Abstract. Dryland regions are characterised by water scarcity and are facingmajor challenges under climate change. One difficulty is anticipating howrainfall will be partitioned into evaporative losses, groundwater, soilmoisture, and runoff (the water balance) in the future, which has importantimplications for water resources and dryland ecosystems. However, in orderto effectively estimate the water balance, hydrological models in drylandsneed to capture the key processes at the appropriate spatio-temporal scales.These include spatially restricted and temporally brief rainfall, highevaporation rates, transmission losses, and focused groundwater recharge.Lack of available input and evaluation data and the high computational costsof explicit representation of ephemeral surface–groundwater interactionsrestrict the usefulness of most hydrological models in these environments.Therefore, here we have developed a parsimonious distributed hydrologicalmodel for DRYland Partitioning (DRYP). The DRYP model incorporates the keyprocesses of water partitioning in dryland regions with limited datarequirements, and we tested it in the data-rich Walnut Gulch ExperimentalWatershed against measurements of streamflow, soil moisture, andevapotranspiration. Overall, DRYP showed skill in quantifying the maincomponents of the dryland water balance including monthly observations ofstreamflow (Nash–Sutcliffe efficiency, NSE, ∼ 0.7),evapotranspiration (NSE > 0.6), and soil moisture (NSE ∼ 0.7). The model showed that evapotranspiration consumes > 90 % of the total precipitation input to the catchment andthat < 1 % leaves the catchment as streamflow. Greater than 90 % of the overland flow generated in the catchment is lost throughephemeral channels as transmission losses. However, only ∼ 35 % of the total transmission losses percolate to the groundwater aquiferas focused groundwater recharge, whereas the rest is lost to the atmosphereas riparian evapotranspiration. Overall, DRYP is a modular, versatile, andparsimonious Python-based model which can be used to anticipate and plan forclimatic and anthropogenic changes to water fluxes and storage in drylandregions.
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Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region
Precipitation is the primary driver of hydrological models, and its spatial and temporal variability have a great impact on water partitioning. However, in data‐sparse regions, uncertainty in precipitation estimates is high and the sensitivity of water partitioning to this uncertainty is unknown. This is a particular challenge in drylands (semi‐arid and arid regions) where the water balance is highly sensitive to rainfall, yet there is commonly a lack of in situ rain gauge data. To understand the impact of precipitation uncertainty on the water balance in drylands, here we have performed simulations with a process‐based hydrological model developed to characterize the water balance in arid and semi‐arid regions (DRYP: DRYland water Partitioning model). We performed a series of numerical analyses in the Upper Ewaso Ng'iro basin, Kenya driven by three gridded precipitation datasets with different spatio‐temporal resolutions (IMERG, MSWEP, and ERA5), evaluating simulations against streamflow observations and remotely sensed data products of soil moisture, actual evapotranspiration, and total water storage. We found that despite the great differences in the spatial distribution of rainfall across a climatic gradient within the basin, DRYP shows good performance for representing streamflow (KGE >0.6), soil moisture, actual evapotranspiration, and total water storage (
- NSF-PAR ID:
- 10483827
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Hydrological Processes
- Volume:
- 37
- Issue:
- 12
- ISSN:
- 0885-6087
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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