ALBERT

Description: Spatially distributed snow cover extent can be derived from remote sensing data with good accuracy. However, such data are available for recent decades only, after satellite missions with proper snow detection capabilities were launched. Yet, longer time series of snow cover area (SCA) are usually required e.g. for hydrological model calibration or water availability assessment in the past. We present a methodology to reconstruct historical snow coverage using recently available remote sensing data and long-term point observations of snow depth from existing meteorological stations. The methodology is mainly based on correlations between station records and spatial snow cover patterns. Additionally, topography and temporal persistence of snow patterns are taken into account. The methodology was applied to the Zerafshan River basin in Central Asia – a very data-sparse region. Reconstructed snow cover was cross-validated against independent remote sensing data and shows an accuracy of about 85%. The methodology can be used to overcome the data gap for earlier decades when the availability of remote sensing snow cover data was strongly limited.

Description: Spatially distributed snow-cover extent can be derived from remote sensing data with good accuracy. However, such data are available for recent decades only, after satellite missions with proper snow detection capabilities were launched. Yet, longer time series of snow-cover area are usually required, e.g., for hydrological model calibration or water availability assessment in the past. We present a methodology to reconstruct historical snow coverage using recently available remote sensing data and long-term point observations of snow depth from existing meteorological stations. The methodology is mainly based on correlations between station records and spatial snow-cover patterns. Additionally, topography and temporal persistence of snow patterns are taken into account. The methodology was applied to the Zerafshan River basin in Central Asia – a very data-sparse region. Reconstructed snow cover was cross validated against independent remote sensing data and shows an accuracy of about 85%. The methodology can be used in mountainous regions to overcome the data gap for earlier decades when the availability of remote sensing snow-cover data was strongly limited.

Description: The Moderate Resolution Imaging Spectroradiometer (MODIS) employed by Terra and Aqua satellites provides spatially snow covered data with 500 m and daily temporal resolution. It delivers public domain data in raster format. The main disadvantage of the MODIS sensor is that it is unable to record observations under cloud covered regions. This is why this study focuses on estimating the pixel cover for cloud covered areas where no information is available. Our step to this product involves employing methodology based on six successive steps that estimate the pixel cover using different temporal and spatial information. The study was carried out for the Kokcha River basin located in northeastern part of Afghanistan. Snow coverage in catchments, like Kokcha, is very important where the melt-water from snow dominates the river discharge in vegetation period for irrigation purposes. Since no snow related observations were available from the region, the performance of the proposed methodology was tested using the cloud generated MODIS snow cover data as possible "ground truth" information. The results show successful performances arising from the methods applied, which resulted in all cloud coverage being removed. A validation was carried out for all subsequent steps, to be outlined below, where each step removes progressively more cloud coverage. Steps 2 to 5 (step 1 was not validated) performed very well with an average accuracy of between 90–96%, when applied one after another for the selected valid days in this study. The sixth step was the least accurate at 78%, but it led to the removal of all remaining cloud cover.

Description: In data sparse mountainous regions it is difficult to derive areal precipitation estimates. In addition, their evaluation by cross validation can be misleading if the precipitation gauges are not in representative locations in the catchment. This study aims at the evaluation of precipitation estimates in data sparse mountainous catchments. In particular, it is first tested whether monthly precipitation fields from downscaled reanalysis data can be used for interpolating gauge observations. Secondly, precipitation estimates from this and other methods are evaluated by comparing simulated and observed discharge, which has the advantage that the data are evaluated at the catchment scale. This approach is extended here in order to differentiate between errors in the overall bias and the temporal dynamics, and by taking into account different sources of uncertainties. The study area includes six headwater catchments of the Karadarya Basin in Central Asia. Generally the precipitation estimate based on monthly precipitation fields from downscaled reanalysis data showed an acceptable performance, comparable to another interpolation method using monthly precipitation fields from multi-linear regression against topographical variables. Poor performance was observed in only one catchment, probably due to mountain ridges not resolved in the model orography of the regional climate model. Using two performance criteria for the evaluation by hydrological modelling allowed a more informed differentiation between the precipitation data and showed that the precipitation data sets mostly differed in their overall bias, while the performance with respect to the temporal dynamics was similar. Our precipitation estimates in these catchments are considerably higher than those from continental- or global-scale gridded data sets. The study demonstrates large uncertainties in areal precipitation estimates in these data sparse mountainous catchments. In such regions with only very few precipitation gauges but high spatial variability of precipitation, important information for evaluating precipitation estimates may be gained by hydrological modelling and a comparison to observed discharge.

