Search

Groundwater is the most significant freshwater source and plays a critical role in the earth's water and energy balance. The lack of groundwater observations with a high spatiotemporal resolution at a global scale hinders our ability to study and model the environment when shallow groundwater has a direct impact on surface soil moisture. This study aims to estimate the spatial and temporal distributions of shallow groundwater-influenced areas at a global scale. We trained an ensemble machine learning algorithm, using outputs from a variably saturated soil moisture flux model, to identify the shallow groundwater occurrence. Model simulations spanned various climate zones and soil types across the globe. The overall accuracy of the algorithm in reproducing the soil moisture flux model results was 95.5%. We applied the algorithm to spaceborne soil moisture observations retrieved by NASA's SMAP satellite and present a global-scale shallow groundwater map derived from the SMAP observations. The derived global distribution of shallow groundwater identifies wetlands, large riparian corridors, and seasonally inundated lowlands. The results showed that 19% of terrestrial land cover had been influenced by shallow groundwater at some point in time during the period of interest (2015–2018). Temporally, shallow groundwater follows an annual cyclic pattern with 2% to 6% of the land surface being influenced globally. This study shows that SMAP observations could be used in estimating shallow groundwater in high spatiotemporal resolution at a global scale, potentially providing invaluable inputs for modeling and environmental monitoring studies.

Hydrological applications rely on the availability and quality of precipitation products, especially model- and satellite-based products for use in areas without ground measurements. It is known that the quality of model- and satellite-based precipitation products is complementary: model-based products exhibit high quality during cold seasons while satellite-based products are better during warm seasons. To explore the complementary behavior of the quality of the precipitation products, this study uses 2-m air temperature as auxiliary information to evaluate high-resolution (0.1°/hourly) precipitation estimates from the Weather Research and Forecasting (WRF) Model and from the version 5 Integrated Multisatellite Retrievals for GPM (IMERG) algorithm (i.e., early and final runs). The products are evaluated relative to the reference NCEP Stage IV precipitation estimates over the central United States during August 2015–July 2017. Results show that the IMERG final-run estimates are nearly unbiased, while the IMERG early-run and the WRF estimates are positively biased. The WRF estimates exhibit high correlations with the reference data when the temperature falls below 280 K. The IMERG estimates, both early and final runs, do so when the temperature exceeds 280 K. Moreover, the complementary behavior of the WRF and the IMERG products conditioned on air temperature does not vary with either season or location.

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

Climate studies and effective environmental management require unbiased climate datasets. This study develops a new bias correction approach using a three-layer feedforward neural network to reduce the biases of climate variables (temperature and precipitation) over northern South America. Air and skin temperature, specific humidity, and net longwave and shortwave radiation are used as inputs to the network for bias correction of 6-hourly temperature. Inputs to the network for bias correction of monthly precipitation are precipitation at lag 0, 1, 2, and 3 months, and also the standard deviation of precipitation from 3 × 3 neighbors around the pixel of interest. The climate model data are provided by the Community Climate System Model, version 3 (CCSM3). Results show that the trained artificial neural network (ANN) can improve the estimation error and correlation of the variables for both calibration and validation periods even when there is a low temporal consistency between the time series of the model data and targets. The developed model is also able to modify the probabilistic structure of the variables although the quantile-based information is not directly considered in the network. The ANN model outperforms linear regression, which is used for comparison purposes. The new method can be used to produce bias-corrected climate variables that can be used as forcing to hydrological and ecological models.

Developing high-quality long-term data sets at uniform space–time resolution is essential for improved climate studies. This article processes the outputs from two global and regional climate models, the Community Climate System Model (CCSM3) and the Regional Climate Model driven by the Hadley Centre Coupled Model (RegCM3). The results are bias-corrected time series of atmospheric variables corresponding to Intergovernmental Panel on Climate Change (IPCC's) historical (20C3M) and future (A2) climate scenarios over the Amazon Basin. We use a series of simple but effective interpolation approaches to produce hourly climate data sets at 1° by 1° grid cells. A quantile-based mapping approach is used to reduce the biases of temperature and precipitation in CCSM3 and RegCM3. Adjustments are also made on specific humidity and downwelling longwave radiation to avoid inconsistency between those variables and bias-corrected temperature values. We also interpolated an already bias-corrected Parallel Climate Model data set (PCM1) from 3-hourly to the hourly resolution. The final climate data sets can be used as forcing of ecosystem and hydrologic models to study climate changes and impact assessments over the Amazon Basin.

