# Identifying Climate and Human Impact Trends in Streamflow: A Case Study in Uruguay

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area and Dataset

^{2}); Catchment 2 (C2), is the mid-size catchment which drains to Fray Marcos station (2744 km

^{2}), upstream from Paso Pache; Catchment 3 (C3), is the smallest catchment limited at Paso de los Troncos (687 km

^{2}), upstream from Fray Marcos.

## 3. Methodology

#### 3.1. Rainfall-Runoff Simulation

#### 3.2. Trend Simulation of Runoff Residuals

^{3}/year/km

^{2}) provided by DINAGUA is used (Figure 2b). Finally, the operator $l()$ indicates a linear operator and $s()$ indicates a polynomial smooth function. The letters “L” and “S” in the model Id indicates whether $TRR$ is modelled by a linear or a smooth function for G, respectively. In this case we used the cubic shrinkage spline [23] for the smooth functions. Additionally, to restrict the flexibility of the splines, the number of “knots” (k) in the spline was limited to a relatively small value (3). Both the shrinkage spline and the low number of knots reduces the risk of overfitting the model.

^{2}as a performance measure as this is directly related to the variance explained in the data. The adjusted r

^{2}is a penalised version of the r

^{2}taking into account the number of parameters in the model:

#### 3.3. Quantifying the Effect of Land Use Change and Water Licenses on Streamflow

## 4. Results

#### 4.1. Rainfall-Runoff Model Performance

#### 4.2. GAMM Analysis of Runoff Residuals

^{3}/year/km

^{2}suggests a reduction in the streamflow. For higher values of $WL$ the streamflow reduction decreases, but the uncertainty increases (wider confidence intervals). This will be further discussed in the discussion. In Figure 7i–l the response curves for C3 indicate the non-significance of $WL$ (Figure 7l) and the only slight significance for $FC$ (indicated by the wide confidence intervals in Figure 7k). The direction of the $s(FC)$ variable might seem counter-intuitive as this suggest stream flow increases with $FC$. However, the overall curve is around 0 (no change in mean $TRR$ with $FC$) and the confidence intervals overlap 0. Overall, this suggests no change in $TRR$ (and therefore streamflow) with $FC$ in C3. The seasonal and global variables at C3 (Figure 7i,j) indicate the same trends as in the S1 model, which were discussed earlier.

#### 4.3. Effect of Land Use Change and Water Licenses on Streamflow

## 5. Discussion

#### 5.1. Seasonal and Global Trends in the Observed Runoff (Aim 1)

#### 5.2. Identifying the Effect of Exogenous Trends (Forest Cover and Water License, Aim 2)

^{2}) is in fact a larger total area in km

^{2}than 20% of C3 (687 km

^{2}) and therefore this could have a large effect on the resulting streamflow. Other explanation could be differences in the hydrological response by forest type [6]. We estimated total forest cover, however in C1, the forest cover is mainly eucalyptus (grandis, dunnii, globulus), however in C2 and C3 the forest type could consist more of native forest and less eucalyptus [41].

