Improving the characteristics of streamflow modeled by regional climate models

Evans, Jason P.

doi:10.1016/j.jhydrol.2003.08.003

Cited by 39 publications

(24 citation statements)

References 62 publications

(65 reference statements)

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“…However, quantifying precipitation patterns in an ecologically meaningful manner has been difficult (19), and we know little about how these patterns affect productivity outside of experimental conditions (20). We used stream discharge data from a US Geological Survey gauging station on a Konza stream (21) as an index of precipitation pattern. High stream discharge for a given amount of precipitation can serve as an index of a precipitation pattern.…”

Section: Resultsmentioning

confidence: 99%

“…High stream discharge for a given amount of precipitation can serve as an index of a precipitation pattern. When precipitation patterns are intense, a smaller fraction of the soil water is retained, and losses to streams increase (21,22). With more water lost to streams, intense precipitation patterns also should lower terrestrial plant productivity.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Timing of climate variability and grassland productivity

Craine

Nippert

Elmore

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

278

185

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Future climates are forecast to include greater precipitation variability and more frequent heat waves, but the degree to which the timing of climate variability impacts ecosystems is uncertain. In a temperate, humid grassland, we examined the seasonal impacts of climate variability on 27 y of grass productivity. Drought and highintensity precipitation reduced grass productivity only during a 110-d period, whereas high temperatures reduced productivity only during 25 d in July. The effects of drought and heat waves declined over the season and had no detectable impact on grass productivity in August. If these patterns are general across ecosystems, predictions of ecosystem response to climate change will have to account not only for the magnitude of climate variability but also for its timing.Konza | net primary production | streamflow | critical climate periods F uture climates are likely to include more frequent droughts, high-intensity precipitation patterns, and heat waves, (i.e., periods of elevated air temperatures) (1, 2). At their most severe, extreme climate events, such as the mid-American heat waves of 1980 and 2011 and the 2003 European heat wave, involve months of hot, dry weather (3, 4), increasing mortality in humans and wildlife (5, 6) while reducing agricultural and natural-systems productivity (7-10). An increase in climate extremes would have unambiguously negative effects on ecosystems. However, most climate variability would not be considered extreme and occurs on much shorter time scales throughout the growing season with temperature and precipitation frequently disassociated. The response of ecosystems to short-term climate variability at different times of year is thought to vary (11-16), but we know little about how the timing of short-duration climate variability impacts key ecosystem dynamics such as plant productivity.To understand better how the timing of climate variability affects grassland productivity, we applied the critical climate period approach (17, 18) to long-term measurements of grass productivity in a humid, temperate grassland. Aboveground net primary productivity of grass (ANPP G ) was measured at the time of peak standing biomass from 1984-2010 in both shallow-soil upland and deep-soil lowland topographic positions in an annually burned, ungrazed watershed that is dominated by grasses with the C 4 photosynthetic pathway. In attempting to understand how the timing of climate variability affects grass productivity, we analyzed long-term records of precipitation, stream discharge, and air temperature to examine how variation in drought, precipitation intensity, and heat waves affect grass productivity at different times of the growing season. Results and DiscussionAcross 27 y, drought reduced grass productivity over a wide range of dates but had declining effects as the season progressed. ANPP G decreased with decreasing precipitation summed from April 15 to August 2 [day of year (DOY) 105-214] (Fig. 1). ANPP G declined 0.60 ± 0.12 g·m −2 for each millimeter decline in p...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Timing of climate variability and grassland productivity

Craine

Nippert

Elmore

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

278

185

View full text Add to dashboard Cite

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“…So, many researchers have selected grid precipitation to drive hydrological models. Evans et al [19] used four RCMs coupled with a CMD-IHACRES hydrological model to compare the different results. Lakhatkia et al [20] coupled MM5 with a THM hydrological model to study water resources and hydrological process response to climate change scenarios.…”

Section: Introductionmentioning

confidence: 99%

Runoff Simulation by SWAT Model Using High-Resolution Gridded Precipitation in the Upper Heihe River Basin, Northeastern Tibetan Plateau

