C´ alculo de Par´ ametros

Dixon and Wilby (2016) investigated the potential for forecasting inflows to Toktogul reservoir. However, there are other large reservoirs in Central Asia for which seasonal forecasts could be beneficial. Therefore, the approach was extended to three other reservoirs in the region: Andijan, Kayrakkum and Nurek. As before, TRMM estimated precipitation and NCEP precipitation and temperature products were available as inputs into the stepwise regression procedure. Each reservoir differs in terms of the timing and volume of inflows, as well as the relative contributions of ice and snowmelt. These differences were used to deepen understanding of model performance.

The operational and research grade models had the same predictors for Andijan (Table 5.3). Unlike Toktogul, gauge adjusted TRMM was not included at any lead time for forecasting inflows to Andijan. This could be explained by the gauge adjustment procedure being better over the Toktogul basin. Inspection of the network of gauges used by TRMM27 shows that none are available within TRMM cells overlapping the Andijan basin, compared with 5 for the Toktogul basin during 2000-2010. Both models outperform the ZOF at all lead times for all metrics except MARE for Andijan (Table 5.4). This can be seen in the forecasted hydrographs, with modelled inflows being generally closer to observed values than the ZOF, clearly seen during peak flows in summer 2003 and 2008 (Figure 5.6). However, the low summer peak during summer 2007 was forecast poorly at all lead times. It is possible that the dummy variable for month is dominating the precipitation input, reducing the variance of the forecast

compared with observed inflows. This could also help to explain the under-prediction of summer 2010 inflows.

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An artefact of the regression model is that negative inflows are forecast into Andijan during winter at all lead times during the period 2001-2010. Similarly to Toktogul, it seems that the long term average actually performs better than the model for the low flow periods. The model seems to be tailored to perform better during the summer months, as the precipitation input uses a long averaging period to best capture winter snowpack accumulation (Table 5.3). A side-effect of this is that winter flows are

predicted from summer precipitation, a mechanism that does not make physical sense. This adds weight to the argument that the statistical model should not be used to

forecast year-round flows. These negative values have been kept in the plotted hydrographs for transparency, even though they are clearly not possible. It should be noted, however, that these negative flows would have to be addressed if the model was used for operational forecasting. Possibilities include the use of ZOF whenever a

negative inflow is forecast, replacing negative values with zero or fitting the model to forecast only winter flows.

Both operational and research grade models also outperform the ZOF when forecasting Kayrakkum inflows using all metrics at all lead times, except AIC after a one-month lead (Table 5.4). Of note is the large reduction in performance of both models when lead times extend beyond one month. Whilst Andijan outflows were still included in the operational model at lead time two months, it is likely that most release from upstream reservoirs has either already entered Kayrakkum, has been stored in subsequent reservoirs or has been used for irrigation by this point. The reliance on Toktogul and Andijan outflows for prediction skill at Kayrakkum is shown during the subdued inflows of 2008/9 and the variable inflows during 2010, which are not well depicted by either model after a lead time of one month (not shown). However, no model forecasts the large inflow during June 2003. From closer inspection of outflows from Toktogul and Andijan there appears to be no anomalous outflows. Therefore, it is surmised that the inflow is due to erroneous data or a large release from one of the reservoirs below Toktogul on the 'Naryn cascade' (e.g. Uch-Kurgansk), but no information on this is available. These results suggest that the management of water above Kayrakkum is the main driver of this reservoir’s inflow regime, rather than flows from unregulated sub- catchments. Therefore, accurate forecasts of Kayrakkum inflows requires detailed information on upstream water management (e.g. irrigation schedules and reservoir operation rules), rather than precipitation and temperature alone.

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The ZOF proves to be a strong benchmark for forecasting inflows to Nurek reservoir, with a Nash-Sutcliffe efficiency of 0.93 (Table 5.4). Only at lead time three months can models outperform the ZOF, even though additional predictors were added to the model at lead times one and two months. The additional skill at lead time three months is provided by the optimal NCEP precipitation cell averaged over four months, which is located in the eastern headwaters of the basin. This lead time, averaging period and location combination likely allows winter snowpack accumulation to be used to forecast summer inflows. Overall, the ZOF performs to such a high standard that inclusion of other predictors in the models results in only minor variations in modelled inflow. The weaker performance of models for Nurek (in southern CA/Pamirs) compared with

Toktogul and Andijan (northern CA/Tien Shan) is consistent with the findings of Schӓr et al. (2004). The comparatively weak skill of Amu Darya seasonal flow forecasts

compared with those for the Syr Darya was explained by less accurate precipitation products and lower quality natural discharge estimates by Schӓr et al. (2004). It is plausible that less accurate precipitation products contribute to the reduced skill

presented here as both TRMM RT and NCEP monthly precipitation are not significantly correlated with observed precipitation at Sari-Tash (Table 3.4).