*3.3.1 Nilo, Cundinamarca*

Having reviewed the supply-demand relationship in this area for different activities and considering the importance of hydroelectric power generation for the country, the need to propose a vulnerability index for this activity was established [9]. Based on the observed and projected data from the previous investigations, both for precipitation and temperature and for flow, as well as on the supply and demand data of the water resource, the WEAP tool was used again to perform a water balance that includes the data mentioned and the operating policies, technical

Given the above, there is a direct relationship between the flow and the volume of the basin with changes in precipitation and temperature. Therefore, the vulnerability index will depend directly on these two variables, its lowest value being the result of a 10% increase in precipitation and a 0.5°C increase in temperature and the highest value the result of a 10% decrease in precipitation and a 3°C increase in

Given the cost overruns in energy production, research similar to the previous one supposes the possibility for decision-makers to establish the vulnerability of their systems to climate change, as well as to implement projects to adapt to and

Finally, it was intended to establish if the availability of water can act as an optimization factor in the generation of hydroelectric power [10]; so, an investigation focused on this topic was carried out. To do so, using both current and future supply and demand values, an adapted scenario was established in which demand decreases by 20% due to the efficient use of water. This makes the critical point at which demand equals supply more distant, having a difference of 5 years on average. As for hydroelectric generation, some system optimization scenarios were established, including pumping from a downstream point of the basin to an upstream point, which regulates the basin and can be used to mitigate events of flood that, as mentioned, are very likely to occur. The optimization scenarios are contingent on pumping with different start dates and the amount of water extracted. It was established that pumping must begin before the critical point established for each

However, from this latest investigation it was concluded that the critical point with measures such as pumping only takes a little longer to occur. Therefore, it is crucial to make changes in demand such as establishing appropriate water management and regulation strategies, optimizing water delivery infrastructure, and establishing priorities. This study and the previous ones encourage decision-makers

demand scenario to avoid regional conflicts over the use of water.

problems, bathymetries, and evaporation in the basin.

*Total unsatisfied demand, Sinú-Caribe basin [8].*

temperature.

**80**

**Figure 2.**

*Resources of Water*

mitigate the effects.

The municipality of Nilo, located in the department of Cundinamarca, is an important region for the production of Cocoa in Colombia. This study seeks to evaluate the water requirements for growing this product in current and future scenarios with climate changes [11].

For this purpose, a baseline was initially established in the period 1975–2005. Then, using variables such as precipitation, temperature, evapotranspiration, among others, a water balance was performed to recognize and characterize the study area, establishing adequate water availability, according to the water resource indices in **Table 6**. This procedure was repeated again considering the change in the projected meteorological variables for the years 2050 and 2070.

Additionally, the water requirements of the crop were established using CropWat software, climatic variables of the baseline and future scenarios, as well as some parameters related to soil, which were established based on fieldwork performed in the study area.

Consequently, crops were delimited due to water deficiency in soil, as a result of an increase in temperature (T) and a decrease in precipitation (PCP). The aforementioned will involve drought stress, a possible increase in pests, and a drastic reduction in crop yield. In addition, there will be a possible increase in the water deficit (Def) in both the pessimistic and optimistic scenarios, changing the Hydric Availability Index (HAI) in the area from optimal to semiarid, as shown in **Table 7**. According to the values in **Table 7**, in terms of the water requirements of the reference crop, a value of 359 mm for the baseline and a consequent increase up to 535 mm were established as a result of climate change.

This study opens up the possibility of planning the use of the land, depending on the water requirements of both current and future crops, in order to make sustainable use of the water resource and can serve as a reference for new studies on this subject. This investigation measured the arithmetic average of the results obtained from the different models and scenarios. However, it does not allow observing the effect of each model, which may differ from each other, either in the magnitude of the change in temperature or in the increase or decrease in precipitation.


**Table 6.** *Description and assessment of the calculated indices.*

Therefore, the scenarios were grouped into four clusters or groups of similar results, in which the centroid value of each of the variables was obtained, as shown in **Table 8**. It was decided to assemble a scenario by assigning weight factors to each cluster, in order to generate a unique scenario with the most adverse effects. Since the municipality of Nilo is mainly engaged in agriculture, it was concluded that the most negative effect in this area is the reduction of precipitation and the increase in temperature. Therefore, the clusters in which this occurs will have a greater value at the time of assigning the weight factor (WF) for each of variable [12].

