| 1. |
- Chen, D., et al.
(författare)
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A high-resolution, gridded dataset for monthly temperature normals (1971 – 2000) in Sweden
- 2007
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Ingår i: Geografiska Annaler. Series A, Physical Geography. - : Informa UK Limited. - 0435-3676 .- 1468-0459. ; 89:4, s. 249-261
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Tidskriftsartikel (refereegranskat)abstract
- A baseline climatology is required in evaluating climate variability and changes on regional and local scales. Gridded climate normals, i.e. averages over a 30-year period, are of special interest since they can be readily used for validation of climate models. This study is aimed at creating an updated gridded dataset for Swedish monthly temperature normals over the period 1971–2000, based on standard 2-m air temperature records at 510 stations in mainland Sweden. Spatial trends of the normal temperatures were modelled as functions of latitude, longitude and elevation by multiple linear regression. The study shows that the temperature normals are strongly correlated with latitude throughout the year and especially in cold months, while elevation was a more important factor in June and July. Longitude played a minor role and was only significant in April and May. Regression equations linking temperature to latitude, longitude and elevation were set up for each month. Monthly temperature normals were detrended by subtracting spatial trends given by the regressions. Ordinary kriging was then applied to both original data (simple method) and de-trended data (composite method) to model the spatial variability and to perform spatial gridding. The multiple regressions showed that between 82% (summer) and 96% (winter) of the variance in monthly temperature normals could be explained by latitude and elevation. Unexplained variances, i.e. the residuals, were modelled with ordinary kriging with exponential semivariograms. The composite grid estimates were calculated by adding the multiple linear trends back to the interpolated residuals at each grid point. Kriged original temperature normals provided a performance benchmark. The cross–validation shows that the interpolation errors of the normals are significantly reduced if the composite method rather than the simple one was used. A gridded monthly dataset with 30-arcsecond spacing was created using the established trends, the kriging model and a digital topographic dataset.
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| 2. |
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| 3. |
- Chen, Deliang, 1961, et al.
(författare)
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Spatial Interpolation of Daily Precipitation in China : 1951-2005
- 2010
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Ingår i: Advances in Atmospheric Sciences. - : Springer Science and Business Media LLC. - 0256-1530 .- 1861-9533. ; 27:6, s. 1221-1232
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Tidskriftsartikel (refereegranskat)abstract
- Climate research relies heavily on good quality instrumental data; for modeling efforts gridded data are needed. So far, relatively little effort has been made to create gridded climate data for China. This is especially true for high-resolution daily data. This work, focuses on identifying an accurate method to produce gridded daily precipitation in China based on the observed data at 753 stations for the period 1951-2005. Five interpolation methods, including ordinary nearest neighbor, local polynomial, radial basis function, inverse distance weighting, and ordinary kriging, have been used and compared. Cross-validation shows that the ordinary kriging based on seasonal semi-variograms gives the best performance, closely followed by the inverse distance weighting with a power of 2. Finally the ordinary kriging is chosen to interpolate the station data to a 18 kmx 18 km grid system covering the whole country. Precipitation for each 0.5A degrees x 0.5A degrees latitude-longitude block is then obtained by averaging the values at the grid nodes within the block. Owing to the higher station density in the eastern part of the country, the interpolation errors are much smaller than those in the west (west of 100A degrees E). Excluding 145 stations in the western region, the daily, monthly, and annual relative mean absolute errors of the interpolation for the remaining 608 stations are 74%, 29%, and 16%, respectively. The interpolated daily precipitation has been made available on the internet for the scientific community.
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| 4. |
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| 5. |
- Gong, Lebing
(författare)
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Data-driven scale extrapolation : estimating yearly discharge for a large region by small sub-basins
- 2014
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Ingår i: Hydrology and Earth System Sciences. - : Copernicus GmbH. - 1027-5606 .- 1607-7938. ; 18:1, s. 343-352
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale hydrological models and land surface models are so far the only tools for assessing current and future water resources. Those models estimate discharge with large uncertainties, due to the complex interaction between climate and hydrology, the limited availability and quality of data, as well as model uncertainties. A new purely data-driven scale-extrapolation method to estimate discharge for a large region solely from selected small sub-basins, which are typically 1-2 orders of magnitude smaller than the large region, is proposed. Those small sub-basins contain sufficient information, not only on climate and land surface, but also on hydrological characteristics for the large basin. In the Baltic Sea drainage basin, best discharge estimation for the gauged area was achieved with sub-basins that cover 5% of the gauged area. There exist multiple sets of sub-basins whose climate and hydrology resemble those of the gauged area equally well. Those multiple sets estimate annual discharge for the gauged area consistently well with 6% average error. The scale-extrapolation method is completely data-driven; therefore it does not force any modelling error into the prediction. The multiple predictions are expected to bracket the inherent variations and uncertainties of the climate and hydrology of the basin.
