The influence of forest loss on rainfall remains poorly understood.
Addressing this challenge Spracklen et al. recently presented a pan-tropical
study of rainfall and land-cover that showed that satellite-derived rainfall
measures were positively correlated with the degree to which model-derived air
trajectories had been exposed to forest cover. This result confirms the
influence of vegetation on regional rainfall patterns suggested in previous
studies. However, we find that the conclusion of Spracklen et al. -- that
differences in rainfall reflect air moisture content resulting from
evapotranspiration while the circulation pattern remains unchanged -- appears
undermined by methodological inconsistencies. We identify methodological
problems with the underlying analyses and the quantitative estimates for
rainfall change predicted if forest cover is lost in the Amazon. We discuss
some alternative explanations that include the distinct role of forest
evapotranspiration in creating low pressure systems that draw moisture from the
oceans to the continental hinterland. Our analysis of meteorological data from
three regions in Brazil, including the central Amazon forest, reveal a tendency
for rainy days during the wet season with column water vapor (CWV) exceeding 50
mm to have higher pressure than rainless days; while at lower CWV rainy days
tend to have lower pressure than rainless days. The coupling between
atmospheric moisture content and circulation dynamics underlines that the
danger posed by forest loss is greater than suggested by focusing only on
moisture recycling alone.
For precipitation retrievals over land, using satellite measurements in
microwave bands, it is important to properly discriminate the weak rainfall
signals from strong and highly variable background surface emission.
Traditionally, land rainfall retrieval methods often rely on a weak signal of
rainfall scattering on high-frequency channels (85 GHz) and make use of
empirical thresholding and regression-based techniques. Due to the increased
ground surface signal interference, precipitation retrieval over
radiometrically complex land surfaces, especially over snow-covered lands,
deserts and coastal areas, is of particular challenge for this class of
retrieval techniques. This paper evaluates the results by the recently proposed
Shrunken locally linear embedding Algorithm for Retrieval of Precipitation
(ShARP), over a radiometrically complex terrain and coastal areas using the
data provided by the Tropical Rainfall Measuring Mission (TRMM) satellite. To
this end, the ShARP retrieval experiments are performed over a region in
Southeast Asia, partly covering the Tibetan Highlands, Himalayas,
Ganges-Brahmaputra-Meghna river basins and its delta. We elucidate promising
results by ShARP over snow covered land surfaces and at the vicinity of
coastlines, in comparison with the land rainfall retrievals of the standard
TRMM-2A12 product. Specifically, using the TRMM-2A25 radar product as a
reference, we provide evidence that the ShARP algorithm can significantly
reduce the rainfall over estimation due to the background snow contamination
and markedly improve detection and retrieval of rainfall at the vicinity of
coastlines. During the calendar year 2013, we demonstrate that over the study
domain the root mean squared difference can be reduced up to 38% annually,
while the reduction can reach up to 70% during the cold months.
The zonal-mean meridional transport of water vapor across the globe is evaluated using the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis for 1948-97. The shape of the meridional profile of the climatological mean transport closely resembles that of previous mean climate descriptions, but values tend to be notably larger than in climatologies derived from radiosonde-only-based analyses. The unprecedented length of the NCEP-NCAR dataset invites a focus on interannual variations in the zonal-mean moisture transport, and these results for northern winter are highlighted here. Although interannual variability in the transport is typically small at most latitudes, a significant ENSO signal is present, marked by a strengthening of water vapor transports over much of the winter hemisphere during warm events. Because of an increase in tropical sea surface temperatures and in the frequency of warm events relative to cold events in the latter half of the 50-yr record, this interannual signal projects onto an overall trend toward enhanced meridional moisture transports in the global hydrological cycle.
The Kalman filter assimilation technique is applied to a simplified soil moisture model for retrieval of the soil moisture profile from near-surface soil moisture measurements. First, the simplified soil moisture model is developed, based on an approximation to the Buckingham-Darcy equation. This model is then used in a 12 month one-dimensional field application, with updating at 1, 5, 10 and 20 day intervals. The data used is for the Nerrigundah field site, Newcastle, Australia. This study has identified: (i) the importance of knowing the depth over which the near-surface soil moisture measurements are representative (ie. observation depth); (ii) that soil porosity and residual soil moisture content are the most important soil parameters for correct retrieval of the soil moisture profile; (iii) the importance of a soil moisture model that represents the dominant soil physical processes correctly; and (iv) that an appropriate forecasting model is far more important than the ...
Next Generation Weather Radar (NEXRAD) multisensor precipitation estimates will be used for a host of applications, including operational streamflow forecasting at the National Weather Service (NWS) River Forecast Centers (RFCs), and non-operational purposes, including studies of weather, climate, and hydrology. Given these expanding applications, it is important to understand the quality and error characteristics of NEXRAD multisensor products. In this paper, we examine the issues involved in evaluating these products through an assessment of a five-and-a-half year record of multisensor estimates from the Arkansas-Red Basin RFC (ABRFC). Our analyses included comparisons of multisensor estimates based on different processing algorithms, comparisons with gage observations from the Oklahoma Mesonet and the Agricultural Research Service (ARS) Micronet, and the application of a validation framework to quantify error characteristics. Our objectives were to examine how known radar b...
This paper describes the Local Analysis and Prediction System (LAPS) and the 20-km horizontal grid version of the Rapid Update Cycle (RUC20) atmospheric analyses datasets, which are available as part of the Cold Land Processes Field Experiment (CLPX) data archive. The LAPS dataset contains spatially and temporally continuous atmospheric and surface variables over Colorado, Wyoming, and parts of the surrounding states. The analysis used a 10-km horizontal grid with 21 vertical levels and an hourly temporal resolution. The LAPS archive includes forty-six 1D surface fields and nine 3D upper-air fields, spanning the period 1 September 2001 through 31 August 2003. The RUC20 dataset includes hourly 3D atmospheric analyses over the contiguous United States and parts of southern Canada and northern Mexico, with 50 vertical levels. The RUC20 archive contains forty-six 1D surface fields and fourteen 3D upper-air fields, spanning the period 1 October 2002 through 31 September 2003. The datasets are archived at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado.
Monthly snowfall totals from over 500 stations in California, some of which date back to 1878, are examined. Most data were accessed through the NOAA archive, but several thousand station months of data were separately keyed in from image files of original documents. Over 26 000 of these entries were new relative to the NOAA archive, generally providing data prior to 1920. The stations were then subdivided into 18 regions for the construction of representative time series of each area. There were problems with the basic data the most difficult with which to deal was the increasing presence of "zero" totals that should have been recorded as "missing." This and other issues reduce the confidence that the regional time series are representative of true variations and trends, especially for regions with few systematically reporting stations. Interpreting linear trends on time series with infrequent large anomalies of one sign (i.e., heavy snowfall years) and unresolved data issues should be done with caution. For those regions characterized by consistent monitoring and with the most robust statistical reproducibility, no statistically significant trends in their periods of record (up to 133 years) nor in the most recent 50 years are found. This result encompasses the main snowfall region of the western slope of the Sierra Nevada Mountains.