Accordingly, a unique scenario (WA) for precipitation and average, minimum and maximum temperatures were established based on the previously assigned weights. This scenario differs from the arithmetic average (AA) of all the scenarios calculated; they were compared with the established baseline (BL), as shown in **Figure 3**.

Establishing a unique scenario as indicated in this investigation allows decisionmakers to establish adaptation measures for that single scenario, focusing efforts on preventing or mitigating the most adverse effects for the area of interest, which

The Coello River basin is located in the department of Tolima. It covers a large percentage of its territory and is of great importance for the region since it supplies the municipalities settled there, as well as their economic activities, mainly agricultural. Likewise, it supplies one of the most important irrigation districts in the

An investigation was conducted in this basin, focused on assessing the implications of climate change on the supply, demand, and indicators of water resource status, namely, indices of aridity, water use, vulnerability to water shortage, and water retention. Its methodology was based on the development of a hydrological model using the Soil and Water Assessment Tool (SWAT), both for the baseline (1976–2005) and for the future period (2020–2050), in which daily precipitation

In the case of temperature, the Delta Method was used as a methodology for the

As a result, a potential increase in annual precipitation was determined as shown in **Figure 4**. However, there was a sharp decrease in its value at daily resolution as illustrated in **Figure 5**, which suggests a possible increase in extreme events since

In the case of the maximum temperature, its value increased progressively as

Once the input variables for the hydrological model have been established, the flow values were obtained throughout the basin, thus establishing the water supply in each of the microbasins. Subsequently, the flows were characterized using flow duration curves, which, compared with the observed value, indicate an increase in the probability of extreme events and a decrease in the average flow that flows

reduction of the geographic scale of the General Circulation Models (GCM) implemented, which consists in establishing the variation of the temperature per month taking as reference the historical data of the GCMs mentioned in **Table 2**. In the case of precipitation, since a daily resolution was required, it was decided to combine the Delta Method for the monthly scale reduction with the Maximum Entropy Method for the disaggregation of said value on a daily basis, depending on

the observed behavior of said variable in each station studied [13].

large amounts of precipitation are concentrated on specific day(s).

through the channel most of the time (**Figure 7**).

is of vital importance for the proper management of water resources in an

*Effects of Climate Change on Water Resources, Indices, and Related Activities in Colombia*

unfavorable future.

**Figure 3.**

*3.3.2 Coello River basin*

illustrated in **Figure 6**.

**83**

region due to its large rice and cotton production.

*Monthly behavior of (a) precipitation (b) average temperature [12].*

*DOI: http://dx.doi.org/10.5772/intechopen.90652*

and monthly temperature were entered as input variables.


**Table 7.**

*Summary of the variation of climatic variables and associated indices [11].*


*The bold values highlight the greater temperature values on the cluster 2 and so the greatest weighting factor for this variables correspond to the scenarios which are part of that cluster. While the minor precipitation value is in the cluster 3, giving it a greater weighting factor to this variable in the scenarios belonging to this cluster.*

#### **Table 8.**

*Summary of climatic variables, their associated indices and weights for each cluster [12].*

*Effects of Climate Change on Water Resources, Indices, and Related Activities in Colombia DOI: http://dx.doi.org/10.5772/intechopen.90652*

**Figure 3.** *Monthly behavior of (a) precipitation (b) average temperature [12].*

Establishing a unique scenario as indicated in this investigation allows decisionmakers to establish adaptation measures for that single scenario, focusing efforts on preventing or mitigating the most adverse effects for the area of interest, which is of vital importance for the proper management of water resources in an unfavorable future.

