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\begin{document}

\title{Localizing Hydrological Drought Early Warning using In-situ Groundwater
Sensors}



\author[1]{William Veness}%
\author[2]{Adrian P. Butler}%
\author[1]{Boris Fernando Ochoa-Tocachi}%
\author[1]{Simon Moulds}%
\author[1]{Wouter Buytaert}%
\affil[1]{Imperial College London}%
\affil[2]{Imperial College of Science, Technology \& Medicine}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\begin{abstract}
Drought Early Warning Systems (DEWSs) aim to spatially monitor and
forecast risk of water shortage to inform early, risk-mitigating
interventions. However, due to the scarcity of in-situ monitoring in
groundwater-dependent arid zones, spatial drought exposure is inferred
using maps of satellite-based indicators such as rainfall anomalies,
soil moisture and vegetation indices. On the local scale, these
coarse-resolution proxy indicators provide a poor inference of
groundwater availability. The improving affordability and technical
capability of modern sensors significantly increases the feasibility of
taking direct groundwater level measurements in data-scarce, arid
regions on a larger scale. Here, we assess the potential of in-situ
monitoring to provide a localized index of hydrological drought in
Somaliland. We find that calibrating a lumped groundwater model with a
short time series of high-frequency groundwater level observations
substantially improves the quantification of local water availability
when compared to satellite-based indices over the same validation
period. By varying the calibration length between 1-30 weeks, we find
that data collection beyond 5 weeks adds little to model calibration at
all three wells. This suggests that a short monitoring campaign is
suitable to improve estimations of local water availability during
drought, and provide superior performance compared to regional-scale
satellite-based indicators. A short calibration period has practical
advantages, as it allows for the relocation of sensors and rapid
characterization of a large number of wells. A monitoring system with
this contextualized, local information can support earlier financing and
better targeting of early actions than regional DEWSs.%
\end{abstract}%



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\textbf{Hosted file}

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