Using two bias reduced forcing data sets we have simulated the initial soil moisture state for the North American continent (1985–1993). Differences between simulations were shown to persist over regions with the greatest soil memory, and are not strongly associated to patterns of difference in forcing fields. Therefore, processes that contribute to soil memory will also limit our ability to accurately estimate its initial state. Moreover, the regions associated with high soil memory may not be directly observable with present and near future sensors due to high vegetation water contents. Thus, research in these regions should be directed toward minimizing forcing variance and extension of observation networks and technology to increase the ability for monitoring soil moisture.

Simulating land surface hydrological states and fluxes requires a comprehensive set of atmospheric forcing data at consistent temporal and spatial scales. At the continental-to-global scale, such data are not available except in weather reanalysis products. Unfortunately, reanalysis products are often biased due to errors in the host weather forecast model. This paper explores whether the error in model predictions of the initial soil moisture status and hydrological fluxes can be minimized through a bias reduction scheme to the European Centre for Medium Range Weather Forecast and National Center for Environmental Prediction/National Center for Atmospheric Research reanalysis products. The bias reduction scheme uses both difference and ratio corrections based upon global observational data sets. Both the corrected and original forcing data were used to simulate land surface states and fluxes with a land surface model (LSM) over North America. Soil moisture, snow depth, and runoff output from the LSM are compared to observations to assess the impact of the bias correction on simulation accuracy. Results of this study demonstrate the sensitivity of LSMs to bias in the forcing data, and that implementation of a bias reduction scheme reduces errors to the simulation of soil moisture, runoff, and snow water equivalence. Accordingly, the initial soil moisture fields produced should be more representative of actual conditions, and therefore more useful to the climate modeling community. Results suggest that modelers using reanalysis products for forcing LSMs, in particular for the establishment of initial conditions, should consider a bias reduction strategy when preparing their input forcing fields.

 In this study, over 36,000 ground-based soil moisture measurements collected during the SGP97, SGP99, SMEX02, and SMEX03 field campaigns were analyzed to characterize the behavior of soil moisture variability across scales. The field campaigns were conducted in Oklahoma and Iowa in the central USA. The Oklahoma study region is sub-humid with moderately rolling topography, while the Iowa study region is humid with low-relief topography. The relationship of soil moisture standard deviation, skewness and the coefficient of variation versus mean moisture content was explored at six distinct extent scales, ranging from 2.5 m to 50 km. Results showed that variability generally increases with extent scale. The standard deviation increased from 0.036 cm³/cm³ at the 2.5-m scale to 0.071 cm³/cm³ at the 50-km scale. The log standard deviation of soil moisture increased linearly with the log extent scale, from 16 m to 1.6 km, indicative of fractal scaling. The soil moisture standard deviation versus mean moisture content exhibited a convex upward relationship at the 800-m and 50-km scales, with maximum values at mean moisture contents of roughly 0.17 cm³/cm³ and 0.19 cm³/cm³, respectively. An empirical model derived from the observed behavior of soil moisture variability was used to estimate uncertainty in the mean moisture content for a fixed number of samples at the 800-m and 50-km scales, as well as the number of ground-truth samples needed to achieve 0.05 cm³/cm³ and 0.03 cm³/cm³ accuracies. The empirical relationships can also be used to parameterize surface soil moisture variations in land surface and hydrological models across a range of scales. To our knowledge, this is the first study to document the behavior of soil moisture variability over this range of extent scales using ground-based measurements. Our results will contribute not only to efficient and reliable satellite validation, but also to better utilization of remotely sensed soil moisture products for enhanced modeling and prediction.

Ground-based measurement of the spatial distribution of soil moisture can be difficult because sampling is essentially made at a point and the choice of both sample depth and sample spacing affects the interpretation of the measurements. Hydrological interest has generally been in soil moisture of the root zone. Microwave Remote Sensing methods are now available that allow the interpretation of spatial distributions of soil moisture, however, their signals respond to moisture in the upper few centimetres of soil. These instruments are still being developed, but one of the questions surrounding their application is how to interpret the surface moisture in a hydrological context. In this study we compare measurements of soil moisture in 0-30 cm of soil with those in 0-6 cm to examine how representative this surface measure is with regard to the root zone. Detailed spatial measurements of soil moisture were conducted at three pasture sites in the 50 km2 Mahurangi River catchment of northern New Zealand as part of a comprehensive hydrology project; MARVEX (MAhurangi River Variability EXperiment). In three field sites, on each of three occasions, field measurements were made using both 30 and 6 cm dielectric-based instruments. Spatial grids of several hundred moisture measurements were collected over 0-30 cm and compared with those collected simultaneously over 0-6 cm. Results indicate that temporal and spatial issues interfere with correlation of the two sets of series. Rapid wetting of 0-6 cm compared with 0-30 cm is seen following storm activity. Some evidence of the decoupling of moisture content response is also evident when sites are measured on days following a storm. Rapid, but not unrealistic, response to intense rainfall was also observed. Implications are that detailed and accurate knowledge of local soil conditions and a sound model of soil water redistribution are required before surface soil moisture measurements can be used to infer root zone behaviour. Such knowledge was not available in this study, from either published data or field observation. In this study, without suitable a priori knowledge, soil property information was found via calibration. © 2003 Elsevier Science B.V. All rights reserved.

Growing conditions for crops such as coffee and wine grapes are shifting to track climate change. Research on these crop responses has focused principally on impacts to food production impacts, but evidence is emerging that they may have serious environmental consequences as well. Recent research has documented potential environmental impacts of shifting cropping patterns, including impacts on water, wildlife, pollinator interaction, carbon storage and nature conservation, on national to global scales. Multiple crops will be moving in response to shifting climatic suitability, and the cumulative environmental effects of these multi-crop shifts at global scales is not known. Here we model for the first time multiple major global commodity crop suitability changes due to climate change, to estimate the impacts of new crop suitability on water, biodiversity and carbon storage. Areas that become newly suitable for one or more crops are Climate-driven Agricultural Frontiers. These frontiers cover an area equivalent to over 30% of the current agricultural land on the planet and have major potential impacts on biodiversity in tropical mountains, on water resources downstream and on carbon storage in high latitude lands. Frontier soils contain up to 177 Gt of C, which might be subject to release, which is the equivalent of over a century of current United States CO2 emissions. Watersheds serving over 1.8 billion people would be impacted by the cultivation of the climate-driven frontiers. Frontiers intersect 19 global biodiversity hotspots and the habitat of 20% of all global restricted range birds. Sound planning and management of climate-driven agricultural frontiers can therefore help reduce globally significant impacts on people, ecosystems and the climate system.