Soil erosion, particularly that caused by agriculture, is closely linked to the global carbon (C) cycle. There is a wide range of contrasting global estimates of how erosion alters soil-atmosphere C exchange. This can be partly attributed to limited understanding of how geomorphology, topography, and management practices affect erosion and oxidation of soil organic C (SOC). This work presents a physically based approach that stresses the heterogeneity at fine spatial scales of SOC erosion, SOC burial, and associated soil-atmosphere C fluxes. The Holcombe's Branch watershed, part of the Calhoun Critical Zone Observatory in South Carolina, USA, is the case study used. The site has experienced some of the most serious agricultural soil erosion in North America. We use SOC content measurements from contrasting soil profiles and estimates of SOC oxidation rates at multiple soil depths. The methodology was implemented in the tRIBS-ECO (Triangulated Irregular Network-based Real-time Integrated Basin Simulator-Erosion and Carbon Oxidation), a spatially and depth-explicit model of SOC dynamics built within an existing coupled physically based hydro-geomorphic model. According to observations from multiple soil profiles, about 32% of the original SOC content has been eroded in the study area. The results indicate that C erosion and its replacement exhibit significant topographic variation at relatively small scales (tens of meters). The episodic representation of SOC erosion reproduces the history of SOC erosion better than models that use an assumption of constant erosion in space and time. The net atmospheric C exchange at the study site is estimated to range from a maximum source of 14.5 g m−2 yr−1 to a maximum sink of −18.2 g m−2 yr−1. The small-scale complexity of C erosion and burial driven by topography exerts a strong control on the landscape's capacity to serve as a C source or a sink.

Using satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces—snow-covered lands, deserts, and coastal areas—are particularly challenging 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) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges–Brahmaputra–Meghna River basins, which is unique in terms of its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year.

The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.

The recently formulated maximum entropy production (MEP) model over land surfaces has been generalized to water-snow-ice surfaces. Analytical solutions of energy budget in terms of the partition of surface radiative fluxes into (turbulent and/or conductive) heat fluxes at the earth-atmosphere interface are derived as functions of surface temperature (e.g., sea surface temperature). The MEP model does not require data of wind speed, air temperature-humidity, and surface roughness. Test of the MEP model using observations from several field experiments is encouraging. Potential applications of the proposed model for understanding long-term trends in surface heat fluxes and for closing global surface energy budget at the Earth's atmosphere are suggested.

Prior research has shown that deforestation in the southwestern Amazon enhances the formation of nonprecipitating shallow cumulus clouds, while deep cumulus convection was favored over forested land. The research presented here further investigates the trends of hydrometeors in the area by examining how precipitation frequency changes as a function of distance to the forest’s edge. Measurements are made from the precipitation radar on the Tropical Rainfall Measuring Mission (TRMM; TRMM 2A25) satellite, and continuous forest coverage is retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS; MODIS MCD12Q1). The event-based areal fractions of precipitation coverage (precipitation fraction) are calculated; referenced to forested, nonforested, and forest-edge land cover; and compared. As results are generally consistent with previous findings, the novel conclusions here extend that precipitation frequency in the southwestern Amazon (i) decreases over regions of nonforests far removed (10-plus km) from forest borders but (ii) increases within several kilometers of the forest edges, particularly over the nonforest side of the transition.

An extremum hypothesis of turbulent transport in the atmospheric surface layer is postulated. The hypothesis has led to a unique solution of Monin–Obukhov similarity equations in terms of simple expressions linking shear stress (momentum flux) and heat flux to mean wind shear and temperature gradient. The extremum solution is consistent with the well-known asymptotic properties of the surface layer. Validation of the extremum solution has been made by comparison to field measurements of momentum and heat fluxes. Furthermore, a modeling test of predicting surface heat fluxes using the results of this work is presented. A critical reexamination of the interpretation of the Obukhov length is given.

Reanalysis data are an important source of information for hydrometeorology applications, which use data assimilation to combine an imperfect atmospheric model with uncertain observations. However, uncertainty estimates are not normally provided with reanalyses. The model “first guess” (6-h forecast) is sometimes saved along with reanalysis estimates, which allows the calculation of the analysis increment (AI), defined as the analysis minus the model first guess. Analysis increment statistics could provide a quantitative index for comparing models in regions with sufficient observations. Monthly analysis increment statistics for the NCEP–NCAR Global Reanalysis 1 (NCEP-R1) and the NCEP/Department of Energy Global Reanalysis 2 (NCEP-R2) are computed for a North American and South American location for zonal and meridional wind and specific humidity at three atmospheric levels for 1998–2001. NCEP-R2 specific humidity was found to have a smaller mean monthly standard deviation of the analysis increment than NCEP-R1 at the North American location at the 300-mb level. The NCEP-R2 specific humidity monthly standard deviation at the South American location is much larger than NCEP-R1 for September–November 1998, which may be related to the transition to La Niña. For both zonal and meridional wind, the monthly AI standard deviations are similar for NCEP-R1 and NCEP-R2 at all atmospheric levels for the North American location. The South American location exhibits similar behavior for the wind AI statistics as for specific humidity: NCEP-R2 has a much larger monthly standard deviation of the AI for September–November 1998. The analysis increment statistics could be one method for quantitatively comparing reanalyses. First guess information should be available to the user in reanalysis archives.

