Base-Flow Index Grid for the Conterminous United States

2003 ◽  
Author(s):  
David M. Wolock
Keyword(s):  
United States ◽  
Base Flow ◽  
Flow Index ◽  
Base Flow Index

scholarly journals Towards a hydrological classification of European soils: preliminary test of its predictive power for the base flow index using river discharge data

2007 ◽  
Vol 11 (4) ◽  
pp. 1501-1513 ◽  
Author(s):  
M. K. Schneider ◽  
F. Brunner ◽  
J. M. Hollis ◽  
C. Stamm
Keyword(s):  
Water Movement ◽  
Base Flow ◽  
Regression Coefficients ◽  
Northern Europe ◽  
Flow Index ◽  
Discharge Data ◽  
Base Flow Index ◽  
The Uk

Abstract. Predicting discharge in ungauged catchments or contaminant movement through soil requires knowledge of the distribution and spatial heterogeneity of hydrological soil properties. Because hydrological soil information is not available at a European scale, we reclassified the Soil Geographical Database of Europe (SGDBE) at 1:1 million in a hydrological manner by adopting the Hydrology Of Soil Types (HOST) system developed in the UK. The HOST classification describes dominant pathways of water movement through soil and was related to the base flow index (BFI) of a catchment (the long-term proportion of base flow on total stream flow). In the original UK study, a linear regression of the coverage of HOST classes in a catchment explained 79% of BFI variability. We found that a hydrological soil classification can be built based on the information present in the SGDBE. The reclassified SGDBE and the regression coefficients from the original UK study were used to predict BFIs for 103 catchments spread throughout Europe. The predicted BFI explained around 65% of the variability in measured BFI in catchments in Northern Europe, but the explained variance decreased from North to South. We therefore estimated new regression coefficients from the European discharge data and found that these were qualitatively similar to the original estimates from the UK. This suggests little variation across Europe in the hydrological effect of particular HOST classes, but decreasing influence of soil on BFI towards Southern Europe. Our preliminary study showed that pedological information is useful for characterising soil hydrology within Europe and the long-term discharge regime of catchments in Northern Europe. Based on these results, we draft a roadmap for a refined hydrological classification of European soils.

scholarly journals Conditioning rainfall-runoff model parameters for ungauged catchments and land management impacts analysis

2009 ◽  
Vol 13 (6) ◽  
pp. 893-904 ◽  
Author(s):  
N. Bulygina ◽  
N. McIntyre ◽  
H. Wheater
Keyword(s):  
Parameter Space ◽  
Land Management ◽  
Base Flow ◽  
Distributed Model ◽  
Flow Index ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Management Interventions ◽  
Simulated Event ◽  
Base Flow Index

Abstract. Data scarcity and model over-parameterisation, leading to model equifinality and large prediction uncertainty, are common barriers to effective hydrological modelling. The problem can be alleviated by constraining the prior parameter space using parameter regionalisation. A common basis for regionalisation in the UK is the HOST database which provides estimates of hydrological indices for different soil classifications. In our study, Base Flow Index is estimated from the HOST database and the power of this index for constraining the parameter space is explored. The method is applied to a highly discretised distributed model of a 12.5 km2 upland catchment in Wales. To assess probabilistic predictions against flow observations, a probabilistic version of the Nash-Sutcliffe efficiency is derived. For six flow gauges with reliable data, this efficiency ranged between 0.70 and 0.81, and inspection of the results shows that the model explains the data well. Knowledge of how Base Flow Index and interception losses may change under future land use management interventions was then used to further condition the model. Two interventions are considered: afforestation of grazed areas, and soil degradation associated with increased grazing intensity. Afforestation leads to median reduction in modelled runoff volume of 24% over the simulated 3 month period; and a median peak flow reduction ranging from 12 to 15% over the six gauges for the largest simulated event. Uncertainty in all results is low compared to prior uncertainty and it is concluded that using Base Flow Index estimated from HOST is a simple and potentially powerful method of conditioning the parameter space under current and future land management.

scholarly journals Controls on hydrologic drought duration in near-natural streamflow in Europe and the USA