Changes in observed daily precipitation over the conterminous United States between two 30-yr periods (1950-79 and 1980-2009) are examined using a 60-yr daily precipitation analysis obtained from the Climate Prediction Center (CPC) Unified Raingauge Database. Several simple measures are used to characterize the changes, including mean, frequency, intensity, and return period. Seasonality is accounted for by examining each measure for four nonoverlapping seasons. The possible role of the El Nin~ o-Southern Oscillation (ENSO) cycle as an explanation for differences between the two periods is also examined. There have been more light (1 mm ≤ P , < mm), moderate (10 mm ≤ P < 25 mm), and heavy (P ≥ 25 mm) daily precipitation events (P) in many regions of the country during the more recent 30-yr period with some of the largest and most spatially coherent increases over the Great Plains and lower Mississippi Valley during autumn and winter. Some regions, such as portions of the Southeast and the Pacific Northwest, have seen decreases, especially during the winter. Increases in multiday heavy precipitation events have been observed in the more recent period, especially over portions of the Great Plains, Great Lakes, and Northeast. These changes are associated with changes in the mean and frequency of daily precipitation during the more recent 30-yr period. Difference patterns are strongly related to the ENSO cycle and are consistent with the stronger El Nin~ o events during the more recent 30-yr period. Return periods for both heavy and light daily precipitation events during 1950-79 are shorter during 1980-2009 at most locations, with some notable regional exceptions.
The relationship between convective precipitation and cloud-to-ground (CG) lightning is examined over a study area in southwest Arizona and northwest Mexico. Using seasonal-to-daily and hourly time resolution, the National Climatic Data Center (NCDC) stage IV precipitation product and the U.S. National Lightning Detection Network lightning data have been analyzed with the aim of developing an improved understanding of the relationship between these variables. A Gaussian method of spatially smoothing discrete lightning counts is used to estimate convective rainfall and improve the quality and spatial coverage of radar-derived precipitation in areas of complex terrain. For testing the dependence of the relationship between CG lightning and precipitation, a precipitation "sensor coverage" analysis has been performed. If locations that have poor sensor coverage are excluded, R-2 between lightning and precipitation improves by up to 15%. A complementary way to estimate convective precipitation is proposed based on 1-h lightning occurrence intervals, which is the maximum time resolution in this study. We find that similar to 67% of the seasonal 2005 precipitation over the analysis domain is associated with CG lightning. Daily precipitation estimates are improved by specifying a "diurnal day" based on the diurnal maxima and minima in precipitation and CG lightning within the domain. Our method for improving quantitative precipitation estimation (QPE) using lightning is able to track and estimate convective precipitation over regions that have poor sensor coverage, particularly in both air mass storms and large multicellular events, with R-2 up to 70%.
The atmospheric moisture budget and surface interactions for the southern Great Plains are evaluated for contrasting May-June periods (1998, 2002, 2006, and 2007) as background for the Cloud and Land Surface Interaction Campaign (CLASIC) of (wet) 7-30 June 2007. Budget components [flux divergence (MFD), storage change (dPW), and inflow (IF/A)] are estimated from North American Regional Reanalysis data. Precipitation (P) is calculated from NCEP daily gridded data, evapotranspiration (E) is obtained as moisture budget equation residual, and the recycling ratio (P-E/P) is estimated using a new equation. Regional averages are presented for months and five daily P categories. Monthly budget results show that E and E - P are strongly positively related to P; E - P generally is positive and balanced by positive MFD that results from its horizontal velocity divergence component (HD, positive) exceeding its horizontal advection component (HA, negative). An exception is 2007 (CLASIC), when E - P and MFD are negative and supported primarily by negative HA. These overall monthly results characterize low P days (<=.0.6 mm), including for nonanomalous 2007, but weaken as daily P approaches 4 mm. In contrast, for 4 < P <= 8 mm day(-1) E - P and MFD are moderately negative and balanced largely by negative HD except in 2007 (negative HA). This overall pattern was accentuated (including for nonanomalous 2007) when daily P > 8 mm. Daily P-E/P ratios are small and of limited range, with P category averages 0.15-0.19. Ratios for 2007 are above average only for daily P <= 4 mm. CLASIC wetness principally resulted from distinctive MFD characteristics. Solar radiation, soil moisture, and crop status/yield information document surface interactions.
In this paper, preliminary results are presented showing that the two record-setting extreme events during 2010 summer (i.e., the Russian heat wave-wildfires and Pakistan flood) were physically connected. It is found that the Russian heat wave was associated with the development of an extraordinarily strong and prolonged extratropical atmospheric blocking event in association with the excitation of a large-scale atmospheric Rossby wave train spanning western Russia, Kazakhstan, and the northwestern China-Tibetan Plateau region. The southward penetration of upper-level vorticity perturbations in the leading trough of the Rossby wave was instrumental in triggering anomalously heavy rain events over northern Pakistan and vicinity in mid- to late July. Also shown are evidences that the Russian heat wave was amplified by a positive feedback through changes in surface energy fluxes between the atmospheric blocking pattern and an underlying extensive land region with below-normal soil moisture. The Pakistan heavy rain events were amplified and sustained by strong anomalous southeasterly flow along the Himalayan foothills and abundant moisture transport from the Bay of Bengal in connection with the northward propagation of the monsoonal intraseasonal oscillation.
It is well known that extreme or prolonged rainfall is the dominant trigger of landslides worldwide. While research has evaluated the spatiotemporal distribution of extreme rainfall and landslides at local or regional scales using in situ data, few studies have mapped rainfall-triggered landslide distribution globally because of the dearth of landslide data and consistent precipitation information. This study uses a newly developed global landslide catalog (GLC) and a 13-yr satellite-based precipitation record from Tropical Rainfall Measuring Mission (TRMM) data. For the first time, these two unique products provide the foundation to quantitatively evaluate the co-occurrence of precipitation and rainfall-triggered landslides globally. Evaluation of the GLC indicates that 2010 had a large number of high-impact landslide events relative to previous years. This study considers how variations in extreme and prolonged satellite-based rainfall are related to the distribution of landslides over the same time scales for three active landslide areas: Central America, the Himalayan arc, and central eastern China. Several test statistics confirm that TRMM rainfall generally scales with the observed increase in landslide reports and fatal events for 2010 and previous years over each region. These findings suggest that the co-occurrence of satellite precipitation and landslide reports may serve as a valuable indicator for characterizing the spatiotemporal distribution of landslide-prone areas in order to establish a global rainfall-triggered landslide climatology. This study characterizes the variability of satellite precipitation data and reported landslide activity at the global scale in order to improve landslide cataloging and attempt to quantify landslide triggering at daily, monthly, and yearly time scales.
Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the relative error structure of Tropical Rainfall Measurement Mission (TRMM) precipitation radar (PR) quantitative precipitation estimation (QPE) at the ground by comparison of 2A25 products with reference values derived from NOAA/NSSL's ground radar-based National Mosaic and QPE system (NMQ/Q2). The primary contribution of this study is to compare the new 2A25, version 7 (V7), products that were recently released as a replacement of version 6 (V6). Moreover, the authors supply uncertainty estimates of the rainfall products so that they may be used in a quantitative manner for applications like hydrologic modeling. This new version is considered superior over land areas and will likely be the final version for TRMM PR rainfall estimates. Several aspects of the two versions are compared and quantified, including rainfall rate distributions, systematic biases, and random errors. All analyses indicate that V7 is in closer agreement with the reference rainfall compared to V6.
This study evaluated 24-, 6-, and 1-h radar precipitation estimated from the National Mosaic and Multisensor Quantitative Precipitation Estimation System (NMQ) and the Weather Surveillance Radar-1988 Doppler (WSR-88D) Precipitation Processing System (PPS) over the conterminous United States (CONUS) for the warm season April September 2009 and the cool season October 2009 March 2010. Precipitation gauge observations from the Automated Surface Observing System (ASOS) were used as the ground truth. Gridded StageIV multisensor precipitation estimates were applied for supplementary verification. The comparison of the two systems consisted of a series of analyses including the linear correlation coefficient (CC) and the root-mean-square error (RMSE) between the radar precipitation estimates and the gauge observations, large precipitation amount detection categorical scores, and the reliability of precipitation amount distribution. Data stratified for the 12 CONUS River Forecast Centers (RFCs) and for the cold rains events with bright-band effects were analyzed additionally. Major results are 1) the linear CC of NMQ versus ASOS are generally higher than that of PPS versus ASOS over CON US, while the spatial variations stratified by the RFCs may switch with seasons; 2) compared to the precipitation distribution of ASOS, NMQ shows less deviation than PPS; 3) for the cold rains verified against ASOS, NMQ has higher CC and PPS has lower RMSE for 6-h and higher RMSE for 1-h cold rains; and 4) for the precipitation detection categorical scores, either NMQ or PPS can be superior, depending on the time interval and season. The verification against StageIV gridded precipitation estimates showed that NMQ consistently had higher correlations and lower biases than did PPS.
Ten years of Tropical Rainfall Measuring Mission precipitation radar data are used to study the physical properties of the precipitating cloud systems that account for the summer rainfall of tropical and subtropical South America. Radar echoes in the continental subtropics tend to be of an intensely convective nature, especially at the eastern foothills of the Andes where diurnally forced deep convective cells of small horizontal scale form when moist low-level flow is driven toward the foothills in connection with a midlatitude disturbance. As the disturbance moves east over the La Plata basin, nocturnal convective systems of larger horizontal scale with wide stratiform regions occur in a zone of general convergence. Precipitation in the continental tropics is generally produced by convective systems with greater stratiform composition. At the northeastern foothills of the central Andes, radar echoes of nocturnal convective systems of medium to large horizontal scale occur where moist low-level flow is lifted over the foothills. Growth of systems to large size is inhibited by daytime divergence at the foothills. Over the Amazon basin, daytime systems are also smaller than nocturnal systems. Radar echoes of precipitation over the Brazilian Highlands are generally smaller in horizontal scale, more convective, and mostly occur during the afternoon over elevated terrain. In the oceanic South Atlantic convergence zone, radar echoes grow to extremely large sizes. They are highly stratiform in nature and occur during all times of the day except late evening when convergence is weakened as a response to continental heating.
The authors investigated the accuracy of snow water equivalent (SWE) observations compiled by 748 Snowpack Telemetry stations and attributed the systematic bias introduced to SWE measurements to drifting snow. Often observed, SWE outpaces accumulated precipitation (AP), which can be statistically and physically explained through 1) precipitation undercatchment and/or 2) drifting snow. Forty-four percent of the 748 stations reported at least one year where the maximum SWE was greater than AP, while 16% of the stations showed this inconsistency for at least 20% of the observed years. Regions with a higher likelihood of inconsistency contained drier snow and are exposed to higher winds speeds, both of which are positively correlated to drifting snow potential as well as gauge undercatch. Differentiating between gauge undercatch and potential drifting scenarios, days when SWE increased but AP remained zero were used. These drift days occurred on an average of 13.3 days per year for all stations, with 31% greater wind speeds at 10 m for such days (using reanalysis winds). Findings suggest marked consistency between SWE and AP throughout the Cascade Mountains and lower elevations of the interior west while indicating notable inconsistency between these two variables throughout the higher elevations of the Rocky Mountains, Utah mountain ranges, and the Sierra Nevada.
Skillful medium-range weather forecasts are critical for water resources planning and management. This study aims to improve 15-day-ahead accumulated precipitation forecasts by combining biweekly weather and disaggregated climate forecasts.Acombination scheme is developed to combine reforecasts from a numerical weather model and disaggregated climate forecasts from ECHAM4.5 for developing 15-day-ahead precipitation forecasts. Evaluation of the skill of the weather-climate information (WCI)-based biweekly forecasts under leave-five-out cross validation shows that WCI-based forecasts perform better than reforecasts in many grid points over the continental United States. Correlation between rank probability skill score (RPSS) and disaggregated ECHAM4.5 forecast errors reveals that the lower the error in the disaggregated forecasts, the better the performance of WCI forecasts. Weights analysis from the combination scheme also shows that the biweekly WCI forecasts perform better by assigning higher weights to the better-performing candidate forecasts (reforecasts or disaggregated ECHAM4.5 forecasts). Particularly, WCI forecasts perform better during the summer months during which reforecasts have limited skill. Even though the disaggregated climate forecasts do not perform well over many grid points, the primary reason WCI-based forecasts perform better than the reforecasts is due to the reduction in the overconfidence of the reforecasts. Since the disaggregated climate forecasts are better dispersed than the reforecasts, combining them with reforecasts results in reduced uncertainty in predicting the 15-day-ahead accumulated precipitation.
Over land, remotely sensed surface soil moisture and rainfall accumulation retrievals contain complementary information that can be exploited for the mutual benefit of both product types. Here, a Kalman filtering–based tool is developed that utilizes a time series of spaceborne surface soil moisture retrievals to enhance short-term (2- to 10-day) satellite-based rainfall accumulation products. Using ground rain gauge data as a validation source, and a soil moisture product derived from the Advanced Microwave Scanning Radiometer aboard the NASA Aqua satellite, the approach is evaluated over the contiguous United States. Results demonstrate that, for areas of low to moderate vegetation cover density, the procedure is capable of improving short-term rainfall accumulation estimates extracted from a variety of satellite-based rainfall products. The approach is especially effective for correcting rainfall accumulation estimates derived without the aid of ground-based rain gauge observations. Special emphasis is placed on demonstrating that the approach can be applied in continental areas lacking ground-based observations and/or long-term satellite data records.