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| 6. |
- Gong, Lebing, et al.
(författare)
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Global-scale river routing-an efficient time-delay algorithm based on HydroSHEDS high-resolution hydrography
- 2011
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Ingår i: Hydrological Processes. - : Wiley. - 0885-6087 .- 1099-1085. ; 25:7, s. 1114-1128
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Tidskriftsartikel (refereegranskat)abstract
- Coupling of global hydrologic and atmospheric models is difficult because of the highly nonlinear hydrological processes to be integrated at large scales. Aggregation of high-resolution data into low-resolution spatial distribution functions is one way to preserve information and account for the nonlinearity. We used HydroSHEDS, presently the most highly resolved (3 '') global hydrography available, to provide accurate control on global river routing through a computationally efficient algorithm. The high resolution of HydroSHEDS allowed discrimination of river-channel pixels, and time-delay distributions were calculated for all such pixels. The distributions were aggregated into network-response functions (NRFs) for each low-resolution cell using an algorithm originally developed for the 1-km-resolution HYDRO1k hydrography. The large size of HydroSHEDS required a modification in algorithm to maintain computational efficiency. The new algorithm was tested with a high-quality local and a more uncertain global weather dataset to identify whether improved routing would provide a gain when weather data quality was limiting. The routing was coupled to the WASMOD-M runoff-generation model to evaluate discharge from the Dongjiang River and the Willamette River basins. The HydroSHEDS-based routing, compared with the HYDRO1k-based routing, provided a small gain in model efficiency, for local and global weather data and for both test basins. The HydroSHEDS-based routing, contrary to the HYDRO1k-based routing, provided physically realistic wave velocities. The most stable runoff-generation parameter values were achieved when HydroSHEDS was used to derive the NRFs. Routing was computed in two steps: first, a preparatory calculation which was a one-time effort and second, the routing during each simulation. The computational efficiency was four to five orders of magnitude better for the simulation step than that for the preparatory step.
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| 7. |
- Gong, Lebing
(författare)
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Large-scale Runoff Generation and Routing : Efficient Parameterisation using High-resolution Topography and Hydrography
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Water has always had a controlling influence on the earth’s evolution. Understanding and modelling the large-scale hydrological cycle is important for climate prediction and water-resources studies. In recent years large-scale hydrological models, including the WASMOD-M evaluated in the thesis, have increasingly become a main assessment tool for global water resources.The monthly version of WASMOD-M, the starting point of the thesis, revealed restraints imposed by limited hydrological and climate data quality and the need to reduce model-structure uncertainties. The model simulated the global water balance with a small volume error but was less successful in capturing the dynamics. In the last years, global high-quality, high-resolution topographies and hydrographies have become available. The main thrust of the thesis was the development of a daily WASMOD-M making use of these data to better capture the global water dynamics and to parameterise local non-linear processes into the large-scale model. Scale independency, parsimonious model structure, and computational efficiency were main concerns throughout the model development.A new scale-independent routing algorithm, named NRF for network-response function, using two aggregated high-resolution hydrographies, HYDRO1k and HydroSHEDS, was developed and tested in three river basins with different climates in China and North America. The algorithm preserves the spatially distributed time-delay information in the form of simple network-response functions for any low-resolution grid cell in a large-scale hydrological model.A distributed runoff-generation algorithm, named TRG for topography-derived runoff generation, was developed to represent the highly non-linear process at large scales. The algorithm, when inserted into the daily WASMOD-M and tested in same three basins, led to the same or a slightly improved performance compared to a one-layer VIC model, with one parameter less to be calibrated. The TRG algorithm also offered a more realistic spatial pattern for runoff generation.The thesis identified significant improvements in model performance when 1) local instead of global climate data were used, and 2) when the scale-independent NRF routing algorithm was used instead of a traditional storage-based routing algorithm. In the same time, spatial resolution of climate input and choice of high-resolution hydrography have secondary effects on model performance.Two high-resolution topographies and hydrographies were used and compared, and new techniques were developed to aggregate their information for use at large scales. The advantages and numerical efficiency of feeding high-resolution information into low-resolution global models were highlighted.
