Previous research into discharge simulation for rivers in CA has been undertaken for several purposes including climate change assessment, improved process
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studies have assessed future water resources under various climate change scenarios and adaptation strategies (e.g. Siegfried et al., 2011 and Ismaiylov et al., 2007). Due to the differing aims of these studies, scant information is provided regarding hydrological model performance and inputs used are not available in real time.
Information about operational seasonal forecasting practices in CA is seldom published. However, it is known that some national hydro-meteorological agencies in the region produce forecasts of April-September discharge each month from January until June (Apel et al., 2017). Empirical methods are used to relate precipitation, temperature and snow water equivalent to seasonal discharge, which is sometimes only available in analogue form as look-up tables or graphs. For example, the Automated Information System of Hydrological Forecasts (AISHF) model is used in Uzbekistan (Agaltseva et al., 1997). AISHF is an empirical model using observed meteorological data as well as modelled snowpack and glacier melt to provide operational forecasts using specialist software allowing interactive analysis. The forecast was reported to meet the
information needs of the users, however, it is unclear at what lead time satisfactory forecasts were produced as this information was not provided (Agaltseva et al., 1997).
Wilby et al. (2011a) used several models to estimate annual inflow to the Kayrakkum and Nurek reservoirs on the Syr and Amu Darya respectively. They used a Water Balance Model (WBM), the Snowmelt Runoff Model (SRM) and a nonlinear multiple regression model. Model inputs included observed meteorological station precipitation and temperature, as well as remotely sensed snow covered areas. Model performance was found to be better for Kayrakkum than Nurek, likely due to upstream reservoirs smoothing climate related variability (Wilby et al., 2011a). Temperature was
successfully used as a proxy for snow and glacier melt in both basins (Wilby et al., 2011b). Following years with higher than average temperature, inflows to Nurek were generally lower. This was attributed to higher evaporative losses and less carry-over of snow and ice between years (Wilby et al., 2011b). This relationship has been reported elsewhere, and has the potential to contain useful information for seasonal forecasting (Schär et al., 2004; Archer and Fowler, 2008). Pereira-Cardenal et al. (2011) utilised TRMM precipitation estimates along with radar altimetry of Toktogul reservoir water levels as a proxy for discharge to attempt to improve real time forecasting. Comparable results were found between modelled inflows and satellite altimetry. However, no
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observed data were used to evaluate the model. Recently, synthetic runoff estimates have been derived from gridded discharge by routing land surface/meteorological model outputs (Zaitchik et al., 2010; Khouakhi, 2017). Although in early stages of
development, such techniques have potential for discharge estimation in areas lacking hydro-meteorological monitoring.
Some studies have focused on seasonal forecasting of river flows in Central Asia. For instance, Baumgartner et al. (2000) used meteorological forecasts as inputs to SRM. Promising results were obtained for forecasting summer flows (r2 = 0.74-0.97), but little information is provided regarding model inputs or lead times used to obtain these
results. Satellite snow cover data were reportedly utilised by Yakovlev (2005) to forecast flows of the Panj River, Tajikistan. Although availability of cloud free imagery is critical for accurate flow forecasts, little information is provided regarding model performance (referenced within Gafurov, 2010).
Schär et al. (2004) used December-April re-analysis precipitation to forecast May-
September withdrawal adjusted flows in both the Syr and Amu Darya rivers. Re-analysis precipitation performed well compared with rain gauge values, due to the more
complete spatial coverage by the re-analysis compared with the sparse network of land- based stations. Forecasts performed better for the Syr Darya (r = 0.92) compared to the Amu Darya. Poor performance may have been caused by lower quality re-analysis precipitation over the Amu Darya basin and/or lower quality withdrawal corrected runoff figures. Successful forecasts of summer inflows to the Mangla Dam, Pakistan, from previous winter observed precipitation and temperature were obtained using a parsimonious liner regression model (Archer and Fowler, 2008). Although outside of CA, a similar hydrological regime (dominated by summer snow and glacier melt) is observed. Winter precipitation was found to be a useful predictor of following spring and summer inflows, giving promise to similar methodologies in CA.
Dixon and Wilby (2016) developed a parsimonious multiple linear regression model to forecast inflows to Toktogul Reservoir on the Syr Darya. Inputs included TRMM as well as observed precipitation and temperature and antecedent flows. Hindcast skill was superior to the mean monthly flow for lead times up to three months. Over 80% of the variance in monthly inflows is explained with three-month lead, and up to 65% for
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summer half-year average. The findings of Schär et al. (2004) and Dixon and Wilby (2016) give promise for seasonal forecasting but neither study used solely operational precipitation data, meaning that forecasts could not be made in real-time. This is of critical importance if forecasts are to be used to support reservoir operation.
Apel et al. (2017) used operationally available data (previous winter observed precipitation, temperature and discharge as well as remotely sensed snow cover) to forecast April-September discharge in 13 basins in CA for the period 2000-2015 (note that data used is not publicly available). Using automatic predictor selection and multiple linear regression R2 values of >0.7 were achieved at a lead time of two months
(February), rising to 0.86-0.96 for zero lead forecasts (issued April 1st). However, results were based on a very limited number of observations (just 16) with no adjustment for sample size. Furthermore, the statistical significance of results was not provided. The authors attributed their high skill scores to the separation of precipitation (winter maximum) and runoff (spring/summer maximum) regimes.
Further potentially operational seasonal forecasting systems have been developed for the region. For example, Tippett et al. (2005) forecast December-March precipitation from October Pacific SSTs. Significant lagged correlations between SST and
precipitation were found across Tajikistan (r = 0.40) and western Kyrgyzstan (r = 0.35) during the period 1999-2003. Gerlitz et al. (2016) used NCEP re-analysis alongside SST to forecast winter and spring precipitation in CA. Correlations of r ~ 0.5 were found between forecast and observed winter-spring precipitation across northern CA at lead time 1.5 months. Correlations were generally stronger for northern CA compared with southern CA. Wet and dry years were well represented by the model, but the variability of precipitation rates was under-estimated. When lead time increases to 4.5 months correlations weaken to r ~ 0.2 in northern CA and less in southern CA. Barlow and Tippett (2008) forecast summer (April-August) river flows from winter Pacific SSTs and winter NCEP re-analysis precipitation for the years 1950-1985. They report modest cross validated correlation (r ~ 0.4) skill between observed and forecast flows in the Vakhsh at Garm. Typically, stations in mountainous regions had the weakest cross validated correlations (r < 0.4). Flow volume was found to be unrelated to model skill. These studies confirm that summer inflows (refill period) to headwater reservoirs can be forecast from winter precipitation/snow accumulation, albeit with varying degrees of
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accuracy. Furthermore, relationships between climate modes and winter precipitation in CA gives hope for extended lead time forecasting of summer inflows.