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  • Author or Editor: Enrique R. Vivoni x
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Enrique R. Vivoni

Abstract

A fundamental problem in ecohydrology is diagnosing impacts of vegetation dynamics on the catchment response. This study uses a distributed hydrologic model and remote sensing data to evaluate the effects of seasonal vegetation greening on the basin water balance and the partitioning of evapotranspiration ET into soil evaporation, transpiration, and evaporation of intercepted water. Using remotely sensed data, updates are made to model vegetation parameters related to radiation, interception, and transpiration as ecosystems respond to precipitation during the North American monsoon (NAM). Comparisons of simulations with static and seasonally varying vegetation parameters reveal lower ET but higher vegetation-mediated ET losses because of the greening. Sensitivity analyses indicate that vegetation fraction is the primary control on ET and its partitioning, while interception parameters play a secondary role. As a result, spatial patterns in ET partitioning in the catchment exhibit a strong signature of vegetation fraction, though fine (coarse)-scale influences of soil moisture (radiation) are also observed. Vegetation-mediated ET losses were significant in large fractions of the catchment and exhibited ecosystem-dependent seasonal evolutions. The numerical simulations presented here provide the first spatially explicit estimates of ET partitioning accounting for vegetation dynamics obtained from remotely sensed data at the catchment scale.

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Theodore J. Bohn
and
Enrique R. Vivoni

Abstract

For their investigation of the impact of irrigated agriculture on hydrometeorological fields in the North American monsoon (NAM) region, Mahalov et al. used the Weather Research and Forecasting (WRF) Model to simulate weather over the NAM region in the summer periods of 2000 and 2012, with and without irrigation applied to the regional croplands. Unfortunately, while the authors found that irrigated agriculture may indeed influence summer precipitation, the magnitude, location, and seasonality of their irrigation inputs were substantially inaccurate because of 1) the assumption that pixels classified as “irrigated cropland” are irrigated during the summer and 2) an outdated land cover map that misrepresents known agricultural districts. The combined effects of these errors are 1) an overestimation of irrigated croplands by a factor of 3–10 along the coast of the Gulf of California and by a factor of 1.5 near the Colorado River delta and 2) a large underestimation of irrigation by a factor of 7–10 in Chihuahua, particularly in 2012. Given the sensitivity of the WRF simulations conducted by Mahalov et al. to the presence of irrigated agriculture, it is expected that the identified errors would significantly impact surface moisture and energy fluxes, resulting in noticeably different effects on precipitation. The authors suggest that the analysis of irrigation effects on precipitation using coupled land–atmospheric modeling systems requires careful specification of the spatiotemporal distribution of irrigated croplands.

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Giuseppe Mascaro
,
Enrique R. Vivoni
, and
Roberto Deidda

Abstract

Evaluating the propagation of errors associated with ensemble quantitative precipitation forecasts (QPFs) into the ensemble streamflow response is important to reduce uncertainty in operational flow forecasting. In this paper, a multifractal rainfall downscaling model is coupled with a fully distributed hydrological model to create, under controlled conditions, an extensive set of synthetic hydrometeorological events, assumed as observations. Subsequently, for each event, flood hindcasts are simulated by the hydrological model using three ensembles of QPFs—one reliable and the other two affected by different kinds of precipitation forecast errors—generated by the downscaling model. Two verification tools based on the verification rank histogram and the continuous ranked probability score are then used to evaluate the characteristics of the correspondent three sets of ensemble streamflow forecasts. Analyses indicate that the best forecast accuracy of the ensemble streamflows is obtained when the reliable ensemble QPFs are used. In addition, results underline (i) the importance of hindcasting to create an adequate set of data that span a wide range of hydrometeorological conditions and (ii) the sensitivity of the ensemble streamflow verification to the effects of basin initial conditions and the properties of the ensemble precipitation distributions. This study provides a contribution to the field of operational flow forecasting by highlighting a series of requirements and challenges that should be considered when hydrologic ensemble forecasts are evaluated.

