No abstract

This paper describes a new distributed hydrological model, called GEOtop. The model accommodates very complex topography and, besides the water balance, unlike most other hydrological models, integrates all the terms in the surface energy balance equation. GEOtop uses a discretization of the landscape based on digital elevation data. These digital elevation data are preprocessed to allow modeling of the effect of topography on the radiation incident on the surface, both shortwave (including shadowing) and longwave (accounting for the sky view factor). For saturated and unsaturated subsurface flow, GEOtop makes use of a numerical solution of the 3D Richards' equation in order to properly model, besides the lateral flow, the vertical structure of water content and the suction dynamics. These characteristics are deemed necessary for consistently modeling hillslope processes, initiation of landslides, snowmelt processes, and ecohydrological phenomena as well as discharges during floods and interstorm periods. An accurate treatment of radiation inputs is implemented in order to be able to return surface temperature. The motivation behind the model is to combine the strengths and overcome the weaknesses of flood forecasting and land surface models. The former often include detailed spatial description and lateral fluxes but usually lack appropriate knowledge of the vertical ones. The latter are focused on vertical structure and usually lack spatial structure and prediction of lateral fluxes. Outlines of the processes simulated and the methods used to simulate them are given. A series of applications of the model to the Little Washita basin of Oklahoma using data from the Southern Great Plains 1997 Hydrology Experiment (SGP97) is presented. These show the model's ability to reproduce the pointwise energy and water balance, showing that just an elementary calibration of a few parameters is needed for an acceptable reproduction of discharge at the outlet, for the prediction of the spatial distribution of soil moisture content, and for the simulation of a full year's streamflow without additional calibration.

Following a brief review of relevant theoretical and empirical spatial results, a theory of space‐time rainfall, applicable to fields advecting without deformation of the coordinates, is presented and tested. In this theory, spatial rainfall fields are constructed from discrete multiplicative cascades of independent and identically distributed (iid) random variables called generators. An extension to space‐time assumes that these generators are iid stochastic processes indexed by time. This construction preserves the spatial structure of the cascades, while enabling it to evolve in response to a nonstationary large‐scale forcing, which is specified externally. The construction causes the time and space dimensions to have fundamentally different stochastic structures. The time dimension of the process has an evolutionary behavior that distinguishes between past and future, while the spatial dimensions have an isotropic stochastic structure. This anisotropy between time and space leads to the prediction of the breakdown of G. I. Taylor's hypothesis of fluid turbulence after a short time, as is observed empirically. General, nonparametric, predictions of the theory regarding the spatial scaling properties of two‐point temporal cross moments are developed and applied to a tracked rainfall field in a case study. These include the prediction of the empirically observed increase of correlation times as resolution decreases and the scaling of temporal cross moments, a new finding suggested by this theory.