Browse

You are looking at 1 - 10 of 2,338 items for :

  • Journal of Hydrometeorology x
  • All content x
Clear All
Sujan Pal, Francina Dominguez, María Eugenia Dillon, Javier Alvarez, Carlos Marcelo Garcia, Stephen W. Nesbitt, and David Gochis

Abstract

Some of the most intense convective storms on Earth initiate near the Sierras de Córdoba mountain range in Argentina. The goal of the RELAMPAGO field campaign was to observe these intense convective storms and their associated impacts. The intense observation period (IOP) occurred during November–December 2018. The two goals of the hydrometeorological component of RELAMPAGO IOP were 1) to perform hydrological streamflow and meteorological observations in previously ungauged basins and 2) to build a hydrometeorological modeling system for hindcast and forecast applications. During the IOP, our team was able to construct the stage–discharge curves in three basins, as hydrological instrumentation and personnel were successfully deployed based on RELAMPAGO weather forecasts. We found that the flood response time in these river locations is typically between 5 and 6 h from the peak of the rain event. The satellite-observed rainfall product IMERG-Final showed a better representation of rain gauge–estimated precipitation, while IMERG-Early and IMERG-Late had significant positive bias. The modeling component focuses on the 48-h simulation of an extreme hydrometeorological event that occurred on 27 November 2018. Using the Weather Research and Forecasting (WRF) atmospheric model and its hydrologic component WRF-Hydro as an uncoupled hydrologic model, we developed a system for hindcast, deterministic forecast, and a 60-member ensemble forecast initialized with regional-scale atmospheric data assimilation. Critically, our results highlight that streamflow simulations using the ensemble forecasting with data assimilation provide realistic flash flood forecast in terms of timing and magnitude of the peak. Our findings from this work are being used by the water managers in the region.

Restricted access
Amar Deep Tiwari, Parthasarathi Mukhopadhyay, and Vimal Mishra

Abstract

The efforts to develop a hydrologic model-based operational streamflow forecast in India are limited. We evaluate the role of bias correction of meteorological forecast and streamflow post-processing on hydrological prediction skill in India. We use the Variable Infiltration Capacity (VIC) model to simulate runoff and root zone soil moisture in the Narmada basin (drainage area: 97,410 km2), which was used as a testbed to examine the forecast skill along with the observed streamflow. We evaluated meteorological and hydrological forecasts during the monsoon (June-September) season for 2000-2018 period. The raw meteorological forecast displayed relatively low skill against the observed precipitation at 1-3 day lead time during the monsoon season. Similarly, the forecast skill was low with mean normalized root mean squared error (NRMSE) more than 0.9 and mean absolute bias larger than 60% for extreme precipitation at the 1-3-day lead time. We used Empirical Quantile Mapping (EQM) to bias correct precipitation forecast. The bias correction of precipitation forecast resulted in significant improvement in the precipitation forecast skill. Runoff and root zone soil moisture forecast was also significantly improved due to bias correction of precipitation forecast where the forecast evaluation is performed against the reference model run. However, bias correction of precipitation forecast did not cause considerable improvement in the streamflow prediction. Bias correction of streamflow forecast performs better than the streamflow forecast simulated using the bias-corrected meteorological forecast. The combination of the bias correction of precipitation forecast and post-processing of streamflow resulted in a significant improvement in the streamflow prediction (reduction in bias from 40% to 5%).

Restricted access
Eli J. Dennis and Ernesto Hugo Berbery

Abstract

Soil hydraulic properties are critical in estimating surface and subsurface processes, including surface fluxes, the distribution of soil moisture, and the extraction of water by root systems. In most numerical weather and climate models, those properties are assigned using maps of soil texture complemented by look-up tables. Comparison of two widely used soil texture databases, the USDA State Soil Geographic database (STATSGO) and Beijing Normal University’s soil texture database (GSDE), reveals that differences are widespread and can be spatially coherent over large areas that can eventually lead to regional climate differences. For instance, over the U.S. Great Plains, GSDE stipulates finer soil grains than STATSGO, while the opposite is true over central Mexico. In this study, we employ the WRF/CLM4 modeling suite to investigate the sensitivity of the simulated regional climate to changes in the prescribed soil maps. Wherever GSDE has finer grains than STATSGO (e.g., over the U.S. Great Plains), the soil retains water more strongly, as evidenced by smaller latent heat flux (−20 W m−2), larger sensible heat flux (+20 W m−2), and correspondingly, a decrease in the 2-m humidity (−1 g kg−1) and an increase in 2-m temperature (+1.5 K). The opposite behavior is found over areas of coarser grains in GSDE (e.g., over central Mexico). Further, the changes in surface fluxes via soil texture lead to differences in the thermodynamic structure of the PBL. Results suggest that neither soil hydraulic properties nor soil moisture solely dictate the strength of surface fluxes, but in combination they alter the land–atmosphere coupling in nontrivial ways.

