# Regional Patterns of Baseflow Variability in Mexican Subwatersheds

^{*}

## Abstract

**:**

## 1. Introduction

^{2}> 0.72). Similar studies associated the baseflow index with geographical, climate and edaphic patterns [10,11].

## 2. Materials and Methods

#### 2.1. Input Data

^{−1}) from 21 Mexican subwatersheds were gathered; the source of this information was El Banco Nacional de Datos de Aguas Superficiales [23]. The subwatersheds were selected so as to represent different climate characteristics (aridity index, seasonality, humidity), as shown by Garcia et al. [24], and landscape characteristics (topography, soil and plant coverage).

^{2}. The analyzed period went from 1950 to 2011, which is the period of available hydrometric data in Mexico.

^{3}·s

^{−1}to depth in mm·day

^{−1}, it is necessary to know the area of the subwatershed that uses the gauging station present as its reference.

^{RM}(Python Software Foundation, Amsterdam, The Netherlands) programming language was utilized to obtain the annual average values for each subwatershed and each variable. The soil texture records were obtained from the Food and Agriculture Organization of the United Nations [31] Soil Database v 1.2.

#### 2.2. Recession Curves’ Selection

^{−1}), Qo is the initial discharge, t is time measured in days and a (RP) and b are the model’s parameters.

#### 2.3. Spatial Predictors of the Response of Baseflow and Symmetry in the Process

^{2}= 0.20) was considered a potentially meaningful correlation [2]. Potential, exponential and linear functions were calculated for all predictors. The fitting criteria were based on the R

^{2}determination coefficient and the root-mean-square error (RMSE). To avoid multiple methods to evaluate data fit, these two criteria were chosen because they are the most widely used in various hydrological calibrations [2,3,15,21].

#### 2.4. Baseflow Separation

## 3. Results

#### 3.1. Recession Curves

^{2}> 0.88). The largest magnitude curve was found to be located in the southwest part of the country (San Pedro, Chiapas, hydrometric station Number 30,067), whereas the lowest value was located in a subwatershed in the Mexican northwest (Río Salado-Anahuac, hydrometric station Number 24,038).

#### 3.2. Baseflow Response Spatial Predictors

^{2}< 0.14), indicating that the soil variables considered do not affect the fitted parameter. Area and slope were slightly predictive of the parameter (R

^{2}> 0.30); however, these variables were not statistically significant (p > 0.05). With regard to normalized precipitation (NP), a marked non-linear trend with RP was observed (R

^{2}> 0.43), and so, this climate variable was chosen as the principal parameter predictor. The remaining 57% of variance was not explained by this variable. Equation (5) represents this dispersion in the model.

^{2}= 0.51, RMSE = 0.12) as compared to the interannual relation (R

^{2}= 0.35, RMSE = 0.71). The interannual variability showed a higher dispersion than the average long-term variation; even so, this trend and the α fitted parameter were similar in both cases (3.88 vs. 4.22). In this study, the model dispersion (Equation (4)) was associated with the predominant type of rock in each subwatershed and its transmissivity. Transmissivity data were available for only seven subwatersheds.

^{2}= 0.96, RSME = 0.57). The model depends on two parameters: μ is the maximum reported transmissivity for Mexican aquifers (which could be fixed), and θ is the model’s variation rate in relation to $\frac{P}{E}$.

#### 3.3. Baseflow Separation

## 4. Discussion

#### 4.1. Recession Curve

#### 4.2. Baseflow Spatial Patterns and Model Parameterization

#### 4.3. Baseflow Separation

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 2.**Recession curves’ selection through the master curve method (dotted line through the year) (

**a**); proposed annual recession curves selection (annual dotted line) (

**b**).

**Figure 4.**Relation between recession parameter and landscape and climate variables among subwatersheds.

**Figure 5.**Subwatershed variability (identified through hydrometric stations) at (

**a**) an interannual scale and (

**b**) subwatershed average long-term variability.

**Figure 6.**Subwatershed variability (identified through hydrometric station) and rock type. Relation between the dispersion parameter estimated in the model (

**a**); Equation (4) and the product of $\frac{P}{E}$ and transmissivity (

**b**).

**Figure 7.**Separation of baseflow by the generalized baseflow model in the sub-basins: (

**a**) El Tecolote, located in the state of Jalisco, Mexico; and (

**b**) Zanatenco, located in the state of Chiapas, México.

Hydrometric Station | Aquifer Identifier | Aquifer Type | Transmissivity (m^{2}·s^{−1}) | Rock Type |
---|---|---|---|---|

