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- Author or Editor: Enrique R. Vivoni x
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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.
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.
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.
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.
Abstract
A new verification method is proposed to test the consistency of ensemble high-resolution precipitation fields forecasted by calibrated downscaling models. The method is based on a generalization of the verification rank histogram and tests the exceedance probability of a fixed precipitation threshold calculated from the observed or ensemble fields. A graphical tool that accounts for random assignments of the rank is proposed to provide guidance in histogram interpretation and to avoid a possible misunderstanding of model deficiencies. The verification method is applied on three numerical experiments carried out in controlled conditions using the space–time rainfall (STRAIN) downscaling model with the aims of investigating (i) the effect of sampling variability on parameter estimation from the observed fields and (ii) model performance when calibration relations between the parameter and a coarse meteorological observable are used to interpret events arising from one or more physical conditions. Results show that (i) ensemble members generated using the parameters estimated on the observed event are overdispersed; (ii) the adoption of a single calibration relation can lead to the generation of consistent ensemble members; and (iii) when a single calibration relation is not able to explain observed event variability, storm-specific calibration relations should be adopted to return consistent forecasts.
Abstract
A new verification method is proposed to test the consistency of ensemble high-resolution precipitation fields forecasted by calibrated downscaling models. The method is based on a generalization of the verification rank histogram and tests the exceedance probability of a fixed precipitation threshold calculated from the observed or ensemble fields. A graphical tool that accounts for random assignments of the rank is proposed to provide guidance in histogram interpretation and to avoid a possible misunderstanding of model deficiencies. The verification method is applied on three numerical experiments carried out in controlled conditions using the space–time rainfall (STRAIN) downscaling model with the aims of investigating (i) the effect of sampling variability on parameter estimation from the observed fields and (ii) model performance when calibration relations between the parameter and a coarse meteorological observable are used to interpret events arising from one or more physical conditions. Results show that (i) ensemble members generated using the parameters estimated on the observed event are overdispersed; (ii) the adoption of a single calibration relation can lead to the generation of consistent ensemble members; and (iii) when a single calibration relation is not able to explain observed event variability, storm-specific calibration relations should be adopted to return consistent forecasts.
Abstract
The North American monsoon (NAM) leads to a large increase in summer rainfall and a seasonal change in vegetation in the southwestern United States and northwestern Mexico. Understanding the interactions between NAM rainfall and vegetation dynamics is essential for improved climate and hydrologic prediction. In this work, the authors analyze long-term vegetation dynamics over the North American Monsoon Experiment (NAME) tier I domain (20°–35°N, 105°–115°W) using normalized difference vegetation index (NDVI) semimonthly composites at 8-km resolution from 1982 to 2006. The authors derive ecoregions with similar vegetation dynamics using principal component analysis and cluster identification. Based on ecoregion and pixel-scale analyses, this study quantifies the seasonal and interannual vegetation variations, their dependence on geographic position and terrain attributes, and the presence of long-term trends through a set of phenological vegetation metrics. Results reveal that seasonal biomass productivity, as captured by the time-integrated NDVI (TINDVI), is an excellent means to synthesize vegetation dynamics. High TINDVI occurs for ecosystems with a short period of intense greening tuned to the NAM or with a prolonged period of moderate greenness continuing after the NAM. These cases represent different plant strategies (deciduous versus evergreen) that can be adjusted along spatial gradients to cope with seasonal water availability. Long-term trends in TINDVI may also indicate changing conditions favoring ecosystems that intensively use NAM rainfall for rapid productivity, as opposed to delayed and moderate greening. A persistence of these trends could potentially result in the spatial reorganization of ecosystems in the NAM region.
