 20 years presented the highest monthly mean VGC-modeled discharge values in January, varying from approximately 340 m³.s⁻¹ to 850 m³.s⁻¹ (Fig. 11). The highest monthly VGC discharge values occurred during summer (considering January, February and March), detected in 2001, 2006, 2007, 2011, 2016, 2017, and 2018 (Fig. 11 b, g, h, l, q, r, and s, respectively). In December, high VGC discharge values were also noticed in 2001, 2007, 2008, 2010, 2015, and 2020, varying from 400 m³.s⁻¹ to 850 m³.s⁻¹ (Fig. 11 b, h, i, k, p, and u, respectively). The lowest values were detected during winter (June, July and August) only in 2002, 2006, and 2010 (Fig. 11 c, g, and k, respectively).

High standard deviation values varying from approximately 300 to 700 m³.s⁻¹ were detected in the climatology for June, July and August (winter) (Fig. 10). These variabilities were detected due to highly anomalous VGC discharge values mainly in June. This was evident in 2012, 2013, 2014, 2016, 2017, and 2019 (Fig. 11 m, n, o, q, r and t, respectively); in some cases, discharge values similar or even superior to the summer-month values were achieved. Such a finding clearly occurred in June of 2012 (Fig. 11 (m)) and 2019 (Fig. 11 (t)), when the VGC discharge values reached 750 m³.s⁻¹ and 610 m³.s⁻¹, respectively. In June, we also detected high standard deviations with average values for this month, at 295 m³.s⁻¹ and 190 m³.s⁻¹ for the model results and data, respectively. Anomalous VGC discharge values were also detected in August 2011, reaching 700 m³.s⁻¹ and 600 m³.s⁻¹ for the data and model results, respectively (Fig. 11 (l)). The highest standard deviations were found in December, January and February, with average values reaching 297 m³.s⁻¹ and 274 m³.s⁻¹ (in January) for the model results and data (Fig. 11), respectively.

4.1 Accumulated Rainfall

The good coherence values between R⁷ and the VGC discharge peaks (Fig. 3) and the maximum correlation found between these terms (Fig. S1) allowed us to use R⁷ as the input for the linear, quadratic, exponential and multiple linear regression considering R^FT models. We also applied R as a spatially and temporally varying field (R^VT) in one of the multiple regression models. The idea of using accumulated rainfall as a single input was to produce simple and reliable statistical models for predicting discharge. Since accumulated precipitation was the only input of these models, these data were essential for this study and controlled all the results. Ribeira de Iguape is a catchment with no dams (CBH-RB 2008; IBAMA 2016), in which the rainfall directly influences the unhindered river discharge. This rainfall/river relationship with no human intervention makes our methodology feasible. A decrease in the freshwater contribution from the Jaguaribe River was detected due to the intensification of dam construction along this catchment (ANA 2008; Dias et al. 2013). The performance and outputs of the analyzed models are discussed in the next paragraphs.

A similar pattern of coincident peaks at subseasonal scales was also detected between the rainfall and runoff data in the HBV rainfall-runoff model (Bergström 1976) at the Leaf River catchment in Mississippi, USA, with high rainfall events being associated with peaks in the runoff time series (Abebe et al. 2010). In this case, seasonal fluctuations in both rainfall and runoff time series were detected similarly to our results. Although these series exhibited similar seasonal variations, it is important to mention that because they are different river basins located in opposite hemispheres, the periods of highest (and lowest) values are expected to differ and, therefore, exhibit distinct peak occurrences. For the Leaf River catchment, the highest values were observed between April and June.

4.2 Regression Models

Peak values (both high and low) are better represented in the MLR models considering R⁷ and R^VT than in the linear, quadratic and exponential models and are especially well-represented by the R^VT-considering model. In general, the modeled VGC discharge presented RMSE values between 15% and 17% lower (Fig. 4) than those output by the previous models (Fig. S2); however, discharge values higher than 1000 m³.s⁻¹ continued to be underestimated (Fig. 4). Such underrepresentations have also been reported for ANN models, including feed forward back propagation (FFBP), radial basis function-based (RBF), and generalized regression neural networks (GRNN) models, and in an MLR model (Cigizoglu and Alp 2004). Nevertheless, these models were capable of reproducing the runoff observations with reasonable MSE values varying from 40 m⁶.s² to 105 m⁶.s² (Cigizoglu and Alp 2004). The FFBP, RBF and MLR models generated similar MSE values, showing that MLR models can produce results as accurate as ANN model results (Cigizoglu and Alp 2004).