#### *3.3.2 Coello River basin*

Therefore, the scenarios were grouped into four clusters or groups of similar results, in which the centroid value of each of the variables was obtained, as shown in **Table 8**. It was decided to assemble a scenario by assigning weight factors to each cluster, in order to generate a unique scenario with the most adverse effects. Since the municipality of Nilo is mainly engaged in agriculture, it was concluded that the most negative effect in this area is the reduction of precipitation and the increase in temperature. Therefore, the clusters in which this occurs will have a greater value

Accordingly, a unique scenario (WA) for precipitation and average, minimum and maximum temperatures were established based on the previously assigned weights. This scenario differs from the arithmetic average (AA) of all the scenarios calculated; they were compared with the established baseline (BL), as shown in

Current 21.4 23.4 22.4 1292.0 124.5 M-L M

*ΔTmax* **(°C)**

Pessimistic 2050 2.0 2.1 2.1 39.8 396.4 L L Optimistic 2050 1.3 2.3 1.8 3.2 31.1 M-L M Average 2050 1.6 2.1 1.8 15.8 152.8 L M-L Pessimistic 2070 2.5 3.5 3.0 36.5 382.8 L L Optimistic 2070 2.5 3.7 3.1 14.3 13.9 M-L M Average 2070 2.1 2.6 2.3 14.0 150.1 L M-L

**Variable/cluster 1 2 3 4** Members 22 7 7 8 Percentage 50 15.91 15.91 18.18 Tmin (°C) 23.08 **24.61** 23.4 23.39 Tmax (°C) 33.89 **35.43** 34.06 34.1 Tavg (°C) 28.49 **30.03** 28.74 28.73 PCP (mm) 1101.25 1149.27 **874.74** 1314.08 Def (mm) 294.52 287.76 525.86 191.01 LI L L L M-L HAI M-L M-L M-L M-L **PCP WF** 0.26 0.68 **4.34** 0.38 **Tmax WF** 0.36 **2.46** 1.35 1.17 **Tmin WF** 0.34 **2.44** 1.39 1.23 **Tavg WF** 0.34 **2.45** 1.38 1.22 *The bold values highlight the greater temperature values on the cluster 2 and so the greatest weighting factor for this variables correspond to the scenarios which are part of that cluster. While the minor precipitation value is in the cluster 3, giving it a*

*ΔTmed* **(°C)**

**(mm)**

*Δ***PCP (%)**

**Def (mm)**

*Δ***Def (%)**

**LI HAI**

**LI HAI**

at the time of assigning the weight factor (WF) for each of variable [12].

**Climate variable/scenario Tmin (°C) Tmax (°C) Tavg (°C) PCP**

*ΔTmin* **(°C)**

*Summary of the variation of climatic variables and associated indices [11].*

*greater weighting factor to this variable in the scenarios belonging to this cluster.*

*Summary of climatic variables, their associated indices and weights for each cluster [12].*

**Figure 3**.

*Resources of Water*

**scenario**

**Table 7.**

**Table 8.**

**82**

**Change in climate variable/**

The Coello River basin is located in the department of Tolima. It covers a large percentage of its territory and is of great importance for the region since it supplies the municipalities settled there, as well as their economic activities, mainly agricultural. Likewise, it supplies one of the most important irrigation districts in the region due to its large rice and cotton production.

An investigation was conducted in this basin, focused on assessing the implications of climate change on the supply, demand, and indicators of water resource status, namely, indices of aridity, water use, vulnerability to water shortage, and water retention. Its methodology was based on the development of a hydrological model using the Soil and Water Assessment Tool (SWAT), both for the baseline (1976–2005) and for the future period (2020–2050), in which daily precipitation and monthly temperature were entered as input variables.

In the case of temperature, the Delta Method was used as a methodology for the reduction of the geographic scale of the General Circulation Models (GCM) implemented, which consists in establishing the variation of the temperature per month taking as reference the historical data of the GCMs mentioned in **Table 2**. In the case of precipitation, since a daily resolution was required, it was decided to combine the Delta Method for the monthly scale reduction with the Maximum Entropy Method for the disaggregation of said value on a daily basis, depending on the observed behavior of said variable in each station studied [13].

As a result, a potential increase in annual precipitation was determined as shown in **Figure 4**. However, there was a sharp decrease in its value at daily resolution as illustrated in **Figure 5**, which suggests a possible increase in extreme events since large amounts of precipitation are concentrated on specific day(s).

In the case of the maximum temperature, its value increased progressively as illustrated in **Figure 6**.

Once the input variables for the hydrological model have been established, the flow values were obtained throughout the basin, thus establishing the water supply in each of the microbasins. Subsequently, the flows were characterized using flow duration curves, which, compared with the observed value, indicate an increase in the probability of extreme events and a decrease in the average flow that flows through the channel most of the time (**Figure 7**).