The accuracy of retrieving the two drop size distribution (DSD) parameters, median volume diameter (D-0), and normalized intercept parameter (N-W), as well as rain rate (R), from polarimetric C-band radar data obtained during a cool-season, long-duration precipitation event in Huntsville, Alabama, is examined. The radar was operated in a special "near-dwelling" mode over two video disdrometers (2DVD) located 15 km away. The polarimetric radar based retrieval algorithms for the DSD parameters and rain rate were obtained from simulations using the 2DVD measurements of the DSD. A unique feature of this paper is the radar-based estimation of the spatial correlation functions of the two DSD parameters and rain rate that are used to estimate the "point-to-area" variance. A detailed error variance separation is performed, including the aforementioned point-to-area variance, along with variance components due to the retrieval algorithm error, radar measurement error, and disdrometer sampling error. The spatial decorrelation distance was found to be smallest for the R (4.5 km) and largest for D-0 (8.24 km). For log(10)(N-W), it was 7.22 km. The proportion of the variance of the difference between radar-based estimates and 2DVD measurements that could be explained by the aforementioned errors was 100%, 57%, and 73% for D-0, log(10)(N-W), and R, respectively. The overall accuracy of the radar-based retrievals for the particular precipitation event quantified in terms of the fractional standard deviation were estimated to be 6.8%, 6%, and 21% for D-0, log(10)(N-W), and R, respectively. The normalized bias was <1%. These correspond to time resolution of similar to 3 min and spatial resolution of similar to 1.5 km.
To understand the sources of temporal and spatial variability of atmospheric evaporative demand across the conterminous United States (CONUS), a mean-value, second-moment uncertainty analysis is applied to a spatially distributed dataset of daily synthetic pan evaporation for 1980-2009. This evaporative demand measure is from the "PenPan" model, which is a combination equation calibrated to mimic observations from U.S. class-A evaporation pans and here driven by six North American Land Data Assimilation System variables: temperature, specific humidity, station pressure, wind speed, and downwelling shortwave and longwave radiation. The variability of evaporative demand is decomposed across various time scales into contributions from these drivers. Contrary to popular expectation and much hydrologic practice, temperature is not always the most significant driver of temporal variability in evaporative demand, particularly at subannual time scales. Instead, depending on the season, one of four drivers (temperature, specific humidity, downwelling shortwave radiation, and wind speed) dominates across different regions of CONUS. Temperature generally dominates in the northern continental interior. This analysis assists land surface modelers in balancing parameter parsimony and physical representativeness. Patterns of dominant drivers are shown to cycle seasonally, with clear implications for modeling evaporative demand in operational hydrology or as a metric of climate change and variability. Depending on the region and season, temperature, specific humidity, downwelling shortwave radiation, and wind speed must together be examined, with downwelling longwave radiation as a secondary input. If any variable may be ignored, it is atmospheric pressure. Parameterizations of evaporative demand based solely on temperature should be avoided at all time scales.
The majority of the world's population growth to 2050 is projected to occur in the tropics. Hence, there is a serious need for robust methods for undertaking water resource assessments to underpin the sustainable management of water in tropical regions. This paper describes the largest and most comprehensive assessment of the future impacts of runoff undertaken in a tropical region using conceptual rainfall-runoff models (RRMs). Five conceptual RRMs were calibrated using data from 115 streamflow gauging stations, and model parameters were regionalized using a combination of spatial proximity and catchment similarity. Future rainfall and evapotranspiration projections (denoted here as GCM(ES)) were transformed to catchment-scale variables by empirically scaling (ES) the historical climate series, informed by 15 global climate models (GCMs), to reflect a 1 degrees C increase in global average surface air temperature. Using the best-performing RRM ensemble, approximately half the GCM(ES) used resulted in a spatially averaged increase in mean annual runoff (by up to 29%) and half resulted in a decrease (by up to 26%). However, similar to 70% of the GCM(ES) resulted in a difference of within +/- 5% of the historical rainfall (1930-2007). The range in modeled impact on runoff, as estimated by five RRMs (for individual GCM(ES)), was compared to the range in modeled runoff using 15 GCM(ES) (for individual RRMs). For mid- to high runoff metrics, better predictions will come from improved GCM(ES) projections. A new finding of this study is that in the wet-dry tropics, for extremely large runoff events and low flows, improvements are needed in both GCM(ES) and rainfall-runoff modeling.
This paper presents the climate change impact on mean annual runoff across continental Australia estimated using the Budyko and Fu equations informed by projections from 15 global climate models and compares the estimates with those from extensive hydrological modeling. The results show runoff decline in southeast and far southwest Australia, but elsewhere across the continent there is no clear agreement between the global climate models in the direction of future precipitation and runoff change. Averaged across large regions, the estimates from the Budyko and Fu equations are reasonably similar to those from the hydrological models. The simplicity of the Budyko equation, the similarity in the results, and the large uncertainty in global climate model projections of future precipitation suggest that the Budyko equation is suitable for estimating climate change impact on mean annual runoff across large regions. The Budyko equation is particularly useful for data-limited regions, for studies where only estimates of climate change impact on long-term water availability are needed, and for investigative assessments prior to a detailed hydrological modeling study. The Budyko and Fu equations are, however, limited to estimating the change in mean annual runoff for a given change in mean annual precipitation and potential evaporation. The hydrological models, on the other hand, can also take into account potential changes in the subannual and other climate characteristics as well as provide a continuous simulation of daily and monthly runoff, which is important for many water availability studies.
The downwelling shortwave radiation on the earth's land surface is affected by the terrain characteristics of slope and aspect. These adjustments, in turn, impact the evolution of snow over such terrain. This article presents a multiscale evaluation of the impact of terrain-based adjustments to incident shortwave radiation on snow simulations over two midlatitude regions using two versions of the Noah land surface model (LSM). The evaluation is performed by comparing the snow cover simulations against the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product. The model simulations are evaluated using categorical measures, such as the probability of detection of "yes" events (PODy), which measure the fraction of snow cover presence that was correctly simulated, and false alarm ratio (FAR), which measures the fraction of no-snow events that was incorrectly simulated. The results indicate that the terrain-based correction of radiation leads to systematic improvements in the snow cover estimates in both domains and in both LSM versions (with roughly 12% overall improvement in PODy and 5% improvement in FAR), with larger improvements observed during snow accumulation and melt periods. Increased contribution to PODy and FAR improvements is observed over the north-and south-facing slopes, when the overall improvements are stratified to the four cardinal aspect categories.A two-dimensional discrete Haar wavelet analysis for the two study areas indicates that the PODy improvements in snow cover estimation drop to below 10% at scales coarser than 16 km, whereas the FAR improvements are below 10% at scales coarser than 4 km.