Both MLR models presented here reproduced the seasonal fluctuations present in the runoff data (Fig. 5 and Fig. 7). The highest runoff peaks were detected during the summer months and in June during some years. Both low- and high-flow events were well-represented by these models (Fig. 5 and Fig. 7), especially the model considering R^VT (Fig. 7-b). In the R^VT model, the field of the maximum correlation coefficient was not equally distributed throughout the catchment (Fig. 6) because precipitation is not homogeneous across this domain (Fig. 2). The maximum correlation was associated with R⁸ and R⁹ in the southern portion, while in the northern part, the maximum correlation was associated with R⁶ and R⁷. These spatial patterns show that the water associated with precipitation in the southern region takes a longer time (~ 8–9 days) to reach the VGC than the water derived from rain in the northern area (~ 6–7 days) (Fig. 6). These spatial differences are associated with the Ribeira de Iguape watershed topography (Fig. 1). The southern region presents higher topography, with elevation values ranging from 800 to 1000 m and a maximum elevation of approximately 2000 m. On the other hand, the northern region presents lower topography, with flatter terrain ranging in elevation from 1 to 100 m (Fig. 1). A catchment with abrupt topography usually presents more permeable bedrock substrates and usually has a larger water storage capacity than a watershed with gentler topography (Sayama et al. 2011). Therefore, an abrupt-topography catchment can sustain lower streamflow responsiveness to precipitation (e.g., Sayama et al. 2011; Wang et al. 2018; Jiang et al. 2012; Muñoz-Villers and McDonnell 2013). Our results are consistent with the findings of previous studies, showing a lower streamflow responsiveness to precipitation in the southern region (steeper topography) of the watershed compared to the northern region (flatter topography) (Fig. 1).

Among the analyzed models, the temporally varying multiple regression model (R^VT) was the most reliable in predicting the discharge time series in the Ribeira de Iguape watershed (Table 2), outperforming the other models. This was a consequence of employing all available spatial information of MERGE over the watershed when computing the multiple regression models, in contrast with the linear, exponential and quadratic regression models, which used only the time series of average spatial precipitation. The skill values of the multiple regression model results were approximately 10% higher than those of the other models. The CDs of the multiple regression models also increased by at least 18% relative to the other models. The RMSE was reduced by at least 30 m³.s⁻¹ and the Pearson's correlation coefficient increased by 16% when applying the multiple regression models. Both multiple regression models presented the highest skill among the models (0.92). However, the R^VT regression model results were marginally better, presenting a CD of 0.67, RMSE of 106.35 m³.s and Pearson correlation of 0.86 (p value < 0.01) compared to the R⁷ regression model results, with a CD equal to 0.64, RMSE of 108.68 m³.s⁻¹ and Pearson correlation of 0.85 (p value < 0.01). Higher CD and lower RMSE values, varying from 0.76 to 0.83 and from 0.5 to 2.5 m³.s⁻¹, respectively, were estimated for the Jhelum catchment in Indica (Dar 2017). These results could be associated with the area of this catchment (~ 8600 km²), which is almost three times smaller than that of the Ribeira de Iguape catchment.

NSE coefficients above 0.74 were found for the MLR models, which increased their performance compared to the other models and showed the robustness of the R⁷ and R^VT regression models (Table 2). Comparable NSE coefficients were found using the SWMM model at the Belo Horizonte catchment (NSE equal to 0.72, Rosa et al. 2020) and in the Taquari-Antas basin at Rio Grande do Sul, Brazil (NSEs varying from 0.40 to 0.84, Collischonn et al. 2007). Similar NSE coefficients with mean and median values of 0.68 and 0.72, respectively, were also obtained for 241 catchments from the CAMELS dataset using the long short-term memory (LSTM) network, a special type of recurrent neural network (Experiment 3 in Kratzert et al. 2018). The underestimation of high-flow occurrences by the models developed here (Fig. 8) was also detected by Kratzert et al. (2018) for both RNN and LSTM models, although the LSTM model outperformed the RNN model. Nevertheless, this LSTM model was capable of reproducing seasonal patterns and fluctuations, similar to our R⁷ and R^VT regression models. It is important to mention that Kratzert et al. (2018) used three different datasets that consisted of 7 different meteorological variables (the day length, precipitation, shortwave downward radiation, maximum and minimum temperature, snow-water equivalent and humidity) as inputs to the LSTM model, while our results used only rainfall as an input and obtained similar NSE coefficients for the R⁷ and R^VT regression model results. This evidence shows that we have implemented much simpler models, with a unique input, with the same robustness in predicting the runoff of a catchment as the LSTM model.

