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Development and application of improved long-term datasets of surface hydrology for Texas

Development and application of improved long-term datasets of surface hydrology for Texas
Development and application of improved long-term datasets of surface hydrology for Texas

Freshwater availability and agricultural production are key factors for sustaining the fast growing population and economy in the state of Texas, which is the third largest state in terms of agricultural production in the United States. This paper describes a long-term (1918-2011) grid-based (1/8°) surface hydrological dataset for Texas at a daily time step based on simulations from the Variable Infiltration Capacity (VIC) hydrological model. The model was calibrated and validated against observed streamflow over 10 Texas river basins. The simulated soil moisture was also evaluated using in situ observations. Results suggest that there is a decreasing trend in precipitation and an increasing trend in temperature in most of the basins. Droughts and floods were reconstructed and analyzed. In particular, the spatially distributed severity and duration of major Texas droughts were compared to identify new characteristics. The modeled flood recurrence interval and the return period were also compared with observations. Results suggest the performance of extreme flood simulations needs further improvement. This dataset is expected to serve as a benchmark which may contribute to water resources management and to mitigating agricultural drought, especially in the context of understanding the effects of climate change on crop yield in Texas.

1687-9309
Lee, Kyungtae
6a813ac0-5953-4b68-840d-a5f8df8a610a
Gao, Huilin
0ab679b9-f396-48c1-b668-3f96e0f6a1c0
Huang, Maoyi
54df46d4-1cbe-4518-84f1-fd9f02148b34
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Shi, Xiaogang
824b77c9-8054-4385-9a32-f484685e743f
Lee, Kyungtae
6a813ac0-5953-4b68-840d-a5f8df8a610a
Gao, Huilin
0ab679b9-f396-48c1-b668-3f96e0f6a1c0
Huang, Maoyi
54df46d4-1cbe-4518-84f1-fd9f02148b34
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Shi, Xiaogang
824b77c9-8054-4385-9a32-f484685e743f

Lee, Kyungtae, Gao, Huilin, Huang, Maoyi, Sheffield, Justin and Shi, Xiaogang (2017) Development and application of improved long-term datasets of surface hydrology for Texas. Advances in Meteorology, 2017, [8485130]. (doi:10.1155/2017/8485130).

Record type: Article

Abstract

Freshwater availability and agricultural production are key factors for sustaining the fast growing population and economy in the state of Texas, which is the third largest state in terms of agricultural production in the United States. This paper describes a long-term (1918-2011) grid-based (1/8°) surface hydrological dataset for Texas at a daily time step based on simulations from the Variable Infiltration Capacity (VIC) hydrological model. The model was calibrated and validated against observed streamflow over 10 Texas river basins. The simulated soil moisture was also evaluated using in situ observations. Results suggest that there is a decreasing trend in precipitation and an increasing trend in temperature in most of the basins. Droughts and floods were reconstructed and analyzed. In particular, the spatially distributed severity and duration of major Texas droughts were compared to identify new characteristics. The modeled flood recurrence interval and the return period were also compared with observations. Results suggest the performance of extreme flood simulations needs further improvement. This dataset is expected to serve as a benchmark which may contribute to water resources management and to mitigating agricultural drought, especially in the context of understanding the effects of climate change on crop yield in Texas.

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More information

Accepted/In Press date: 8 February 2017
e-pub ahead of print date: 6 March 2017
Published date: 2017
Additional Information: A correction has been attached to this output located at https://doi.org/10.1155/2017/2632468

Identifiers

Local EPrints ID: 479551
URI: http://eprints.soton.ac.uk/id/eprint/479551
ISSN: 1687-9309
PURE UUID: a2544a27-bdc4-4215-807a-097742fc2ce9
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

Catalogue record

Date deposited: 26 Jul 2023 16:37
Last modified: 17 Mar 2024 03:40

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Contributors

Author: Kyungtae Lee
Author: Huilin Gao
Author: Maoyi Huang
Author: Xiaogang Shi

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