Giuseppe Mascaro

Observed relation between evapotranspiration and soil moisture in the North American monsoon region

] Soil moisture control on evapotranspiration is poorlyunderstood in ecosystems experiencing seasonal greening.In this study, we utilize a set of multi-year observations atfour eddy covariance sites along a latitudinal gradient invegetation greening to infer the ET-q relation during theNorth American monsoon. Results reveal significantseasonal, interannual and ecosystem variations in theobserved ET-q relation directly linked to vegetationgreening. In particular, monsoon-dominated ecosystemsadjust their ET-q relation, through changes in unstressedET and plant stress threshold, to cope with differences inwater availability. Comparisons of the observed relations tothe North American Regional Reanalysis dataset reveallarge biases that increase where vegetation greening is moresignificant. The analysis presented here can be used to guideimprovements in land surface model parameterization inwater-limited ecosystems.

A modeling approach reveals differences in evapotranspiration and its partitioning in two semiarid ecosystems in Northwest Mexico

Seasonal vegetation changes during the North American monsoon play a major role in modifying water, energy, and momentum fluxes. Nevertheless, most models parameterize plants as a static component or with averaged seasonal variations that ignore interannual differences and their potential impact on evapotranspiration (ET) and its components. Here vegetation parameters derived from remote sensing data were coupled with a hydrologic model at two eddy covariance (EC) sites with observations spanning multiple summers. Sinaloan thornscrub (ST) and Madrean woodland (MW) sites, arranged at intermediate and high elevations along mountain fronts in northwest Mexico, occupy specific niches related to climate conditions and water availability that are poorly understood. We found that simulations with a dynamic representation of vegetation greening tracked well the seasonal evolution of observed ET and soil moisture (SM). A switch in the dominant component of ET from soil evaporation (E) to plant transpiration (T) was observed for each ecosystem depending on the timing and magnitude of vegetation greening that is directly tied to rainfall characteristics. Differences in vegetation greening at the ST and MW sites lead to a dominance of transpiration at ST (T/ET = 57%), but evaporation-dominant conditions at MW (T/ET = 19%). Peak transpiration occurred at 5 and 20 days after the full canopy development in the ST and MW sites, respectively. These results indicate that evapotranspiration timing and partitioning varies considerably in the two studied ecosystems in accordance with different modes of vegetation greening. Intermediate-elevation ecosystems follow an intensive water use strategy with a rapid and robust transpiration response to water availability. In contrast, higher elevation sites have delayed and attenuated transpiration, suggesting an extensive water use strategy persisting beyond the North American monsoon.

Implications of Ensemble Quantitative Precipitation Forecast Errors on Distributed Streamflow Forecasting

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 r...

Impacts of climate change on precipitation and discharge extremes through the use of statistical downscaling approaches in a Mediterranean basin.

Mediterranean region is characterized by high precipitation variability often enhanced by orography, with strong seasonality and large inter-annual fluctuations, and by high heterogeneity of terrain and land surface properties. As a consequence, catchments in this area are often prone to the occurrence of hydrometeorological extremes, including storms, floods and flash-floods. A number of climate studies focused in the Mediterranean region predict that extreme events will occur with higher intensity and frequency, thus requiring further analyses to assess their effect at the land surface, particularly in small- and medium-sized watersheds. In this study, climate and hydrologic simulations produced within the Climate Induced Changes on the Hydrology of Mediterranean Basins (CLIMB) EU FP7 research project were used to analyze how precipitation extremes propagate into discharge extremes in the Rio Mannu basin (472.5km(2)), located in Sardinia, Italy. The basin hydrologic response to climate forcings in a reference (1971-2000) and a future (2041-2070) period was simulated through the combined use of a set of global and regional climate models, statistical downscaling techniques, and a process based distributed hydrologic model. We analyzed and compared the distribution of annual maxima extracted from hourly and daily precipitation and peak discharge time series, simulated by the hydrologic model under climate forcing. For this aim, yearly maxima were fit by the Generalized Extreme Value (GEV) distribution using a regional approach. Next, we discussed commonality and contrasting behaviors of precipitation and discharge maxima distributions to better understand how hydrological transformations impact propagation of extremes. Finally, we show how rainfall statistical downscaling algorithms produce more reliable forcings for hydrological models than coarse climate model outputs.

A New Verification Method to Ensure Consistent Ensemble Forecasts through Calibrated Precipitation Downscaling Models

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.

Temporal downscaling and statistical analysis of rainfall across a topographic transect in northwest Mexico

AbstractIn this study a temporal statistical downscaling scheme of rainfall is calibrated using observations from 2007 to 2010 at eight sites located along a 14-km topographic transect of 784 m in elevation in northwest Mexico. For this purpose, the rainfall statistical properties over a wide range of temporal scales (3 months–1 min) for the summer (July–September) and winter (November–March) seasons are first analyzed. Rainfall accumulation is found not to be significantly correlated with elevation in either season, and a strong diurnal cycle is found to be present only in summer, peaking in the late afternoon. Winter rainfall events are highly correlated between individual stations across the transect even at short aggregation times (<30 min), and summer storms are more localized in space and time. Spectral and scale invariance analyses showed the presence of three (two) scaling regimes in summer (winter), which are associated with typical meteorological phenomena of the corresponding time scales (front...