2016 ◽  
Vol 20 (10) ◽  
pp. 4043-4059 ◽  
Author(s):  
Erik Tijdeman ◽  
Sophie Bachmair ◽  
Kerstin Stahl
Keyword(s):  
Large Scale ◽  
Aridity Index ◽  
Base Flow ◽  
Classification Systems ◽  
Flow Index ◽  
Climate Classification ◽  
Drought Duration ◽  
Base Flow Index ◽  
The Usa ◽  
Hydrologic Drought

Abstract. Climate classification systems, such as Köppen–Geiger and the aridity index, are used in large-scale drought studies to stratify regions with similar hydro-climatic drought properties. What is currently lacking is a large-scale evaluation of the relation between climate and observed streamflow drought characteristics. In this study we explored how suitable common climate classifications are for differentiating catchments according to their characteristic hydrologic drought duration and whether drought durations within the same climate classes are comparable between different regions. This study uses a dataset of 808 near-natural streamflow records from Europe and the USA to answer these questions. First, we grouped drought duration distributions of each record over different classes of four climate classification systems and five individual climate and catchment controls. Then, we compared these drought duration distributions of all classes within each climate classification system or classification based on individual controls. Results showed that climate classification systems that include absolute precipitation in their classification scheme (e.g., the aridity index) are most suitable for differentiating catchments according to drought duration. However, differences in duration distributions were found for the same climate classes in Europe and the USA. These differences are likely caused by differences in precipitation, in catchment controls as expressed by the base flow index and in differences in climate beyond the total water balance (e.g., seasonality in precipitation), which have been shown to exert a control on drought duration as well. Climate classification systems that include an absolute precipitation control can be tailored to drought monitoring and early warning systems for Europe and the USA to define regions with different sensitivities to hydrologic droughts, which, for example, have been found to be higher in catchments with a low aridity index. However, stratification of catchments according to these climate classification systems is likely to be complemented with information of other climate classification systems (Köppen–Geiger) and individual climate and catchment controls (precipitation and the base flow index), especially in a comparative study between Europe and the USA.

scholarly journals Quantification of Temporal Variations in Base Flow Index Using Sporadic River Data: Application to the Bua Catchment, Malawi

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 901 ◽  
Author(s):  
Laura Kelly ◽  
Robert M. Kalin ◽  
Douglas Bertram ◽  
Modesta Kanjaye ◽  
Macpherson Nkhata ◽  
...  
Keyword(s):  
Water Resources ◽  
River Flow ◽  
Temporal Variations ◽  
Dry Season ◽  
Base Flow ◽  
Flow Index ◽  
Wet Season ◽  
Data Application ◽  
Base Flow Index ◽  
Long Term Trends

This study investigated how sporadic river datasets could be used to quantify temporal variations in the base flow index (BFI). The BFI represents the baseflow component of river flow which is often used as a proxy indicator for groundwater discharge to a river. The Bua catchment in Malawi was used as a case study, whereby the smoothed minima method was applied to river flow data from six gauges (ranging from 1953 to 2009) and the Mann-Kendall (MK) statistical test was used to identify trends in BFI. The results showed that baseflow plays an important role within the catchment. Average annual BFIs > 0.74 were found for gauges in the lower reaches of the catchment, in contrast to lower BFIs < 0.54 which were found for gauges in the higher reaches. Minimal difference between annual and wet season BFI was observed, however dry season BFI was >0.94 across all gauges indicating the importance of baseflow in maintaining any dry season flows. Long term trends were identified in the annual and wet season BFI, but no evidence of a trend was found in the dry season BFI. Sustainable management of the investigated catchment should, therefore, account for the temporal variations in baseflow, with special regard to water resources allocation within the region and consideration in future scheme appraisals aimed at developing water resources. Further, this demonstration of how to work with sporadic river data to investigate baseflow serves as an important example for other catchments faced with similar challenges.

scholarly journals Conditioning rainfall-runoff model parameters for ungauged catchments and land management impacts analysis

2009 ◽  
Vol 6 (2) ◽  
pp. 1907-1938 ◽  
Author(s):  
N. Bulygina ◽  
N. McIntyre ◽  
H. Wheater
Keyword(s):  
Parameter Space ◽  
Land Management ◽  
Base Flow ◽  
Distributed Model ◽  
Flow Index ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Management Interventions ◽  
Simulated Event ◽  
Base Flow Index