Estimates of the net convergence of atmospheric moisture flux over the Amazon Basin, [C], derived using data products from three global reanalyses, the NCEP–NCAR reanalysis (NCEP-1), the NCEP/Department of Energy reanalysis (NCEP-2), and the 40-yr ECMWF Re-Analysis (ERA-40), are compared. Two types of uncertainty in these [C] estimates are distinguished and quantified: “model-associated uncertainty,” which necessarily arises from imperfections in the numerical weather models or data assimilation algorithms, and “postprocessing uncertainty” introduced by operations performed on the original reanalysis data products to compute [C], particularly the finite-difference approximation of divergence. Model-associated uncertainty is found to overwhelm the postprocessing error. A closer look at the time series of this field extending over the period 1980–2001, and their comparison to basin-averaged precipitation and runoff data, reveals the signatures of two potential sources of model-associated errors. 1) ERA-40 estimates of [C] exhibit an artificial shift in 1987, possibly produced by the start of assimilation of Special Sensor Microwave Imager (SSM/I) data. The estimates preceding 1988 are negatively biased relative to the remaining time series, and hence subsequent analysis is limited to the 14-yr period 1988–2001. 2) NCEP-1 and NCEP-2 estimates of [C] show a negative bias over the period 1992–98, which likely originates in biased Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) data assimilated by these reanalyses. A measure of the random error in the [C] time series produced by each reanalysis, computed using river discharge data as reference, indicates that ERA-40 gives the most accurate estimates of net atmospheric moisture flux convergence for the aforementioned 14-yr period.

Spatially averaged evapotranspiration [ET] over the Amazon Basin is computed as the residual of the basin’s atmospheric water balance equation, at the monthly time scale and for the period 1988–2001. Basin-averaged rainfall [P] is obtained from the Global Precipitation Climatology Project (GPCP) dataset, and alternative estimates of the net convergence of atmospheric water vapor flux over the basin [C] are derived from three global reanalyses: the NCEP–NCAR and NCEP–Department of Energy (DOE) reanalyses and the 40-yr ECMWF Re-Analysis (ERA-40). Additionally, a best estimate of [C] is obtained by taking a weighted average of data from these three sources, in which the weight factors are based on the random error attributed to each reanalysis’ [C] estimates by comparison to river discharge data. The resulting time series is dominated by ERA-40’s contribution, which was found to be the most accurate over the study period. Data products from the three reanalyses are also employed to compute the monthly tendencies of total precipitable water over the basin. While the seasonal signature of this “accumulation term” provides important insight into the Amazon Basin’s hydrological cycle, its magnitude is found to be negligible relative to the other components of the water budget. The value of mean annual [ET] presented in this work is significantly lower than other published estimates that are based on simulations by various land surface models. Furthermore, when the best estimate of [C] is used, the resulting [ET] time series exhibits a seasonal cycle that is in phase with that of basin-averaged surface net radiation, suggesting that Amazonian evapotranspiration is prevalently limited by energy availability. In contrast, most land surface models, including that of the NCEP–NCAR reanalysis, simulate water-limited evapotranspiration in the Amazon Basin. The analysis presented here supports the hypothesis that most Amazonian trees sustain elevated evapotranspiration rates during the dry season through deep roots, which tap into large reservoirs of soil water that are replenished during the following wet season.

This study investigates the use of a previously published algorithm for estimating ground heat flux (GHF) at the global scale. The method is based on an analytical solution of the diffusion equation for heat transfer in a soil layer and has been shown to be effective at the point scale. The algorithm has several advantageous properties: 1) it only needs a single-level input of surface (skin) temperature, 2) the time-mean GHF can be derived directly from time-mean skin temperature, 3) it has reduced sensitivity to the variability in soil thermal properties and moisture, 4) it does not requires snow depth, and 5) it is computationally effective. A global map of the necessary thermal inertia parameter is derived using reanalysis data as a function of soil type. These parameter estimates are comparable to values obtained from in situ observations. The new global GHF estimates are generally consistent with the reanalysis GHF output simulated using two-layer soil hydrology models. The authors argue that the new algorithm is more robust and trustworthy in regions where they differ. The proposed algorithm offers potential benefits for direct assimilation of observations of surface temperature as well as GHF into the reanalysis models at various time scales.

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

The benefits of short-term (1–6 h), distributed quantitative precipitation forecasts (DQPFs) are well known. However, this area is acknowledged to be one of the most challenging in hydrometeorology. Previous studies suggest that the “state of the art” methods can be enhanced by exploiting relevant information from radar and numerical weather prediction (NWP) models, using process physics and data-dictated tools where each fits best. Tests indicate that improved results are obtained by decomposing the overall problem into component processes, and that each process may require alternative tools ranging from simple interpolation to statistical time series models and artificial neural networks (ANNs). A new hybrid modeling strategy is proposed for DQPF that utilizes measurements from radar [Weather Surveillance Radar-1998 Doppler (WSR-88D) network: 4 km, 1 h] and outputs from NWP models (48-km Eta Model: 48 km, 6 h). The proposed strategy improves distributed QPF over existing methods like radar extr...

