# The Hydroological Balance in Micro-Watersheds Is Affected by Climate Change and Land Use Changes
## 3 Results and Discussion
### Hydrological Balance
#### 3.4.2 Hydrological Environmental Service  
Distributions of inflows and outflows of the hydrological balance by watershed as a percentage of the evolution (Ev%) of the change in the proposed scenarios are shown in Table 7. The evolution was estimated based on the current flows of the study area [27]. The results indicated that under the conditions of the positive scenario, together with the increase in the precipitation values estimated by the GCMs, there would be an increase in the water kept in the soil the preceding year: by 138.3% according to the CNRM model, by 102.7% according to the HadGEM3 model and by 91.5% according to the MPI model. These percentages mean that in the parts of the micro-watersheds where temperate forest cover predominates, 77.4% (CNRM), 61% (HadGEM3), and 60% (MPI) would be stored more than in the current scenario at the micro-watershed level. Soil cover significantly influences the distribution of soil moisture [90; 91]. However, although it is estimated that there would be much more moisture in the soil that would lead to the infiltration and recharge of the aquifers, the simulations indicate that there would be a decrease in the base flow, even under the conditions of the positive scenario: \(-\)52.5% (CNRM), \(-\)61.3% (HadGEM3) and \(-\)62.5% (MPI). This decrease could be related to the increase in evapotranspiration: 50.8% (CNRM), 43.3% (HadGEM3), and 30.2% (MPI). Climate change will accelerate the hydrological cycle with a tendency to increase temperature [10]. An increase in air temperature leads to higher rates of evapotranspiration [14]. These results coincide with the research conducted by [PERSON] et al. [10], who reported that the greatest increase in evapotranspiration rates will occur in the period 2071-2100. Given that evapotranspiration is key in the hydrological balance, a disturbance to this flow can change the distribution between the dominant water flows, such as infiltration and surface runoff [65]. Some simulation studies suggest that increased precipitation, together with warmer temperatures in the summer, cause severe reductions in base flows [92; 93]. However, at the local scale, high summer temperatures and increased evapotranspiration rates could lead to an increase in convective precipitation, which would compensate for the reductions in base flow [94]. The decrease in base flow may be related to the increase in forest cover, mainly due to the establishment of reforestation that has positive responses to the increase in soil moisture [95]. Regarding the evolution of the change that the surface runoff would present, under a negative scenario, it is expected that climatic changes will considerably increase the surface runoff in these parts of the micro-watersheds: 275% (CNRM), 127.7% (HadGEM3) and 133.8% (MPI). This result represents that in this part of the micro-watersheds, 81.6% (CNRM), 68.1% (HadGEM3), and 93.8% (MPI) of the total runoff from the micro-watersheds are generated. Removal of vegetative cover and soil organic matter leads to changes in hydrological processes by reducing interception of precipitation and altering soil structure (compaction and erosion), which results in an increase in the amount and speed of surface runoff [23; 24; 96]. The decrease in forest cover as a consequence of forest fires influences the increase in surface runoff, erosion, and the increase in sediments to the channels [66; 67; 68]. However, under the conditions of the positive scenario of land use change, the results contrary to the data presented at the microbasin level are observed, and it is emphasized that the variations in climate change would be contained at least in the parts of the microbasins where forest cover is present. The simulations indicate that there would be an average decrease in surface runoff of \(-\)69.9% (CNRM), \(-\)86.5% (HadGEM3), and \(-\)89.3% (MPI). This represents a decrease of \(-\)194.2% (CNRM), \(-\)121.1% (HadGEM3), and \(-\)101.9% (MPI) with respect to the data presented at the microbasin level for the same scenarios.