Open access
Mu Xiao, Sarith P. Mahanama, Yongkang Xue, Fei Chen, and Dennis P. Lettenmaier

Abstract

When compared with differences in snow accumulation predicted by widely used hydrological models, there is a much greater divergence among otherwise “good” models in their simulation of the snow ablation process. Here, we explore differences in the performance of the Variable Infiltration Capacity model (VIC), Noah land surface model with multiparameterization options (Noah-MP), the Catchment model, and the third-generation Simplified Simple Biosphere model (SiB3) in their ability to reproduce observed snow water equivalent (SWE) during the ablation season at 10 Snowpack Telemetry (SNOTEL) stations over 1992–2012. During the ablation period, net radiation generally has stronger correlations with observed melt rates than does air temperature. Average ablation rates tend to be higher (in both model predictions and observations) at stations with a large accumulation of SWE. The differences in the dates of last snow between models and observations range from several days to approximately a month (on average 5.1 days earlier than in observations). If the surface cover in the models is changed from observed vegetation to bare soil in all of the models, only the melt rate of the VIC model increases. The differences in responses of models to canopy removal are directly related to snowpack energy inputs, which are further affected by different algorithms for surface albedo and energy allocation across the models. We also find that the melt rates become higher in VIC and lower in Noah-MP if the shrub/grass present at the observation sites is switched to trees.

Restricted access
Simon R. Osborne and Graham P. Weedon

Abstract

A meteorological drought in 2018 led to senescence of the C3 grass at Cardington, Bedfordshire, United Kingdom. Observations of near-surface atmospheric variables and soil moisture are compared to simulations by the JULES land surface model (LSM) as used for Met Office forecasts. In years without drought, JULES provides better standalone simulations of evapotranspiration (ET) and soil moisture when the canopy height and rooting depth are reduced to match local conditions. During drought with the adjusted configuration, JULES correctly estimates total ET, but the components are in the wrong proportions. Several factors affect the estimation of ET including modeled skin temperatures, dewfall, and bare-soil evaporation. A diurnal range of skin temperatures close to observed is produced via the adjusted configuration and doubling the optical extinction coefficient. Although modeled ET during drought matches observed ET, this includes simulation of transpiration but in reality the grass was senescent. Excluding transpiration, the modeled bare-soil evaporation underestimates the observed midday latent heat flux. Part of the missing latent heat may relate to inappropriate parameterization of hydraulic properties of dry soils and part may be due to insufficient evaporation of dew. Dew meters indicate dewfall of up to 20 W m−2 during drought when the surface is cooling radiatively and turbulence is minimal. These data demonstrate that eddy-covariance techniques fail to reliably record the times, intensity, and variations in negative latent heat flux. Furthermore, the parameterization of atmospheric turbulence as used in LSMs fails to represent accurately dewfall during calm conditions when the surface is radiatively cooled.

Restricted access
Prabhakar Shrestha

Abstract

A 10-year simulation of shallow groundwater table (GWT) depth over a temperate region in northwestern Europe, using a physics based integrated hydrological model at km-scale, exhibits a strong seasonal cycle. This is also well captured in terms of near surface soil moisture anomalies, terrestrial water storage anomalies and shallow GWT depth anomalies from observations over the region. The modeled monthly anomaly of GWT depth exhibits a statistically significant (p<0.05) moderate positive/negative correlation with non-rain and rain affected monthly anomalies of incoming solar radiation. The vegetation cover also produces a strong local control on the variability of shallow GWT depth. Thus, much of the variability in the simulated seasonal cycle of shallow GWT depth could be linked to the distribution of clouds and vegetation.

The spatiotemporal distribution of clouds, partly influenced by the Rhein Massif, modulates the seasonal variability of incoming solar radiation and precipitation, over the region. Particularly, the southwestern and northern part of the Rhien Massif divided by the Rhein valley exhibits a dipole behavior with relatively high/low shallow GWT depth fluctuations, associated with positive/negative anomaly of incoming solar radiation and negative/positive anomaly of precipitation.