Mesoscale circulations forced by a random distribution of surface sensible heat flux have been investigated using a three-dimensional numerical model. The complex land surface is modeled as a homogeneous random field characterized by a spectral distribution. Standard deviation and length scale of the sensible heat flux at the surface have been identified as the important parameters that describe the thermal variability of land surface. The form of the covariance of the random surface forcing is not critical in driving the mesoscale circulation. The thermally induced mesoscale circulation is significant and extends up to about 5 km when the atmosphere is neutral. It becomes weak and is suppressed when the atmosphere is stable. The mesoscale momentum flux is much stronger than the corresponding turbulent momentum flux in the neutral atmosphere, while the two are comparable in the stable atmosphere. The mesoscale heat flux has a different vertical profile than turbulent heat flux and may provide a major heat transport mechanism beyond the planetary boundary layer. The impact of synoptic wind on the mesoscale circulations is relatively weak. Nonlinear advection terms are responsible for momentum flux in the absence of synoptic wind.

It has long been shown both in theory and in observation that emission from rain drops in a raining cloud results in upwelling brightness temperature above that caused by the sea surface alone. High brightness temperatures at microwave frequencies (e.g. 37 and 19 GHz) have usually been quantitatively associated with rainfall using physical or statistical models. By comparing concurrent special sensor microwave/imager and radar data, however, we noticed many cases where there is no appreciable rainfall in a field of view (FOV) which exhibits high brightness temperature (T(sub B)) at 37 and 19 GHz. On the basis of calculations and past literature it is shown that such high brightness temperatures can be caused by nonprecipitating clouds and by wind. The effect of the wind is to create wave and high-emissivity foam on the sea surface. A model is developed to relate T(sub B) to the fractional coverage of rain, f, within a FOV. The parameters of the model are calibrated by fitting the model to the observed brightness temperature and fractional rain coverage data. The critical parameter of the model, T(sub B min.), which is the threshold brightness temperature for the presence of rain, depends on the strength of the storm. The strength of the storm is characterized by the fraction of the FOVs within a large area that have T(sub B) higher than 240 K, which is readily obtainable from satellite data alone. The instantaneous FOV rain rate R can then be obtained through the f approximately R relationship which is empirically derived using radar data. An algorithm has been proposed based on the T(sub B) approximately f and f approximately R relationship. Application of the algorithm to TOGA-COARE and Darwin storms results in reasonable instantaneous FOV rain estimate. When averaged over the entire radar scan, a more accurate and unbiased areal rain estimate can be achieved.

We use physical arguments and statistical analysis to formulate stochastic models of rainfall intensity in time during intense convective storms. Special attention is given to the issue of multiplicative versus additive model structure and to the type of self-similarity that can be displayed by rainfall under the constraints of stationarity and nonnegativity. We show that some multiscaling models proposed in the past do not satisfy these constraints. Using a set of six high-resolution records, we find that the best fitting models are multiplicative, with a log (rain rate) spectrum of the segmented power type, i.e., of the form |ω| -β , with β that varies in different frequency ranges. Four spectral regimes are identified between scales from a few seconds to several hours. Nowhere do we find a log (rain rate) spectrum of the |ω| -1 type or scaling of the moments, which would be consistent with a conserved multifractal model. Other moment and spectral analyses as well as theoretical arguments lead us to reject also nonconserved multiscaling representations. The stochastic model we finally propose is lognormal with a segmented log spectrum and is not scaling. Two versions of the model are considered, one stationary for the central portion of the storm and the other nonstationary to include the buildup and decay phases. Compared to existing alternatives, the model is very easy to fit to data and to simulate.

Rainfall data collected by radar in the vicinity of Darwin, Australia, have been analyzed in terms of their mean, variance, autocorrelation of area-averaged rain rate, and diurnal variation. It is found that, when compared with the well-studied GATE (Global Atmospheric Research Program Atlantic Tropical Experiment) data, Darwin rainfall has larger coefficient of variation (CV), faster reduction of CV with increasing area size, weaker temporal correlation, and a strong diurnal cycle and intermittence. The coefficient of variation for Darwin rainfall has larger magnitude and exhibits larger spatial variability over the sea portion than over the land portion within the area of radar coverage. Stationary, and nonstationary models have been used to study the sampling errors associated with space-based rainfall measurement. The nonstationary model shows that the sampling error is sensitive to the starting sampling time for some sampling frequencies, due to the diurnal cycle of rain, but not for others. Sampling experiments using data also show such sensitivity. When the errors are averaged over starting time, the results of the experiments and the stationary and nonstationary models match each other very closely. In the small areas for which data are available for I>oth Darwin and GATE, the sampling error is expected to be larger for Darwin due to its larger CV.

The behavior of a numerical cloud model is investigated in terms of its sensitivity to perturbations with two kinds of lateral boundary conditions: 1) with cyclic lateral boundary conditions, the model is sensitive to many aspects of its structure, including a very small potential temperature perturbation at only one grid point, changes in time step, and small changes in parameters such as the autoconversion rate from cloud water to rainwater and the latent heat of vaporization; 2) with prescribed lateral boundary conditions, growth and decay of perturbations are highly dependent on the flow conditions inside the domain. It is shown that under relatively uniform (unidirectional) advection across the domain, the perturbations will decay. On the other hand, convergence, divergence, or, in general, flow patterns with changing directions support error growth. This study shows that it is the flow structure inside the model domain that is important in determining whether the prescribed lateral boundary conditions will result in decaying or growing perturbations. The numerical model is inherently sensitive to initial perturbations, but errors can decay due to advection of information from lateral boundaries across the domain by uniform flow. This result provides one explanation to the reported results in earlier studies showing both error growth and decay.

