Over the oceans, approximately 90% of net radiation produces evaporation (Budyko, 1974), primarily in the tropics. Over continents, net radiation heats the surface, evaporates water from water bodies or moist soils, or provides plants with energy to remove water from soils (Pitman, 2003; Istanbulluoglu and Bras, ANALYSISAt the watershed scale, soil moisture is the major control for rainfall-runoff response, especially where saturation excess runoff processes dominate. From the ecological point of view, the pools of soil moisture are fundamental ecosystem resources providing the transpirable water for plants. In drylands particularly, soil moisture is one of the major controls on the structure, function, and diversity in ecosystems. In terms of the global hydrological cycle, the overall quantity of soil moisture is small, ∼0.05%; however, its importance to the global energy balance and the distribution of precipitation far outweighs its physical amount. In soils it governs microbial activity that aff ects important biogeochemical processes such as nitrifi cation and CO 2 production via respiration. During the past 20 years, technology has advanced considerably, with the development of diff erent electrical sensors for determining soil moisture at a point. However, modeling of watersheds requires areal averages. As a result, point measurements and modeling grid cell data requirements are generally incommensurate. We review advances in sensor technology, particularly emerging geophysical methods and distributed sensors, aimed at bridging this gap. We consider some of the data analysis methods for upscaling from a point to give an areal average. Finally, we conclude by off ering a vision for future research, listing many of the current scientifi c and technical challenges.

Abstract:We want to develop a dialogue between geophysicists and hydrologists interested in synergistically advancing process based watershed research. We identify recent advances in geophysical instrumentation, and provide a vision for the use of electrical and magnetic geophysical instrumentation in watershed scale hydrology. The focus of the paper is to identify instrumentation that could significantly advance this vision for geophysics and hydrology during the next 3-5 years. We acknowledge that this is one of a number of possible ways forward and seek only to offer a relatively narrow and achievable vision. The vision focuses on the measurement of geological structure and identification of flow paths using electrical and magnetic methods. The paper identifies instruments, provides examples of their use, and describes how synergy between measurement and modelling could be achieved. Of specific interest are the airborne systems that can cover large areas and are appropriate for watershed studies. Although airborne geophysics has been around for some time, only in the last few years have systems designed exclusively for hydrological applications begun to emerge. These systems, such as airborne electromagnetic (EM) and transient electromagnetic (TEM), could revolutionize hydrogeological interpretations. Our vision centers on developing nested and cross scale electrical and magnetic measurements that can be used to construct a three-dimensional (3D) electrical or magnetic model of the subsurface in watersheds. The methodological framework assumes a 'top down' approach using airborne methods to identify the large scale, dominant architecture of the subsurface. We recognize that the integration of geophysical measurement methods, and data, into watershed process characterization and modelling can only be achieved through dialogue. Especially, through the development of partnerships between geophysicists and hydrologists, partnerships that explore how the application of geophysics can answer critical hydrological science questions, and conversely provide an understanding of the limitations of geophysical measurements and interpretation.

Complex impedance data were collected for eight sandstones at various levels of water saturation [Formula: see text] in the frequency range of 5 Hz to 4 MHz. The measurements were made using a two‐electrode technique with platinum electrodes sputtered onto the flat faces of disk‐ shaped samples. Presentation of the data in the complex impedance plane shows clear separation of the response due to polarization at the sample‐electrode interface from the bulk sample response. Electrode polarization effects were limited to frequencies of less than 60 kHz, allowing us to study the dielectric constant κ′ of the sandstones in the frequency range of 60 kHz to 4 MHz. κ′ of all samples at all levels of saturation shows a clear power‐law dependence upon frequency. Comparing the data from the eight sandstones at [Formula: see text], the magnitude of the frequency dependence was found to be proportional to the surface area‐to‐volume ratio of the pore space of the sandstones. The surface area‐to‐volume ratio of the pore space of each sandstone was determined using a nitrogen gas adsorption technique and helium porosimetry. κ′ also exhibits a strong dependence on [Formula: see text]. κ′ increases rapidly with [Formula: see text] at low saturations, up to some critical saturation above which κ′ increases more gradually and linearly with [Formula: see text]. Using the surface area‐to‐volume ratios of the sandstones, the critical saturation in the dielectric response was found to correspond to water coverage of approximately 2 nm on the surface of the pore space. Our interpretation of the observed dependence of κ′ on both frequency and [Formula: see text] is that it is the ratio of surface water to bulk water in the pore space of a sandstone that controls the dielectric response through a Maxwell‐Wagner type of mechanism.

We have developed an open source 3D, MATLAB based, resistivity inversion package. The forward solution to the governing partial differential equation is efficiently computed using a second-order finite volume discretization coupled with a preconditioned, biconjugate, stabilized gradient algorithm. Using the analytical solution to a potential field in a homogeneous half space, we evaluate the accuracy of our numerical forward solution and, subsequently, develop a source correction factor that reduces forward modeling errors associated with boundary effects and source electrode singularities. For the inversion algorithm we have implemented an inexact Gauss-Newton solver, with the model update being calculated using a preconditioned conjugate gradient algorithm. The inversion uses a combination of zero and first order Tikhonov regularization. Two synthetic examples demonstrate the usefulness of this code. The first example considers a surface resistivity survey with 3813 measurements. The discretized model space contains 19,040 cells. For this example, the inversion package converges in approximately [Formula: see text] on a [Formula: see text] Pentium 4, with [Formula: see text] of RAM. The second example considers the case of borehole based data acquisition. For this example there were 4704 measurements and 13,200 model cells. The inversion for this example requires [Formula: see text] of computational time.