Activitats: casos pràctics

To reach objective 6, the suitability of employing an artificial neural network to estimate regional recharge was assessed in a small region (13000 hectares) of Southwest Colombia (Patia Valley). Similarly to Canterbury, the land cover in Patia Valley is mainly composed of crops and irrigated grasslands (Vergara Varela, 2015), and groundwater is the primary source for irrigation (CRC, 2017) . Groundwater allocation is based on land surface recharge calculated employing a water balance i.e. groundwater recharge volumes are calculated as the inputs from rainfall minus the outputs from evapotranspiration and runoff. Given that Colombia is a developing country, the availability of data is a big constraint when assessing new approaches. The methodology involved the estimation of LSR based on data that is available region-wide i.e. Rainfall, ET and River Flow data. Although lysimeter-measured infiltration data is available in the region, access to those data was not granted this study. Therefore the procedure consisted of training a NN to predict LSR based on monthly LSR estimated previously by the regional council through water balance calculations (CRC, 2017).

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4.2.1 Site Description

The Patia Valley is located at 1° 15’ N, 77° W and 2° 15’ N, 77° 20’ W between the Western and Central Andes of Colombia (Figure 9), with a mean annual rainfall of 1,600 mm (Vergara Varela, 2015). The Patia Valley is approximately 120 km long and 20 km wide. The Patia River, flows south along the eastern base of the Western Andes, and discharges into the Pacific Ocean (Ramirez et al., 2018). Low lying open plains form the centre of the upper Patia Valley. Those plains are about 4 to 5 km wide and located at 550 to 600 m above sea level. The higher parts of the plains are located at 1,000 – 1,200 m above sea level and are formed by layers of tuff and gravel covering the steeply dipping Tertiary rock formations beneath. Land cover in the area has been changing over the last few years from a dry forest ecosystem to crops and irrigated grasslands for livestock development (Vergara Varela, 2015).

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4.2.2 Data Analysis

Rainfall, evapotranspiration and river flow data were available for analysis in this study and were provided by the regional council, which manages water allocation in the area. The regional council has estimated land surface recharge for the Patia region employing a water balance, whereby the amount of water that infiltrates through the soil column equals the rainfall minus evapotranspiration losses and runoff (CRC, 2017). As opposed to the methodology for the Canterbury region, river flow was considered here as an input for the NN given its inclusion on previous LSR estimations reported by the regional council.

Because of the strong variability of rainfall in time, the regional council plans water allocation based on what an average scenario of recharge would be. This average scenario of recharge is calculated based on what an average scenario of rainfall, evapotranspiration and runoff would also be. By average, the council refers to rainfall depths which present a 50% probability of exceedance. The latter refers to the probability of occurrence of e.g. a rainfall event greater than some given value P drawn from a Weibull distribution (Weibull, 1939) which ranges from 0 to 100%. In general, as the rainfall amount increases, its probability of exceedance (occurrence) decreases. These rainfall depths can only be obtained by a thoroughly analysis of long time series of historic rainfall data. Through a long term time series analysis, the council defines an average scenario employing rainfall, ET and runoff depth with a 50% probability of exceedance.

An analysis of the long-term records of climate data was performed. 30 years of monthly data were recorded by the rainfall, ET and river flow stations located in the area (Figure 10). Surrounding the valley, there are 4 ET stations i.e. open pan evaporation sites, 10 rainfall stations and two stations to measure flow in the Patia River, one located in the upper reaches of the catchment and one in the lowest part of the catchment. After defining the average scenarios (i.e. the events that presented a 50% probability of occurrence), a spatial mapping of rainfall and ET was performed. Kriging interpolation was used as the method to map the spatial behaviour of the variables that were used later for training the NN. Álvarez-Villa et al. (2011) have shown that kriging interpolation is able to capture the main physical characteristics of rainfall in Colombia.

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Figure 10. Rainfall and ET stations in the area

4.2.3 Model development

Given that lysimeter data is not publicly available, the procedure was to train a NN in order to check whether this model can match the previous LSR estimates calculated employing a water balance. Three different approaches were utilised:

 Approach I

A simple feed forward neural network was trained on a monthly basis with 60% of the data available and tested with the remaining 40% using three inputs (i.e. the three parameters measured in the region).

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 Approach II

River flow was removed from model development since it is a parameter that is not available region-wide. Also, the ratio of training and testing data was adjusted i.e. 70% of the data was used for training and 30% for testing, and only the rainfall and ETc were used as inputs for the NN.

 Approach III

To check improvements in model performance, a simple NN was trained with Dropout (i.e. an approximation of a Bayesian NN) on a monthly basis. The inputs were the same as in approach II and the ratio of training and testing data was 70/30.

4.2.4 Model Uncertainty

The process for estimating model uncertainty was the same as described in 4.1.4.