5.4. Results and discussion
5.4.3. Conjunctive management of surface-groundwater resources using the PSO-Hybrid
5.4.3.1. Computation of the irrigation water deficit (IWR)
The results of the previous section indicate that the PSO- computed optimal amount of surface water conveyed (CW) by the Gavoshan dam into the Miandarband plain cannot satisfy the amount of irrigation water recommended originally (in year 1993) by Iran’s Ministry of Power. The water difference to satisfy the crop needs in the plain can only be supplied from groundwater resources which, by nature of the very dry climate in that part of the world with low precipitation and high evapotranspiration (see Table 5.2), ergo low aquifer recharge, may themselves not be sustainable neither in the long run. To overcome this problem, an appropriate surface- groundwater conjunctive management strategy for the Miandarband plain is inevitable. This task is carried out by firstly determining the irrigation water requirement (IWR) of all crops supposed to be cultivated in the plain based on the original agricultural development plan of 1993, which is listed in Table 5.4.
Table 5.4. Cultivation pattern and percentage of cultivated area (~200 km2) in the Miandarband plain.
Crop Area (km2) Crop Area (km2) Crop Area (km2)
Wheat 53.8 Clover 11.6 Apple 4
Alfalfa 27 Dry beans 11.5 Water melon 3.8
Barely 26.8 Chick beans 11.5 Tomato 3.8
Sweet corn 13.4 Sugar beet 9.6 Soybean 3.8
Field corn 11.6 Grape 4 Sun flower 3.8
It must be emphasized that this IWR should not be confounded with the originally recommended volume of irrigation water (RIW) for the Miandarband plain of 176.2 MCM/a, and as the former had not yet been calculated at that time, it has been done by (Zare and Koch, 2016c), wherefore IWR is defined as the difference between the total crop evapotranspiration ETc during the growth season and the input precipitation P, multiplied by an irrigation network
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efficiency factor e. ETc was computed using the FAO-56 Penman-Monteith method with empirical crop coefficients Kc (Allen et al., 1998). For further details see (Zare and Koch, 2016c) and we provide here only the monthly results for the IWR in Table 5.4 Also listed in the table are the monthly RIW, taken from Table 5.1, the volumes of optimal conveyed water from the Gavoshan dam (CW) using PSO, and the ensuing water deficit, which is now the difference between IWR and CW.
Table 5.5. Monthly RIW calculated by Iran’s Ministry of Power (1993); IWR obtained with the FAO-56 method and using empirical crop coefficients (Zare and Koch, 2016a), volumes of optimal conveyed water from the Gavoshan dam (CW) using PSO, and the ensuing water deficit. All values are in MCM/a.
Parameter Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Sum
RIW 10.2 1.4 0 0.6 0 0.5 7 28 48 41.5 23.8 15.2 176.2
IWR 7.6 1.4 0 0.1 1.2 3.9 13 29.3 66.7 65.8 55.1 21.7 265.8
Optimal CW 3.8 0 0 0 0 0 0 15.2 33.1 32.3 18.7 9.6 112.7
Water deficit
(IWR-CW) 3.8 1.4 0 0.1 1.2 3.9 13 14.1 33.6 33.5 36.4 12.1 153.1
Table 5.5 indicates that whereas the original planned RIW is 176.2 MCM/a, the agricultural FAO-56- calculated irrigation demand (IWR) is with 265.8 MCM/a much higher, which shows insufficient water planning at that time (1993). Not only that, since the optimal amount of conveyed water (CW) from the Gavoshan Dam to the agricultural irrigation network in the Miandarband plain is only 112.7 MCM/a, there is total water deficit of 153.1 MCM/a with respect to the biologically necessary crop IWR and of still 63.5MCM/a with respect to the RIW. The problem of poor RIW- planning is even more tangible during the crops’ high demand months June, July and August.
All these results mean that another source of water, i.e. groundwater must be tapped to satisfy the IWR. Doing so would also help to resolve the problem of waterlogging occurring frequently in the plain, as has been found out in a previous study of the authors on the effects of the construction of an irrigation/drainage network on water-logging in the Miandarband plain (Zare and Koch, 2014). In addition, the groundwater withdrawal should be somehow limited to avoid ecologically unacceptable drops of the groundwater levels.
