Stratified models

Due to the clear differences in the key drivers of flow between summer and winter, models were stratified to forecast inflows during summer only (April-September),

accepting the long-term monthly mean as winter inflows. This aimed to both reduce the problems of forecasting winter flows, as well as improve forecast skill during the

important summer months. Due to upstream regulation rather than natural processes dominating the inflow regime to Kayrakkum, no further model development was undertaken for this reservoir.

For both Toktogul and Andijan reservoirs stratified models outperform full year models at lead times one, two and four months for all metrics except MARE (Table 5.5). As MARE is sensitive to errors occurring at low discharges, it is not of significant

importance for forecasting summer peak inflows. Conversely, the other four metrics are sensitive to errors occurring at higher magnitude discharges (see section 4.2.3),

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arguably more important for CA as a large proportion of flow occurs during the summer peak. At a lead time of three months, summer only models for Toktogul and Andijan include NCEP precipitation lagged by three months with no averaging period. Full year models on the other hand include the same NCEP precipitation but averaged over four (Toktogul) and two (Andijan) months. It is known that winter precipitation is a skilful predictor of summer inflows in the Syr Darya (Schӓr et al., 2004; Table 5.2). This extra information regarding winter precipitation (and by inference snowpack accumulation) is, therefore, the likely reason that full year models outperform summer only models at lead time three months. Summer only models at lead times one and two months for both Toktogul and Andijan utilise a relatively long averaging period for their precipitation input (between 3-6 months), providing skill from the previous winter’s accumulated

precipitation. The ZOF for both Toktogul and Andijan is weaker using only summer months, as would be expected and summer only models outperform the ZOF at all lead times for all metrics except MARE.

The large residuals in forecasted inflows to Andijan during the summers of 2007 and 2010 are reduced by around half when using stratified compared with full year models (Figure 5.7). However, negative inflows are still forecast by stratified models at lead times one, two and four months for September. As inflows to Andijan are snow- rather than ice-melt dominated (Savoskul and Smakhtin, 2013), summer inflows are moisture limited rather than energy (temperature) limited. It is likely that with higher mean air temperatures, snow melt occurs earlier in the year (Sorg et al., 2012; Siegfried et al., 2012). In such circumstances, stratified models could be built for a shorter or earlier period of the year, possibly leading to improvements in model skill for the important summer peak. At lead times one and two months, stratified models generally forecast the summer inflows to Toktogul more accurately (smaller residuals) than full year models (Figure 5.8). However, as shown by the skill metrics, full year models

outperform stratified models for a lead time of three months. Stratifying models did not improve forecasts of Nurek inflows, with additional predictors (on top of the monthly dummy variable) only included at lead time three months. The ZOF continued to be a strong benchmark when forecasting only summer inflows with a NSE of 0.81. It is

possible that either the poor accuracy of precipitation products over the region (Schӓr et al., 2004) (Table 3.4) and/or the greater relative importance of glacier melt (Savoskul

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and Smakhtin, 2013) reduce precipitation products skill for forecasting summer inflows at this site.

The significant positive correlations between mean winter precipitation (TRMM and NCEP derived) and mean summer runoff found for both Toktogul and Andijan basins are of comparable strength to similar forecasting attempts in the region (Schӓr et al., 2004; Archer and Fowler, 2008; Barlow and Tippett, 2008). Previous studies have generally forecasted mean summer discharge, meaning comparisons are less easily drawn with the monthly time step used in this study. However, the inability of both full year and stratified models to improve on the ZOF for Nurek inflows aligns closely with the findings of Schӓr et al. (2004) and Barlow and Tippett (2008) for the Vakhsh and nearby Murgab Rivers respectively. Comparable R2 values are found using stratified models compared with the findings of Schӓr et al. (2004) (R2 = 0.85) at a lead of one month for Toktogul (R2adj = 0.80) and Andijan (R2adj = 0.77) reservoirs. However,

stratified model skill cannot compete with the mean summer flow forecast skill of Apel et al (2017) at any lead time or Schӓr et al. (2004) at a lead time longer than one month. Archer and Fowler (2008) found better model performance for April-June than April- September mean discharge for the River Jhelum, Pakistan. A similar shortening the target period may be beneficial in CA, focusing the forecast on the months during which snowmelt dominates the flow regime.

6.2 Climate mode based seasonal forecasting 6.2.1 Correlation analysis

Correlation analysis between NAO, Niño 3.4 and DMI with CA precipitation and

temperature was undertaken. Previous studies have shown weak concurrent negative correlations between NAO and precipitation over parts of CA (Aizen et al., 2001, Syed et al., 2006). Results presented here generally agree with earlier research, showing insignificant negative correlations between NAO and precipitation over the same areas. Positive correlations were found between Niño 3.4 and CA precipitation confirming previous findings. The association is strongest during the winter half-year, consistent with Dai and Wigley (2001) who report positive annual precipitation anomalies of up to 50 mm over CA during El Niño phases. Syed et al. (2006) and Mariotti (2007) showed the strongest correlations occur during autumn and spring. October, November,

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December and March have areas of significant positive correlations across parts of central and western Tajikistan (Figure 5.9). However, the statistically significant positive correlations found by Mariotti (2007) for south-east Kyrgyzstan did not emerge from this analysis. Only an insignificant positive correlation was detected over north-east

Kyrgyzstan during winter months.

