scholarly journals Can Precipitation and Temperature from Meteorological Reanalyses Be Used for Hydrological Modeling?

2016 ◽  
Vol 17 (7) ◽  
pp. 1929-1950 ◽  
Author(s):  
Gilles R. C. Essou ◽  
Florent Sabarly ◽  
Philippe Lucas-Picher ◽  
François Brissette ◽  
Annie Poulin
Keyword(s):  
United States ◽  
Hydrological Modeling ◽  
The United States ◽  
Hydrological Models ◽  
River Flows ◽  
Surface Precipitation ◽  
Climatic Regions ◽  
Precipitation Seasonality ◽  
Regional Reanalysis ◽  
Precipitation And Temperature

Abstract This paper investigates the potential of reanalyses as proxies of observed surface precipitation and temperature to force hydrological models. Three global atmospheric reanalyses (ERA-Interim, CFSR, and MERRA), one regional reanalysis (NARR), and one global meteorological forcing dataset obtained by bias-correcting ERA-Interim [Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI)] were compared to one gridded observation database over the contiguous United States. Results showed that all temperature datasets were similar to the gridded observation over most of the United States. On the other hand, precipitation from all three global reanalyses was biased, especially in summer and winter in the southeastern United States. The regional reanalysis precipitation was closer to observations since it indirectly assimilates surface precipitation. The WFDEI dataset was generally less biased than the reanalysis datasets. All datasets were then used to force a global conceptual hydrological model on 370 watersheds of the Model Parameter Estimation Experiment (MOPEX) database. River flows were computed for each watershed, and results showed that the flows simulated using NARR and gridded observations forcings were very similar to the observed flows. The simulated flows forced by the global reanalysis datasets were also similar to the observations, except in the humid continental and subtropical climatic regions, where precipitation seasonality biases degraded river flow simulations. The WFDEI dataset led to better river flows than reanalysis in the humid continental and subtropical climatic regions but was no better than reanalysis—and sometimes worse—in other climatic zones. Overall, the results indicate that global reanalyses have good potential to be used as proxies to observations to force hydrological models, especially in regions with few weather stations.

scholarly journals Variations of U.S. Regional Precipitation and Simulations by the NCEP CFS: Focus on the Southwest

2009 ◽  
Vol 22 (12) ◽  
pp. 3211-3231 ◽  
Author(s):  
Song Yang ◽  
Yundi Jiang ◽  
Dawei Zheng ◽  
R. Wayne Higgins ◽  
Qin Zhang ◽  
...  
Keyword(s):  
United States ◽  
Atmospheric Circulation ◽  
Annual Cycle ◽  
West Coast ◽  
Time Scale ◽  
Gulf Coast ◽  
The United States ◽  
The West ◽  
Western Atlantic ◽  
Precipitation Seasonality

Abstract Variations of U.S. regional precipitation in both observations and free-run experiments with the NCEP Climate Forecast System (CFS) are investigated. The seasonality of precipitation over the continental United States and the time–frequency characteristics of precipitation over the Southwest (SW) are the focus. The differences in precipitation variation among different model resolutions are also analyzed. The spatial distribution of U.S. precipitation is characterized by high values over the East and the West Coasts, especially over the Gulf Coast and southeast states, and low values elsewhere except over the SW in summer. A large annual cycle of precipitation occurs over the SW, northern plains, and the West Coast. Overall, the CFS captures the above features reasonably well, except for the SW. However, it overestimates the precipitation over the western United States, except the SW in summer, and underestimates the precipitation over the central South, except in springtime. It also overestimates (underestimates) the precipitation seasonality over the intermountain area and Gulf Coast states (SW, West Coast, and northern Midwest). The model using T126 resolution captures the observed features more realistically than at the lower T62 resolution over a large part of the United States. The variability of observed SW precipitation is characterized by a large annual cycle, followed by a semiannual cycle, and the oscillating signals on annual, semiannual, and interannual time scales account for 41% of the total precipitation variability. However, the CFS, at both T62 and T126 resolution, fails in capturing the above feature. The variability of SW precipitation in the CFS is much less periodic. The annual oscillation of model precipitation is much weaker than that observed and it is even much weaker than the simulated semiannual oscillation. The weakly simulated annual cycle is attributed by the unrealistic precipitation simulations of all seasons, especially spring and summer. On the annual time scale, the CFS fails in simulating the relationship between the SW precipitation and the basinwide sea surface temperature (SST) and the overlying atmospheric circulation. On the semiannual time scale, the model exaggerates the response of the regional precipitation to the variations of SST and atmospheric circulation over the tropics and western Atlantic, including the Gulf of Mexico. This study also demonstrates a challenge for the next-generation CFS, at T126 resolution, to predict the variability of North American monsoon climate.