Description: In data sparse regions, as in many mountainous catchments, it is a challenge to generate suitable precipitation input fields for hydrological modelling, as station data do not provide enough information to derive areal precipitation estimates. This study presents a method using the spatial variation of precipitation from downscaled reanalysis data for the interpolation of gauge observations. The second aim of this study is the evaluation of different precipitation estimates by hydrological modelling. Study area is the Karadarya catchment in Central Asia (11 700 km2). ERA-40 reanalysis data are downscaled with the regional climate model Weather Research and Forecasting Model (WRF). Precipitation data from gauge observations are interpolated (i) using monthly accumulated WRF precipitation data, (ii) using monthly fields from multiple linear regression against topographical variables and (iii) with the inverse distance approach. These precipitation data sets are also compared to (iv) the direct use of the precipitation output from the WRF downscaled ERA-40 data and (v) precipitation from the APHRODITE data set. Our study suggests that using monthly fields from downscaled reanalysis data can be a good approach for the interpolation of station data in data sparse mountainous regions. Compared to mean annual precipitation from continental and global scale gridded data sets our precipitation estimates for the study area are considerably higher. The introduction of a calibrated precipitation bias factor for the comparison of different precipitation estimates by hydrological modelling allows for a more informed differentiation with regard to the temporal dynamics, on the one hand, and the overall bias, on the other hand. Uncertainty and sensitivity analyses suggest that our results are robust against uncertainties in the calibration parameters, other model parameters and inputs, and the selected calibration period.

Description: Snow cover information is of central importance for the estimation of water storage in cold mountainous regions. It is difficult to assess distributed snow cover information in a catchment in order to estimate possible water resources. It is especially a challenge to obtain snow cover information for high mountainous areas. Usually, snow depth is measured at meteorological stations, and it is relatively difficult to extrapolate this spatially or temporally since it highly depends on available energy and topography. The snow coverage of a catchment gives detailed information about the catchment's potential source for water. Many regions lack meteorological stations that measure snow, and usually no stations are available at high elevations. Satellite information is a very valuable source for obtaining several environmental parameters. One of the advantages is that the data is mostly provided in a spatially distributed format. This study uses satellite data to estimate snow coverage on high mountainous areas. Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover data is used in the Kokcha Catchment located in the north-eastern part of Afghanistan. The main disadvantage of MODIS data that restricts its direct use in environmental applications is cloud coverage. This is why this study is focused on eliminating cloud covered cells and estimating cell information under cloud covered cells using six logical, spatial and temporal approaches. The results give total cloud removal and mapping of snow cover for the study areas.

Description: Despite the large variety of methods for estimating slope erosion intensity, it is still difficult to obtain accurate erosion rates. Therefore, our goal was to develop a method to provide accurate estimates of sheet and rill erosion intensities, and evaluate denudation quantities due to abrasion, landslides and talus processes using a high-precision laser scanning system (Trimble® GX). Differential maps between all stages of surveying and TIN-models were built directly on point clouds in "Trimble® RealWorks" software. Inspection and cross-section tools were used for detailed study of ground movements on the slope surface and the development of linear erosion forms. A new method for accurate estimates of the erosion has been developed using terrestrial laser scanning techniques. It makes it possible to assess the denudation–accumulation balance on erosive slopes, determine the dynamics of the volume of material moved on different parts of the slope in various surface runoff events, and identify spatial regularities forming rill washouts.

Description: This study focuses on modelling water balances for catchments with limited data availability. The objective was to use globally available data for water balance modelling of meso-scale catchments. The study is carried out in two catchments; one having enough data for the performance check of the model and the other with very few data for model validation. Globally available meteorological and geographical data is used for the basic model inputs. Dissaggregation of the global data, both spatially and temporally, was conducted to distribute the available data across the watershed and to attain higher resolution input data for the model. In addition, a glacier module was developed for the regions covered by glaciers. The HBV-IWS model developed at the Institute of Hydraulic Engineering at the University of Stuttgart is applied. The outcomes of the modelling provide noteworthy results for both catchments that can be used in water resources planning and management issues. Moreover, the research presents the potential for modelling water balances using predominantly globally available data and proposes appropriate disaggregation methods for global data usage.

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.