Restricted access
Rhonalyn V. Macalalad, Roy A. Badilla, Olivia C. Cabrera, and Gerry Bagtasa

Abstract

The Philippines is frequently affected by tropical cyclones (TCs) and understanding the flood response of the PRB from TC-induced rain is needed in effective disaster risk management. As large uncertainties remain in TC rain forecasting, we propose a simple checklist method for flood forecasting of the PRB which depends on the general TC track, season, and accumulated rainfall. To this end, flood events were selected based on the alert, alarm, and critical river height levels established by the Philippine Atmospheric, Geophysical and Astronomical ServicesAdministration (PAGASA). Results show that all flood events in the PRB were induced by TCs. All intense TCs that directly traversed the PRB resulted in critical level floods. These TCs also had the shortest flood onset of 7-27 hours from alert to critical level. Flooding from distant landfalling TCs, on the other hand, are dependent on season. TCs traversing north (south) of the PRB induced flooding only during the southwest (northeast) monsoon season. These TCs can raise water levels from alert to critical in 11 to 48 hours. Remote precipitation from non-landfalling TCs can also induce critical level flooding but with a longer onset time of 59 hours. These results indicate that a simple checklist method can serve as a useful tool for flood forecasting in regions with limited data and forecasting resources.

Restricted access
Liqing Peng, Zhongwang Wei, Zhenzhong Zeng, Peirong Lin, Eric F. Wood, and Justin Sheffield

Abstract

Downward shortwave radiation ( Rsd ) determines the surface energy balance, alters evapotranspiration and hydrological conditions, and feeds back to the regional and global climate. Large-scale Rsd estimates are usually retrieved from satellite based on the top-of-atmosphere radiation and cloud parameters. These estimates are subject to biases and temporal inhomogeneity due to errors in atmospheric parameters, algorithms, and sensor changes. We found that three satellite products overestimate Rsd by 8-10% over Asia for 1984-2006, particularly in high latitudes. We used the Model Ensemble Tree (MTE) machine-learning algorithm and commonly-used ensemble averaging methods to integrate ground observations and satellite products. Validations based on test stations and independent networks showed that the MTE approach reduces the median relative biases from 8-10% to 2%, which is more effective than the ensemble averaging methods. We further evaluated the impacts of uncertainty in radiation forcing on surface energy and water balances using the land surface model Noah-MP. The uncertainty of radiation data affects the prediction of sensible heat the most, and also largely affects latent heat prediction in humid regions. Holding the other variables constant, a 10% positive bias in Rsd can lead to a 20% to 60% positive bias in the monthly median sensible heat. The simulated hydrological responses to changing radiation forcing are nonlinear as a result of the interactions among ET, snowpack, and soil moisture. Our analysis concludes that reducing uncertainty of radiation data is beneficial for predicting regional energy and water balances, which requires more high-quality ground observations and improved satellite retrieval algorithms.

Restricted access
Arianna Cauteruccio, Enrico Chinchella, Mattia Stagnaro, and Luca G. Lanza

Abstract

The hotplate precipitation gauge operates by means of a thermodynamic principle. It is composed by a small size disk with two thin aluminium heated plates on the upper and lower faces. Each plate has three concentric rings to prevent the hydrometeors from sliding off in strong wind. As for the more widely used tipping-bucket and weighing gauges, measurements are affected by the wind-induced bias due to the bluff-body aerodynamics of the instrument outer shape. Unsteady Reynolds-Averaged Navier-Stokes equations were numerically solved, using a k-ω shear stress transport closure model, to simulate the aerodynamic influence of the gauge body on the airflow. Wind tunnel tests were conducted to validate simulation results. Solid particle trajectories were modelled using a Lagrangian Particle Tracking model to evaluate the influence of the airflow modification on the ability of the instrument to collect the incoming hydrometeors. A suitable parameterization of the particle size distribution, as a function of the snowfall intensity, was employed to calculate the Collection Efficiency (CE) under different wind conditions. Results reveal a relevant role of the three rings in enhancing the collection performance of the gauge. Below 7.5 m s-1, the CE curves linearly decrease with increasing the wind speed, while beyond that threshold, the blocking caused by the rings counter effects the aerodynamic induced undercatch, and the CE curves quadratically increase with the wind speed. At high wind speed, the undercatch vanishes and the instrument exhibits a rapidly increasing overcatch. For operational purposes, adjustment curves were formulated as a function of the wind speed and the measured snowfall intensity.

Restricted access
Yalei You, S. Joseph Munchak, Christa Peters-Lidard, and Sarah Ringerud

Abstract

Rainfall retrieval algorithms for passive microwave radiometers often exploits the brightness temperature depression due to ice scattering at high frequency channels (≥ 85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low frequency channels (19, 24 and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounder (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Unit-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all-10-satellites, 5-imagers, 6-satellites with very different equator crossing times, and GMI-only. Results show that Δe from all-10-satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, comparing with the integrated multi-satellite retrievals (IMERG) final run product. The 6-satellites scheme has comparable performance with all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.

Restricted access