scholarly journals Atmospheric Moisture Transport over the United States and Mexico as Evaluated in the NCEP Regional Reanalysis

2005 ◽  
Vol 6 (5) ◽  
pp. 710-728 ◽  
Author(s):  
Kingtse C. Mo ◽  
Muthuvel Chelliah ◽  
Marco L. Carrera ◽  
R. Wayne Higgins ◽  
Wesley Ebisuzaki
Keyword(s):  
United States ◽  
Gulf Of Mexico ◽  
Great Plains ◽  
Moisture Transport ◽  
Gulf Of California ◽  
Phase Relationship ◽  
The United States ◽  
Low Level ◽  
Low Level Jet ◽  
Regional Reanalysis

Abstract The large-scale atmospheric hydrologic cycle over the United States and Mexico derived from the 23-yr NCEP regional reanalysis (RR) was evaluated by comparing the RR products with satellite estimates, independent sounding data, and the operational Eta Model three-dimensional variational data assimilation (3DVAR) system (EDAS). In general, the winter atmospheric transport and precipitation are realistic. The climatology and interannual variability of the Pacific, subtropical jet streams, and low-tropospheric moisture transport are well captured. During the summer season, the basic features and the evolution of the North American monsoon (NAM) revealed by the RR compare favorably with observations. The RR also captures the out-of-phase relationship of precipitation as well as the moisture flux convergence between the central United States and the Southwest. The RR is able to capture the zonal easterly Caribbean low-level jet (CALLJ) and the meridional southerly Great Plains low-level jet (GPLLJ). Together, they transport copious moisture from the Caribbean to the Gulf of Mexico and from the Gulf of Mexico to the Great Plains, respectively. The RR systematically overestimates the meridional southerly Gulf of California low-level jet (GCLLJ). A comparison with observations suggests that the meridional winds from the RR are too strong, with the largest differences centered over the northern Gulf of California. The strongest winds over the Gulf in the RR extend above 700 hPa, while the operational EDAS and station soundings indicate that the GCLLJ is confined to the boundary layer.

A Comparison of the U.S. Climate Reference Network Precipitation Data to the Parameter-Elevation Regressions on Independent Slopes Model (PRISM)

2020 ◽  
Vol 21 (10) ◽  
pp. 2391-2400
Author(s):  
Michael S. Buban ◽  
Temple R. Lee ◽  
C. Bruce Baker
Keyword(s):  
United States ◽  
Soil Moisture ◽  
High Resolution ◽  
The United States ◽  
Reference Network ◽  
Socioeconomic Impacts ◽  
Surface Precipitation ◽  
Using Data ◽  
Elevation Model ◽  
The U.S

AbstractSince drought and excessive rainfall can have significant socioeconomic impacts, it is important to have accurate high-resolution gridded datasets that can help improve analysis and forecasting of these conditions. One such widely used dataset is the Parameter-Elevation Regressions on Independent Slopes Model (PRISM). PRISM uses a digital elevation model (DEM) to obtain gridded elevation analyses and then uses a regression analysis along with approximately 15 000 surface precipitation measurements to produce a 4-km resolution daily precipitation product over the conterminous United States. The U.S. Climate Reference Network (USCRN) consists of 114 stations that take highly accurate meteorological measurements across all regions of the United States. A comparison between the USCRN and PRISM was performed using data from 2006 to 2018. There were good comparisons between the two datasets across nearly all seasons and regions; most mean daily differences were <1 mm, with most absolute daily differences ~5 mm. The most general characteristics were for a net dry bias in the PRISM data in the Southwest and a net moist bias in the southern United States. Verifying the PRISM dataset provides us with confidence it can be used with estimates of evapotranspiration, high-resolution gridded soil properties, and vegetation datasets to produce a daily gridded soil moisture product for operational use in the analyses and prediction of drought and excessive soil moisture conditions.