Abstract. Data scarcity and model over-parameterisation, leading to model equifinality and large prediction uncertainty, are common barriers to effective hydrological modelling. The problem can be alleviated by constraining the prior parameter space using parameter regionalization. A common basis for regionalization in the UK is the HOST database which provides estimates of hydrological indices for different soil classifications. In our study, Base Flow Index is estimated from the HOST database and the power of this index for constraining the parameter space is explored. The method is applied to a highly discretized distributed model of a 12.5 km2 upland catchment in Wales. To assess probabilistic predictions against flow observations, a probabilistic version of the Nash-Sutcliffe efficiency is derived. For six flow gauges with reliable data, this efficiency ranged between 0.70 and 0.81, and inspection of the results shows that the model explains the data well. Knowledge of how Base Flow Index and interception losses may change under future land use management interventions was then used to further condition the model. Two interventions are considered: afforestation of grazed areas, and soil degradation associated with increased grazing intensity. Afforestation leads to median reduction in modelled runoff volume of 24% over the simulated 3 month period; and a median peak flow reduction ranging from 12–15% over the six gauges for the largest simulated event. Uncertainty in all results is suprisingly low and it is concluded that using Base Flow Index estimated from HOST is a simple and potentially powerful method of conditioning the parameter space under current and future land management.

Landscape Influences on Stream Habitats and Biological Assemblages

2006 ◽  
Keyword(s):  
Rainbow Trout ◽  
Brown Trout ◽  
Brook Trout ◽  
Coho Salmon ◽  
Base Flow ◽  
Flow Index ◽  
Great Lakes Basin ◽  
Habitat Features ◽  
Base Flow Index ◽  
Local Habitat

<em>Abstract.</em>—Effective management of salmonid populations in the Great Lakes basin requires understanding how their distribution and density vary spatially. We used a hierarchical approach to evaluate the predictive capabilities of landscape conditions, local habitat features, and potential effects from coinhabiting salmonids on the distribution and densities of rainbow trout <em>Oncorhynchus mykiss</em>, brook trout <em>Salvelinus fontinalis, </em>brown trout <em>Salmo trutta</em>, and coho salmon <em>O. kisutch </em>within the majority of the Canadian tributaries of Lake Ontario. We collected fish assemblage, instream habitat, and water temperature data from 416 wadeable stream sites. Landscape characteristics were obtained for each site’s catchment and summarized into six key attributes (drainage area, base flow index, percent impervious cover (PIC), reach slope, elevation, and location with respect to permanent fish barriers). Classification trees indicated that PIC in a catchment was a critical predictor of salmonid distribution, in that beyond a threshold of 6.6–9 PIC, all salmonids were predicted to be absent. Base flow index and barriers were also important predictors of the distribution of salmonids. Models generally provided higher classification success at predicting absence (86–98%) than predicting presence (63–87%). Landscape features were the best predictors of densities of rainbow and brook trout (adjusted <em>r</em><sup>2</sup> = 0.49 and 0.30 respectively), although the local habitat features were almost as effective for predicting brook trout (<em>r</em><sup>2</sup> = 0.23). Local habitat features (proportion of riffles and pools, substrate, cover, and stream temperature), and presence of other salmonids produced the best predictive model for brown trout. Coho salmon was only locally distributed in the basin, and the derived model was driven by spatial characteristics rather than ecological processes. Our models estimate 653,000 juvenile rainbow trout and 231,000 brook trout (all age-classes) in our study streams. Finally, we estimate that current brook trout distribution in our study area is only 21% of its historic range.

Regional Estimation of Base Recharge to Ground Water Using Water Balance and a Base-Flow Index

Ground Water ◽  
2003 ◽  
Vol 41 (4) ◽  
pp. 504-513 ◽  
Author(s):  
Jozsef Szilagyi ◽  
F. Edwin Harvey ◽  
Jerry F. Ayers
Keyword(s):  
Water Balance ◽  
Ground Water ◽  
Base Flow ◽  
Flow Index ◽  
Base Flow Index ◽  
Regional Estimation
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