#### 5.3. Further Considerations

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Gleick, P.H. Global Freshwater Resources: Soft-Path Solutions for the 21st Century. Science
**2003**, 302, 1524–1528. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Aparicio, J.; Lafragua, J.; Lopez, A.; Mejia, R.; Aguilar, E.; Mejía, M. Water Resources Assessment: Integral Water Balance in Basins, phi-vii ed.; Number 14 in Technical Document; UNESCO Office Montevideo and Regional Bureau for Science in Latin America and the Caribbean: Montevideo, Uruguay, 2008. [Google Scholar]
- Hölzel, H.; Diekkrüger, B. Predicting the impact of linear landscape elements on surface runoff, soil erosion, and sedimentation in the Wahnbach catchment, Germany. Hydrol. Process.
**2012**, 26, 1642–1654. [Google Scholar] [CrossRef] - Ren, L.; Wang, M.; Li, C.; Zhang, W. Impacts of human activity on river runoff in the northern area of China. J. Hydrol.
**2002**, 261, 204–217. [Google Scholar] [CrossRef] - Gao, Z.; Zhang, L.; Zhang, X.; Cheng, L.; Potter, N.; Cowan, T.; Cai, W. Long-term streamflow trends in the middle reaches of the Yellow River Basin: Detecting drivers of change: Streamflow Trends in the Middle Reach of the Yellow River Basin. Hydrol. Process.
**2016**, 30, 1315–1329. [Google Scholar] [CrossRef] - Zhang, M.; Liu, N.; Harper, R.; Li, Q.; Liu, K.; Wei, X.; Ning, D.; Hou, Y.; Liu, S. A global review on hydrological responses to forest change across multiple spatial scales: Importance of scale, climate, forest type and hydrological regime. J. Hydrol.
**2017**, 546, 44–59. [Google Scholar] [CrossRef] - Yang, L.; Feng, Q.; Yin, Z.; Wen, X.; Si, J.; Li, C.; Deo, R.C. Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe River, Northwest China. Hydrol. Process.
**2017**, 31, 1100–1112. [Google Scholar] [CrossRef] - Mekonnen, D.F.; Duan, Z.; Rientjes, T.; Disse, M. Analysis of combined and isolated effects of land-use and land-cover changes and climate change on the upper Blue Nile River basin’s streamflow. Hydrol. Earth Syst. Sci.
**2018**, 22, 6187–6207. [Google Scholar] [CrossRef] - Silveira, L.; Gamazo, P.; Alonso, J.; Martínez, L. Effects of afforestation on groundwater recharge and water budgets in the western region of Uruguay. Hydrol. Process.
**2016**, 30, 3596–3608. [Google Scholar] [CrossRef] - Silveira, L.; Alonso, J. Runoff modifications due to the conversion of natural grasslands to forests in a large basin in Uruguay. Hydrol. Process.
**2009**, 23, 320–329. [Google Scholar] [CrossRef] - Berri, G.J.; Ghietto, M.A.; García, N.O. The Influence of ENSO in the Flows of the Upper Paraná River of South America over the Past 100 Years. J. Hydrometeorol.
**2002**, 3, 57–65. [Google Scholar] [CrossRef] - Camilloni, I.A.; Barros, V.R. Extreme discharge events in the Paraná River and their climate forcing. J. Hydrol.
**2003**, 278, 94–106. [Google Scholar] [CrossRef] - Krepper, C.M.; García, N.O.; Jones, P.D. Interannual variability in the Uruguay river basin. Int. J. Climatol.
**2003**, 23, 103–115. [Google Scholar] [CrossRef] - Kendall, M.G.; Gibbons, J.D. Rank Correlation Methods, 5th ed.; Arnold, E., Ed.; Oxford University Press: London, UK; New York, NY, USA, 1990. [Google Scholar]
- Mann, H.B. Nonparametric Tests Against Trend. Econometrica
**1945**, 13, 245. [Google Scholar] [CrossRef] - Pettitt, A.N. A Non-Parametric Approach to the Change-Point Problem. J. Appl. Stat.
**1979**, 28, 126. [Google Scholar] [CrossRef] - Burn, D.H.; Hag Elnur, M.A. Detection of hydrologic trends and variability. J. Hydrol.
**2002**, 255, 107–122. [Google Scholar] [CrossRef] - Tramblay, Y.; El Adlouni, S.; Servat, E. Trends and variability in extreme precipitation indices over Maghreb countries. Nat. Hazards Earth Syst. Sci.
**2013**, 13, 3235–3248. [Google Scholar] [CrossRef] [Green Version] - Villarini, G.; Smith, J.A.; Serinaldi, F.; Ntelekos, A.A. Analyses of seasonal and annual maximum daily discharge records for central Europe. J. Hydrol.
**2011**, 399, 299–312. [Google Scholar] [CrossRef] - Zeng, S.; Xia, J.; Du, H. Separating the effects of climate change and human activities on runoff over different time scales in the Zhang River basin. Stoch. Environ. Res. Risk Assess.