Characterizing Predictability of Precipitation Means and Extremes over the Conterminous United States, 1949–2010*

2016 ◽  
Vol 29 (7) ◽  
pp. 2621-2633 ◽  
Author(s):  
Mingkai Jiang ◽  
Benjamin S. Felzer ◽  
Dork Sahagian
Keyword(s):  
United States ◽  
Pacific Northwest ◽  
West Coast ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
The United States ◽  
Monthly Precipitation ◽  
The West ◽  
Precipitation Seasonality

Abstract The proper understanding of precipitation variability, seasonality, and predictability are important for effective environmental management. Precipitation and its associated extremes vary in magnitude and duration both spatially and temporally, making it one of the most challenging climate parameters to predict on the basis of global and regional climate models. Using information theory, an improved understanding of precipitation predictability in the conterminous United States over the period of 1949–2010 is sought based on a gridded monthly precipitation dataset. Predictability is defined as the recurrent likelihood of patterns described by the metrics of magnitude variability and seasonality. It is shown that monthly mean precipitation and duration-based dry and wet extremes are generally highly variable in the east compared to those in the west, while the reversed spatial pattern is observed for intensity-based wetness indices except along the Pacific Northwest coast. It is thus inferred that, over much of the U.S. landscape, variations of monthly mean precipitation are driven by the variations in precipitation occurrences rather than the intensity of infrequent heavy rainfall. It is further demonstrated that precipitation seasonality for means and extremes is homogeneously invariant within the United States, with the exceptions of the West Coast, Florida, and parts of the Midwest, where stronger seasonality is identified. A proportionally higher role of variability in regulating precipitation predictability is demonstrated. Seasonality surpasses variability only in parts of the West Coast. The quantified patterns of predictability for precipitation means and extremes have direct applications to those phenomena influenced by climate periodicity, such as biodiversity and ecosystem management.

scholarly journals An Application of Filtered Renewal Processes in Hydrology

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mario Lefebvre ◽  
Fatima Bensalma
Keyword(s):  
United States ◽  
The United States ◽  
Poisson Processes ◽  
Renewal Processes ◽  
River Flows

Filtered renewal processes are used to forecast daily river flows. For these processes, contrary to filtered Poisson processes, the time between consecutive events is not necessarily exponentially distributed, which is more realistic. The model is applied to obtain one- and two-day-ahead forecasts of the flows of the Delaware and Hudson Rivers, both located in the United States. Better results are obtained than with filtered Poisson processes, which are often used to model river flows.

scholarly journals The Use of Reanalyses and Gridded Observations as Weather Input Data for a Hydrological Model: Comparison of Performances of Simulated River Flows Based on the Density of Weather Stations

2017 ◽  
Vol 18 (2) ◽  
pp. 497-513 ◽  
Author(s):  
Gilles R. C. Essou ◽  
François Brissette ◽  
Philippe Lucas-Picher
Keyword(s):  
Hydrological Modeling ◽  
Strong Impact ◽  
Weather Station ◽  
River Flows ◽  
Mountainous Regions ◽  
Rough Approximation ◽  
Weather Stations ◽  
Precipitation And Temperature ◽  
Station Density ◽  
Better Than