9080 | 0859 [39] | unconfined | 0.0241 | Riolite-tuff-acid, basalt, alluvial |

11,012 | 1802 [40] | unconfined | 0.0131 | Riolite-tuff-acid, basalt, alluvial |

12,601 | 1502 [41] | unconfined | 0.0370 | Alluvial, riolite |

18,271 | 1701 [42] | unconfined | 0.0180 | Basalt, sandstone |

23,022 | 0711 [43] | unconfined | 0.0018 | Basalt |

24,038 | 0512 [44] | unconfined | 0.1761 | Limestone, sandstone |

24,150 | 0507 [45] | unconfined | 0.0902 | Alluvial, limestone |

Hydrometric Station | Subwatershed Name | Longitude (°) | Latitude (°) | Number of Recessions | Surface (km^{2}) | Fitted Value | R^{2} |
---|---|---|---|---|---|---|---|

9010 | R. Bavispe-Angostura | −109.36 | 30.61 | 3 | 14,188 | 6.4 | 0.92 |

9080 | R. Papigochic | −108.30 | 29.13 | 4 | 1856 | 14.3 | 0.96 |

10,098 | R. Alamos | −108.76 | 26.59 | 4 | 1813 | 12.7 | 0.91 |

11,012 | R. San Pedro | −105.14 | 21.96 | 4 | 11,924 | 36.0 | 0.92 |

15,010 | R. Purificación | −104.50 | 19.56 | 4 | 168 | 54.8 | 0.93 |

18,157 | R, Atoyac | −98.23 | 19.23 | 6 | 258 | 125.3 | 0.95 |

18,169 | R. Tilostoc | −100.11 | 19.17 | 4 | 154 | 212.6 | 0.93 |

18,271 | R. Apatlaco | −99.22 | 18.84 | 6 | 364 | 15.3 | 0.88 |

18,466 | R. Tilostoc-Anahuac | −100.25 | 19.27 | 3 | 124 | 100.0 | 0.91 |

18,489 | R. Tilostoc-set | −100.12 | 19.22 | 3 | 317 | 113.4 | 0.95 |

23,011 | R. Zanatenco | −93.74 | 16.08 | 6 | 166 | 43.0 | 0.96 |

23,022 | R. Sesecapa | −92.87 | 15.46 | 3 | 125 | 90.9 | 0.90 |

24,038 | R. Salado | −100.13 | 27.22 | 3 | 23,475 | 4.0 | 0.97 |

24,150 | R. Salado de Nadadores | −100.94 | 27.42 | 6 | 21,520 | 25.0 | 0.94 |

24,198 | R. Monterrey | −100.36 | 25.66 | 6 | 5412 | 91.0 | 0.94 |

26,268 | R. Tampán | −99.21 | 21.65 | 4 | 8722 | 22.0 | 0.92 |

27,083 | R. Necaxa | −97.87 | 20.25 | 5 | 562 | 140.3 | 0.98 |

28,135 | R. Papaloapan | −95.84 | 18.30 | 3 | 20,263 | 87.5 | 0.92 |

30,067 | R. San Pedro Mar | −93.09 | 16.06 | 5 | 42 | 235.0 | 0.92 |

12,574 | R. Gavia | −99.87 | 19.42 | 5 | 37 | 3.5 | 0.93 |

12,601 | R. Sila | −99.71 | 19.77 | 3 | 36 | 12.5 | 0.96 |

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**MDPI and ACS Style**

Salas-Aguilar, V.; Macedo-Cruz, A.; Paz, F.; Palacios, E.; Ortiz, C.; Quevedo, A.
Regional Patterns of Baseflow Variability in Mexican Subwatersheds. *Water* **2016**, *8*, 98.
https://doi.org/10.3390/w8030098

**AMA Style**

Salas-Aguilar V, Macedo-Cruz A, Paz F, Palacios E, Ortiz C, Quevedo A.
Regional Patterns of Baseflow Variability in Mexican Subwatersheds. *Water*. 2016; 8(3):98.
https://doi.org/10.3390/w8030098

**Chicago/Turabian Style**

Salas-Aguilar, Víctor, Antonia Macedo-Cruz, Fernando Paz, Enrique Palacios, Carlos Ortiz, and Abel Quevedo.
2016. "Regional Patterns of Baseflow Variability in Mexican Subwatersheds" *Water* 8, no. 3: 98.
https://doi.org/10.3390/w8030098