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Abstract An analytical model is developed for mean annual groundwater evapotranspiration (GWET) at the watershed scale based on a three‐stage precipitation partitioning framework. The ratio of mean annual GWET to precipitation, defined as GWET ratio, is modeled as a function of climate aridity index (CAI), storage capacity index, the shape parameter ‘a’ for the spatial distribution of storage capacity, and the shape parameter ‘b’ for the spatial distribution of available water for GWET. In humid regions, GWET ratio tends to increase with increasing CAI due to the limited energy supply and shallower depth to water table (DWT) for a given storage capacity index. In contrast, in arid regions, the GWET ratio tends to decrease as the CAI increases because of the limited water availability and the presence of a deeper DWT for a given storage capacity index. In arid regions, the GWET ratio decreases as the parameter ‘a’ increases, mainly because of increased ET from a thicker unsaturated zone in environments with a deeper DWT. GWET ratio increases as parameter ‘b’ increases due to more watershed area with larger available water for GWET. The storage capacity index and shape parameters are estimated for 31 study watersheds in Tampa Bay Florida area based on the simulated GWET from an integrated hydrologic model and for 21 watersheds from literature. A possible correlation has been identified between the two shape parameters in the Tampa Bay watersheds. The analytical model for mean annual GWET can be further tested in other watersheds if data are available.
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Diagnosis toward predicting mean annual runoff in ungauged basins
Abstract. Prediction of mean annual runoff is of great interest but still poses achallenge in ungauged basins. The present work diagnoses the prediction inmean annual runoff affected by the uncertainty in estimated distribution ofsoil water storage capacity. Based on a distribution function, a waterbalance model for estimating mean annual runoff is developed, in which theeffects of climate variability and the distribution of soil water storagecapacity are explicitly represented. As such, the two parameters in themodel have explicit physical meanings, and relationships between theparameters and controlling factors on mean annual runoff are established.The estimated parameters from the existing data of watershed characteristicsare applied to 35 watersheds. The results showed that the model couldcapture 88.2 % of the actual mean annual runoff on average across thestudy watersheds, indicating that the proposed new water balance model ispromising for estimating mean annual runoff in ungauged watersheds. Theunderestimation of mean annual runoff is mainly caused by theunderestimation of the area percentage of low soil water storage capacitydue to neglecting the effect of land surface and bedrock topography. Higherspatial variability of soil water storage capacity estimated through theheight above the nearest drainage (HAND) and topographic wetness index (TWI)indicated that topography plays a crucial role in determining the actualsoil water storage capacity. The performance of mean annual runoffprediction in ungauged basins can be improved by employing better estimationof soil water storage capacity including the effects of soil, topography,and bedrock. It leads to better diagnosis of the data requirement forpredicting mean annual runoff in ungauged basins based on a newly developedprocess-based model finally.
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- Award ID(s):
- 1804770
- PAR ID:
- 10342037
- Date Published:
- Journal Name:
- Hydrology and Earth System Sciences
- Volume:
- 25
- Issue:
- 2
- ISSN:
- 1607-7938
- Page Range / eLocation ID:
- 945 to 956
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/hess-25-945-2021