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| 8. |
- Gong, Lebing, et al.
(författare)
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Large-scale runoff generation - parsimonious parameterisation using high-resolution topography
- 2011
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Ingår i: Hydrology and Earth System Sciences. - : Copernicus GmbH. - 1027-5606 .- 1607-7938. ; 15:8, s. 2481-2494
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Tidskriftsartikel (refereegranskat)abstract
- World water resources have primarily been analysed by global-scale hydrological models in the last decades. Runoff generation in many of these models are based on process formulations developed at catchments scales. The division between slow runoff (baseflow) and fast runoff is primarily governed by slope and spatial distribution of effective water storage capacity, both acting at very small scales. Many hydrological models, e. g. VIC, account for the spatial storage variability in terms of statistical distributions; such models are generally proven to perform well. The statistical approaches, however, use the same runoff-generation parameters everywhere in a basin. The TOPMODEL concept, on the other hand, links the effective maximum storage capacity with real-world topography. Recent availability of global high-quality, high-resolution topographic data makes TOPMODEL attractive as a basis for a physically-based runoff-generation algorithm at large scales, even if its assumptions are not valid in flat terrain or for deep groundwater systems. We present a new runoff-generation algorithm for large-scale hydrology based on TOPMODEL concepts intended to overcome these problems. The TRG (topography-derived runoff generation) algorithm relaxes the TOPMODEL equilibrium assumption so baseflow generation is not tied to topography. TRG only uses the topographic index to distribute average storage to each topographic index class. The maximum storage capacity is proportional to the range of topographic index and is scaled by one parameter. The distribution of storage capacity within large-scale grid cells is obtained numerically through topographic analysis. The new topography-derived distribution function is then inserted into a runoff-generation framework similar VIC's. Different basin parts are parameterised by different storage capacities, and different shapes of the storage-distribution curves depend on their topographic characteristics. The TRG algorithm is driven by the HydroSHEDS dataset with a resolution of 3 '' (around 90 m at the equator). The TRG algorithm was validated against the VIC algorithm in a common model framework in 3 river basins in different climates. The TRG algorithm performed equally well or marginally better than the VIC algorithm with one less parameter to be calibrated. The TRG algorithm also lacked equifinality problems and offered a realistic spatial pattern for runoff generation and evaporation.
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| 9. |
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| 10. |
- Gong, Lebing, et al.
(författare)
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Sensitivity of the Penman–Monteith reference evapotranspiration to key climatic variables in the Changjiang (Yangtze River) basin
- 2006
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 329:3-4, s. 620-629
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Tidskriftsartikel (refereegranskat)abstract
- Sensitivity analysis is important in understanding the relative importance of climatic variables to the variation of reference evapotranspiration (ETref). In this study, a non-dimensional relative sensitivity coefficient was employed to predict responses of ETref to perturbations of four climatic variables in the Changjiang (Yangtze River) basin. ETref was estimated with the FAO-56 Penman–Monteith equation. A 41-year historical dataset of daily air temperature, wind speed, relative humidity and daily sunshine duration at 150 national meteorological observatory stations was used in the analysis. Results show that the response of ETref can be precisely predicted under perturbation of relative humidity or shortwave radiation by their sensitivity coefficients; the predictive power under perturbations of air temperature and wind speed depended on the magnitude of the perturbation, season and region. The prediction errors were much smaller than the seasonal and regional variation of their sensitivity coefficients. The sensitivity coefficient could also be used to predict the response of ETref to co-perturbation of several variables. The accuracy of the prediction increases from the lower to the upper region. Spatial variations of long-term average monthly and yearly sensitivity coefficients were obtained by interpolation of station estimates. In general, relative humidity was the most sensitive variable, followed by shortwave radiation, air temperature and wind speed. The actual rank of the four climatic variables in terms of their sensitivity varied with season and region. The large spatial variability of the sensitivity coefficients of all the climatic variables in the middle and lower regions of the basin was to a large extent determined by the distinct wind-speed patterns in those two regions.
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