**2014**, 28, 401–413. [Google Scholar] [CrossRef] - Koutsoyiannis, D. Nonstationarity versus scaling in hydrology. J. Hydrol.
**2006**, 324, 239–254. [Google Scholar] [CrossRef] [Green Version] - Simpson, G.L. Modelling palaeoecological time series using generalized additive models. bioRxiv
**2018**. [Google Scholar] [CrossRef] - Wood, S.N. Generalized Additive Models: An Introduction with R, 2nd ed.; Chapman & Hall/CRC Texts in Statistical Science; CRC Press/Taylor & Francis Group: Boca Raton, FL, USA, 2017. [Google Scholar]
- Wang, Q.; Liu, R.; Men, C.; Guo, L.; Miao, Y. Effects of dynamic land use inputs on improvement of SWAT model performance and uncertainty analysis of outputs. J. Hydrol.
**2018**, 563, 874–886. [Google Scholar] [CrossRef] - Serinaldi, F.; Kilsby, C.G.; Lombardo, F. Untenable nonstationarity: An assessment of the fitness for purpose of trend tests in hydrology. Adv. Water Resour.
**2018**, 111, 132–155. [Google Scholar] [CrossRef] - Perrin, C.; Michel, C.; Andréassian, V. Improvement of a parsimonious model for streamflow simulation. J. Hydrol.
**2003**, 279, 275–289. [Google Scholar] [CrossRef] - Amoussou, E.; Tramblay, Y.; Totin, H.S.; Mahé, G.; Camberlin, P. Dynamique et modélisation des crues dans le bassin du Mono à Nangbéto (Togo/Bénin). Hydrolog. Sci. J.
**2014**, 59, 2060–2071. [Google Scholar] [CrossRef] - Arnaud, P.; Lavabre, J.; Fouchier, C.; Diss, S.; Javelle, P. Sensitivity of hydrological models to uncertainty in rainfall input. Hydrolog. Sci. J.
**2011**, 56, 397–410. [Google Scholar] [CrossRef] - Santos, L.; Thirel, G.; Perrin, C. Continuous state-space representation of a bucket-type rainfall-runoff model: A case study with the GR4 model using state-space GR4 (version 1.0). Geosci. Model. Dev.
**2018**, 11, 1591–1605. [Google Scholar] [CrossRef] - Coron, L.; Thirel, G.; Delaigue, O.; Perrin, C.; Andréassian, V. The suite of lumped GR hydrological models in an R package. Environ. Model. Softw.
**2017**, 94, 166–171. [Google Scholar] [CrossRef] - R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2018. [Google Scholar]
- Gräler, B.; Pebesma, E.; Heuvelink, G. Spatio-Temporal Interpolation using gstat. RFID J.
**2016**, 8, 204–218. [Google Scholar] [CrossRef] - Pebesma, E.J. Multivariable geostatistics in S: The gstat package. Comput. Geosci.
**2004**, 30, 683–691. [Google Scholar] [CrossRef] - Andrews, F.; Croke, B.; Jakeman, A. An open software environment for hydrological model assessment and development. Environ. Model. Softw.
**2011**, 26, 1171–1185. [Google Scholar] [CrossRef] - Bennett, N.D.; Croke, B.F.; Guariso, G.; Guillaume, J.H.; Hamilton, S.H.; Jakeman, A.J.; Marsili-Libelli, S.; Newham, L.T.; Norton, J.P.; Perrin, C.; et al. Characterising performance of environmental models. Environ. Model. Softw.
**2013**, 40, 1–20. [Google Scholar] [CrossRef] - Bos, M.G.; Kselik, R.; Allen, R.; Molden, D. Water Requirements for Irrigation and the Environment; Springer: Dordrecht, The Netherlands, 2009. [Google Scholar] [CrossRef]
- Schilling, K.E.; Jha, M.K.; Zhang, Y.K.; Gassman, P.W.; Wolter, C.F. Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions. Water Resour. Res.
**2008**, 44. [Google Scholar] [CrossRef] - Oudin, L.; Andréassian, V.; Lerat, J.; Michel, C. Has land cover a significant impact on mean annual streamflow? An international assessment using 1508 catchments. J. Hydrol.
**2008**, 357, 303–316. [Google Scholar] [CrossRef] - King, R.S.; Baker, M.E.; Whigham, D.F.; Weller, D.E.; Jordan, T.E.; Kazyak, P.F.; Hurd, M.K. Spatial considerations for linking watershed land cover to ecological indicators in streams. Ecol. Appl.
**2005**, 15, 137–153. [Google Scholar] [CrossRef] - Guzha, A.; Rufino, M.; Okoth, S.; Jacobs, S.; Nóbrega, R. Impacts of land use and land cover change on surface runoff, discharge and low flows: Evidence from East Africa. J. Hydrol. Reg. Stud.
**2018**, 15, 49–67. [Google Scholar] [CrossRef] - MGAP. Cartografía Forestal 2018; Dirección General Forestal: Montevideo, Uruguay, 2018. [Google Scholar]

