stoPET v1.0: a stochastic potential evapotranspiration generator for simulation of climate change impacts

Asfaw, Dagmawi Teklu; Singer, Michael Bliss; Rosolem, Rafael; MacLeod, David; Cuthbert, Mark O.; Miguitama, Edisson Quichimbo; Gaona, Manuel Felipe Rios; Michaelides, Katerina

doi:10.5194/gmd-16-557-2023

Cited by 4 publications

(2 citation statements)

References 52 publications

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“…Finally, considering the variability of forcing datasets, exploring a broader range of spatial and temporal variability in precipitation and potential evapotranspiration for quantifying water partitioning will also aid in reducing uncertainty. Utilizing tools to explore the stochastic behaviour of forcing datasets (e.g., stoPET v1.0 Asfaw et al (2023), STORM (Rios Gaona et al, 2023; Singer et al, 2018) will help understand how the variability in forcing datasets impacts different components of the water balance, thereby improving model estimations. This, in turn, will enhance our understanding of the influence of climate change variability on surface and subsurface water balance components, particularly groundwater.…”

Section: Discussionmentioning

confidence: 99%

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

Quichimbo,

Singer,

Michaelides

et al. 2023

Hydrological Processes

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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 (r >0.5). However, the choice of precipitation datasets greatly influences surface (infiltration, runoff, and transmission losses) and subsurface fluxes (groundwater recharge and discharge) across different climatic zones of the Ewaso Ng'iro basin. Within humid areas, evapotranspiration does not show sensitivity to the choice of precipitation dataset, however, in dry lowland areas it becomes more sensitive to precipitation rates as water‐limited conditions develop. The analysis shows that the highest rates of precipitation produce high rates of diffuse recharge in Ewaso uplands and also propagate into runoff, transmission losses and, ultimately focused recharge, with the latter acting as the main mechanism of groundwater recharge in low dry areas. The results from this modelling exercise suggest that care must be taken in selecting forcing precipitation data to drive hydrological modelling efforts, especially in basins that span a climatic gradient. These results also suggest that more effort is required to reduce uncertainty between different precipitation datasets, which will in turn result in more consistent quantification of the water balance.

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Section: Discussionmentioning

confidence: 99%

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

Quichimbo,

Singer,

Michaelides

et al. 2023

Hydrological Processes

Self Cite

View full text Add to dashboard Cite

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“…The new STORM could also be integrated with stochastic models characterizing atmospheric evaporative demand (e.g., Asfaw et al, 2023), which would allow for closure of the water balance.…”

Section: Storm Applicationsmentioning

confidence: 99%

STORM v.2: A simple, stochastic decision-support tool for exploring the impacts of climate and climate change at, and near the land surface in gauged watersheds

Gaona

Michaelides

Singer

2023

Preprint

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Abstract. Climate change is expected to have major impacts on land surface and subsurface processes through its expression on the hydrological cycle, but the impacts to any particular basin or region are highly uncertain. Non-stationarities in the frequency, magnitude, duration, and timing of rainfall events have important implications for human societies, water resources, and ecosystems. The conventional approach for assessing the impacts of climate change is to downscale global climate model output and use it to drive regional and local models that express the climate within hydrology near the land surface. While this approach may be useful for linking global general circulation models to regional hydrological cycle, it is limited for examining the details of hydrological response to climate forcing for a specific location over timescales relevant to decision makers. For example, management of flood or drought hazard requires detailed information that includes uncertainty based on variability in storm characteristics, rather than on differences between models within an ensemble. To fill this gap, we present the second version of our STOchastic Rainfall Model (STORM), an open-source, parsimonious and user-friendly modeling framework for simulating climatic expression as rainfall fields over a basin. This work showcases the use of STORM in simulating ensembles of realistic sequences, and spatial patterns of rainstorms for current climate conditions, and bespoke climate change scenarios that are likely to affect the water balance near the Earth's surface. We outline, and detail STORM's new approaches: one copula for linking marginal distributions of storm intensity and duration; orographic stratification of rainfall using the copula approach; a radial decay-rate for rainfall intensity which takes into consideration potential, but unrecorded, maximum storm intensities; an optional component to simulate storm start date-times via circular/directional statistics; and a simple implementation for modelling future climate scenarios. We also introduce a new pre-processing module that facilitates the generation of model input in the form of probability density functions (PDFs) from historical data for subsequent stochastic sampling. Independent validation showed that the average performance of STORM falls within a 5.5 % of the historical seasonal total rainfall in the Walnut Gulch Experimental Watershed (Arizona, USA) that ocurred in the current century.

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A Monte Carlo Model for WWTP Effluent Flow Treatment through Enhanced Willow Evapotranspiration

Tegos

2024

Hydrology

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The effectiveness of using enhanced evapotranspiration rates of willow plantation is a modern environmentally friendly practice for advanced treatment of effluent WWTP flow. The key idea is that through advanced willow evapotranspiration rates, a significant proportion of the effluent flow can be transferred into the atmosphere through the physical process of evapotranspiration. This study further discusses the concept in a real-world problem using a wide dataset consisting of a recent PET monthly remote dataset namely RASPOTION, monthly recorded rainfall gauge, and experimental willow evapotranspiration surveys across Ireland, to identify the monthly cropping pattern. A Monte Carlo water balance model has been developed for the period 2003–2016. The model was applied in an existing willow plantation at Donard WWTP co. Wicklow, Ireland to identify the exceedance probability of willow plantation runoff against estimated low flows (i.e., Q95, Q99) at the adjacent small tributary. In this case study, any failure which can lead to river quality deterioration was not assessed. The overall framework aims to provide new insights considering the multiple sources of uncertainty (i.e., monthly willow cropping pattern and WWTP effluent flow) in associated environmental engineering problems.

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stoPET v1.0: a stochastic potential evapotranspiration generator for simulation of climate change impacts

Cited by 4 publications

References 52 publications

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

STORM v.2: A simple, stochastic decision-support tool for exploring the impacts of climate and climate change at, and near the land surface in gauged watersheds

A Monte Carlo Model for WWTP Effluent Flow Treatment through Enhanced Willow Evapotranspiration

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