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Various remote sensing products are used to identify spatial-temporal trends in snow cover in river basins originating in the Himalayas and adjacent Tibetan-Qinghai plateau
. It is shown that remote sensing allows detection of spatial-temporal patterns of snow cover across large areas in inaccessible terrain, providing useful information on a critical component of the hydrological cycle. Results show large variation in snow cover between years while an increasing trend from west to east is observed. Of all river basins the Indus basin is, for its water resources, most dependent on snow and ice melt and large parts are snow covered for prolonged periods of the year. A significant negative winter snow cover trend was identified for the upper Indus basin. For this basin a hydrological model is used and forced with remotely sensed derived precipitation and snow cover. The model is calibrated using daily discharges from 2000 to 2005 and stream flow in the upper Indus basin can be predicted with a high degree of accuracy. From the analysis it is concluded that there are indications that regional warming is affecting the hydrology of the upper Indus basin due to accelerated glacial melting during the simulation period. This warming may be associated with global changes in air temperature resulting from anthropogenic forcings. This conclusion is primarily based on the observation that the average annual precipitation over a five year period is less than the observed stream flow and supported by positive temperature trends in all seasons. © 2008 Elsevier Inc. All rights reserved
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Various remote sensing products are used to identify spatial-temporal trends in snow cover in river basins originating in the Himalayas and adjacent Tibetan-Qinghai plateau
. It is shown that remote sensing allows detection of spatial-temporal patterns of snow cover across large areas in inaccessible terrain, providing useful information on a critical component of the hydrological cycle. Results show large variation in snow cover between years while an increasing trend from west to east is observed. Of all river basins the Indus basin is, for its water resources, most dependent on snow and ice melt and large parts are snow covered for prolonged periods of the year. A significant negative winter snow cover trend was identified for the upper Indus basin. For this basin a hydrological model is used and forced with remotely sensed derived precipitation and snow cover. The model is calibrated using daily discharges from 2000 to 2005 and stream flow in the upper Indus basin can be predicted with a high degree of accuracy. From the analysis it is concluded that there are indications that regional warming is affecting the hydrology of the upper Indus basin due to accelerated glacial melting during the simulation period. This warming may be associated with global changes in air temperature resulting from anthropogenic forcings. This conclusion is primarily based on the observation that the average annual precipitation over a five year period is less than the observed stream flow and supported by positive temperature trends in all seasons.
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Calibrating spatially distributed hydrological models is complex due to the lack of reliable data, uncertainty in representing the physical features of a river catchment, and the implementation of hydrological processes in a simulation model
. In this paper, an innovative approach is presented which incorporates remote sensing derived evapotranspiration in the calibration of the Soil and Water Assessment Tool (SWAT) in a catchment of the Krishna basin in southern India. The Gauss–Marquardt–Levenberg algorithm is implemented to optimise different combination of land use, soil, groundwater, and meteorological model parameters. In the best performing optimisation, the r2 between monthly sub-basin simulated and measured actual evapotranspiration (ETact) was increased from 0.40 to 0.81. ETact was more sensitive to the groundwater and meteorological parameters than the soil and land use parameters. Traditional calibration on a limited number of discharge stations lumps all hydrological processes together and chances on the equifinality problem are larger. In this study we have shown this problem can be constrained by using spatially distributed observations with a monthly temporal resolution. At a spatial resolution below the sub-basin level further study is required to fine-tune the calibration procedure
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Numerical simulation models are frequently ap- plied to assess the impact of climate change on hydrology and agriculture
. A common hypothesis is that unavoidable model errors are reflected in the reference situation as well as in the climate change situation so that by comparing ref- erence to scenario model errors will level out. For a polder in The Netherlands an innovative procedure has been intro- duced, referred to as the Model-Scenario-Ratio (MSR), to express model inaccuracy on climate change impact assess- ment studies based on simulation models comparing a ref- erence situation to a climate change situation. The SWAP (Soil Water Atmosphere Plant) model was used for the case study and the reference situation was compared to two cli- mate change scenarios. MSR values close to 1, indicating that impact assessment is mainly a function of the scenario itself rather than of the quality of the model, were found for most indicators evaluated. A climate change scenario with enhanced drought conditions and indicators based on thresh- old values showed lower MSR values, indicating that model accuracy is an important component of the climate change impact assessment. It was concluded that the MSR approach can be applied easily and will lead to more robust impact as- sessment analyses
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Variations in climate, land-use and water consumption can have profound effects on river runoff
. There is an increasing demand to study these factors at the regional to river basin-scale since these effects will particularly affect water resources management at this level. This paper presents a method that can help to differentiate between the effects of man-made hydrological developments and climate variability (including both natural variability and anthropogenic climate change) at the basin scale. We show and explain the relation between climate, water consumption and changes in runoff for the Krishna river basin in central India. River runoff variability due to observed climate variability and increased water consumption for irrigation and hydropower is simulated for the last 100 years (1901–2000) using the STREAM water balance model. Annual runoff under climate variability is shown to vary only by about 14–34 millimetres (6–15%). It appears that reservoir construction after 1960 and increasing water consumption has caused a persistent decrease in annual river runoff of up to approximately 123 mm (61%). Variation in runoff under climate variability only would have decreased over the period under study, but we estimate that increasing water consumption has caused runoff variability that is three times higher
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