In the case of demand, having a projection of the meteorological variables and the crop areas, as well as the type of product grown, the water demand was established in terms of the irrigation requirement of said crops using the CropWat tool, as shown in **Table 9**.

**Figure 7.**

**Model (Mm3**

**Model (Mm<sup>3</sup>**

**Model (Mm<sup>3</sup>**

**Table 9.**

**85**

*Flow duration curve for all the models and RCPs selected, year 2050 [13].*

*DOI: http://dx.doi.org/10.5772/intechopen.90652*

/**year) (Mm3**

/**year) (Mm3**

/**year) (Mm3**

*Irrigation requirement for the crops in the study area [13].*

**Demand/crop Pane cane Banana Plantain Arracacha Rice**

*Effects of Climate Change on Water Resources, Indices, and Related Activities in Colombia*

Current 13.5 10.6 5.4 2.0 296.3 IPSL\_LR45 20.8 12.4 6.4 1.8 393.1 IPSL\_MR45 16.3 14.2 7.3 2.2 367.5 MIROC5\_45 20.4 10.3 5.3 1.7 314.4 IPSL\_LR85 19.1 19.5 10.0 1.7 390.8 IPSL\_MR85 21.2 14.1 7.2 2.2 362.9 MIROC5\_85 13.1 10.2 5.2 1.9 322.2 **Demand/crop Bean Corn Yucca Cocoa Coffee**

/**year) (Mm3**

/**year) (Mm3**

/**year) (Mm3**

Current 2.6 0.042 0.014 0.33 12.3 IPSL\_LR45 3.6 0.064 0.018 0.53 16.3 IPSL\_MR45 3.2 0.054 0.018 0.47 14.7 MIROC5\_45 3.0 0.042 0.018 0.38 12.2 IPSL\_LR85 3.5 0.063 0.018 0.51 16.1 IPSL\_MR85 3.1 0.055 0.017 0.47 14.8 MIROC5\_85 3.09 0.046 0.018 0.39 13.1

Current 1.2 1.1 0.7 36.7 183.5 IPSL\_LR45 1.6 1.3 0.98 50.7 245.5 IPSL\_MR45 1.7 1.4 0.96 45.6 234.4 MIROC5\_45 1.7 1.3 0.89 42.7 210.4 IPSL\_LR85 1.5 1.3 0.94 49.8 243.6 IPSL\_MR85 1.6 1.4 0.97 44 227.3 MIROC5\_85 1.7 1.3 0.9 43.4 211.4 **Demand/crop Mango Avocado Soursop Lemon Cotton**

/**year) (Mm<sup>3</sup>**

/**year) (Mm<sup>3</sup>**

/**year) (Mm<sup>3</sup>**

/**year) (Mm<sup>3</sup>**

/**year) (Mm<sup>3</sup>**

/**year) (Mm<sup>3</sup>**

/**year)**

/**year)**

/**year)**

**Figure 4.** *Monthly behavior of precipitation. (a) RCP 4.5 (b) RCP 8.5 [13].*

**Figure 5.** *Daily behavior of precipitation. (a) RCP 4.5 (b) RCP 8.5 [13].*

**Figure 6.** *Annual behavior of temperature. (a) RCP 4.5 (b) RCP 8.5 [13].*

*Effects of Climate Change on Water Resources, Indices, and Related Activities in Colombia DOI: http://dx.doi.org/10.5772/intechopen.90652*

#### **Figure 7.** *Flow duration curve for all the models and RCPs selected, year 2050 [13].*


#### **Table 9.**

*Irrigation requirement for the crops in the study area [13].*

In the case of demand, having a projection of the meteorological variables and

the crop areas, as well as the type of product grown, the water demand was established in terms of the irrigation requirement of said crops using the CropWat

tool, as shown in **Table 9**.

*Resources of Water*

**Figure 5.**

**Figure 6.**

**84**

**Figure 4.**

*Daily behavior of precipitation. (a) RCP 4.5 (b) RCP 8.5 [13].*

*Annual behavior of temperature. (a) RCP 4.5 (b) RCP 8.5 [13].*

*Monthly behavior of precipitation. (a) RCP 4.5 (b) RCP 8.5 [13].*
