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Research Collection Research Collection Journal Article Assessment of CHADFDM satellite-based input dataset for the groundwater recharge estimation in arid and data scarce regions Author(s): Salehi Siavashani, Nafiseh; Jimenez-Martinez, Joaquin; Vaquero, Guillermo; Elorza, Francisco J.; Sheffield, Justin; Candela, Lucila; Serrat-Capdevila, Aleix Publication Date: 2021-06 Permanent Link: https://doi.org/10.3929/ethz-b-000492881 Originally published in: Hydrological Processes 35(6), http://doi.org/10.1002/hyp.14250 Rights / License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Received: 29 May 2020 Revised: 5 May 2021 Accepted: 14 May 2021 DOI: 10.1002/hyp.14250 RESEARCH ARTICLE Assessment of CHADFDM satellite-based input dataset for the groundwater recharge estimation in arid and data scarce regions Nafiseh Salehi Siavashani1,2,3 | Joaquin Jimenez-Martinez4,5 | Guillermo Vaquero2,3 | Francisco J. Elorza6 | Justin Sheffield7 | Lucila Candela2 | Aleix Serrat-Capdevila8 1Department of Civil and Environmental Engineering, Technical University of Catalonia, Abstract Barcelona, Spain Aquifer natural recharge estimations are a prerequisite for understanding hydrologic 2 IMDEA Agua, Alcala de Henares, Spain systems and sustainable water resources management. As meteorological data series 3Fundacion Gomez Pardo, Madrid, Spain collection is difficult in arid and semiarid areas, satellite products have recently 4Department of Water Resources and Drinking Water, Eawag, Dübendorf, become an alternative for water resources studies. A daily groundwater recharge Switzerland estimation in the NW part of the Lake Chad Basin, using a soil–plant-atmosphere 5Department of Civil, Environmental and model (VisualBALAN), from ground- and satellite-based meteorological input dataset Geomatic Engineering, ETH Zurich, Zürich, Switzerland for non-irrigated and irrigated land and for the 2005–2014 period is presented. Aver- 6 School of Mining and Energy Engineering, age annual values were 284 mm and 30C for precipitation and temperature in Technical University of Madrid, Madrid, Spain ground-based gauge stations. For the satellite-model-based Lake Chad Basin Flood 7Departmentof Geography, University of Southampton, Southampton, UK and Drought Monitor System platform (CHADFDM), average annual precipitation 8Water Global Practice, The World Bank, and temperature were 417 mm and 29C, respectively. Uncertainties derived from Washington, D.C., USA satellite data measurement could account for the rainfall difference. The estimated Correspondence mean annual aquifer recharge was always higher from satellite- than ground-based Nafiseh Salehi Siavashani, Department of Civil and Environmental Engineering, Technical data, with differences up to 46% for dryland and 23% in irrigated areas. Recharge University of Catalonia, Barcelona, Spain. response to rainfall events was very variable and results were very sensitive to: Email: [email protected] wilting point, field capacity and curve number for runoff estimation. Obtained results provide plausible recharge values beyond the uncertainty related to data input and modelling approach. This work prevents on the important deviations in recharge esti- mation from weighted-ensemble satellite-based data, informing in decision making to both stakeholders and policy makers. KEYWORDS CHADFDM data set, ground-satellite meteorological data, groundwater recharge modelling, Lake Chad Basin This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2021 The Authors. Hydrological Processes published by John Wiley & Sons Ltd. Hydrological Processes. 2021;35:e14250. wileyonlinelibrary.com/journal/hyp 1of15 https://doi.org/10.1002/hyp.14250 2of15 SALEHI SIAVASHANI ET AL. 1 | INTRODUCTION et al., 2016; Lu, Wang, et al., 2016; Nogueira et al., 2018). For ground- water recharge quantifications based on the water balance equation, Scarce precipitation, interannual dry periods, marked seasonal and the use of field and satellite products has been assessed in similar cli- spatial variability and extreme rainfall events are the main features of mate areas, ranging between less than 100 km2 and 104 km2, as sum- arid and semiarid zones that condition groundwater recharge (Lerner marized by Coelho et al. (2017). Most approaches generally use et al., 1990). Therefore, reliable rainfall and temperature data in these rainfall and evapotranspiration inputs for studies conducted in the areas are extremely important for accurately assessing both soil water Guarani aquifer (Lucas et al., 2015), Pakistan (Usman et al., 2015) and balance and aquifer recharge (Wu et al., 1996). Beyond inherent in the West Bank (Khalaf & Donoghue, 2012); the lately also incorpo- uncertainties to available in situ data, that is, from ground-based rates irrigation data parameters and surface water input. For arid weather stations, one of the main difficulties is field data scarcity Northern Brazil, the work of Coelho et al. (2017) considers spatially (Bhowmik & Costa, 2014; Dumolard et al., 2007). Meteorological sta- varying runoff and soil moisture in the water balance equation, along tions are generally scarce, unevenly distributed, and may operate dur- with ancillary land use/land cover data. Wu et al. (2019) determined ing short periods or imply major gaps. This can, in turn, lead to the aquifer annual and long-term recharge trend at regional scale with problems if long-term values are required when only short dataset GRACE and GLDAS in the semiarid region of the Ordos Basin, China. periods are available (Lerner et al., 1990). Data and information are There was no obvious long-term trend observed, and the annual also often scattered among different agencies with limited access, recharge can be explained by the variability in precipitation. For which makes it difficult to obtain complete records. This generally modelling purposes, several physically based distributed numerical results in information that is not very suitable for practical needs. To codes, found in different water balance approaches such as overcome this challenge, researchers have traditionally increased data SAHYSMOD (ILRI, 2005), VisualBALAN (Samper et al., 2005), TOPOG availability using different statistical methods (Wagner et al., 2012), (CSIRO, 2008) or SWB2 (Westenbroek et al., 2018), among others, especially by bridging gaps in time series, interpolating between data are currently applied. The generally available requested input data, points, introducing uncertainties given rainfall intermittency and large estimated reasonably accurately, and the facility to modify or substi- distances between observation points. tute different input datasets, make VisualBALAN an excellent candi- An alternative and complementary source of information comes date for recharge calculations. VisualBALAN v2.0 offers an from satellite products (e.g., TRMM, CMORPH, TMPA, PERSIANN), intermediate level of difficulty. This model code estimates a sequential which can provide meteorological data series over large areas, and are water balance for the soil, the unsaturated zone and the aquifer, and useful for making multiple applications in hydrology (Beck, van Dijk, has proven successful in calculating groundwater recharge in various et al., 2017; Beck, Vergopolan, et al., 2017; Habib et al., 2008; Mzirai hydrogeological conditions (Samper et al., 1999; Espinha-Marques et al., 2005; Sheffield et al., 2018; Velpuri & Senay, 2013). For exam- et al., 2011; Touhami et al., 2013). ple, these sources of information have been used to develop large- The quantification of natural groundwater recharge is a basic scale drought monitoring systems with also the goal of stakeholders requirement for efficient water resources management. The diffuse use in mind, which monitor in near real-time the terrestrial water cycle recharge is a complex function that results from the coupling of sev- based on remote sensing data and land surface hydrological modelling eral factors: precipitation (volume, intensity, duration), air tempera- (Sheffield et al., 2014). ture, topography, vegetation (cropping pattern, rooting depth) and Given the general ground data scarcity in arid and semiarid zones, evapotranspiration, soil and subsoil types, flow mechanisms in the satellite remote sensing products are a potentially useful direct or unsaturated zone, bedrock geology and available groundwater storage indirect data source of most hydrological cycle components. Never- (Scanlon et al., 2002). Of these, precipitation and evapotranspiration theless, satellite products use is influenced/limited by uncertainties are the system's driven forces. Depending on relief, not concentrated resulting from its wide space–time coverage and spatial resolution, recharge from runoff and ponding may be also dominant mechanisms which is especially relevant in these regions due to the high spatial in arid environments. Several reviews on aquifer recharge quantifica- and temporal variability of surface meteorology and elevation- tion based on different methods have been conducted in the past, dependent biases (Bitew & Gebremichael, 2010). In some cases, satel- and have focused
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