Data for water vapor adsorption and evaporation are presented for a bare soil (sandy loam, clay content 15%) in a southern Spanish olive grove. Water losses and gains were measured using eight high-precision minilysimeters, placed around an olive tree, which had been irrigated until the soil reached field capacity (∼0.22 m3 m−3). They were subsequently left to dry for 10 days. A pair of lysimeters was situated at each of the main points of the compass (N, E, S, W), at a distance of 1 m (the inner set of lysimeters; ILS) and 2 m (the outer set of lysimeters; OLS), respectively, from the tree trunk.
Distinct periods of moisture loss (evaporation) and moisture gain (vapor adsorption) could be distinguished for each day. Vapor adsorption often started just after noon and generally lasted until the (early) evening. Values of up to 0.7 mm of adsorbed water per day were measured. Adsorption was generally largest for the OLS (up to 100% more on a daily basis), and increased during the dry down. This was mainly the result of lower OLS surface soil moisture contents (period-average absolute difference ∼0.005 m3 m−3), as illustrated using various analyses employing a set of micrometeorological equations describing the exchange of water vapor between bare soil and the atmosphere. These analyses also showed that the amount of water vapor adsorbed by soils is very sensitive to changes in atmospheric forcing and surface variables. The use of empirical equations to estimate vapor adsorption is therefore not recommended.
In this study different parameterizations for land surface models currently employed in meteorological models at ECMWF [Tiled ECMWF Surface Scheme for Exchange Processes over Land (TESSEL)] and NCEP (Noah) are evaluated for a semiarid region in Ghana, West Africa. Both schemes utilize the Jarvis–Stewart approach to calculate canopy conductance as the critical variable for partitioning the available energy into sensible and latent heat flux. Additionally, an approach within Noah is tested to calculate canopy conductance based on plant physiology (A-gs method), where the photosynthetic assimilation is coupled to the leaf stomatal conductance.
All parameterizations were run offline for a seasonal cycle in 2002/03 using observations as forcings at two test sites. The two locations are in the humid tropical southern region and in the drier northern region. For the purpose of forcing and evaluation, a new set of data has been utilized to include surface fluxes obtained by scintillometry. The measurements include the rapid wet-to-dry transition after the wet season at both sites.
As a general trend, it has been found that during the wet period of a season net radiation is described well by all parameterizations. During the drying process the errors in modeled net radiation increased at both sites. The models perform poorly in simulating soil heat fluxes with larger errors for TESSEL for both sites. The evolution in time for sensible heat flux and latent heat flux was tackled in different ways by the utilized parameterizations and sites with enhanced model performance for the more southern site. Soil moisture in the upper soil layers is modeled with small errors for the different parameterizations.
Key adjustments for reducing net radiation during the dry period of a season are discussed. In particular, the ratio of roughness length of momentum and heat was found to be an important parameter, but will require seasonal adjustments.
Daily precipitation retrievals from three algorithms [the Tropical
Rainfall Measuring Mission 3B42 rain product (TRMM-3B42), the Climate
Prediction Center morphing technique (CMORPH), and the second version
(RFEv2) of the Famine Early Warning System (FEWS)] and CloudSat
retrievals of cloud liquid water, ice amount, and cloud fraction are
used to document the cloud structures associated with rainfall location
and intensity in the West African monsoon. The different rainfall
retrieval approaches lead to contrasting cloud sensitivities between all
three algorithms most apparent in the onset period of June and July.
During the monsoon preonset phase, CMORPH produces a precipitation peak
at around 12°N associated with upper-level cirrus clouds, while FEWS
and TRMM both produce rainfall maxima collocated with the
tropospheric-deep convective cloud structures at 4°-6°N. In July
similar relative displacements of the rainfall maxima are observed.
Conditional sampling of several hundred convection systems proves that,
while upper-level cirrus is advected northward relative to the motion of
the convective system cores, the reduced cover and water content of
lower-tropospheric clouds in the northern zone could be due to signal
attenuation as the systems there appear to be more intense, producing
higher ice water contents. Thus, while CMORPH may overestimate rainfall
in the northern zone due to its reliance on cloud ice, TRMM and FEWS are
likely underestimating precipitation in this zone, potentially due to
the use of infrared based products in TRMM and FEWS when microwave is
not available. Mapping the CloudSat retrievals as a function of rain
rate confirms the greater sensitivity of CMORPH to ice cloud and
indicates that high-intensity rainfall events are associated with
systems that are deeper and of a greater spatial scale.
Information related to land surface is immensely important to global change science. For example, land surface changes can alter regional climate through its effects on fluxes of water, energy, and carbon. In the past decades, data sources and methodologies for characterizing land surface heterogeneity (e.g., land cover, leaf area index, fractional vegetation cover, bare soil, and vegetation albedos) from remote sensing have evolved rapidly. The double ECOCLIMAP database-constituted of a land cover map and land surface variables and derived from Advanced Very High Resolution Radiometer (AVHRR) observations acquired between April 1992 and March 1993-was developed to support investigations that require information related to spatiotemporal dynamics of land surface. Here is the description of ECOCLIMAP-II: a new characterization of the land surface heterogeneity based on the latest generation of sensors, which represents an update of the ECOCLIMAP-I database over Africa. Owing to the many features of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors (more accurate in spatial resolution and spectral information compared to the AVHRR sensor), a variety of methods have been developed for an extended period of 8 yr (2000-07) to strengthen consistency between land surface variables as required by the meteorological and ecological communities. The relative accuracy (or performance) quality of ECOCLIMAP-II was assessed (i.e., by comparison with other global datasets). Results illustrate a substantial refinement; for instance, the fractional vegetation cover resulting in a root-mean-square error of 34% instead of 64% in comparison with the original version of ECOCLIMAP.
Multispectral Spinning Enhanced Visible and IR Interferometer (SEVIRI) data, calibrated with daily rain gauge estimates, were used to produce daily high-resolution rainfall estimates over Africa. An artificial neural network (ANN) approach was used, producing an output of satellite pixel-scale daily rainfall totals. This product, known as the Rainfall Intensity Artificial Neural Network African Algorithm (RIANNAA), was calibrated and validated using gauge data from the highland Oromiya region of Ethiopia. Validation was performed at a variety of spatial and temporal scales, and results were also compared against Tropical Applications of Meteorology Using Satellite Data (TAMSAT) single-channel IR-based rainfall estimates. Several versions of RIANNAA, with different combinations of SEVIRI channels as inputs, were developed. RIANNAA was an improvement over TAMSAT at all validation scales, for all versions of RIANNAA. However, the addition of multispectral data to RIANNAA only provided a statistically significant improvement over the single-channel RIANNAA at the highest spatial and temporal-resolution validation scale. It appears that multispectral data add more value to rainfall estimates at high-resolution scales than at averaged time scales, where the cloud microphysical information that they provide may be less important for determining rainfall totals than larger-scale processes such as total moisture advection aloft.