Significant negative correlations found here between NAO and January and February temperature across Tajikistan are stronger than those described by Syed et al. (2006) and Hurrell (1997). This could reflect methodological differences, as Syed et al. (2006) used a composite of temperature, rather than correlation analysis. Furthermore, the four-month period used by both studies could be masking the significant values found here during January and February only. El Niño typically results in positive winter temperature anomalies in CA (Hurrell, 1997; Syed et al. 2010), a finding corroborated by the significant positive correlations found during January-February across

Kyrgyzstan (Figure 5.10). No previous studies have reported the strong, significant negative correlation during May across northern Kyrgyzstan and Kazakhstan. In

mountainous northern Kyrgyzstan, May is often the wettest month. Hence, temperature anomalies during this period are important in determining whether precipitation falls in liquid or frozen form at higher elevations.

The non-parametric Spearman’s Rank correlation was used to test associations between mean summer (Apr-Sep) inflows and preceding winter NAO, Niño 3.4 or DMI over the periods 1941-1980 and 2001-2010. Significant (p < 0.05) negative correlations between January-February NAO and following summer inflows to all three reservoirs during 1941-1980 is consistent with the modest negative correlations between NAO and precipitation during January-March across some parts of CA (Table 5.6). The result suggests that there could be a negative association between NAO and winter rainfall/ snow accumulation, leading to an indirect link between winter NAO and summer inflows.

Highly significant (p < 0.01) positive correlations between Nurek summer inflows and previous winter Niño 3.4 during 1941-1980 align closely with the positive correlations between winter precipitation and Niño 3.4 across Tajikistan (Table 5.6 and Figure 5.9). This suggests that Niño 3.4 has power to discriminate winter precipitation

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correlations between winter Niño 3.4 and following summer inflows to Andijan and Toktogul again fits with the positive, though insignificant, correlations with precipitation over north eastern Kyrgyzstan during several winter months (Figure 5.9). Positive, but insignificant, correlations persist during the years 2001-2010 between winter Niño 3.4 and following summer inflows to all three reservoirs.

Overall, significant correlations between selected winter months Niño 3.4 and NAO to following summer reservoir inflows suggest potential predictability. In particular, forecasting Nurek summer inflows from winter Niño 3.4 appears most feasible (Table 5.6). Moreover, by using Niño 3.4 in November-December, it would be possible to issue an early outlook of summer inflows – well before the end of the winter snowpack

accumulation period (October-March). Even so, these results must still be interpreted with care because of the large number of statistical tests performed and likelihood of Type I errors (i.e. false correlations). Given an error rate p = 0.05, approximately 4 significant results could have occurred by chance alone when correlating Niño 3.4 to following summer inflows. However, with 44 significant associations between Niño 3.4 and summer inflows actually detected the link is robust.

6.2.2 Composite forecasting

Composite analysis of data in the years 1941-1980 supports the findings of the

correlation tests, with Niño 3.4 emerging as the most promising variable for predicting inflows, especially for Nurek (Figure 5.11). Differences of up to ±11% occur in summer inflows to Andijan following contrasting NAO phases, but differences were not

statistically significant for any reservoir (Table 5.7 and Table 5.8). Inflows after positive NAO show greater differences with neutral conditions compared with negative NAO. The below average summer flows after positive NAO phases found here is consistent with Aizen et al. (2001) and the prior correlation analysis. Negative correlation between NAO and winter precipitation, as reported by Aizen et al. (2001) and Syed et al. (2006), would translate into less snowpack thence lower summer reservoir inflows as shown in Table 5.6.

Typically, El Niño conditions during winter are followed by higher summer inflows to all three reservoirs. The summer inflow anomaly for Nurek reservoir after a winter El Niño

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can be as much as +37%. Figure 5.11 as well as Table 5.7 and Table 5.8 suggest a southwest to northeast gradient in the influence of Niño 3.4 index phase on discharge. Previous research suggests that the link between Pacific SSTs and CA rainfall

anomalies is not direct, but via intervening atmospheric circulations over the Arabian Sea and Middle East (Mariotti, 2007; Barlow and Tippett, 2008). The gradient in

predictability seen here could reflect depth of moisture penetration from the southwest. The Pamirs hinder moisture from reaching the Tien Shan, thereby reducing the

connection between El Niño and precipitation in northwest CA (see section 6.2.5).

The influence of concurrent air temperature on summer inflows varies by reservoir. It might be expected that higher temperatures would be associated with greater meltwater and therefore, increased summer inflows, as seen for Nurek (Figure 5.12). However, the opposite occurs for Toktogul and Andijan reservoirs following neutral of El Niño

conditions, with above average temperatures associated with below average inflows. Similar results were found by Archer and Fowler (2008) for the Indus basin, attributed to higher evaporative losses (Singh and Bengtsson, 2005). This is a plausible cause for both Toktogul and Andijan, as Nurek has a greater proportion of glacier-derived flow into Nurek compared with Toktogul and Andijan (Savoskul and Smakhtin, 2013). If all winter snow accumulation melts each year regardless of the temperature anomaly, summer runoff will be moisture rather than energy (temperature) limited. Research suggests that winter precipitation totals are a useful predictor of summer flow,

particularly for the Syr Darya, giving weight to this hypothesis (Schӓr et al., 2004; Dixon and Wilby, 2016).