Precipitation recycling is the contribution of evaporation within a region to precipitation in that same region. The recycling rate is a diagnostic measure of the potential for interactions between land surface hydrology and regional climate. In this paper we present a model for describing the seasonal and spatial variability of the recycling process. The precipitation recycling ratio, rho, is the basic variable in describing the recycling process. Rho is the fraction of precipitation at a certain location and time which is contributed by evaporation within the region under study. The recycling model is applied in studyiing the hydrologic cycle in the Amazon basin. It is estimated that about 25% of all the rain that falls in the Amazon basin is contributed by evaporation within the basin. This estimate is based on analysis of a data set supplied by the European Centre for Medium-range Weather Forecasts (ECMWF). The same analysis is repeated using a different data set from the Geophysical Fluid Dynamics Laboratory (GFDL). Based on this data set, the recycling ratio is estimated to be 35%. The seasonal variability of the recycling ratio is small compared with the yearly average. The new estimates of the recycling ratio are compared with results of previous studies, and the differences are explained.

Recent studies on the Amazon deforestation problem predict that removal of the forest will result in a higher surface temperature, a significant reduction in evaporation and precipitation, and possibly significant changes in the tropical circulation. Here, we discuss the basic mechanisms contributing to the response of the tropical atmosphere to deforestation. A simple linear model of the tropical atmosphere is used in studying the effects of deforestation on climate. It is suggested that the impact of large-scale deforestation on the circulation of the tropical atmosphere consists of two components: the response of the tropical circulation to the negative change in precipitation (heating), and the response of the same circulation to the positive change in surface temperature. Owing to their different signs, the changes in predicted temperature and precipitation excite competing responses working in opposite directions. The predicted changes in tropical circulation determines the change, if any, in atmospheric moisture convergence, which is equivalent to the change in run-off. The dependence of run-off predictions on the relative magnitudes of the predicted changes in precipitation and surface temperature implies that the predictions about run-off are highly sensitive, which explains, at least partly, the disagreement between the different models concerning the sign of the predicted change in Amazonian run-off.

Recent studies on the Amazon deforestation problem predict that removal of the forest will result in a higher surface temperature, a significant reduction in evaporation and precipitation, and possibly significant changes in the tropical circulation. Here, we discuss the basic mechanisms contributing to the response of the tropical atmosphere to deforestation. A simple linear model of the tropical atmosphere is used in studying the effects of deforestation on climate. It is suggested that the impact of large-scale deforestation on the circulation of the tropical atmosphere consists of two components: the response of the tropical circulation to the negative change in precipitation (heating), and the response of the same circulation to the positive change in surface temperature. Owing to their different signs, the changes in predicted temperature and precipitation excite competing responses working in opposite directions. The predicted change in tropical circulation determines the change, if any, in atmospheric moisture convergence, which is equivalent to the change in run-off. The dependence of run-off predictions on the relative magnitudes of the predicted changes in precipitation and surface temperature implies that the predictions about run-off are highly sensitive, which explains, at least partly, the disagreement between the different models concerning the sign of the predicted change in Amazonian run-off.

The existence of analytical solutions for two-dimensional nonlinear semigeostrophic models of moist frontogenesis is investigated. Two different schemes for the modeling of stratiform cloud thermodynamics are taken into account, one based on the assumption of an everywhere cloudy environment, while in the other the atmosphere is considered to be exactly saturated and only condensation effects are relevant. In the first case, an exact analytical solution is derived for arbitrary boundary conditions, which satisfies the requirements for the validation of the semigeostrophic approximation even when the atmosphere is conditionally unstable with respect to slantwise convection. The growth of symmetric instabilities with no short-wave cutoff is predicted for this conditionally unstable atmosphere, even under the approximations of the semigeostrophic theory. An analogous, but approximate, analytical solution is then proposed for the second case. The errors introduced by the approximation are, however, n...

The fraction of the grid cell area covered by rainfall, mu, is an essential parameter in descriptions of land surface hydrology in climate models. A simple procedure is presented for estimating this fraction, based on extensive observations of storm areas and rainfall volumes. Storm area and rainfall volume are often linearly related; this relation can be used to compute the storm area from the volume of rainfall simulated by a climate model. A formula is developed for computing mu, which describes the dependence of the fractional coverage of rainfall on the season of the year, the geographical region, rainfall volume, and the spatial and temporal resolution of the model. The new formula is applied in computing mu over the Amazon region. Significant temporal variability in the fractional coverage of rainfall is demonstrated. The implications of this variability for the modeling of land surface hydrology in climate models are discussed.

