Data-driven scale extrapolation: estimating yearly discharge for a large region by small sub-basins
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...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-01-01
|
| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/18/343/2014/hess-18-343-2014.pdf |
| _version_ | 1818548879886385152 |
|---|---|
| author | L. Gong |
| author_facet | L. Gong |
| author_sort | L. Gong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T08:26:09Z |
| format | Article |
| id | doaj.art-835132f994a64326acc5569a32685fcd |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-12T08:26:09Z |
| publishDate | 2014-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-835132f994a64326acc5569a32685fcd2022-12-22T00:31:15ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382014-01-0118134335210.5194/hess-18-343-2014Data-driven scale extrapolation: estimating yearly discharge for a large region by small sub-basinsL. Gong0Department of Earth Sciences, Uppsala University, Uppsala, SwedenLarge-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.http://www.hydrol-earth-syst-sci.net/18/343/2014/hess-18-343-2014.pdf |
| spellingShingle | L. Gong Data-driven scale extrapolation: estimating yearly discharge for a large region by small sub-basins Hydrology and Earth System Sciences |
| title | Data-driven scale extrapolation: estimating yearly discharge for a large region by small sub-basins |
| title_full | Data-driven scale extrapolation: estimating yearly discharge for a large region by small sub-basins |
| title_fullStr | Data-driven scale extrapolation: estimating yearly discharge for a large region by small sub-basins |
| title_full_unstemmed | Data-driven scale extrapolation: estimating yearly discharge for a large region by small sub-basins |
| title_short | Data-driven scale extrapolation: estimating yearly discharge for a large region by small sub-basins |
| title_sort | data driven scale extrapolation estimating yearly discharge for a large region by small sub basins |
| url | http://www.hydrol-earth-syst-sci.net/18/343/2014/hess-18-343-2014.pdf |
| work_keys_str_mv | AT lgong datadrivenscaleextrapolationestimatingyearlydischargeforalargeregionbysmallsubbasins |