Abstract
The North American monsoon (NAM) leads to a large increase in summer rainfall and a seasonal change in vegetation in the southwestern United States and northwestern Mexico. Understanding the interactions between NAM rainfall and vegetation dynamics is essential for improved climate and hydrologic prediction. In this work, the authors analyze long-term vegetation dynamics over the North American Monsoon Experiment (NAME) tier I domain (20°–35°N, 105°–115°W) using normalized difference vegetation index (NDVI) semimonthly composites at 8-km resolution from 1982 to 2006. The authors derive ecoregions with similar vegetation dynamics using principal component analysis and cluster identification. Based on ecoregion and pixel-scale analyses, this study quantifies the seasonal and interannual vegetation variations, their dependence on geographic position and terrain attributes, and the presence of long-term trends through a set of phenological vegetation metrics. Results reveal that seasonal biomass productivity, as captured by the time-integrated NDVI (TINDVI), is an excellent means to synthesize vegetation dynamics. High TINDVI occurs for ecosystems with a short period of intense greening tuned to the NAM or with a prolonged period of moderate greenness continuing after the NAM. These cases represent different plant strategies (deciduous versus evergreen) that can be adjusted along spatial gradients to cope with seasonal water availability. Long-term trends in TINDVI may also indicate changing conditions favoring ecosystems that intensively use NAM rainfall for rapid productivity, as opposed to delayed and moderate greening. A persistence of these trends could potentially result in the spatial reorganization of ecosystems in the NAM region.
Abstract
This work studies precipitation recycling as part of the dynamic North American monsoon system (NAMS) to understand how moisture and energy fluxes modulate recycling variability at the daily-to-intraseasonal time scale. A set of land–atmosphere variables derived from North American Regional Reanalysis (NARR) data are used to represent the hydroclimatology of the monsoon. The recycling ratio is estimated using the Dynamic Recycling Model, which provides recycling estimates at the daily time scales. Multichannel singular spectrum analysis (M-SSA) is used to extract trends in the data while at the same time selecting only the variability common to all of the variables.
The 1985–2006 climatological analysis of NAMS precipitation recycling reveals a positive feedback mechanism between monsoon precipitation and subsequent increase in precipitation of recycled origin. Recycling ratios during the monsoon are consistently above 15% and can be as high as 25%. While monsoon precipitation and evapotranspiration are predominantly located in the seasonally dry tropical forests in the southwestern part of the domain, recycling is enhanced northeast of this region, indicating a relocation of soil moisture farther inland to drier regions in the northeast. The three years with the longest monsoons in the 22-yr period present an asynchronous pattern between precipitation and recycling ratio. The longest monsoons have a characteristic double peak in precipitation, with enhanced recycling ratios during the intermediate dry period. This indicates that, even when large-scale moisture advection decreases, evapotranspiration provides moisture to the overlying atmosphere, contributing to precipitation. Through the negative feedback present during long monsoons and by relocation of soil moisture, precipitation recycling brings favorable conditions for vegetation sustenance in the NAMS region.
Abstract
This work studies precipitation recycling as part of the dynamic North American monsoon system (NAMS) to understand how moisture and energy fluxes modulate recycling variability at the daily-to-intraseasonal time scale. A set of land–atmosphere variables derived from North American Regional Reanalysis (NARR) data are used to represent the hydroclimatology of the monsoon. The recycling ratio is estimated using the Dynamic Recycling Model, which provides recycling estimates at the daily time scales. Multichannel singular spectrum analysis (M-SSA) is used to extract trends in the data while at the same time selecting only the variability common to all of the variables.
The 1985–2006 climatological analysis of NAMS precipitation recycling reveals a positive feedback mechanism between monsoon precipitation and subsequent increase in precipitation of recycled origin. Recycling ratios during the monsoon are consistently above 15% and can be as high as 25%. While monsoon precipitation and evapotranspiration are predominantly located in the seasonally dry tropical forests in the southwestern part of the domain, recycling is enhanced northeast of this region, indicating a relocation of soil moisture farther inland to drier regions in the northeast. The three years with the longest monsoons in the 22-yr period present an asynchronous pattern between precipitation and recycling ratio. The longest monsoons have a characteristic double peak in precipitation, with enhanced recycling ratios during the intermediate dry period. This indicates that, even when large-scale moisture advection decreases, evapotranspiration provides moisture to the overlying atmosphere, contributing to precipitation. Through the negative feedback present during long monsoons and by relocation of soil moisture, precipitation recycling brings favorable conditions for vegetation sustenance in the NAMS region.
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.
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.