**Figure 1.**(

**a**) Relief, monitoring system and sub-catchments of Santa Lucía at Paso Pache (C1, 4896 km${}^{2}$), Fray Marcos (C2, 2744 km${}^{2}$) and Paso de los Troncos (C3, 687 km${}^{2}$). (

**b**) Main land uses observed during 2015.

**Figure 3.**(

**a**) Boxplot of monthly precipitation, (

**b**) potential evapotranspiration, (

**c**) runoff at Paso de los Troncos, and (

**d**) mean temperature for the period 1981–2016.

**Figure 4.**Shematic representation of the steps used to indentify the origin of trends and quantify the runoff change.

**Figure 5.**(

**a**) Transformation of Relative Residuals ($TRR$, gray points), linear regression of $TRR$ (black line) and histogram of $TRR$ (bars) for C1 (

**a**,

**b**), C2 (

**c**,

**d**), and C3 (

**e**,

**f**).

**Figure 6.**(

**a**,

**c**,

**e**) Global (G) and (

**b**,

**d**,

**f**) seasonal (S) terms (continuous black line) and standard error bounds (dashed line) for S1 models ($TRR=l(G)+s(S)$).

**Figure 7.**Smooth terms (continuous black lines) and standard error bounds (dashed lines) of S4 model ($TRR=s(G)+s(S)+s(FC)+s(WL)$) for C1 (

**a**–

**c**), C2 (

**e**–

**g**) and C3 (

**i**–

**k**) catchments.

**Figure 8.**Effect of land use change and water licenses in Flow Duration Curves of Santa Lucía at Paso Pache (

**a**), Fray Marcos (

**b**), and Paso de los Troncos (

**c**).

Model Id. | Equation (TRR Equal to) |
---|---|

L1 | $l(G)+s(S)$ |

S1 | $s(G)+s(S)$ |

L2 | $l(G)+s(S)+s(FC)$ |

S2 | $s(G)+s(S)+s(FC)$ |

L3 | $l(G)+s(S)+s(WL)$ |

S3 | $s(G)+s(S)+s(WL)$ |

L4 | $l(G)+s(S)+s(FC)+s(WL)$ |

S4 | $s(G)+s(S)+s(FC)+s(WL)$ |

Daily Scale | Monthly Scale | |||||
---|---|---|---|---|---|---|

Catchment | NSE | BIAS | ${\mathit{r}}^{2}$ | NSE | BIAS | ${\mathit{r}}^{2}$ |

C1 | 0.82 | −0.04 | 0.85 | 0.91 | −0.04 | 0.91 |

C2 | 0.81 | −0.05 | 0.84 | 0.85 | −0.03 | 0.86 |

C3 | 0.57 | −0.07 | 0.79 | 0.78 | −0.10 | 0.80 |

Catchment | Model Id. | Intercept | G | S | FC | W | Adjusted ${\mathit{r}}^{2}$ |
---|---|---|---|---|---|---|---|

C1 | L1 | ** | ** | *** | ⊗ | ⊗ | 0.115 |

S1 | *** | *** | ⊗ | ⊗ | 0.188 | ||

L2 | *** | *** | ⊗ | 0.208 | |||

S2 | *** | *** | ⊗ | 0.211 | |||

L3 | *** | ** | *** | ⊗ | * | 0.144 | |

S3 | *** | *** | ⊗ | 0.188 | |||

L4 | *** | *** | * | 0.222 | |||

S4 | *** | *** | 0.214 | ||||

C2 | L1 | ** | * | ⊗ | ⊗ | 0.096 | |

S1 | * | * | * | ⊗ | ⊗ | 0.072 | |

L2 | * | * | ⊗ | 0.125 | |||

S2 | * | ** | ⊗ | 0.117 | |||

L3 | . | * | ⊗ | ** | 0.134 | ||

S3 | * | ⊗ | *** | 0.139 | |||

L4 | . | * | ** | 0.134 | |||

S4 | * | . | ** | 0.149 | |||

C3 | L1 | *** | *** | *** | ⊗ | ⊗ | 0.17 |

S1 | *** | *** | ⊗ | ⊗ | 0.285 | ||

L2 | *** | *** | *** | *** | ⊗ | 0.269 | |

S2 | *** | *** | . | ⊗ | 0.295 | ||

L3 | *** | *** | *** | ⊗ | ** | 0.229 | |

S3 | *** | *** | ⊗ | 0.285 | |||

L4 | *** | *** | *** | *** | 0.269 | ||

S4 | *** | *** | . | 0.295 |

Catchment | Slope (G) | p-Value |
---|---|---|

C1 | $1.12\times {10}^{-3}$ | $8.19\times {10}^{-5}$ |

C2 | $1.27\times {10}^{-3}$ | $1.92\times {10}^{-2}$ |

C3 | $1.93\times {10}^{-3}$ | $9.18\times {10}^{-4}$ |

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**MDPI and ACS Style**

Navas, R.; Alonso, J.; Gorgoglione, A.; Vervoort, R.W.
Identifying Climate and Human Impact Trends in Streamflow: A Case Study in Uruguay. *Water* **2019**, *11*, 1433.
https://doi.org/10.3390/w11071433

**AMA Style**

Navas R, Alonso J, Gorgoglione A, Vervoort RW.
Identifying Climate and Human Impact Trends in Streamflow: A Case Study in Uruguay. *Water*. 2019; 11(7):1433.
https://doi.org/10.3390/w11071433

**Chicago/Turabian Style**

Navas, Rafael, Jimena Alonso, Angela Gorgoglione, and R. Willem Vervoort.
2019. "Identifying Climate and Human Impact Trends in Streamflow: A Case Study in Uruguay" *Water* 11, no. 7: 1433.
https://doi.org/10.3390/w11071433