The variability of crop and soil states due to uncertain climatic inputs and soil properties is quantified using a mathematical representation of the physiological, biochemical, hydrological, and physical processes related to plant growth. The components of the state-space model of the soil-crop-climate interactions are a plant growth, a moisture transport, and a solute transport model. A linear model for the perturbations of the state and the inputs around the nominal (first-order mean) values is derived. The linear model is used for second-moment uncertainty propagation due to fluctuations of the climatic forcing in time and due to the spatial variability of the soil properties. The most important climatic variables affecting crop production are identified in a case study. Correlation of climatic inputs between days is found to increase the crop yield variance. Significant variance reduction is found in transforming random soil properties to soil-state variables and then to plant-state variables. 31 refs., 4 figs.

Weather radar, in combination with a distributed rainfall-runoff model, promises to significantly improve real-time flood forecasting. This paper investigates the value of radar-derived precipitation in forecasting streamflow in the Sieve River basin, near Florence, Italy. The basin is modeled with a distributed rainfall-runoff model that exploits topographic information available from digital elevation maps. The sensitivity of the flood forecast to various properties of the radar-derived rainfall is studied. It is found that use of the proper radar reflectivity-rainfall intensity (Z-R) relationship is the most crucial factor in obtaining correct food hydrographs. Errors resulting from spatially averaging radar rainfall are acceptable, but the use of discrete point information (i.e., raingage) can lead to serious problems. Reducing the resolution of the 5-min radar signal by temporally averaging over 15 and 30 min does not lead to major errors. Using 3-bit radar data (rather than the usual 8-bit ...

There have been numerous attempts to detect the presence of deterministic chaos by estimating the correlation dimension. The values of reported correlation dimension for various geophysical time series vary between 1.3 and virtually infinity (i.e., no saturation). It is pointed out that analyzing variables that depend on physical constraints and thresholds, like precipitation, may lead to underestimation of the correlation dimension of the underlying dynamical system.

A general framework has been developed to study the predictability of space–time averages of mesoscale rainfall in the tropics. A comparative ratio between the natural variability of the rainfall process and the prediction error is used to define the predictability range. The predictability of the spatial distribution of precipitation is quantified by the cross correlation between the control and the perturbed rainfall fields. An upper limit of prediction error, called normalized variability, has been derived as a function of space–time averaging. Irrespective of the type and amplitude of perturbations, a space–time averaging set of 25 km2–15 min (or larger time averaging) is found to be necessary to limit the error growth up to or below the prescribed large-scale mean rainfall.

Persistent and prolonged periods of dry or moist conditions are often evident in the interannual variability of continental-type climates This variability appears as fluctuations around several distinct and preferred moisture states. These fluctuations and transitions between the preferred states are commonly attributed to large-scale changes in atmospheric circulation patterns possibly caused by oceanic influence. This paper argues that a major contributing factor to the persistent dry or moist behavior could be due to feedback and nonlinear interaction between the components of the hydrologic cycle in both the land and the atmosphere. A model that couples the water balance of continental landmasses and the overlying atmosphere is presented. The large-scale variabilities in atmospheric circulation are introduced by way of simple randomness in key forcing parameters. The result is a multiplicative-noise stochastic differential equation for the water balance dynamics of continental-type climates t...

[1] We hypothesize that the system of liquid water in leaf tissues and the water vapor in the atmosphere tends to evolve towards a potential equilibrium as quickly as possible by maximization of the transpiration rate. We make two assumptions in formulating the transpiration rate: (1) stomatal aperture is directly controlled by guard cell turgor (or leaf water potential); (2) CO2 flux can be used as a nonparametric equivalent of stomatal conductance for a given stomatal function (not necessarily optimal in terms of the water use efficiency for photosynthesis). Transpiration is then expressed as a function of leaf temperature, CO2 flux (as a surrogate of stomatal conductance), and sensible heat flux characterizing the transport mechanism at a given level of radiative energy input. Maximization of transpiration constrained by the energy balance equation leads to vanishing derivatives of transpiration with respect to leaf temperature and CO2 flux. We have obtained observational evidence in support of the proposed hypothesis.

[1] Recent experimental and theoretical studies support the notion that bed load in mountain rivers can both enhance incision rates through wear and inhibit incision rates by covering the bed. These effects may play an important role in landscape evolution and, in particular, the response of river channels to tectonic or climatic perturbation. We use the channel-hillslope integrated landscape development (CHILD) numerical model with two different bedrock incision models that include the dual role of the sediment flux to explore the transient behavior of fluvial landscapes. Both models predict that steady state channel slopes increase in landscapes with higher rock uplift rates. However, the incision models predict different transient responses to an increase in uplift rate, and the behavior of each incision model depends on both the magnitude of change in uplift rate and the local drainage area. In some cases, the transient channel behavior is indistinguishable from that predicted for transport-limited alluvial rivers. In other cases, knickpoints form in some or all of the drainage network, as predicted by the detachment-limited stream power model. In all cases the response in the lower parts of the network is highly dependent on the response in the upper parts of the network as well as the hillslopes. As the upper parts of the network send more sediment downstream, channel incision rates may rise or fall, and slopes in the lower parts of the channel may, in fact, decrease at times during the transient adjustment to an increase in rock uplift rate. In some cases, channel incision in the upper parts of the network ceases during the transient while the hillslopes adjust to the new uplift rate; drainage density may also change as a function of uplift rate. Our results suggest that if the sediment flux strongly controls bedrock incision rates, then (1) the transient fluvial response will take longer than predicted by the detachment-limited stream power model, (2) changes in channel slope may be much more complex than predicted by the detachment-limited stream power model, and (3) changes in the fluvial system will be closely tied to sediment delivery from the hillslopes. Importantly, our results outline quantitative differences in system behavior produced by competing models and provide a framework for identifying locations in natural systems where differences in channel morphology can be used to discern between competing fluvial erosion models.

[1] The stream power erosion law, which describes the erosion rate as a function of channel discharge and gradient, has often been used for modeling landscape evolution in regions dominated by fluvial processes. However, most previous studies utilizing the stream power erosion law simply use drainage area as a surrogate for channel discharge. Despite its convenience this simplification has important shortcomings. Specifically, it ignores the effects of precipitation properties on channel discharge and hence erosion rate, and it ignores the interactions between mountain ranges and precipitation properties. By using the stream power erosion law together with the geomorphoclimatic instantaneous unit hydrograph we provide a method for linking the landscape evolution and precipitation properties directly. Our results demonstrate that the channel profile is sensitive not only to the total precipitation but also to precipitation properties like the rainfall frequency, intensity, duration, and their distribution in space. The channel profile is most sensitive to the variation of rainfall intensity and less sensitive to rainfall frequency and duration. Shorter and more intense rainfall could lead to significantly higher erosion rate and flatter channel profiles compared to longer and less intense rainfall. The spatial variation of precipitation can also influence the evolution of channel profile. Even if the total precipitation remains spatially homogeneous, different spatial behavior of rainfall intensity and rainfall duration may lead to different steady state river profiles. The channel profile tends to be flatter under the conditions of increasing rainfall intensity and decreasing rainfall duration with elevation and vice versa.

[1] Head advance due to plunge pool erosion is a common process in gullies incising resistant soils. A model of headcut retreat resulting from plunge pool erosion is developed and implemented in the channel-hillslope integrated landscape development (CHILD) model, an existing three-dimensional landscape evolution modeling framework. The model estimates horizontal headcut retreat as a function of discharge, height of the headcut, upstream slope, and relevant land surface and soil properties for soil erosion. We analyze the sensitivity of headcut retreat to flow discharge, upstream slope and surface roughness, and headcut height. CHILD simulations indicate that headcut retreat is most significant in zones with either gentle slopes or large headcut heights. Model parameters have contrasting effects on the retreat rates depending on the size and depth of the pool beneath the headcut and upstream flow hydraulics, making the process difficult to predict as a function of topographic thresholds and simple geomorphic transport laws.

[1] The first near-global high-resolution digital elevation model (DEM) of the Earth has recently been released following the successful Shuttle Radar Topography Mission (SRTM) of 2000. This data set will have applications in a wide range of fields and will be especially valuable in the Earth sciences. Prior to widespread dissemination and use, it is important to acquire knowledge regarding the accuracy characteristics. In this work a comprehensive analysis of the vertical errors present in the data set and the assessment of their effects on different hydrogeomorphic products is performed. In particular, the work consisted of (1) measuring the vertical accuracy of the data set in two areas with different topographic characteristics; (2) characterizing the error structure by comparing elevation residuals with terrain attributes; (3) assessing a wavelet-based filter for removing speckle; and (4) assessing the effects of vertical errors on hydrogeomorphic products and on slope stability modeling. The results indicate that in the two sites, relief has a strong effect on the vertical accuracy of the SRTM DEM. In the high-relief terrain, large errors and data voids are frequent, and their location is strongly influenced by topography, while in the low- to medium-relief site, errors are smaller, although the hilly terrain still produces an effect on the sign of the errors. Speckling generates deviations in the drainage network in one of the investigated areas, but the application of a wavelet filter proved to be an effective tool for removing vertical noise, although further fine tuning is necessary. Vertical errors cause differences in automatically extracted hydrogeomorphic products that range between 4 and 1090.