Multiscale Spatial and Temporal Statistical Properties of Rainfall in Central Arizona

AbstractThe statistical properties of the rainfall regime in central Arizona are investigated using observations from the early 1980s of the Flood Control District of Maricopa County (FCDMC) network, currently consisting of 310 gauges ranging in elevation from 220 to 2325 m MSL. A set of techniques is applied to analyze the properties across a wide range of temporal scales (from 1 min to years) and the associated spatial variability. Rainfall accumulation is characterized by (i) high interannual variability, which is partially explained by teleconnections with El Nino–Southern Oscillation; (ii) marked seasonality, with two distinct maxima in summer (July–September) and winter (November–March); (iii) significant orographic control; and (iv) strong diurnal cycle in summer, peaking in early afternoon at higher elevations and at nighttime in lower desert areas. The annual maximum rainfall intensities occur in the summer months and increase with elevation, suggesting that higher terrain enhances the strength o...

Comparison of Statistical and Multifractal Properties of Soil Moisture and Brightness Temperature From ESTAR and PSR During SGP99

Global soil moisture products are based on passive microwave sensors of brightness temperature at different frequencies, including Land C-bands. Two airborne sensors used to develop and test retrieval algorithms for soil moisture estimation are the L-band Electronically Scanned Thinned Array Radiometer (ESTAR) and the C-band Polarimetric Scanning Radiometer (PSR). In this letter, we compare the statistical and multifractal properties of soil moisture and brightness temperature from ESTAR and PSR during the Southern Great Plains Experiment in 1999. We show that differences between products are minimized at a support scale close to the satellite resolution. Then, we compare the subfootprint variabilities of each product in eight coarse domains of 25.6 × 25.6 km2. Scale-invariance and multifractal properties were found in all the three available soil moisture products, but multifractality was negligible in brightness temperature fields, implying that the nonlinear transformations in retrieval algorithms introduce the multifractal behavior in soil moisture. Subfootprint variability and the multifractal behavior for diverse wetness conditions are also different for the three products. Since discrepancies between the subfootprint variabilities of PSR and ESTAR brightness temperatures are small, the factors leading to the differences among the soil moisture products are mainly due to the level of detail of the retrieval algorithm and to the spatial variability of forcing data and parameters related to surface roughness and vegetation.

Irrigation Impacts on Scaling Properties of Soil Moisture and the Calibration of a Multifractal Downscaling Model

Irrigation is an anthropogenic factor that can introduce significant spatial heterogeneity in the distributions of soil moisture (θ) and potentially affect the performance of downscaling models of coarse satellite products. In this paper, we propose a framework to: 1) quantitatively analyze and compare the scale invariance and multifractal properties of θ in the presence of irrigation; and 2) filter out the effect of irrigated croplands in the application of a multifractal downscaling algorithm based on the hypothesis of spatial homogeneity. For this aim, we use the θ data sets of the National Airborne Field Experiments 2005 (NAFE05) and 2006 (NAFE06) campaigns in Australia. Results show that irrigation affects the scale-invariance properties (tested from 32 to 1 km) in a large high-density agricultural district in the semi-arid NAFE06 site, although it does not have a significant impact on the sparser agricultural districts of the temperate NAFE05 region. The multifractal downscaling model was calibrated using a strategy that attenuates the effect of irrigation on θ fields, thus mimicking natural settings. Performances, tested against aircraft and, for the first time, ground-based observations, are adequate in most cases. Some deficiencies are found for drier conditions in regions with a higher percentage of irrigated fields, suggesting the need to further refine the techniques for detecting irrigated croplands. Overall, the findings of this work reveal that the impact of irrigation on the soil moisture statistical variability and downscaling is larger in drier regions or conditions, where irrigation creates a drastic contrast with the surrounding areas.

Performance of the CORDEX‐Africa regional climate simulations in representing the hydrological cycle of the Niger River basin

The water resources of the Niger River basin (NRB) in West Africa are crucial to support the socioeconomic development of nine countries. In this study, we compared and evaluated performances of simulations at 0.44° resolution of several regional climate models (RCMs) of the Coordinated Regional climate Downscaling Experiment (CORDEX) in reproducing the statistical properties of the hydrological cycle of the NRB in the current climate. To capture the large range of climatic zones in the region, analyses were conducted by spatially averaging the water balance components in four nested subbasins. Most RCMs overestimate (order of +10% to +400%, depending on model and subbasin) the mean annual difference between precipitation (P) and evaporation (E), whose observed value was assumed equal to the long-term discharge based on the mass conservation principle. This is due to a tendency to simulate larger mean annual P and a weak hydrological cycle in the E channel. Some exceptions appear in the humid most-upstream subbasin, where a few RCMs underestimate P. Overall, the representation of the water balance is mostly sensitive to the parameterized land surface and atmospheric processes of the nested RCMs, with less influence of the driving general circulation model. This finding is supported by further analyses on seasonal cycle and spatial variability of the water balance components and on model performances in reproducing observed climatology. Results of this work should be considered when RCMs are used directly or in impact studies to develop policies and plan investments aimed at ensuring water sustainability in the NRB.