Ruan

Zou

Yang

et al. 2017

Water

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Abstract:The scarcity and uneven distribution of precipitation stations in the inland river basins of the Northeastern Tibetan Plateau restrict the application of the distributed hydrological model and spatial analysis of water balance component characteristics. This study used the upper Heihe River Basin as a case study, and daily gridded precipitation data with 3 km resolution based on the spatial interpolation of gauged stations and a regional climate model were used to construct a soil and water assessment tool (SWAT) model. The aim was to validate the precision of high-resolution gridded precipitation for hydrological simulation in data-scarce regions; a scale transformation method was proposed by building virtual stations and calculating the lapse rate to overcome the defects of the SWAT model using traditional precipitation station data. The gridded precipitation was upscaled from the grid to the sub-basin scale to accurately represent sub-basin precipitation input data. A satisfactory runoff simulation was achieved, and the spatial variability of water balance components was analysed. Results show that the precipitation lapse rate ranges from 40 mm/km to 235 mm/km and decreases from the southeastern to the northwestern areas. The SWAT model achieves monthly runoff simulation compared with gauged runoff from 2000 to 2014; the determination coefficients are higher than 0.71, the Nash-Sutcliffe efficiencies are higher than 0.76, and the percentage bias is controlled within ±15%. Meadow and sparse vegetation are the major water yield landscapes, and the elevation band from 3500 m to 4500 m is the major water yield area. Precipitation and evapotranspiration present a slightly increasing trend, whereas water yield and soil water content present a slightly decreasing trend. This finding indicates that the high-resolution gridded precipitation data fully depict its spatial heterogeneity, and scale transformation significantly promotes the application of the distributed hydrological model in inland river basins. The spatial variability of water balance components can be quantified to provide references for the integrated assessment and management of basin water resources in data-scarce regions.

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“…From a historical point of view, the interest in large-scale hydrological models stems from the need to have modelling tools for the land phase of the hydrological cycle in global circulation models (Sausen et al, 1994;Wood et al, 1992;Evans, 2003), and also because of the need to manage international conflicts related to water in transboundary basins (Andersen et al, 2001). Models are also needed for hydrological forecasting (Bremicker et al, 2004), to assess the effects of widespread land cover change on streamflow (Matheussen et al, 2000), and to estimate climate change effects on streamflow (Guo et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

The MGB-IPH model for large-scale rainfall—runoff modelling

Collischonn

Allasia

Silva

et al. 2007

Hydrological Sciences Journal

259

192

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Recent developments in hydrological modelling of river basins are focused on prediction in ungauged basins, which implies the need to improve relationships between model parameters and easilyobtainable information, such as satellite images, and to test the transferability of model parameters. A large-scale distributed hydrological model is described, which has been used in several large river basins in Brazil. The model parameters are related to classes of physical characteristics, such as soil type, land use, geology and vegetation. The model uses two basin space units: square grids for flow direction along the basin and GRU-group response units-which are hydrological classes of the basin physical characteristics for water balance. Expected ranges of parameter values are associated with each of these classes during the model calibration. Results are presented of the model fitting in the Taquari-Antas River basin in Brazil (26 000 km 2 and 11 flow gauges). Based on this fitting, the model was then applied to the Upper Uruguay River basin (52 000 km 2 ), having similar physical conditions, without any further calibration, in order to test the transferability of the model. The results in the Uruguay basin were compared with recorded flow data and showed relatively small errors, although a tendency to underestimate mean flows was found.Key words South America; River Uruguay; River Taquari; hydrological model; large basins; ungauged basins; parameter fitting Le modèle MGB-IPH pour la modélisation pluie-débit à grande échelle Résumé De récents développements en modélisation hydrologique de bassins versants sont centrés sur la prévision en bassins non jaugés, ce qui nécessite d'améliorer les relations entre les paramètres du modèle et les informations facilement accessibles, comme les images satellitales, et de tester la tranférabilité des paramètres de modélisation. Cet article décrit un modèle hydrologique distribué à grande échelle, qui a déjà été utilisé pour plusieurs grands bassins versants au Brésil. Les paramètres du modèle sont liés à des classes de caractéristiques physiques, telles que le type de sol, l'occupation du sol, la géologie et la végétation. Le modèle s'appuie sur deux unités spatiales: des mailles carrées pour les directions d'écoulement à travers le bassin et des UGR-unités groupées de réponse-qui sont des classes de caractéristiques physiques du bassin vis à vis du bilan hydrologique. Les gammes attendues des valeurs des paramètres sont associées à chacune de ces classes lors du calage du modèle. Les résultats du calage du modèle pour le bassin Brésilien de la Rivière Taquari-Antas (26 000 km² et 11 stations de jaugeage) sont présentés. Puis, à partir de ce calage, le modèle a été appliqué au bassin du cours supérieur de la Rivière Uruguay (52 000 km²), qui présente des conditions similaires, sans aucun calage supplémentaire, afin de tester la transférabilité du modèle. Les résultats dans le bassin de l'Uruguay ont été comparés avec des données de débit observées et des erreurs relativ...

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Improving the characteristics of streamflow modeled by regional climate models

Cited by 39 publications

References 62 publications

Timing of climate variability and grassland productivity

Timing of climate variability and grassland productivity

Runoff Simulation by SWAT Model Using High-Resolution Gridded Precipitation in the Upper Heihe River Basin, Northeastern Tibetan Plateau

The MGB-IPH model for large-scale rainfall—runoff modelling

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