High-resolution rain fields are a prerequisite to many hydrometeorological studies. For some applications, the required resolution may be as fine as 1 km in space and 5 min in time. At these scales, rainfall is strongly intermittent, variable in space, and correlated in time because of the propagation of the rainy systems. This paper compares two interpolation approaches to generate high-resolution rain fields from rain gauge measurements: (i) a classic interpolation technique that consists in interpolating independently the rain intensities at each time step (Eulerian kriging) and (ii) a simple dynamic interpolation technique that incorporates the propagation of the rainy systems (Lagrangian kriging). For this latter approach, three propagation models are tested. The different interpolation techniques are evaluated over three climatically contrasted areas in West Africa where a multiyear 5-min rainfall dataset has been collected during the African Monsoon Multidisciplinary Analyses (AMMA) campaigns. The dynamic interpolation technique is shown to perform better than the classic approach for a majority of the rainy events. The performances of the three propagation models differ from one another, depending on the evaluation criteria used. One of them provides a satisfactory time of arrival of rainfall but slightly smooths the rain intensities. The two others reproduce well the rain intensities, but the time of arrival of the rain is sometimes delayed. The choice of an appropriate propagation algorithm will thus depend on the operational objectives underlying the rain field generation.
A fine-scale model of blowing snow is used to simulate the characteristics of snowcover in a low-Arctic catchment with moderate topography and partial shrub cover. The influence of changing shrub characteristics is investigated by performing a sequence of simulations with varying shrub heights and coverage. Increasing shrub height gives an increase in snow depth within the shrub-covered areas, up to a limit determined by the supply of falling and blowing snow, but increasing shrub coverage gives a decrease in snow depths within shrubs as the supply of blowing snow imported from open areas is reduced. A simulation of snow redistribution over the existing topography without any shrub cover gives much greater accumulations of snow on slopes in the lee of the prevailing wind than on windward slopes; in contrast, shrubs are able to trap snow on both lee and windward slopes. A spatially aggregated, or tiled, model is developed in which snow is relocated by wind transport from sparsely vegetated tiles to more densely vegetated tiles. The vegetation distribution is not specified, but the simulation is parametrized using average fetch lengths along the major transport axis. The aggregated model is found to be capable of matching the average snow accumulation in shrub and open areas predicted by the distributed model reasonably well but with much less computational cost.
Land surface models (LSMs) used in climate modeling include detailed above-ground biophysics but usually lack a good representation of runoff. Both processes are closely linked through soil moisture. Soil moisture however has a high spatial variability that is unresolved at climate model grid scales. Physically based vertical and horizontal aggregation methods exist to account for this scaling problem. Effects of scaling and aggregation have been evaluated in this study by performing catchment-scale LSM simulations for the Rhône catchment. It is found that evapotranspiration is not sensitive to soil moisture over the Rhône but it largely controls total runoff as a residual of the terrestrial water balance. Runoff magnitude is better simulated when the vertical soil moisture fluxes are resolved at a finer vertical resolution. The use of subgrid-scale topography significantly improves both the timing of runoff on the daily time scale (response to rainfall events) and the magnitude of summer baseflow (from seasonal groundwater recharge). Explicitly accounting for soil moisture as a subgrid-scale process in LSMs allows one to better resolve the seasonal course of the terrestrial water storage and makes runoff insensitive to the used grid scale. However, scale dependency of runoff to above-ground hydrology cannot be ignored: snowmelt runoff from the Alpine part of the Rhône is sensitive to the spatial resolution of the snow scheme, and autumnal runoff from the Mediterranean part of the Rhône is sensitive to the spatial resolution of precipitation.
This work proposes a relatively simple methodology for creating ensembles of precipitation inputs that are consistent with the spatial and temporal scale necessary for regional crop modeling. A high-quality reference precipitation dataset [the European Land Data Assimilation System (ELDAS)] was used as a basis to define the uncertainty in an operational precipitation database [the Crop Growth Monitoring System (CGMS)]. The distributions of precipitation residuals (CGMS ¿ ELDAS) were determined for classes of CGMS precipitation and transformed to a Gaussian distribution using normal score transformations. In cases of zero CGMS precipitation, the occurrence of rainfall was controlled by an indicator variable. The resulting normal-score-transformed precipitation residuals appeared to be approximately multivariate Gaussian and exhibited strong spatial correlation; however, temporal correlation was very weak. An ensemble of 100 precipitation realizations was created based on back-transformed spatially correlated Gaussian residuals and indicator realizations. Quantile¿quantile plots of 100 realizations against the ELDAS reference data for selected sites revealed similar distributions (except for the 100th percentile, owing to some large residuals in the realizations). The semivariograms of realizations for sampled days showed considerable variability in the overall variance; the range of the spatial correlation was similar to that of the ELDAS reference dataset. The intermittency characteristics of wet and dry periods were reproduced well for most of the selected sites, but the method failed to reproduce the dry period statistics in semiarid areas (e.g., southern Spain). Finally, a case study demonstrates how rainfall ensembles can be used in operational crop modeling and crop yield forecasting.
Near-surface air temperature observations often have periods of missing data, and many applications using these datasets require filling in all missing periods. Multiple methods are available to fill missing data, but the comparative accuracy of these approaches has not been assessed. In this comparative study, five techniques were used to fill in missing temperature data: spatiotemporal correlations in the form of empirical orthogonal functions (EOFs), time series diurnal interpolation, and three variations of lapse rate-based filling. The method validation used sets of hourly surface temperature observations in complex terrain from five regions. The most accurate method for filling missing data depended on the number of available stations and the number of hours of missing data. Spatiotemporal correlations using EOF reconstruction were most accurate provided that at least 16 stations were available. Temporal interpolation was the most accurate method when only one or two stations were available or for 1-h gaps. Lapse rate-based filling was most accurate for intermediate numbers of stations. The accuracy of the lapse rate and EOFmethods was found to be sensitive to the vertical separation of stations and the degree of correlation between them, which also explained some of the regional differences in performance. Horizontal distance was less significantly correlated with method performance. From these findings, guidelines are presented for choosing a filling method based on the duration of the missing data and the number of stations.
A network of eddy covariance (EC) and micrometeorological flux (METFLUX) stations over corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] canopies was established as part of the Soil Moisture-Atmosphere Coupling Experiment (SMACEX) in central Iowa during the summer of 2002 to measure fluxes of heat, water vapor, and carbon dioxide (CO2) during the growing season. Additionally, EC measurements of water vapor and CO2 fluxes from an aircraft platform complemented the tower-based measurements. Sensible heat, water vapor, and CO2 fluxes showed the greatest spatial and temporal variability during the early crop growth stage. Differences in all of the energy balance components were detectable between corn and soybean as well as within similar crops throughout the study period. Tower network-averaged fluxes of sensible heat, water vapor, and CO2 were observed to be in good agreement with area-averaged aircraft flux measurements.