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Qiuhong Tang
,
Enrique R. Vivoni
,
Francisco Muñoz-Arriola
, and
Dennis P. Lettenmaier

Abstract

The links between vegetation, evapotranspiration (ET), and soil moisture (SM) are prominent in western Mexico—a region characterized by an abrupt increase in rainfall and ecosystem greenup during the North American monsoon (NAM). Most regional-scale land surface models use climatological vegetation and are therefore unable to capture fully the spatiotemporal changes in these linkages. Interannually varying and climatological leaf area index (LAI) were prescribed, both inferred from the space-borne Moderate Resolution Imaging Spectroradiometer (MODIS), as the source of vegetation parameter inputs to the Variable Infiltration Capacity (VIC) model applied over the NAM region for 2001–08. Results at two eddy covariance tower sites for three summer periods were compared and evaluated. Results show that both vegetation greening onset and dormancy dates vary substantially from year to year with a range of more than half a month. The model using climatological LAI tends to predict lower (higher) ET than the model using observed LAI when vegetation greening occurs earlier (later) than the mean greening date. These discrepancies were especially large during approximately two weeks at the beginning of the monsoon. The effect of LAI on ET estimates was about 10% in the Sierra Madre Occidental and 30% in the continental interior. VIC-estimated ET based on interannually varying LAI had high interannual variability at the greening onset and dormancy periods corresponding to the vegetation dynamics. The greening onset date was highly related to ET early in the monsoon season, indicating the potential usefulness of LAI anomalies for predicting early season ET.

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Agustín Robles-Morua
,
Enrique R. Vivoni
, and
Alex S. Mayer

Abstract

A distributed hydrologic model is used to evaluate how runoff mechanisms—including infiltration excess (RI ), saturation excess (RS ), and groundwater exfiltration (RG )—influence the generation of streamflow and evapotranspiration (ET) in a mountainous region under the influence of the North American monsoon (NAM). The study site, the upper Sonora River basin (~9350 km2) in Mexico, is characterized by a wide range of terrain, soil, and ecosystem conditions obtained from best available data sources. Three meteorological scenarios are compared to explore the impact of spatial and temporal variations of meteorological characteristics on land surface processes and to identify the value of North American Land Data Assimilation System (NLDAS) forcing products in the NAM region. The following scenarios are considered for a 1-yr period: 1) a sparse network of ground-based stations, 2) raw forcing products from NLDAS, and 3) NLDAS products adjusted using available station data. These scenarios are discussed in light of spatial distributions of precipitation, streamflow, and runoff mechanisms during annual, seasonal, and monthly periods. This study identified that the mode of runoff generation impacts seasonal relations between ET and soil moisture in the water-limited region. In addition, ET rates at annual and seasonal scales were related to the runoff mechanism proportions, with an increase in ET when RS was dominant and a decrease in ET when RI was more important. The partitioning of runoff mechanisms also helps explain the monthly progression of runoff ratios in these seasonally wet hydrologic systems. Understanding the complex interplay between seasonal responses of runoff mechanisms and evapotranspiration can yield information that is of interest to hydrologists and water managers.

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Enrique R. Vivoni
,
Kinwai Tai
, and
David J. Gochis

Abstract

Through the use of a mesoscale meteorological model and distributed hydrologic model, the effects of initial soil moisture on rainfall generation, streamflow, and evapotranspiration during the North American monsoon are examined. A collection of atmospheric fields is simulated by varying initial soil moisture in the meteorological model. Analysis of the simulated rainfall fields shows that the total rainfall, intensity, and spatial coverage increase with higher soil moisture. Hydrologic simulations forced by the meteorological fields are performed using two scenarios: (i) fixed soil moisture initializations obtained via a drainage experiment in the hydrologic model and (ii) adjusted initializations to match conditions in the two models. The scenarios indicate that the runoff ratio increases with higher rainfall, although a change is observed from a linear (fixed initialization) to a nonlinear response (adjusted initialization). Variations in basin response are attributed to controls exerted by rainfall, soil, and vegetation properties for varying initial conditions. Antecedent wetness significantly influences the runoff response through the interplay of different runoff generation mechanisms and also controls the evapotranspiration process. The authors conclude that a regional increase in initial soil moisture promotes rainfall generation, streamflow, and evapotranspiration for this warm-season case study.

