Variability and Trends in Precipitation, Temperature and Drought Indices in the State of California

He, Minxue; Gautam, Mahesh R.

doi:10.3390/hydrology3020014

Cited by 45 publications

(53 citation statements)

References 86 publications

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“…This study further shows that there is generally no change in annual, seasonal, and monthly precipitation in the study watersheds. This finding is consistent with what is shown in previous studies [22,31,76]. A new finding of this study is that the April-July runoff volumes of the study watersheds show no sign of changes in the study period.…”

Section: Discussionsupporting

confidence: 82%

“…This indicates that though the increasing minimum temperature and average temperature have not explicitly led to changes in the amount of snowpack (peak) and runoff (multi-monthly), they generally lead to increasing variability in those variables. This is in agreement with what have been previously found out in precipitation with increasing variability but no significant changes to the mean value [31,82,83]. The implication is that the occurrence frequency of extreme events is increasing.…”

Section: Discussionsupporting

confidence: 82%

“…It is also the case for four operational predictors (i.e., 1 April snow water equivalent, October-March precipitation and runoff, April-June precipitation). This highlights that though the occurrence frequency of extreme storm and runoff events may be increasing as reported in previous work [31,[77][78][79], their total volume at a longer temporal scale (multi-monthly) remains largely unchanged. The practical implication of this finding is that the current operational regression equations are still valid and usable since both the predictand and predictors are stationary so far.…”

Section: Discussionmentioning

confidence: 56%

“…They argued that a 20-year window provides enough sample size to develop reliable variances and persistence, although a longer period would be more desirable. Most recently, [31] examined the variability of temperature and precipitation in seven climatic divisions of California using a 30-year window. The current study has an 86-year record of period (from water years .…”

Section: Study Metricsmentioning

confidence: 99%

See 3 more Smart Citations

Predictability of Seasonal Streamflow in a Changing Climate in the Sierra Nevada

He¹,

Russo²,

Anderson³

2016

Climate

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Section: Discussionsupporting

confidence: 82%

Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 56%

Section: Study Metricsmentioning

confidence: 99%

See 2 more Smart Citations

Predictability of Seasonal Streamflow in a Changing Climate in the Sierra Nevada

He¹,

Russo²,

Anderson³

2016

Climate

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“…To face this hazard, men tried to appreciate the phenomenon by trying to understand the climate cycles through the study of the variability and the trends of different climatic variables especially rainfall and temperatures [BALLING et al 1998;HE, GAUTAM 2016;NICHOLSON 1989;VINNIKOV et al 1990;ZHAI, PAN 2003]. …”

Section: Introductionmentioning

confidence: 99%

Drought monitoring in the Seybouse basin (Algeria) over the last decades

Khezazna¹,

Amarchi²,

Derdous³

et al. 2017

Journal of Water and Land Development

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Algeria is amongst the African countries most affected by climate change impacts especially by drought which caused considerable economic losses in the past decades. In this paper, drought monitoring for the period between 1970 and 2011 was conducted in the Seybouse watershed by analysing annual rainfall data in terms of variability and trends along with the calculation of the standardized precipitation index (SPI). The results indicated important inter-annual rainfall fluctuation and a significant increasing trend. The estimated drought indices indicated that the Seybouse watershed experienced in the past a long dry period with a moderate severity followed by a long wet period at the majority of the study area. Moreover, the interpolation of the standardized precipitation indices (SPI) on the entire Seybouse basin in GIS allowed visualizing and evaluating the spatial--temporal evolution of drought in the region which should help the decision-makers in the management of water resources, agriculture and other activities that may be affected by drought.

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Both real‐time and long‐term environmental data perform well in predicting shorebird distributions in managed habitat

Conlisk

Golet

Reynolds

et al. 2022

Ecological Applications

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Highly mobile species, such as migratory birds, respond to seasonal and interannual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species‐distribution models typically rely on long‐term average habitat conditions, mostly because large‐extent, temporally resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real‐time environmental data increases the predictive ability of distribution models, relative to using long‐term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16 days), and 17‐year long‐term average surface water data. Using abundance‐weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. Although modeling with both real‐time and long‐term average data provided good fit to withheld validation data (the area under the receiver operating characteristic curve, or AUC, averaged between 0.79 and 0.89 for all taxa), there were small differences in model performance. The best models incorporated long‐term average conditions and spatial pattern information for real‐time flooding (e.g., perimeter‐area ratio of real‐time water bodies). There was not a substantial difference in the performance of real‐time and long‐term average data models within time periods when real‐time surface water differed substantially from the long‐term average (specifically during drought years 2013–2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real‐time data may ultimately be best for guiding dynamic, adaptive conservation actions, whereas models based on long‐term averages may be more helpful for guiding permanent wetland protection and restoration.

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Variability and Trends in Precipitation, Temperature and Drought Indices in the State of California

Cited by 45 publications

References 86 publications

Predictability of Seasonal Streamflow in a Changing Climate in the Sierra Nevada

Predictability of Seasonal Streamflow in a Changing Climate in the Sierra Nevada

Drought monitoring in the Seybouse basin (Algeria) over the last decades

Both real‐time and long‐term environmental data perform well in predicting shorebird distributions in managed habitat

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