The Ribeira de Iguape catchment is the main source (60%) of freshwater on the southeastern coast of Brazil (Afonso 2006; Marta-Almeida et al. 2021) and therefore strongly influences the salinity in the South Brazil Bight due to the freshwater flowing through the VGC. A discharge time series was produced for the Valo Grande Channel for the period from 2000 to 2020 resulting from the most reliable model (Fig. S6). In the time-series results, two moments presented negative discharges, which physically impossible. This is a limitation of the model and should thus be considered depending on the model application (Fig. S6). Nevertheless, negative flow estimations were also detected in other rainfall-runoff models, including RBF neural networks and MLR models (Cigizoglu and Alp 2004). With the predicted time series, we qualitatively provided and validated the monthly mean discharge (Fig. 10) and presented the monthly climatology obtained for the VGC discharge (Fig. 11).

The resulting RMSE values found for our MLR models were at least 50% lower than the RMSE values of a similar rainfall-runoff model applied for the Madhya Pradesh watershed in India (Patel et al. 2016). Our MLR models presented similar NSE and correlation coefficient values compared to the most reliable LSTM model developed by Xiang et al. (2020). Our results were also consistent with those of other rainfall-runoff models. As a comparison, the semidistributed HBV-96 version conceptual model (Lindström et al. 1997a) presented R²_V (Lindström 1997b) values ranging from 0.6 to 0.8. In addition, our NSE values derived for the MLR models were higher than the NSE values found in 604 basins in the USA using a coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting Model (Newman et al. 2015). For the calibration period, 90% (604) of 671 basins showed NSE values greater than 0.55, and in 34% (225) of the basins, the model provided NSEs higher than 0.8 (Newman et al. 2015).

The monthly mean rainfall information from the MERGE dataset presented the highest values from October to March (Fig. 9-a), which is consistent with the period described by Carvalho et al. (2018). The maximum river discharge during summer for the Ribeira de Iguape can be explained by the rainfall distribution in this region (Carvalho et al. 2018). The VGC runoff follows this same pattern (Fig. 9-b). Generally, by the end of the year (from September to March), the river discharges between Florianópolis and Bertioga, including in Ribeira do Iguape, Paranaguá, Itapocú, and São João, are approximately twice as large as those in the drier months around July (Marta-Almeida et al. 2021).

The seasonal variability is evident in the modeled VGC discharge (Fig. 11), with intense runoff events occurring in the summer, mainly in January and December (Fig. 11), with high standard deviation values varying from 300 to 800 m³.s⁻¹ in the climatology of December, January, and February (Fig. 10). Discharge peaks were also observed during winter (mainly in June, with some in August), with high standard deviation values varying from 300 m³.s⁻¹ to 700 m³.s⁻¹ in the climatology for June, July, and August (Fig. 10). These intense discharge events were associated with high rainfall events. In general, the highest discharge peaks were observed on 4 August 2011, at 2164 m³.s⁻¹, followed by 13 January 2016, at 1806 m³.s⁻¹, and 1 January 2018, at approximately 1797 m³.s⁻¹ (Fig. 11). Marta-Almeida et al. (2021) found that the highest peaks occurred in February, June, and December in 2014 but did not observe highest peaks in these months in other years (except for a low peak in July 2016). We observed peaks in February and June of 2014 but not in December of 2014 (Fig. 11). The reconstructed discharge time series and the monthly climatology presented here can be used in oceanic models to represent the discharge from the Ribeira de Iguape watershed system in estuarine and coastal hydrodynamic forecast and hindcast simulations. This application should consider that, in general, the modeled discharge peaks present some underrepresentation of reality, which might limit some studies related to extreme events.

