Estimation of annual baseflow at ungauged sites in Indiana USA

Ahiablame, Laurent; Chaubey, Indrajeet; Engel, Bernard A.; Cherkauer, Keith A.; Merwade, Venkatesh

doi:10.1016/j.jhydrol.2012.10.002

Cited by 81 publications

(79 citation statements)

References 55 publications

(121 reference statements)

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“…2). Previous studies in the Midwest and northern Great Plains states also reported similar BFI values (Santhi et al, 2008;Ahiablame et al, 2013;Schilling and Helmers, 2008). As baseflow is an important component of streamflow, understanding the spatial-temporal variation of baseflow is of great value to development of water resources (Santhi et al, 2008).…”

Section: Mean Variability and Spatial Distribution Of Baseflowmentioning

confidence: 67%

“…The Eckhardt filter method (Eckhardt, 2005(Eckhardt, , 2008 was used on daily streamflow for baseflow separation in this study. The Eckhardt filter has been validated and widely used in similar analyses (Eckhardt, 2008;Lim et al, 2005;Ahiablame et al, 2013). It is mathematically expressed as (Eckhardt, 2005(Eckhardt, , 2008:…”

Section: Baseflow Separationmentioning

confidence: 99%

“…Under the condition that Q bf Q sf , baseflow for the first time step was assumed 50% of streamflow by the algorithm. Baseflow index is the proportion of baseflow in long-term streamflow records, expressed as a percentage (e.g., Eckhardt, 2005Eckhardt, , 2008Santhi et al, 2008;Ahiablame et al, 2013). The two parameters of Eckhardt filter consist of recession constant and BFImax (Eckhardt, 2005).…”

Section: Baseflow Separationmentioning

confidence: 99%

“…Baseflow is an important component of streamflow (Santhi et al, 2008;Ahiablame et al, 2013;Rumsey et al, 2015). As such, understanding its availability and contribution to streamflow are critical for appropriate planning and management of water resources (Santhi et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Research showed that baseflow represents more than 50% of total streamflow in the United States Midwest and Great Plains regions (e.g. Santhi et al, 2008;Ahiablame et al, 2013;Rumsey et al, 2015). To continue to meet critical water demands, as mentioned above, there is a need to provide relevant science-based information to increase understanding of the role of regional controls on MORB baseflow.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Annual baseflow variations as influenced by climate variability and agricultural land use change in the Missouri River Basin

Ahiablame

Sheshukov

Rahmani

et al. 2017

Journal of Hydrology

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a b s t r a c tThe Missouri River system has a large water storage capacity, where baseflow plays an important role. Understanding historical baseflow characteristics with respect to climate and land use impacts is essential for effective planning and management of water resources in the Missouri River Basin (MORB). This study evaluated statistical trends in baseflow and precipitation for 99 MORB minimally disturbed watersheds during 1950-2014. Elasticity of baseflow to climate variability and agricultural land use change were quantified for the 99 studied watersheds. Baseflow was derived from daily streamflow records with a recursive digital filter method. The results showed that baseflow varied between 38 and 80% (0 and 331 mm/year) of total streamflow with an average of 60%, indicating that more than half of streamflow in the MORB is derived from baseflow. The trend analysis revealed that precipitation increased during the study period in 78 out of 99 watersheds, leading to 1-3.9% noticeable increase in baseflow for 68 of 99 watersheds. Although the changes in baseflow obtained in this study were a result of the combined effects of climate and land use change across the basin, upward trends in baseflow generally coincide with increased precipitation and agricultural land use trends in the basin. Agricultural land use increase mostly led to a 0-5.7% decrease in annual baseflow in the basin, except toward east of the basin where baseflow mostly increased with agricultural land use increase (0.1-2.0%). In general, a 1% increase in precipitation and a 1% increase in agricultural land use resulted in 1.5% increase and 0.2% decrease in baseflow, respectively, during the study period. These results are entirely dependent on the quality of data used; however, they provide useful insight into the relative influence of climate and land use change on baseflow conditions in the Great Plains region of the USA.