In this study of snow-atmosphere coupling strength, the previous snow-atmosphere coupled modeling experiment is extended to investigate the separate impacts on the atmosphere of the radiatively driven snow albedo effect and the snow hydrological effect that operates through soil moisture, evapotranspiration, and precipitation feedbacks. The albedo effect is governed by snow cover fraction, while the hydrological effect is controlled by anomalies in snow water equivalent. Realistic snow cover from satellite estimates is prescribed and compared with model-generated values to isolate the snow albedo effect. Similarly, imparting realistic snow water equivalent from the Global Land Data Assimilation System in the model allows for estimation of the snow hydrological effect. The snow albedo effect is found to be active before, and especially during, the snowmelt period, and regions of strong albedo-driven coupling move northward during spring, with the retreating edge of the snowpack in the Northern Hemisphere. The snow hydrological effect appears first during snowmelt and can persist for months afterward. The contributing factors to the snow albedo effect are analyzed in a theoretical framework.
This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado) that are part of the 2002/03 Cold Land Processes Field Experiment (CLPX). The assimilated snow albedo products are 1) the standard Moderate Resolution Imaging Spectrometer (MODIS) product (MOD10A1) and 2) retrievals from MODIS observations with the recently developed Pattern-Based Semiempirical (PASS) approach. The performance of the Noah simulations, with and without assimilation, is evaluated using the in situ measurements of snow albedo, upward shortwave radiation, and snow depth. The results show that simulations with albedo assimilation agree better with the measurements. However, because of the limited impact of snow albedo updates after subsequent snowfall, the mean (or seasonal) error statistics decrease significantly for only two of the three CLPX sites. Though the simulated snow depth and duration for the snow season benefit from the assimilation, the greatest improvements are found in the simulated upward shortwave radiation, with root mean squared errors reduced by about 30%. As such, this study demonstrates that assimilation of satellite-observed snow albedo can improve LSM simulations, which may positively affect the representation of hydrological and surface energy budget processes in runoff and numerical weather prediction models.
This study examines simulations of two flooding events in Alberta, Canada, during June 2005, made using the Weather Research and Forecasting Model (WRF). The model was used in a manner readily accessible to nonmeteorologists (e.g., accepting default choices and parameters) and with a relatively large spatial resolution for rapid model runs. The simulations were skillful: strong storms were developed having the correct timing and location, generating precipitation rates close to observations, and with precipitation amounts near that observed. The model was then used to examine the sensitivity of the two storms to the topography of the Rocky Mountains. Comparing model results using the actual topographic grid with those of a reduced-mountain grid, it is concluded that a reduction in mountain elevation decreases maximum precipitation by roughly 50% over the mountains and foothills. There was little sensitivity to topography in the precipitation outside the mountains.
A climatology of wetlands has been derived at a low spatial resolution (0.25 degrees 3 0.25 degrees equal-area grid) over a 15-yr period by combining visible and near-infrared satellite observations and passive and active microwaves. The objective of this study is to develop a downscaling technique able to retrieve wetland estimations at a higher spatial resolution (about 500 m). The proposed method uses an image-processing technique applied to synthetic aperture radar (SAR) information about the low and high wetland season. This method is tested over the densely vegetated basin of the Amazon. The downscaling results are satisfactory since they respect the spatial hydrological features of the SAR data and the temporal evolution of the low-resolution wetland estimates. A new long-term and high-resolution wetland dataset has been generated for 1993-2007 for the Amazon basin. This dataset represents a new and unprecedented source of information for climate and land surface modeling of the Amazon and for the definition of future hydrology-oriented satellite missions such as Surface Water and Ocean Topography (SWOT).
Heavy precipitation and the resulting flooding are the most serious weather-related hazards over the west coast of North America. This paper analyzes the trends in heavy precipitation for the period 1950-2009 by examining the decadal distributions of the top 60, 40, and 20 two-day precipitation events for a collection of stations along the coastal zone of the United States and British Columbia, as well as the decadal distribution of maximum daily discharge for unregulated rivers from northern California to Washington State. During the past 60 years there has been a modest increase in heavy precipitation events over southern and central coastal California, a decline in heavy events from northern California through the central Oregon coast, a substantial increase in major events over Washington, and a modest increase over coastal British Columbia. Most of these trends are not significantly different from zero at the 95% level. The trends in maximum daily discharge of unregulated rivers are consistent with the above pattern, with increasing discharges over the past three decades over Washington and northern Oregon and declines over the remainder of Oregon and northern California. Finally, the above trends in heavy rainfall and daily discharge are compared to the future patterns indicated by general circulation models under various global warming scenarios.
Anewmethodology for assessing the impact of surface heat fluxes on precipitation is applied to data from the North American Regional Reanalysis (NARR) and to output from the Geophysical Fluid Dynamics Laboratory's Atmospheric Model 2.1 (AM2.1). The method assesses the sensitivity of afternoon convective rainfall frequency and intensity to the late-morning partitioning of latent and sensible heating, quantified in terms of evaporative fraction (EF). Over North America, both NARR and AM2.1 indicate sensitivity of convective rainfall triggering to EF but no appreciable influence of EF on convective rainfall amounts. Functional relationships between the triggering feedback strength (TFS) metric and mean EF demonstrate the occurrence of stronger coupling formeanEF in the range of 0.6 to 0.8. To leading order,AM2.1 exhibits spatial distributions and seasonality of the EF impact on triggering resembling those seen in NARR: rainfall probability increases with higher EF over the eastern United States and Mexico and peaks in Northern Hemisphere summer. Over those regions, the impact of EF variability on afternoon rainfall triggering in summer can explain up to 50% of seasonal rainfall variability. However, the AM2.1 metrics also exhibit some features not present in NARR, for example, strong coupling extending northwestward from the central Great Plains into Canada. Sources of disagreement may include model hydroclimatic biases that affect the mean patterns and variability of surface flux partitioning, with EF variability typicallymuch lower in NARR. Finally, the authors also discuss the consistency of their results with other assessments of land-precipitation coupling obtained from different methodologies.