In this work, simple models based on MERGE rainfall data were shown to be capable of providing information about discharge in the Ribeira de Iguape watershed. We evaluated 5 different models that use rainfall data from MERGE to provide discharge estimations for the VGC. We applied linear regression, quadratic and exponential models and two different MLR models. In the first multiple regression model, R was estimated for 7 days (R⁷), and in the second model, the number of days used to compute R varied spatially (R^VT); in addition, we calculated the standard errors and p values for each grid cell from the MERGE precipitation data. The VGC discharge time series predicted by each model was compared with estimated discharges from data collected from the DAEE. Among the applied models, the multiple regression with R^VT was the most accurate (RMSE of 103.70 m³.s⁻¹) and had the highest coefficient of determination (CD of 0.75), skill (0.92) and NSE (0.75) values. We attribute this better representation of the VGC discharge to this type of statistical model considering individual grid points from the MERGE rainfall data instead of using spatially averaged rainfall, as the other models did. Moreover, the multiple regression model that considered R^VT was more reliable than the regression that considered a constant period of accumulation (R⁷). This finding is the result of the first model also considering varying days of accumulation based on the highest correlation value between the R and VGC discharge.

Discharge measurements are important for constructing and calibrating numerical models for oceanic predictions, leading to improved salinity fields and ocean dynamic simulations in continental shelf and estuarine regions. The VGC discharge time series and the monthly climatology results presented here can be included in operational oceanic models at small and mesoscales in the region and can be used to calibrate numerical simulations. Forecasting river discharge can improve the quality of ocean models in reproducing salinity fields and ocean dynamics in continental shelf and estuarine regions. In addition, it is fundamental to estimate the dispersion of materials and pollutants through rivers and channels and to evaluate their impacts. The results presented here can also be used to investigate the hydrodynamics in the CIELS and how pollutants and other substances are transported in this system.

The methodology presented here can be replicated in other watersheds around the world if sufficiently long training datasets are available. The models require as inputs precipitation maps constructed over time for a given watershed. This information is used to set the MLR model. MLR can be considered a powerful statistical tool to generate highly simple and reliable rainfall-runoff models. Such an MLR model can provide similar results compared to ANN models, such as FFBPNN and GRNN models (Turhan 2021). MLR models have also presented similar results as ANN models when applied together with wavelet transformation (Partal 2017).

Funding:

Author Paula Birocchi has received a PhD scholarship from CNPQ (Conselho Nacional de Desenvolvimento Científico e Tecnológico).

Competing interests:

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval:

Not applicable

Consent to participate:

The authors Paula Birocchi, Danilo Augusto Silva, Dalton Kei Sasaki and Marcelo Dottori consent to participate in this article.

Consent for publication:

Not applicable

Data Availability:

The MERGE datasets used and analyzed during the current study are available at http://ftp.cptec.inpe.br/modelos/tempo/MERGE/GPM/DAILY

For more information about the data results for the implemented models please contact Paula Birocchi.

Code availability:

The Python codes used here can be requested via email to [email protected]

Author Contributions:

All authors contributed to the study's conception, design, and investigation. The methodology was chosen and the formal analysis was made by Paula Birocchi, Danilo Augusto Silva, and Dalton Kei Sasaki. The software usage and validation were made by Paula Birocchi. The visual elements of the paper were made by Paula Birocchi and Dalton Kei Sasaki. The first draft of the manuscript was written by Paula Birocchi and all authors commented on previous versions of the manuscript. All authors contributed to writing the manuscript, reviewing and editing it. All authors read and approved the final manuscript.

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Estimating river discharge from rainfall satellite data through simple statistical models

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Journal Publication

Version 1

Abstract

Figures

1. Introduction

1.1 Study area

2. Methods

2.1 A daily precipitation dataset: MERGE

2.2 Valo Grande Channel discharge estimate

2.3 Models

3. Results

3.1 Accumulated rainfall (R)

3.2 Regression models

3.2.1 Multiple linear regression models

3.3 Model comparison

3.4 Reconstruction of complete time series and seasonal comparisons (2000-2020)

4. Discussion

4.1 Accumulated Rainfall

4.2 Regression Models

5. Concluding Remarks

Declarations

Data Availability:

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1