Abstract Precipitation forcing is critical for hydrological modeling as it has a strong impact on the accuracy of simulated river flows. In general, precipitation data used in hydrological modeling are provided by weather stations. However, in regions with sparse weather station coverage, the spatial interpolation of the individual weather stations provides a rough approximation of the real precipitation fields. In such regions, precipitation from interpolated weather stations is generally considered unreliable for hydrological modeling. Precipitation estimates from reanalyses could represent an interesting alternative in regions where the weather station density is low. This article compares the performances of river flows simulated by a watershed model using precipitation and temperature estimates from reanalyses and gridded observations. The comparison was carried out based on the density of surface weather stations for 316 Canadian watersheds located in three climatic regions. Three state-of-the-art atmospheric reanalyses—ERA-Interim, CFSR, and MERRA—and one gridded observations database over Canada—Natural Resources Canada (NRCan)—were used. Results showed that the Nash–Sutcliffe values of simulated river flows using precipitation and temperature data from CFSR and NRCan were generally equivalent regardless of the weather station density. ERA-Interim and MERRA performed significantly better than NRCan for watersheds with weather station densities of less than 1 station per 1000 km2 in the mountainous region. Overall, these results indicate that for hydrological modeling in regions with high spatial variability of precipitation such as mountainous regions, reanalyses perform better than gridded observations when the weather station density is low.

scholarly journals Assessment on latitudinal tree species richness using environmental factors in the southeastern United States

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6781
Author(s):  
Youngsang Kwon ◽  
Taesoo Lee ◽  
Alison Lang ◽  
Dorian Burnette
Keyword(s):  
United States ◽  
Species Richness ◽  
Environmental Factors ◽  
Tree Species ◽  
The United States ◽  
Forested Area ◽  
Tree Species Richness ◽  
Mean Temperature ◽  
Precipitation Seasonality ◽  
Southeastern Region

The southeastern region of the United States exhibits an unusual trend of decreasing tree species richness (TSR) from higher to lower latitudes over the Florida peninsula. This trend contradicts the widely marked latitudinal diversity gradient where species richness is highest in tropical zones and decreases towards extratropical regions. This study aims to assess the environmental factors that prompt this atypical inverse latitudinal gradient seen in TSR using the USDA Forest Service’s Forest Inventory and Analysis (FIA) database. Fifteen variables under four categories of forested area, groundwater, soil properties, and climate groups were examined to model TSR in the region. Generalized linear models (GLMs) with Poisson distributions first assessed individual variables to test explanatory power then the LASSO regularization method was utilized to extract two subsets of the most influential variables to predict TSR. Forest area and four climate variables (mean annual temperature, precipitation seasonality, mean temperature of coldest quarter, and mean precipitation of driest quarter) were the top five variables during the initial GLM assessment implying their potential individual influence in regulating TSR. Two subsets of LASSO models contained seven and three predictor variables, respectively. Frist subset includes seven predictors, presented in highest to low standardized coefficient, mean temperature of coldest quarter, forested area, precipitation seasonality, mean precipitation of driest quarter, water table depth, spodosol, and available water storage. The other subset further excluded four lowest influential variables from the first set, leaving the top three variables from the first subset. The first subset of the LASSO model predicted TSR with 63.4% explained deviance while the second subset reproduced 60.2% of deviance explained. With only three variables used, the second model outperformed the first model evaluated by the AIC value. We conclude that forest patch area, mean temperature of coldest quarter, and precipitation seasonality are the highly influential variables of TSR among environmental factors in the southeastern region of U.S., but evolutionary or historic cause should be further incorporated to fully understand tree species diversity pattern in this region.

The “wound” metaphor in the border narratives of Ana Castillo and Graciela Limón

2021 ◽  
Vol 10 (1) ◽  
pp. 49-60
Author(s):  
Sonia Farid
Keyword(s):  
United States ◽  
Mexican American ◽  
The United States ◽  
Ana Castillo ◽  
River Flows ◽  
Border Towns ◽  
Border Narratives ◽  
Detrimental Impact

This paper explores the manifestations of the wound metaphor in two Mexican-American border novels: The Guardians (2007) by Ana Castillo and The River Flows North by Graciela Limón (2009). This will be done by analyzing the metaphor as tackled by Anzaldúa and Fuentes then examining the detrimental impact of the border on characters that are affected by it in one way or another whether through attempting to cross to the United States, crossing back to Mexico, or living in border towns.

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