This paper describes a comprehensive assessment of a new high-resolution, gauge-satellite-based analysis of daily precipitation over continental South America during 2004. This methodology is based on a combination of additive and multiplicative bias correction schemes to get the lowest bias when compared with the observed values ( rain gauges). Intercomparisons and cross-validation tests have been carried out between independent rain gauges and different merging techniques. This validation process was done for the control algorithm [Tropical Rainfall Measuring Mission(TRMM) Multisatellite Precipitation Analysis real-time algorithm] and five different merging schemes: additive bias correction; ratio bias correction; TRMM Multisatellite Precipitation Analysis, research version; and the combined scheme proposed in this paper. These methodologies were tested for different months belonging to different seasons and for different network densities. All compared, merging schemes produce better results than the control algorithm; however, when finer temporal ( daily) and spatial scale ( regional networks) gauge datasets are included in the analysis, the improvement is remarkable. The combined scheme consistently presents the best performance among the five techniques tested in this paper. This is also true when a degraded daily gauge network is used instead of a full dataset. This technique appears to be a suitable tool to produce real-time, high-resolution, gauge-and satellite-based analyses of daily precipitation over land in regional domains. Pages: 533-543
The Environmental Modeling Center (EMC) at the National Centers for Environmental Prediction (NCEP) and the University of Washington (UW) run parallel drought monitoring systems over the continental United States based on the North American Land Data Assimilation System (NLDAS). The NCEP system uses four land surface models (LSMs): Variable Infiltration Capacity (VIC), Noah, Mosaic, and Sacramento (SAC). The UW system uses VIC, SAC, Noah, and the Community Land Model (CLM). An assessment of differences in drought characteristics using both systems for the period 1979-2008 was performed. For soil moisture (SM) percentiles and runoff indices, differences are relatively small among different LSMs in the same system. However, the ensemble mean differences between the two systems are large over the western United States in some cases exceeding 20% for SM and runoff percentile differences. These differences are most apparent after 2002 when the NCEP system transitioned to use the real-time North American Regional Reanalysis (NARR) and its precipitation gauge station data. (The UW system went into real-time operation in 2005.) Experiments were performed to address the sources of uncertainties. Comparison of simulations using the two systems with different model forcings indicates that the precipitation forcing differences are the primary source of the SM and runoff differences. While temperature, shortwave and longwave radiation, and wind speed forcing differences are also large after 2002, their contributions to SM and runoff differences are much smaller than precipitation.
The North American Regional Reanalysis (NARR) is a state-of-the-art land atmosphere reanalysis product that provides improved representation of the terrestrial hydrologic cycle compared to previous global reanalyses, having the potential to provide an enhanced picture of hydrologic extremes such as floods and droughts and their driving mechanisms. This is partly because of the novel assimilation of observed precipitation, state-of-the-art land surface scheme, and higher spatial resolution. NARR is evaluated in terms of the terrestrial water budget and its depiction of drought at monthly to annual time scales against two offline land surface model [Noah v2.7.1 and Variable Infiltration Capacity (VIC)] simulations and observation-based runoff estimates over the continental United States for 1979-2003. An earlier version of the Noah model forms the land component of NARR and so the offline simulation provides an opportunity to diagnose NARR land surface variables independently of atmospheric feedbacks. The VIC model has been calibrated against measured streamflow and so provides a reasonable estimate of large-scale evapotranspiration. Despite similar precipitation, there are large differences in the partitioning of precipitation into evapotranspiration and runoff. Relative to VIC, NARR and Noah annual evapotranspiration is biased high by 28% and 24%, respectively, and the runoff ratios are 50% and 40% lower. This is confirmed by comparison with observation-based runoff estimates from 1130 small, relatively unmanaged basins across the continental United States. The overestimation of evapotranspiration by NARR is largely attributed to the evapotranspiration component of the Noah model, whereas other factors such as atmospheric forcings or biases induced by precipitation assimilation into NARR play only a minor role. A combination of differences in the parameterization of evapotranspiration and in particular low stomatal resistance values in NARR, the seasonality of vegetation characteristics, the near-surface radiation and meteorology, and the representation of soil moisture dynamics, including high infiltration rates and the relative coupling of soil moisture with baseflow in NARR, are responsible for the differences in the water budgets. Large-scale drought as quantified by soil moisture percentiles covaries closely over the continental United States between the three datasets, despite large differences in the seasonal water budgets. However, there are large regional differences, especially in the eastern United States where the VIC model shows higher variability in drought dynamics. This is mostly due to increased frequency of completely dry conditions in NARR that result from differences in soil depth, higher evapotranspiration, early snowmelt, and early peak runoff. In the western United States, differences in the precipitation forcing contribute to large discrepancies between NARR and Noah/VIC simulations in the representation of the early 2000s drought.
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. VICestimated 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 themonsoon season, indicating the potential usefulness of LAI anomalies for predicting early seasonET.
The data required to drive distributed hydrological models are significantly limited within mountainous terrain because of a scarcity of observations. This study evaluated three common configurations of forcing data: 1) one low-elevation station, combined with empirical techniques; 2) gridded output from the Weather Research and Forecasting Model (WRF); and 3) a combination of the two. Each configuration was evaluated within the heavily instrumented North Fork American River basin in California during October-June 2000-10. Simulations of streamflow and snowpack using the Distributed Hydrology Soil and Vegetation Model (DHSVM) highlighted precipitation and radiation as variables whose sources resulted in significant differences. The best source of precipitation data varied between years. On average, the WRF performed as well as the single station distributed using the Parameter Regression on Independent Slopes Model (PRISM). The average percent biases in simulated streamflow were 3%and 1%, for configurations 1 and 2, respectively, even though precipitation compared directly with gauge measurements was biased high by 6% and 17%, suggesting that gauge undercatch may explain part of the bias. Simulations of snowpack using empirically estimated longwave irradiance resulted in melt rates lower than those observed at high-elevation sites, while at lower elevations the same forcing caused significant midwinter melt that was not observed. These results highlight the complexity of how forcing data sources impact hydrology over different areas (high- versus low-elevation snow) and different time periods. Overall, results support the use of output from the WRF model over empirical techniques in regions with limited station data.
Estimating evapotranspiration using the complementary relationship can serve as a proxy to more sophisticated physically based approaches and can be used to better understand water and energy budget feedbacks. The authors investigated the existence of complementarity between actual evapotranspiration (ET) and potential ET (ETp) over natural vegetation in semiarid desert ecosystems of southern Idaho using only the forcing data and simulated fluxes obtained from Noah land surface model (LSM) and North American Regional Reanalysis (NARR) data. To mitigate the paucity of long-term meteorological data, the Noah LSM-simulated fluxes and the NARR forcing data were used in the advection-aridity (AA) model to derive the complementary relationship (CR) for the sagebrush and cheatgrass ecosystems. When soil moisture was a limiting factor for ET, the CR was stable and asymmetric, with b values of 2.43 and 1.43 for sagebrush and cheatgrass, respectively. Higher b values contributed to decreased ET and increased ETp, and as a result ET from the sagebrush community was less compared to that of cheatgrass. Validation of the derived CR showed that correlations between daily ET from the Noah LSM and CR-based ET were 0.76 and 0.80 for sagebrush and cheatgrass, respectively, while the root-mean-square errors were 0.53 and 0.61 mm day-1.
An automatic device for measurement of the amount (weight) of deposited precipitation developed at the Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic, is described. Examples of measurements of various types of deposited precipitation are presented. The paper also discusses the response of the measuring instrument to falling precipitation and the influence of wind on the measurements. The results of first measurements proved that the instrument is suitable for automatic and continuous monitoring of deposited precipitation.
In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent.