Choosing appropriate techniques for quantifying groundwater recharge

Scanlon, Bridget R.; Healy, Richard W.; Cook, Peter

doi:10.1007/s10040-002-0200-1

Cited by 490 publications

(767 citation statements)

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“…Methods of estimating groundwater recharge are broadly categorized as waterbudget methods, modeling methods, physical methods involving saturated and unsaturated zones, and tracer techniques involving chemical, isotopic, and heat tracers (Healy 2010). In spite of the variety of the available techniques, precise recharge estimation is difficult because of high levels of uncertainty associated with most estimation methods (Scanlon et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Mapping mean annual groundwater recharge in the Nebraska Sand Hills, USA

et al. 2011

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[2000][2001][2002][2003][2004][2005][2006][2007][2008][2009] period was estimated at a 1-km spatial resolution as the difference of mean annual precipitation (P) and evapotranspiration (ET). Monthly P values came from the PRISM dataset, while monthly ET values were derived from linear transformations of the MODIS daytime land-surface temperature values into pixel ET rates with the help of ancillary atmospheric data (air temperature, humidity, and global radiation). The study area receives about 73 mm of recharge (with an error bound of ±73 mm) annually, which is about 14 ± 14% of the regional mean annual P value of 533 mm. The largest recharge rates (about 200 ± 85 mm or 30 ± 12% of P) occur in the south-eastern part of the Sand Hills due to smoother terrain and more abundant precipitation (around 700 mm), while recharge is the smallest (about 40 ± 59 mm or 10 ± 14% of P) in the western part, where annual precipitation is only about 420 mm. Typically, lakes, wetlands, wet inter-dunal valleys, rivers, irrigated crops (except in the south-eastern region) and certain parts of afforested areas in the south-central portion of the study area act as discharge areas for groundwater.

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Section: Introductionmentioning

confidence: 99%

Mapping mean annual groundwater recharge in the Nebraska Sand Hills, USA

et al. 2011

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“…However, according to SCANLON et al (2002), the biggest limitation of the water balance method is that the precision of the estimate of recharge depends on the accuracy with which the components of the equation were measured or estimated. It should be noted that in any case involving the quantification of information, the accuracy of the data collected is a desirable and necessary requirement, and the designation made by SCANLON et al (2002) is not a limitation but rather a requirement for a good estimate of the recharge water balance method.…”

Section: Water Balance Methodsmentioning

confidence: 99%

“…Recharge is calculated as follows SCANLON et al, 2002;WAHNFREID & HIRATA, 2005): (03) in which R d is the direct recharge (LT -1 ), S y is the aquifer specific yield (dimensionless); h is the groundwater level (L), and t is time (T).…”

Section: Methods Of Free Surface Fluctuationmentioning

confidence: 99%

Groundwater recharge estimate at Alto Rio Grande - MG watershed

Silva¹,

Silva

Coelho

et al. 2012

Eng. Agríc.