Although originally an RIW of 176.2 MCM/a was planned to be allocated to agricultural irrigation in the Miandarband plain (see Table 5.5), it was found out that only in the first year of the irrigation network’s operation (2007) a volume of 168.2 MCM/a, i.e. close to the target, were conveyed, whereas in the following 6 years the CW- volumes ranged only between 43 MCM/a (2014) and 130 MCM/a (2012) (see Table 5.6). Thus there has been a significant shortage of surface water for satisfying the RIW (row RIW-CW), let alone the more realistic IWR (row IWR-CW), in the last few years, all of which hints of the necessity of more efficient water resources management.
Table 5.6. Observed CW for years 2007-2014 and differences RIW-CW (based on RIW = 176.2 MCM/a) and effective water deficit IWR-CW (based on IWR of 265.8 MCM/a)
Water Year 2007-8 2008-9 2009-10 2010-11 2011-12 2012-13 2013-14
CW (MCM/a) 168.2 73 71.4 102.9 129.9 100.7 43
RIW-CW (MCM/a) 8 103.2 104.8 73.3 46.3 75.5 133.2
IWR-CW (MCM/a) 97.6 192.8 194.4 162.9 135.9 165.1 222.8
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5.4.3.2. Groundwater pumping induced head fluctuations estimated by the hybrid Wavelet-ANFIS/FCM model
The effective agricultural water difference IWR-CW in Table 5.6 must be made up by groundwater withdrawal (GW). However, the latter should be done in a way that the plain will neither face groundwater levels (GL) too high, leading to waterlogging, nor drops too low.
Using the algorithm of the hybrid wavelet-ANFIS/FCM-model as sketched in Figure 5.12 GL- fluctuations during the time period 2007-2013 are simulated first. The results are shown in Figure 5.18 for the training and testing phases of the data period and they indicate that the simulated GLs match the observed ones rather well.
Figure 5.18. Wavelet-ANFIS/FCM – simulated and observed GL-changes for the training (upper panel) and testing (lower panel) phases.
More details of the statistical results of the hybrid Wavelet-ANFIS/FCM model are illustrated by the linear regression plots of the simulated over the observed GL-data in Figure 5.19, with the regression equations and coefficients of determination R2 as indicated, wherefore the latter is slightly higher for the training- than for the test- phase.
All data-driven prediction methods are based on the idea that the random errors are drawn from a normal distribution and the hybrid Wavelet-ANFIS model is not an exemption. Figure 5.20 illustrates that the a posteriori computed errors of the GLs computed with this model follow indeed such a normal distribution. In addition, the absolute maximum errors for one month are only 0.53m and 0.59m in the training and test phases, respectively.
All of the above results clearly indicate that the selected hybrid wavelet-ANFIS model delivers good and reliable predictions of the “GL fluctuation” of the Miandarband plain.
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Figure 5.19. Regression plots of Wavelet-ANFIS (Sym4 mother wavelet) - simulated over observed GL-data for training (left panel) and testing (right) phases
Figure 5.20. Distribution of the changes in GL- random errors of the wavelet-ANFIS/FCM model for the training- (top row) and testing phase (bottom row)
With the statistical assumptions of the hybrid wavelet-ANFIS model as indicated by Figure 5.20 satisfied, the latter can be applied to the ANN- Eq. (5-12) to predict the average monthly changes in GL (output) using the optimal average monthly GW- and CW- volumes computed earlier as input and which are shown again in Figure 5.21.
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Figure 5.21. Optimal monthly conveyed water (CW) from the Gavoshan Dam to the Miandraband plain and optimal monthly groundwater withdrawal (GW) used as input in the hybrid wavelet-ANFIS
model to predict the GL.
The results of these GL- predictions are shown in Figure 5.22, together with the 95%- confidence interval computed using the RMSE obtained earlier in the testing phase of the hybrid wavelet-ANFIS model (see Figure 5.20). Also drawn in the figure are the critical maximum (water-logging) (about 1m below the land surface) and the minimum GL-threshold levels (see also Figure 5.4). Figure 5.22 clearly indicates that the computed optimized CW- and GW- volumes are such that the groundwater levels stay well within the allowed range, i.e.
neither high water-logging- prone- nor unacceptably low groundwater tables will occur.
Figure 5.22. Simulated monthly GL using optimal monthly inputs for CW and GW with 95% -confidence interval. Also shown are the critical maximum (waterlogging) (about 1m below the land
surface) and the minimum GL-threshold levels (not to scale).