Stochastic Rainfall-runoff Model with Explicit Soil Moisture Dynamics

Stream runoff is perhaps the most poorly represented process in ecohydrological stochastic soil moisture models. Here we present a rainfall-runoff model with a new stochastic description of runoff linked to soil moisture dynamics. We describe the rainfall-runoff system as the joint probability density function (PDF) of rainfall, soil moisture and runoff forced by random, instantaneous jumps of rainfall. We develop a master equation for the soil moisture PDF that accounts explicitly for a general state-dependent rainfall-runoff transformation. This framework is then used to derive the joint rainfall-runoff and soil moisture-runoff PDFs. Runoff is initiated by a soil moisture threshold and a linear progressive partitioning of rainfall based on the soil moisture status. We explore the dependence of the PDFs on the rainfall occurrence PDF (homogeneous or state-dependent Poisson process) and the rainfall magnitude PDF (exponential or mixed-exponential distribution). We calibrate the model to 63 years of rainfall and runoff data from the Upper Little Tennessee watershed (USA) and show how the new model can reproduce the measured runoff PDF

Temporal stability of soil moisture and radar backscatter observed by the advanced Synthetic Aperture Radar (ASAR)

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R-2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments

Space-time modeling of soil moisture: Stochastic rainfall forcing with heterogeneous vegetation

The present paper complements that of Isham et al. (2005), who introduced a space-time soil moisture model driven by stochastic space-time rainfall forcing with homogeneous vegetation and in the absence of topographical landscape effects. However, the spatial variability of vegetation may significantly modify the soil moisture dynamics with important implications for hydrological modeling. In the present paper, vegetation heterogeneity is incorporated through a two dimensional Poisson process representing the coexistence of two functionally different types of plants (e.g., trees and grasses). The space-time statistical structure of relative soil moisture is characterized through its covariance function which depends on soil, vegetation, and rainfall patterns. The statistical properties of the soil moisture process averaged in space and time are also investigated. These properties are especially important for any modeling that aggregates soil moisture characteristics over a range of spatial and temporal scales. It is found that particularly at small scales, vegetation heterogeneity has a significant impact on the averaged process as compared with the uniform vegetation case. Also, averaging in space considerably smoothes the soil moisture process, but in contrast, averaging in time up to 1 week leads to little change in the variance of the averaged process

The effects of soil moisture, soil texture, and host orientation on the ability of Heterorhabditis bacteriophora (Rhabditida: Heterorhabditidae) to infect Galleria mellonella (Lepidoptera: Pyralidae)

Abstract Entomopathogenic nematodes (EPN) demonstrate potential as a biological control for soil dwelling insects. However, edaphic factors, such as soil moisture and texture impact the efficacy of EPN on a host. The objectives were to examine the effects of soil texture and moisture on 1) the infection rate of Galleria mellonella L. by EPN and; 2) the ability of H. bacteriophora (Poinar) to move through the soil to find a host at different orientations. Soil textures consisted of sand, a sand/silt/peat mixture, and a silt/peat mixture at 50% and 100% moisture. A general linear model was used to evaluate infection rates and EPN movement. Both soil moisture (p \u3c 0.05) and texture (p \u3c 0.05) had significant effects on nematodes infection rates of G. mellonella. Texture, moisture, and host orientation did not significantly affect (p \u3e 0.05) the ability of EPN to find a host. While EPN were able to find a host within a variety of soil types, soils that held more water had higher infection rates than soils that held less water, suggesting that moisture may be a key component in facilitating infection by EPN. By understanding the factors that influence the ability of EPN to find and infect a host, improved bio-control programs using EPN can be developed

Global soil moisture bimodality in satellite observations and climate models

A new diagnostic metric based on soil moisture bimodality is developed in order to examine and compare soil moisture from satellite observations and Earth System Models. The methodology to derive this diagnostic is based on maximum likelihood estimator encoded into an iterative algorithm, which is applied to the soil moisture probability density function. This metric is applied to satellite data from the Advanced Microwave Scanning Radiometer for the Earth Observing System and global climate models data from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Results show high soil moisture bimodality in transitional climate areas and high latitudes, potentially associated with land-atmosphere feedback processes. When comparing satellite versus climate models, a clear difference in their soil moisture bimodality is observed, with systematically higher values in the case of CMIP5 models. These differences appear related to areas where land-atmospheric feedback may be overestimated in current climate models

The effect of soil moisture content on leaf extension rate and yield of perennial ryegrass

peer-reviewedThree experiments are described that were designed to evaluate the relationship between soil moisture and perennial ryegrass growth and leaf extension rate (LER) in loam or silt clay loam soil. When soil moisture was maintained at a range of proportions (0.5, 0.75, 1.0, 1.25) of field capacity (FC) in a pot experiment in a glasshouse, 0.75FC had consistently higher growth and LER than 0.5FC and, to a lesser extent, 1.25FC. The quadratic relationship between herbage growth and amount of water applied to maintain target field capacity, was stronger than for that between LER and the amount of water applied, with a maximum response at an application of about 2.5 L/m2 per day. In a microsward (soil depth of 30 cm in boxes 56 cm × 72 cm) trial inducing drought by withholding water for a range of durations resulted in a progressive decline in LER. When soil moisture content fell to about 0.4 of that of the consistently watered control LER was less than 0.1 of the control. However within one week of receiving water, even in the relatively severe drought treatment, LER was not significantly lower than the control treatment. LER was quadratically related to soil moisture content when soil was drying or after rewatering. In a further experiment on the microswards, reducing soil moisture content to about 0.18 g/g by limiting water in May-June resulted in a severe reduction in LER and growth rate and a decline in tillering rate. However, after application of the equivalent of 3 mm precipitation per day in late June, while soil moisture content remained relatively low (about 0.2 to 0.25 g/g soil), LER and herbage growth increased rapidly to as high as in consistently watered microswards. In a treatment in which soil moisture content eventually exceeded FC, LER and herbage growth declined with increase in excess above FC, concurring with findings in the steady state soil moisture experiment. Implications of the data for prediction of production from sown grass swards using temperate maritime grass-growth models are that: (1) during drought, when rainfall resumes, regrowth will be influenced more by amount of rainfallthan soil moisture content and (2) excess soil moisture should be taken into account, including effects of reduced nutrient uptake and post-anoxia stress

Data documentation for the bare soil experiment at the University of Arkansas

The reflectivities of several controlled moisture test plots were investigated. These test plots were of a similar soil texture which was clay loam and were prepared to give a desired initial soil moisture and density profile. Measurements were conducted on the plots as the soil water redistributed for both long term and diurnal cycles. These measurements included reflectivity, gravimetric and volumetric soil moisture, soil moisture potential, and soil temperature

Optional Soil Moisture Sensor Protocol

The purpose of this resource is to measure the water content of soil based on the electrical resistance of soil moisture sensors. Students install soil moisture sensors in holes that are 10 cm, 30 cm, 60 cm, and 90 cm deep. They take daily readings of soil moisture data by connecting a meter to the sensors and using a calibration curve to determine the soil water content at each depth. Educational levels: Middle school, High school