Ecohydrological Controls on Grass and Shrub Above-ground Net Primary Productivity in a Seasonally Dry Climate

Seasonally dry, water‐limited regions are often co‐dominated by distinct herbaceous and woody plant communities with contrasting ecohydrological properties. We investigated the shape of the above‐ground net primary productivity (ANPP) response to annual precipitation (Pa) for adjacent grassland and shrubland ecosystems in Southern California, with the goal of understanding the role of these ecohydrological properties on ecosystem function. Our synthesis of observations and modelling demonstrates grassland and shrubland exhibit distinct ANPP‐Pa responses that correspond with characteristics of the long‐term Pa distribution and mean water balance fluxes. For annual grassland, no ANPP occurs below a ‘precipitation compensation point,’ where bare soil evaporation dominates the water balance, and ANPP saturates above the Pawhere deep percolation and runoff contribute to the modelled water balance. For shrubs, ANPP increases at a lower and relatively constant rate across the Pa gradient, while deep percolation and runoff account for a smaller fraction of the modelled water balance. We identify precipitation seasonality, root depth, and water stress sensitivity as the main ecosystem properties controlling these responses. Observed ANPP‐Paresponses correspond to notably different patterns of rain‐use efficiency (RUE). Grass RUE exceeds shrub RUE over a wide range of typical Pa values, whereas grasses and shrubs achieve a similar RUE in particularly dry or wet years. Inter‐annual precipitation variability, and the concomitant effect on ANPP, plays a critical role in maintaining the balance of grass and shrub cover and ecosystem‐scale productivity across this landscape

Rainfall-runoff as spatial stochastic processes : data collection and synthesis.

Thesis. 1975. Sc.D.--Massachusetts Institute of Technology. Dept. of Civil Engineering.Vita.Bibliography: leaves 213-221.Sc.D

Application of a Hillslope-Scale Soil Moisture Data Assimilation System to Military Trafficability Assessment

Soil moisture is an important environmental variable that impacts military operations and weapons systems. Accurate and timely forecasts of soil moisture at appropriate spatial scales, therefore, are important for mission planning. We present an application of a soil moisture data assimilation system to military trafficability assessment. The data assimilation system combines hillslope-scale (e.g., 10s to 100s of m) estimates of soil moisture from a hydrologic model with synthetic L-band microwave radar observations broadly consistent with the planned NASA Soil Moisture Active–Passive (SMAP) mission. Soil moisture outputs from the data assimilation system are input to a simple index-based model for vehicle trafficability. Since the data assimilation system uses the ensemble Kalman Filter, the risks of impaired trafficability due to uncertainties in the observations and model inputs can be quantified. Assimilating the remote sensing observations leads to significantly different predictions of trafficability conditions and associated risk of impaired trafficability, compared to an approach that propagates forward uncertainties in model inputs without assimilation. Specifically, assimilating the observations is associated with an increase in the risk of “slow go” conditions in approximately two-thirds of the watershed, and an increase in the risk of “no go” conditions in approximately 40% of the watershed. Despite the simplicity of the trafficability assessment tool, results suggest that ensemble-based data assimilation can potentially improve trafficability assessment by constraining predictions to observations and facilitating quantitative assessment of the risk of impaired trafficability

A weather generator for hydrological, ecological, and agricultural applications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95112/1/wrcr11029.pd

Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico

This paper presents the development of a rainfall-triggered landslide module within an existing physically based spatially distributed ecohydrologic model. The model, tRIBS-VEGGIE (Triangulated Irregular Networks-based Real-time Integrated Basin Simulator and Vegetation Generator for Interactive Evolution), is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics are resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the study area of Luquillo Forest. The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards' equation (present in tRIBS-VEGGIE but not in tRIBS), which better represents the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the factor of safety (FS) to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the infinite slope model, creating a powerful tool for the assessment of rainfall-triggered landslide risk.United States. National Aeronautics and Space Administration (Project NNX07AD29G

Spatial distribution of precipitation recycling in the Amazon basin Elfatih

A free-boundary theory for the shape of the ideal dripping icicle Phys. Fluids 18, 083101 (2006) Grain growth theories and the isothermal evolution of the specific surface area of snow J. Appl. Phys. 95, 6175 (2004) Automated spectro-goniometer: A spherical robot for the field measurement of the directional reflectance of snow Rev. Sci. Instrum. 74, 5179 (2003) Comment on "Grain boundary ridge on sintered bonds between ice crystals" [J. Appl. Phys. 90, 5782 (2001)] J. Appl. Phys. 93, 783 (2003) Sintering in a dry snow cover J. Appl. Phys. 84, 4585 (1998) Additional information on AIP Conf. Proc. ABSTRACT Precipitation recycling is the contribution of evaporation within a large region to precipitation in that same region. The rate of recycling is a diagnostic measure of the coupling of land surface hydrology and regional climate. Here we describe the spatial and seasonal variability of the precipitation recycling process over the Amazon basin. The results are based on data of evaporation and water vapor fluxes from the European Center for Medium Range Weather Forecast (ECMWF). We estimate that 25% of all the rain that falls in the Amazon basin is contributed by evaporation within the basin. The contribution of recycled water vapor increases westward and southward with significantly different spatial distributions in the different seasons. INTRODUCTION Hydrology affects climate in many different ways. Evaporation provides the water vapor necessary for precipitation processes. Latent heat fluxes associated with evaporation and condensation provide an important energy transport mechanism in the Earth's atmosphere. Because land surface hydrology plays such a significant role in maintaining the equilibrium of regional climate, many recent studies t,2,3 suggest that anthropogenic changes in surface hydrology, e.g., deforestation of the Amazon basin, may result in serious impacts on climate. The precipitation recycling rate is a diagnostic measure of the current degree of coupling and the potential interactions of land surface hydrology and regional climate. Previous studies suggested different ways for computing precipitation recycling. Budyko 4 provides a spatially lumped estimate of precipitation recycling. It describes the seasonal but not the spatial distribution of the recycling rate. Lettau 5 describes precipitation recycling along a single streamline. We study both the spatial and seasonal variability of the recycling process. We consider two species of water vapor molecules; those which evaporate outside the region and molecules which evaporate within the region. The definition of the word 'region' includes all the area under study which is the Amazon basin. It is not restricted to the area of a single grid point. For a finite control volume of the atmosphere, conservation of mass requires the following relations

Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

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

Combined Assimilation of Satellite Precipitation and Soil Moisture: A Case Study Using TRMM and SMOS Data

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)

Topographic variability and the influence of soil erosion on the carbon cycle

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

Compressive Earth Observatory: An Insight from AIRS/AMSU Retrievals

We demonstrate that the global fields of temperature, humidity and geopotential heights admit a nearly sparse representation in the wavelet domain, offering a viable path forward to explore new paradigms of sparsity-promoting data assimilation and compressive recovery of land surface-atmospheric states from space. We illustrate this idea using retrieval products of the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) on board the Aqua satellite. The results reveal that the sparsity of the fields of temperature is relatively pressure-independent while atmospheric humidity and geopotential heights are typically sparser at lower and higher pressure levels, respectively. We provide evidence that these land-atmospheric states can be accurately estimated using a small set of measurements by taking advantage of their sparsity prior.Comment: 12 pages, 8 figures, 1 tabl