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Section: Mean Variability and Spatial Distribution Of Baseflowmentioning

confidence: 67%

Section: Baseflow Separationmentioning

confidence: 99%

Section: Baseflow Separationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Annual baseflow variations as influenced by climate variability and agricultural land use change in the Missouri River Basin

Ahiablame

Sheshukov

Rahmani

et al. 2017

Journal of Hydrology

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Global patterns in base flow index and recession based on streamflow observations from 3394 catchments

et al. 2013

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[1] Numerous previous studies have constructed models to estimate base flow characteristics from climatic and physiographic characteristics of catchments and applied these to ungauged regions. However, these studies generally used streamflow observations from a relatively small number of catchments (<200) located in small, homogeneous study areas, which may have led to less reliable models with limited applicability elsewhere. Here, we use streamflow observations from a highly heterogeneous set of 3394 catchments (<10,000 km 2 ) worldwide to construct reliable, widely applicable models based on 18 climatic and physiographic characteristics to estimate two important base flow characteristics: (1) the base flow index (BFI), defined as the ratio of long-term mean base flow to total streamflow; and (2) the base flow recession constant (k), defined as the rate of base flow decay. Regression analysis results revealed that BFI and k were related to several climatic and physiographic characteristics, notably mean annual potential evaporation, mean snow water equivalent depth, and abundance of surface water bodies. Ensembles of artificial neural networks (ANNs; obtained by subsampling the original set of catchments) were trained to estimate the base flow characteristics from climatic and physiographic data. The catchment-scale estimation of the base flow characteristics demonstrated encouraging performance with R 2 values of 0.82 for BFI and 0.72 for k. The connection weights of the trained ANNs indicated that climatic characteristics were more important for estimating k than BFI. Global maps of estimated BFI and k were obtained using global climatic and physiographic data as input to the derived models. The resulting global maps are available for free download at http://www.hydrology-amsterdam.nl.

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Regionalization of land‐use impacts on streamflow using a network of paired catchments

Ochoa‐Tocachi

Buytaert

Bièvre

2016

Water Resources Research

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Quantifying the impact of land use and cover (LUC) change on catchment hydrological response is essential for land‐use planning and management. Yet hydrologists are often not able to present consistent and reliable evidence to support such decision‐making. The issue tends to be twofold: a scarcity of relevant observations, and the difficulty of regionalizing any existing observations. This study explores the potential of a paired catchment monitoring network to provide statistically robust, regionalized predictions of LUC change impact in an environment of high hydrological variability. We test the importance of LUC variables to explain hydrological responses and to improve regionalized predictions using 24 catchments distributed along the Tropical Andes. For this, we calculate first 50 physical catchment properties, and then select a subset based on correlation analysis. The reduced set is subsequently used to regionalize a selection of hydrological indices using multiple linear regression. Contrary to earlier studies, we find that incorporating LUC variables in the regional model structures increases significantly regression performance and predictive capacity for 66% of the indices. For the runoff ratio, baseflow index, and slope of the flow duration curve, the mean absolute error reduces by 53% and the variance of the residuals by 79%, on average. We attribute the explanatory capacity of LUC in the regional model to the pairwise monitoring setup, which increases the contrast of the land‐use signal in the data set. As such, it may be a useful strategy to optimize data collection to support watershed management practices and improve decision‐making in data‐scarce regions.

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Estimation of annual baseflow at ungauged sites in Indiana USA

Cited by 81 publications

References 55 publications

Annual baseflow variations as influenced by climate variability and agricultural land use change in the Missouri River Basin

Annual baseflow variations as influenced by climate variability and agricultural land use change in the Missouri River Basin

Global patterns in base flow index and recession based on streamflow observations from 3394 catchments

Regionalization of land‐use impacts on streamflow using a network of paired catchments

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