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Springs are outcrops of aquifers surface, and the water cycle in this environment pass through the recharge, generally defined as the amount of water added to the aquifer, which may occur locally from rainwater infiltration. This study uses the Water Table Fluctuation (WTF) method to estimate the direct recharge and a groundwater balance to estimate the deep recharge on unconfined aquifers. The WTF method employs data of the aquifer water levels and its specific yield to estimate the direct recharge. The groundwater balance considers the direct recharge estimated by the WTF method, as the water input in the system and outputs as the base flow and deep recharge. The recharge was estimated at four areas at the watershed of Alto Rio Grande city, Minas Gerais (MG) state, in Brazil. The direct recharge estimate was 121.11; 64.62; 83.99; 152.46 (mm/year) for the L1, L2, M1 and M2 areas. The effect of the presence of forest in the recharge area can prevail over slope of relief, allowing more direct recharge, even in sources with steeper relief. The runoff from the springs in the study period exceeded the direct recharge, indicating a situation in which the saturated zone feeds the vadose zone. The annual flow was above the direct recharge pointing a situation of over exploitation of the aquifer, a non sustainable situation. The specific yield of the aquifers could also have been underestimated. KEYWORDS:Groundwater recharge; Aquifer surface; Springs. RECARGA DE AQUÍFEROS SUPERFICIAIS NA REGIÃO DO ALTO RIO GRANDE-MG RESUMO:As nascentes são afloramentos dos aquíferos superficiais, e o ciclo da água neste ambiente passa pela recarga que é definida, de maneira geral, como a quantidade de água que é acrescentada ao aquífero, podendo ocorrer localmente a partir da infiltração das águas das chuvas. Neste trabalho, apresenta-se o método de flutuação das superfícies livres (Water Table Fluctuation-WTF) para estimativa da recarga direta e um balanço hídrico subterrâneo para estimativa da recarga profunda, em aquíferos livres. O método WTF utiliza dados de monitoramento do nível do aquífero e rendimento específico do aquífero para a determinação da recarga direta. O balanço hídrico subterrâneo assume a recarga direta estimada pelo método WTF como sendo a entrada de água no sistema, e as saídas, como sendo o escoamento de base e a recarga profunda. As estimativas de recarga foram aplicadas a quatro áreas de recarga de aquíferos superficiais da região do Alto Rio Grande-MG. A estimativa da recarga direta foi de 121,11; 64,62; 83,99 e 152,46 (mm/ano) para as áreas L1, L2, M1 e M2. O efeito da presença de mata na área de recarga pode prevalecer sobre o da declividade do relevo, proporcionando maior recarga direta, mesmo em áreas de recarga com relevo mais íngreme. A drenagem do aquífero superficial (deflúvio propiciado pelas nascentes) no período avaliado superou a recarga direta, indicando uma situação em que a zona saturada, alimenta a zona vadosa.

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“…Thus, one can improve the accuracy of modeling of such complex phenomena by selecting new variables, assuming their relevance and by exploring their relationships to the predicted values. Similarly, estimation of groundwater recharge and discharge (R/D) rates can be based on multiple existing physically based models (Scanlon et al 2002). However, the measurements of such physically based models are difficult and expensive to collect, and a physically based inverse approach provides an alternative, employing readily available information to decrease the processing time (Lin et al 2008b).…”

Section: Introductionmentioning

confidence: 99%

A framework for accurate geospatial modeling using image ranking and machine learning

Bajcsy

Lin

Yahja

et al. 2010

Journal of Hydroinformatics

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There is a large class of modeling problems where the complexity of the underlying phenomena is overwhelming and hence the accuracy of mathematical models is limited. Our approach to this class of problems is to design frameworks that bring together physically based and data-driven models, and incorporate the tacit knowledge of experts by providing visual exploration and feedback capabilities. This paper presents such a novel computer-assisted framework for accurate geospatial modeling applied to improve groundwater recharge and discharge (R/D) patterns. The novelty of our work is in designing a methodology for ranking and extracting relationships, as well as in developing a general framework for building accurate geospatial models. The framework combines variables derived using physically based inverse modeling with auxiliary geospatial variables directly sensed, ranks variables and extracts variable relationships using data-driven (''machine learning'') techniques, and supports partially expert-driven trial-and-error experimentation and more rigorous optimization, as well as visual explorations, to derive more accurate models for R/D pattern estimation. When the framework was tested by experts, it led to a high level of consistency between the machine-learning-based knowledge and the experts' knowledge about R/D distribution. The prototype solution of the framework is available for downloading at http://isda.ncsa.uiuc.edu/Sp2Learn/.

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Choosing appropriate techniques for quantifying groundwater recharge

Cited by 490 publications

References 0 publications

Mapping mean annual groundwater recharge in the Nebraska Sand Hills, USA

Mapping mean annual groundwater recharge in the Nebraska Sand Hills, USA

Groundwater recharge estimate at Alto Rio Grande - MG watershed

A framework for accurate geospatial modeling using image ranking and machine learning

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