Automated approaches for regional runoff mapping in the northeastern United States

Bishop, Gary D.; Church, M. Robbins

doi:10.1016/0022-1694(92)90126-g

Cited by 23 publications

(12 citation statements)

References 22 publications

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“…Increasing the accuracy of the soil water holding capacity data plane is a n important step in improving the accuracy of the regional predictions. We are currently pursuing validation of PnET-I1 water yield predictions at the regional scale through comparison with regional extrapolations from gauged watershed data (Bishop & Church 1992), and regional forest production estimates by comparison with U.S. Forest Service Forest Inventory and Analysis data. The assumption of doubled water use efficiency in a 2 x C 0 2 atmosphere is also crucial to this conclusion, making this an important area for future research.…”

Section: Discussionmentioning

confidence: 99%

Predicting the effects of climate change on water yield and forest production in the northeastern United States

Aber¹,

Ollinger²,

Federer³

et al. 1995

Clim. Res.

300

228

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Rap~d and simultaneous changes in temperature, precipitation and the atmospheric concentration of CO, are predicted to occur over the next century. Simple, well-validated models of ecosystem function are required to predict the effects of these changes. This paper describes an improved version of a forest carbon and water balance model (PnET-11) and the application of the model to predict stand-and regional-level effects of changes in temperature, precipitation and atmospheric CO2 conceniraiion. PnET-ii is d s u~~p i e , y e~~e l d i i~e d , l~lu~lii~iy ii~~ie-btep ~nociel of water and carbon "vlances (gross and net) driven by nitrogen availability as expressed through foliar N concentration. Improvements from the orig~nal model include a complete carbon balance and improvements in the prediction of canopy phenology, as well as in the computation of canopy structure and photosynthesis. The model was parameterized and run for 4 forestkite con~binations and validated against available data for water yield, gross and net carbon exchange and biomass production. The validation exercise suggests that the determination of actual water availability to stands and the occurrence or non-occurrence of soilbased water stress are critical to accurate modeling of forest net primary production (NPP) and net ecosystem production (NEP). The model was then run for the entire NewEngland/New York (USA) region using a 1 km resolution geographic information system. Predicted long-term NEP ranged from -85 to +275 g C m-2 yr" for the 4 forest/site combinations, and from -150 to 350 g C m-' yr-' for the region, with a regional average of 76 g C m-2 yr-l A con~bination of increased temperature (+6OC), decreased precipitation (-15%) and increased water use efficiency (2x, due to doubling of CO,) resulted generally in increases in NPP and decreases in water yield over the region.

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Section: Discussionmentioning

confidence: 99%

Predicting the effects of climate change on water yield and forest production in the northeastern United States

Aber¹,

Ollinger²,

Federer³

et al. 1995

Clim. Res.

300

228

View full text Add to dashboard Cite

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“…There are a number of approaches to predicting runoff in ungauged catchments, including processbased modeling (e.g., Müller et al, 2014), graphical methods based on the construction of isolines (e.g., Bishop and Church, 1992), and statistical approaches. Statistical approaches are often implemented via linear regression, wherein the runoff signature of interest is considered to be an unobservable random variable correlated with observable features of both gauged and ungauged basins (e.g.…”

Section: Linear Modelsmentioning

confidence: 99%

A topological restricted maximum likelihood (TopREML) approach to regionalize trended runoff signatures in stream networks

Müller¹,

Thompson²

2015

Preprint

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Abstract. We introduce TopREML as a method to predict runoff signatures in ungauged basins. The approach is based on the use of linear mixed models with spatially correlated random effects. The nested nature of streamflow networks is taken into account by using water balance considerations to constrain the covariance structure of runoff and to account for the stronger spatial correlation between flow-connected basins. The restricted maximum likelihood (REML) framework generates the best linear unbiased predictor (BLUP) of both the predicted variable and the associated prediction uncertainty, even when incorporating observable covariates into the model. The method was successfully tested in cross validation analyses on mean streamflow and runoff frequency in Nepal (sparsely gauged) and Austria (densely gauged), where it matched the performance of comparable methods in the prediction of the considered runoff signature, while significantly outperforming them in the prediction of the associated modeling uncertainty. TopREML's ability to combine deterministic and stochastic information to generate BLUPs of the prediction variable and its uncertainty makes it a particularly versatile method that can readily be applied in both densely gauged basins, where it takes advantage of spatial covariance information, and data-scarce regions, where it can rely on covariates, which are increasingly observable thanks to remote sensing technology.

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“…There are a number of approaches to predicting runoff in ungauged catchments, including process-based modeling (e.g., Müller et al, 2014), graphical methods based on the construction of isolines (e.g., Bishop and Church, 1992), and statistical approaches. Statistical approaches are often implemented via linear regression, wherein the runoff signature of interest is considered to be an unobservable random variable correlated with observable features of both gauged and ungauged basins (e.g., rainfall, topography).…”

Section: Linear Modelsmentioning

confidence: 99%

TopREML: a topological restricted maximum likelihood approach to regionalize trended runoff signatures in stream networks

Müller

Thompson

2015

Hydrol. Earth Syst. Sci.

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Abstract. We introduce topological restricted maximum likelihood (TopREML) as a method to predict runoff signatures in ungauged basins. The approach is based on the use of linear mixed models with spatially correlated random effects. The nested nature of streamflow networks is taken into account by using water balance considerations to constrain the covariance structure of runoff and to account for the stronger spatial correlation between flow-connected basins. The restricted maximum likelihood (REML) framework generates the best linear unbiased predictor (BLUP) of both the predicted variable and the associated prediction uncertainty, even when incorporating observable covariates into the model. The method was successfully tested in cross-validation analyses on mean streamflow and runoff frequency in Nepal (sparsely gauged) and Austria (densely gauged), where it matched the performance of comparable methods in the prediction of the considered runoff signature, while significantly outperforming them in the prediction of the associated modeling uncertainty. The ability of TopREML to combine deterministic and stochastic information to generate BLUPs of the prediction variable and its uncertainty makes it a particularly versatile method that can readily be applied in both densely gauged basins, where it takes advantage of spatial covariance information, and data-scarce regions, where it can rely on covariates, which are increasingly observable via remote-sensing technology.

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Automated approaches for regional runoff mapping in the northeastern United States

Cited by 23 publications

References 22 publications

Predicting the effects of climate change on water yield and forest production in the northeastern United States

Predicting the effects of climate change on water yield and forest production in the northeastern United States

A topological restricted maximum likelihood (TopREML) approach to regionalize trended runoff signatures in stream networks

TopREML: a topological restricted maximum likelihood approach to regionalize trended runoff signatures in stream networks

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