Abstract
A detailed intercomparison was performed for the period January 1998–June 1999 of three different sets of rainfall observations over the watershed covered by the National Weather Service Arkansas–Red Basin River Forecast Center (ABRFC). The rainfall datasets were 1) hourly 4-km-resolution ABRFC-produced P1 estimates, 2) 15-min 2-km resolution NOWrad estimates produced and marketed by Weather Services International Corporation (WSI), and 3) conventional hourly rain gauge observations available from the operational observing network. Precipitation estimates from the three products were compared at monthly, daily, and hourly timescales for the Arkansas–Red River basin and the Illinois River basin. Results indicate that the P1 products had a higher correlation and smaller bias relative to rain gauges than did the WSI products. The fact that the P1 estimates are bias corrected using gauges themselves makes an independent assessment difficult. WSI monthly accumulations seemed to overestimate (underestimate) total rainfall relative to gauges during the warm (cold) season. WSI and P1 estimates had very good agreement overall with correlation coefficients of daily accumulations generally greater than 0.7. The P1 hourly estimates were characterized by a large proportion of extremely light rainfall rates (less than 2 mm h−1). This is likely due to the P1 bias correction algorithm's use of sparse gauge data during low-level stratiform precipitation events. Finally, analyses of mean areal precipitation, fractional coverage, and storm total rainfall for the Illinois River basin demonstrate the potential impact of these rainfall products on hydrologic models that use these precipitation estimates as meteorological forcing.
Abstract
A detailed intercomparison was performed for the period January 1998–June 1999 of three different sets of rainfall observations over the watershed covered by the National Weather Service Arkansas–Red Basin River Forecast Center (ABRFC). The rainfall datasets were 1) hourly 4-km-resolution ABRFC-produced P1 estimates, 2) 15-min 2-km resolution NOWrad estimates produced and marketed by Weather Services International Corporation (WSI), and 3) conventional hourly rain gauge observations available from the operational observing network. Precipitation estimates from the three products were compared at monthly, daily, and hourly timescales for the Arkansas–Red River basin and the Illinois River basin. Results indicate that the P1 products had a higher correlation and smaller bias relative to rain gauges than did the WSI products. The fact that the P1 estimates are bias corrected using gauges themselves makes an independent assessment difficult. WSI monthly accumulations seemed to overestimate (underestimate) total rainfall relative to gauges during the warm (cold) season. WSI and P1 estimates had very good agreement overall with correlation coefficients of daily accumulations generally greater than 0.7. The P1 hourly estimates were characterized by a large proportion of extremely light rainfall rates (less than 2 mm h−1). This is likely due to the P1 bias correction algorithm's use of sparse gauge data during low-level stratiform precipitation events. Finally, analyses of mean areal precipitation, fractional coverage, and storm total rainfall for the Illinois River basin demonstrate the potential impact of these rainfall products on hydrologic models that use these precipitation estimates as meteorological forcing.
Abstract
This study presents a first attempt to address the propagation of radar rainfall nowcasting errors to flood forecasts in the context of distributed hydrological simulations over a range of catchment sizes or scales. Rainfall forecasts with high spatiotemporal resolution generated from observed radar fields are used as forcing to a fully distributed hydrologic model to issue flood forecasts in a set of nested subbasins. Radar nowcasting introduces errors into the rainfall field evolution that result from spatial and temporal changes of storm features that are not captured in the forecast algorithm. The accuracy of radar rainfall and flood forecasts relative to observed radar precipitation fields and calibrated flood simulations is assessed. The study quantifies how increases in nowcasting errors with lead time result in higher flood forecast errors at the basin outlet. For small, interior basins, rainfall forecast errors can be simultaneously amplified or dampened in different flood forecast locations depending on the forecast lead time and storm characteristics. Interior differences in error propagation are shown to be effectively averaged out for larger catchment scales.
Abstract
This study presents a first attempt to address the propagation of radar rainfall nowcasting errors to flood forecasts in the context of distributed hydrological simulations over a range of catchment sizes or scales. Rainfall forecasts with high spatiotemporal resolution generated from observed radar fields are used as forcing to a fully distributed hydrologic model to issue flood forecasts in a set of nested subbasins. Radar nowcasting introduces errors into the rainfall field evolution that result from spatial and temporal changes of storm features that are not captured in the forecast algorithm. The accuracy of radar rainfall and flood forecasts relative to observed radar precipitation fields and calibrated flood simulations is assessed. The study quantifies how increases in nowcasting errors with lead time result in higher flood forecast errors at the basin outlet. For small, interior basins, rainfall forecast errors can be simultaneously amplified or dampened in different flood forecast locations depending on the forecast lead time and storm characteristics. Interior differences in error propagation are shown to be effectively averaged out for larger catchment scales.
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.
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.