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Hernan A. Moreno
,
Enrique R. Vivoni
, and
David J. Gochis

Abstract

Flood forecasting in mountain basins remains a challenge given the difficulty in accurately predicting rainfall and in representing hydrologic processes in complex terrain. This study identifies flood predictability patterns in mountain areas using quantitative precipitation forecasts for two summer events from radar nowcasting and a distributed hydrologic model. The authors focus on 11 mountain watersheds in the Colorado Front Range for two warm-season convective periods in 2004 and 2006. The effects of rainfall distribution, forecast lead time, and basin area on flood forecasting skill are quantified by means of regional verification of precipitation fields and analyses of the integrated and distributed basin responses. The authors postulate that rainfall and watershed characteristics are responsible for patterns that determine flood predictability at different catchment scales. Coupled simulations reveal that the largest decrease in precipitation forecast skill occurs between 15- and 45-min lead times that coincide with rapid development and movements of convective systems. Consistent with this, flood forecasting skill decreases with nowcasting lead time, but the functional relation depends on the interactions between watershed properties and rainfall characteristics. Across the majority of the basins, flood forecasting skill is reduced noticeably for nowcasting lead times greater than 30 min. The authors identified that intermediate basin areas [~(2–20) km2] exhibit the largest flood forecast errors with the largest differences across nowcasting ensemble members. The typical size of summer convective storms is found to coincide well with these maximum errors, while basin properties dictate the shape of the scale dependency of flood predictability for different lead times.

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Theodore J. Bohn
,
Kristen M. Whitney
,
Giuseppe Mascaro
, and
Enrique R. Vivoni

Abstract

Accurate characterization of precipitation P at subdaily temporal resolution is important for a wide range of hydrological applications, yet large-scale gridded observational datasets primarily contain daily total P. Unfortunately, a widely used deterministic approach that disaggregates P uniformly over the day grossly mischaracterizes the diurnal cycle of P, leading to potential biases in simulated runoff Q. Here we present Precipitation Isosceles Triangle (PITRI), a two-parameter deterministic approach in which the hourly hyetograph is modeled with an isosceles triangle with prescribed duration and time of peak intensity. Monthly duration and peak time were derived from meteorological observations at U.S. Climate Reference Network (USCRN) stations and extended across the United States, Mexico, and southern Canada at 6-km resolution via linear regression against historical climate statistics. Across the USCRN network (years 2000–13), simulations using the Variable Infiltration Capacity (VIC) model, driven by P disaggregated via PITRI, yielded nearly unbiased estimates of annual Q relative to simulations driven by observed P. In contrast, simulations using the uniform method had a Q bias of −11%, through overestimating canopy evaporation and underestimating throughfall. One limitation of the PITRI approach is a potential bias in snow accumulation when a high proportion of P falls on days with a mix of temperatures above and below freezing, for which the partitioning of P into rain and snow is sensitive to event timing within the diurnal cycle. Nevertheless, the good overall performance of PITRI suggests that a deterministic approach may be sufficiently accurate for large-scale hydrologic applications.

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Enrique R. Vivoni
,
Dara Entekhabi
,
Rafael L. Bras
,
Valeriy Y. Ivanov
,
Matthew P. Van Horne
,
Christopher Grassotti
, and
Ross N. Hoffman

Abstract

The predictability of hydrometeorological flood events is investigated through the combined use of radar nowcasting and distributed hydrologic modeling. Nowcasting of radar-derived rainfall fields can extend the lead time for issuing flood and flash flood forecasts based on a physically based hydrologic model that explicitly accounts for spatial variations in topography, surface characteristics, and meteorological forcing. Through comparisons to discharge observations at multiple gauges (at the basin outlet and interior points), flood predictability is assessed as a function of forecast lead time, catchment scale, and rainfall spatial variability in a simulated real-time operation. The forecast experiments are carried out at temporal and spatial scales relevant for operational hydrologic forecasting. Two modes for temporal coupling of the radar nowcasting and distributed hydrologic models (interpolation and extended-lead forecasting) are proposed and evaluated for flood events within a set of nested basins in Oklahoma. Comparisons of the radar-based forecasts to persistence show the advantages of utilizing radar nowcasting for predicting near-future rainfall during flood event evolution.

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