ALBERT

Description: Long-term monitoring of water resources and climate parameters at the scale of river basins requires networks of continuously operated in-situ stations. Since 2009, GFZ and CAIAG, in cooperation with the National Hydrometeorological Services (NHMS), are establishing such a regional monitoring network in Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Afghanistan) which is collecting observations of meteorological and hydrological parameters and delivering them to the end-users. The network design focuses mainly on the higher elevations where the recent decline of monitoring stations and networks established in Soviet times was strongest, and the resulting observational gap hinders research on climate and hydrological change as well as operational tasks in water management such as the seasonal runoff forecast. The newly developed and installed Remotely Operated Multi-Parameter Stations (ROMPS) do not only monitor standard meteorological and hydrological parameters, but also deliver GPS data for atmospheric sounding as well as tectonic studies. The observational data from the ROMPS is transmitted at least once a day to a centralized geo-database infrastructure for long-term storage and data redistribution. Users can access the data manually using a web-interface or automatically using SOS requests; in addition, data is distributed to the NHMS through standard communication and data exchange channels.

Description: Long-term monitoring of water resources and climate parameters at the scale of river basins requires networks of continuously operated in-situ stations. Since 2009, GFZ and CAIAG, in cooperation with the National Hydrometeorological Services (NHMS) of Central Asia, are establishing such a regional monitoring network in Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and lately Afghanistan to collect observations of meteorological and hydrological parameters and to deliver them to the end-users for operational tasks and scientific studies. The newly developed and installed remotely operated multi-parameter stations (ROMPS) do not only monitor standard meteorological and hydrological parameters, but also deliver Global Navigation Satellite System (GNSS) data for atmospheric sounding as well as tectonic studies. Additionally, three stations integrate seismic sensors for earthquake monitoring. The observational data from the ROMPS is transmitted nominally in near-real time, but at least once a day to a centralized geo-database infrastructure for long-term storage and data redistribution. Users can access the data manually using a web-interface or automatically using SOS requests; in addition, data is planed to be distributed to the NHMS through standard communication and data exchange channels.

Description: The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien-Shan and Pamirs. During the summer months the snow and glacier melt dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for a sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydromet services, this study aims at the development of a generic tool for deriving statistical forecast models of seasonal river discharge. The generic model is kept as simple as possible in order to be driven by available hydrological and meteorological data, and be applicable for all catchments with their often limited data availability in the region. As snowmelt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature as recorded by climatological stations in the catchments. These data sets are accompanied by snow cover predictors derived from the operational ModSnow tool, which provides cloud free snow cover data for the selected catchments based on MODIS satellite images. In addition to the meteorological data antecedent streamflow is used as a predictor variable. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to 3 or 4 predictors. A user selectable number of best models according to pre-defined performance criteria is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross validation. Based on the cross validation the predictive uncertainty was quantified for every prediction model. According to the official procedures of the hydromet services forecasts of the mean seasonal discharge of the period April to September are derived every month starting from January until June. The application of the model for several catchments in Central Asia - ranging from small to the largest rivers – for the period 2000-2015 provided skillful forecasts for most catchments already in January. The skill of the prediction increased every month, with R2 values often in the range 0.8 – 0.9 in April just before the prediction period. The forecasts further improve in the following months, most likely due to the integration of spring precipitation, which is not included in the predictors before May, or spring discharge, which contains indicative information for the overall seasonal discharge. In summary, the proposed generic automatic forecast model development tool provides robust predictions for seasonal water availability in Central Asia, which will be tested against the official forecasts in the upcoming years, with the vision of eventual operational implementation.

Description: The Regional Research Network „Water in Central Asia“ (CAWa) funded by the German Federal Foreign Office consists of 19 remotely operated multi-parameter stations (ROMPS) in Central Asia. These stations were installed by the German Research Centre for Geosciences (GFZ) in Potsdam, Germany in close cooperation with the Central-Asian Institute for Applied Geosciences (CAIAG) in Bishkek, Kyrgyzstan, the national hydrometeorological services in Uzbekistan and Tajikistan, the Ulugh Beg Astronomical Institute in Tashkent, Uzbekistan, and the Kabul Polytechnic University, Afghanistan. The primary objective of these stations is to support the establishment of a reliable data basis of meteorological and hydrological data especially in remote areas with extreme climate conditions in Central Asia for applications in climate and water monitoring. Up to now, ten years of data are provided for an area of scarce station distribution and with limited open access data which can be used for a wide range of scientific or engineering applications. This dataset provides different types of raw hydrometeorological data such as air temperature, relative humidity, air pressure, wind speed and direction, precipitation, solar radiation, soil moisture and soil temperature as well as snow parameters and river discharge information for selected sites. The data has not undergone any quality control mechanism and should, therefore, be seen as raw data. A visual inspection of the data set has been made and some errors and quality degradation are listed in Zech et al. (2020) but does not claim to be complete. A quality control is strongly recommended by the authors before using the data. Each station data has its own storage directory at the data dissemination server named with the abbreviation (4-letter code) of the station. The data is sampled with a 5-minute interval and stored in hourly files separated by the type of data. These files are then archived as monthly files named with the station abbreviation, type of data, year and month. After one year, these monthly files are further archived to a yearly file. A detailed description for the stations is provided by the Station Exposure Descriptions. Further information about the dataset can be found in Zech et al. (2020). All data is compiled as ASCII data in two different formats which are explained in the documents GITW-SSP-FMT-GFZ-003.pdf (for the stations ALAI, ALA6, and SARY) and CAWA-SSP-FMT-GFZ-006.pdf (for all other stations). Monthly, the data will be dynamically extended as long as data can be acquired from the stations. Additionally, the near real-time data can be displayed and downloaded without any registration from the Sensor Data Storage System (SDSS) hosted at the Central-Asian Institute for Applied Geosciences (CAIAG) in Bishkek, Kyrgyzstan.

Description: Внедрение интегрированного управления водными и земельны- ми ресурсами (ИУВЗР) в Централь- ной Азии сегодня сталкивается с существенными проблемами. Са- мой основной задачей является создание и развитие потенциала на индивидуальном и организацион- ном уровнях. Аналитическая записка описывает подходы по наращиванию потен- циала, принятые Германской во- дной инициативой для Централь- ной Азии (“Берлинский процесс”), в частности: (1) краткосрочные про- фессиональные курсы, предлагае- мые исследовательским проектом ЦАВа; (2) региональная магистер- ская программа “Интегрированное управление водными ресурсами” реализуемая в Казахстанско-Не- мецком Университете в Алматы; (3) обучающий модуль по бассей- новому планированию, разрабо- танный в рамках программы GIZ “Трансграничное Управление во- дными ресурсами в Центральной Азии”. Эти подходы в основном на- правлены на развитие потенциала на индивидуальном уровне, но про- грамма GIZ по созданию бассейно- вых речных комиссий направленa также на развитие потенциала на институциональном уровне. Ключевыми факторами успеха стали региональные и транс-секторальные подходы, принятые всеми тремя программами, объединение науки и практики, и соответствие содержа- ния тренинга практическим потреб- ностям участников.