[1] Topography acts as a template for numerous landscape processes that include hydrologic, ecologic, and biologic phenomena. These processes not only interact with each other but also contribute to shaping the landscape as they influence geomorphic processes. We have investigated the effects of vegetation on thresholds for channel initiation and landform evolution using both analytical and numerical approaches. Vegetation is assumed to form a uniform ground cover. Runoff erosion is modeled based on a power function of excess shear stress, in which shear stress efficiency is inversely proportional to vegetation cover. This approach is validated using data. Plant effect on slope stability is represented by additional cohesion provided by plant roots. Vegetation cover is assumed to reduce sediment transport rates due to physical creep processes (rainsplash, dry ravel, and expansion and contraction of sediments) according to a negative exponential relationship. Vegetation grows as a function of both available cover and unoccupied space by plants and is killed by geomorphic disturbances (runoff erosion and landsliding) and wildfires. Analytical results suggest that in an equilibrium basin with a fixed vegetation cover, plants may cause a transition in the dominant erosion process at the channel head. A runoff erosion–dominated landscape, under none or poor vegetation cover, may become landslide dominated under a denser vegetation cover. The sign of the predicted relationship between drainage density and vegetation cover depends on the relative influence of vegetation on different erosion phenomena. With model parameter values representative of the Oregon Coast Range (OCR), numerical experiments conducted using the Channel Hillslope Integrated Landscape Development (CHILD) model confirm the findings based on the analytical theory. A highly dissected fluvial landscape emerges when surface is assumed bare. When vegetation cover is modeled, landscape relief increases, resulting in hollow erosion dominated by landsliding. Interestingly, our simulations underscore the importance of vegetation disturbances by geomorphic events and wildfires on the landscape structure. Simulated landscapes resemble real-world catchments in the OCR when such disturbances are considered.

[1] Gully erosion is most commonly triggered by fluvial erosion following natural and anthropogenic disturbances or as a response to changes in climate and tectonic forcing and base level drop. Field observations attribute the headward growth and widening of many gully systems to gravitational mass-wasting processes of oversteepened sidewalls. Soil saturation, groundwater sapping, and tension crack development contribute to the instability. Recent landscape evolution models treat such mass failures as slope-dependent continuous sediment transport processes, sometimes conditioned on a slope threshold or with nonlinear dependence on slope gradient. In this study we first present an explicit physically based theory for the stability analysis of gully heads and walls. The theory is based on the force balance equation of an assumed planar failure geometry of a steep gully wall, with a potential failure plane dipping into the incised gully bed and tension cracks developing behind the scarp face. Then, we test the theory against field data collected in our field site in Colorado and against other published data. Second, the theory is implemented in a one-dimensional hillslope profile development model and the three-dimensional channel-hillslope integrated landscape development (CHILD) to study the effects of soil cohesion, erosion thresholds, and stochastic climate on the tempo of gully development and morphology. Preliminary results indicate that wider and shallower gullies develop and integrate, forming wide valleys, when soil cohesion is small. As soil cohesion increases, erosion slows down, gullies become deeper with vertical walls, and episodic mass failures occur. Differences in storm intensity-duration characteristics and erosion thresholds are predicted to have a significant impact on gully development. Vertical gully walls develop rapidly, and gullies enlarge by slab failures in a climate characterized by high-intensity, short-duration storm pulses. However, under low-intensity, long-duration storms, gullies quickly stabilize, and vertical walls are eliminated and rounded, forming diffusion-dominated hilltops. Erosion thresholds have a similar impact on the tempo of gully erosion but in the opposite direction. Lowering the erosion threshold enhances gully widening by slab failures. Gully walls stabilize when the erosion threshold is high due to a reduction in the erosion of the failure material on the toe of gully walls.

[1] From rainfall interception at the canopy to added soil cohesion within the root zone, plants play a significant role in directing local geomorphic dynamics, and vice versa. The consequences at the regional scale, however, are known in less quantitative terms. In light of this, the numerical Channel-Hillslope Integrated Landscape Development model is equipped with coupled vegetation-erosion dynamics, allowing for sensitivity analysis on the various aspects of vegetation behavior. The processes considered are plant growth, plant death, and the additional resistance imparted by plants against erosion. With each process is associated a single parameter, whose effects on the spatiotemporal nature of a 1 km2 basin is studied. Through their inhibition of erosion, plants steepened the topography and reduced drainage density, yet, in doing so, made erosive events more extreme. Plants more susceptible to erosion act to decouple neighboring cells and cause extensive and perennial network and channel adjustments.

This paper investigates the dependence of parameters of a stationary point process model on seasonal and spatial variability of rainfall. Twenty-four years of hourly rainfall data for two stations from the Arno River basin in central Italy have been used to estimate parameters for each month, assuming local stationarity within the month. The estimated model parameters are physically reasonable, vouching for the model's ability to capture the general temporal and spatial structure of rainfall events. Parameter sensitivity analysis provide useful guidelines for understanding the model structure and devising effective parameter estimation strategies.

The nature and the spatial distribution of cumuli within an unforced cumulus cloud field are investigated. The thermodynamic effects of convection are quantified as functions of changes of convective available potential energy (CAPE) induced by the convective overturning, and the time rate of change of CAPE is parameterized in terms of a kernel of influence or stabilization function. A three-dimensional cloud model is used to infer and quantify stabilization functions by performing single-cloud experiments. On the basis of the results obtained, a new hypothesis with respect to the spatial distribution of cumuli is postulated, which states that, under completely homogeneous external conditions and assuming a spatially random distribution of cloud-triggering mechanisms, the spatial distribution of cumuli in the resulting cloud field must be regular, as opposed to either random or clustered, because cumulus clouds tend to reduce the available energy for convection, thereby inhibiting further convection nearby.