Featured hydrological tools:

  1. 1.AWE-GEN

  2. 2.Tethys-Chloris

  3. 3.tRIBS


    Most natural hydrologic phenomena are so complex that they are beyond comprehension, or exact laws governing such phenomena have not been fully discovered. Before such laws can ever be found, complicated hydrologic phenomena (the prototype) can only be approximated by modeling.

 Ven Te Chow


3. tRIBS

The TIN-based Real-Time Integrated Basin Simulator (tRIBS) is a physically-based, distributed hydrologic model developed at the Ralph M. Parsons Laboratory, Massachusetts Institute of Technology (Ivanov et al., 2004). The model emphasizes th
e dynamic relationship between the vadose and saturated zones and the land surface response to continuous storm and interstorm cycle. This is performed by tracking the evolution of moisture fronts within computational elements in relation to the water table depth position. One-dimensional infiltration in the surface normal direction is redistributed by both the lateral fluxes in the vadose zone and in the phreatic aquifer during storm and interstorm periods. By modeling these detailed processes, tRIBS is capable of producing runoff through a number of generation mechanisms including infiltration-excess, saturation from below and perched saturation-excess runoff. In combination, these processes allow tRIBS to model the development of partial contributing areas within the basin. Runoff produced from these areas is routed to the channel network using a hydrologic routing scheme; the kinematic wave approach is utilized to route flood waves in streams.

In its early developmental stages, tRIBS was an event-based model operating on a raster grid cell computational domain (RIBS, Garrote and Bras, 1995). The model has been subsequently extended to perform on a continuous basis with the addition of a new infiltration scheme for multiple storm events and hydrologic redistribution during interstorm periods due to groundwater flow and evapotranspiration demand. In addition, developments in TIN-based terrain modeling have also been incorporated into the model by restructuring the computational architecture. No longer bound to a raster grid, the computational domain is adjusted to better fit the terrain, allowing for significant savings in computation demand. In this framework, the Voronoi cell serves as the essential element for hydrologic modeling.

In its current state, tRIBS is capable of using the spatially distributed terrain data to their fullest capacity, while maintaining the efficiency required for treating larger watersheds. In addition, the model is capable of using other spatially distributed descriptors of the basin properties, specifically the soil type, vegetation and land cover data. Coupled with the ability to ingest precipitation estimates and forecasts from NEXRAD-based maps, tRIBS is able to take advantage of driving reasons for utilizing radar rainfall data, spatial coverage and temporal/spatial variability.

Primary tRIBS web-page is maintained by our colleague and collaborator Prof. Enrique Vivoni at:


Garrote, L. and Bras, R. L. (1995). A Distributed Model for Real-time Flood Forecasting using Digital Elevation Models. Journal of Hydrology. 167: 279-306.

Ivanov, V.Y., Vivoni, E.R., Bras, R.L., and Entekhabi, D., (2004). Catchment hydrologic response with a fully-distributed triangulated irregular network model. Water Resources Research, 40(11), W11102, doi:10.1029/2004WR003218.


2. Tethys-Chloris (T&C)

The  idea to develop Tethys-Chloris model originates from the emerging interest that in the last ten years has been reversed toward eco-hydrological studies and related scientific questions. According to the spirit of “HYDROWIT” group a physical-based mechanistic tool has been developed to account for the coupled interactions of energy-water-vegetation in a variety of environments and climates where water is the key component. Applications can span from temperate Mediterranean climates to cold semiarid mountains environment. Tethys-Chloris applications aim to highlight eco-hydrological patterns and dynamics through numerical simulations with the will to solve or to sharp scientific questions.  All essential components of hydrological cycle are included.  Energy and mass exchanges in the atmospheric surface layer are treated thoroughly with an accurate resistance analogy scheme. A simplified module of saturated and unsaturated soil water dynamics governs the subsurface hydrology.  Up to two layers of vegetation (e.g. trees and grasses) can be accounted for. In cold environment a snowpack evolution module controls the energy exchanges, the snow accumulation and the snow melting that can be eventually mediated by vegetation interactions. Vegetation structure and dynamics are parsimoniously parameterizes including plant life-cycle processes, photosynthesis, phenology, carbon allocation and tissues turnover.

Land-Surface Hydrological Components

  1. absorption, reflection, and transmittance of solar shortwave radiation and atmospheric longwave radiation;

  2. sensible and latent heat fluxes, partition of latent heat into evaporation and transpiration, ground heat flux and incoming heat with precipitation;

  3. resistance scheme for water and energy fluxes, including aerodynamic resistance, leaf boundary layer resistance, soil resistance and stomatal resistance; 

  4. snow hydrology component, including snowpack energy balance, snowmelt and snow interception;

  5. rainfall interception, throughfall,  stem flow and splash erosion; 

  6. infiltration and water movement into a multi-layer soil including unsaturated zone dynamics, saturated zone and different types of  runoff formation. 

Biochemical Components
  1. stomatal physiology and photosynthesis.  These processes are highly coupled with the hydrological components and  the vegetation dynamics 

Vegetation Dynamics Components

  1. net primary productivity and plant respiration;

  2. carbon allocation and translocation to and from carbon compartments;

  3. tissue turnover and stress-induced foliage loss; 

  4. carbon balance; 

  5. vegetation phenology; 

The model simulates the energy and water budgets of bare soil,  vegetated, water and, snow covered surfaces for a basic computational element. A computational element typically represents an area with similar topographic features that can span 25-1000 m2. The union of many computational elements may form a hillslope or a watershed. Spatial distributed applications of Tethys-Chloris explicitly consider  topographic representation of the terrain, and interaction  with the neighbors elements.  Such interactions are represented through surface and subsurface moisture transfers among the elements. A scheme of subsurface and surface flow routing is thus implemented within Tethys-Chloris.

The model resolves the mass and energy budgets at the hourly scale, and the carbon vegetation dynamics at the daily scale. Vegetation and hydrology interacts at fine time scales through the influence of biochemical process on energy and water fluxes. Water availability on turn influences vegetation growth, health and structure in a longer time scale, leading to a wonderful coupled natural mechanism. 

The model names may seem unfamiliar, however… Tethys (Greek: Τηθύς)  in Greek mythology, was an archaic Titaness and aquatic sea goddess and she was considered as an embodiment of the waters of the world. Chloris (Greek: χλωρις),  was a Nymph associated with spring, flowers and new growth … thus nothing could be more familiar to us. 


Fatichi, S., V.Y. Ivanov, and E. Caporali (2012). A mechanistic ecohydrological model to investigate complex interactions in cold and warm water controlled environments. 1. Theoretical framework and plot-scale analysis. Journal of Advances in Modeling Earth Systems, doi:10.1029/2011MS000086.

Fatichi, S., V.Y. Ivanov, and E. Caporali (2012). A mechanistic ecohydrological model to investigate complex interactions in cold and warm water controlled environments. 2. Distributed analysis. Journal of Advances in Modeling Earth Systems, doi:10.1029/2011MS000087.



Records of meteorological variables around the world are often very short, with substantial gaps and low spatial coverage. This creates a problem of data inadequacy in numerous  applications.  To overcome such problems, weather generators, as the tools capable of generating consistent time-series of climatic variables, have been proposed and used in the past.  Weather generators infer from meteorological variables of an observed climate set of parameters that allow to numerically  reproduce  continuous and longer  time series  of such  climate. Several studies on  agricultural and crop productivity,  water resource evaluation,  flood risk analysis, etc., requires the use of weather generators.  Other possible applications are related to the generation of inputs to hydrological models, ecosystem models, or in long-term land management and erosion studies. 

AWE-GEN (Advanced WEather GENerator) is an hourly weather generator  capable of reproducing low and high-frequency characteristics of hydro-climatic variables and the essential statistical properties of these variables. The weather generator  employs both the physically-based and stochastic approaches and is a substantial evolution of the model presented by Ivanov et al., 2007.  It includes a formulation of the precipitation module based on the Poisson-Cluster process; stochastic-physical based modules of cloud-cover and air temperature. It further includes modules simulating vapor pressure, wind speed, atmospheric pressure, and shortwave radiation partition into various type and spectral bands including the simulation of the photosynthetically active radiation. AWE-GEN is also capable of simulating the inter-annual variability of the precipitation process and a wide set of statistics including extremes. Furthermore, a procedure to take into account non stationary change of climate has been incorporated in the AWE-GEN framework. The procedure is based on a stochastic downscaling of GCM predictions. The Matlab source code of the generator and test cases can be downloaded here:


Fatichi, S.,  Ivanov, V.Y.,  E. Caporali (2011). Simulation of future climate scenarios with a weather generator, Advances in Water Resources, 34, 448–467, doi:10.1016/j.advwatres.2010.12.013

Ivanov, V.Y., Bras, R.L., and Curtis, D.C. (2007). A weather generator for hydrological, ecological, and agricultural applications, Water Resources Research, 43, W10406, doi: 10.1029/ 2006WR005364.



Over the recent years, it has become more evident that vegetation, as well as its dynamics, is the often-ignored linchpin of the land-surface hydrology. This work emphasizes the coupled nature of vegetation-water-energy dynamics by considering linkages at time scales that vary from hourly to interannual. A dynamic ecohydrological model, tRIBS+VEGGIE, is constructed (Ivanov et al., 2008a,b; Ivanov, 2006), which represents the essential water and energy processes over the complex topography of a river basin and links them to the basic plant life regulatory processes. In this framework, which is particularly focused on ecohydrology of semi-dry environments, soil water is considered to be the key limiting resource affecting vegetation structure and organization. The mechanisms through which water limitation influences plant behavior are related to carbon assimilation via the control of photosynthesis and stomatal behavior, carbon allocation, stress-induced foliage loss, as well as recruitment and phenology patterns.

The system simulates a number of processes that manifest numerous dynamic feedbacks among various components of the coupled vegetation-hydrology system:

•Biophysical processes: absorption, reflection, and transmittance of solar shortwave radiation; absorption, reflection, and emission of longwave radiation; sensible and latent heat fluxes, partition of latent heat into canopy and soil evaporation, and transpiration; stomatal physiology; ground heat flux.

•Hydrological processes: interception, throughfall, and stem flow; infiltration in a multi-layer soil; lateral water transfer in the unsaturated and saturated zones; runoff and runon with re-infiltration.

•Biochemical processes and vegetation dynamics: photosynthesis and primary productivity; plant respiration; tissue turnover and stress-induced foliage loss; carbon allocation; vegetation phenology; plant recruitment.

The model simulates the energy and water budgets of both vegetated and non-vegetated surfaces that can be simultaneously present within a given element. In a domain of study, the dynamics of each computational element are simulated separately. Spatial dependencies are introduced by considering the surface and subsurface moisture transfers among the elements, which affect the local dynamics via the coupled energy-water interactions. Consequently, when applied to a catchment system, the model offers a quasi-three-dimensional framework in which lateral moisture transfers may lead to the spatio-temporal variability of states. The model accounts for the hydraulic, thermal, and albedo properties of different soil types.

While the models of biophysical processes operate at an hourly time scale, the routines simulating the processes of infiltration, lateral moisture transfer, and runoff (runon) use a finer time step (7.5-15 min.). Consequently, at the hourly time scale, the stomatal response to environmental conditions is the only vegetation process that affects the water and energy budgets. At the daily and longer time scales, vegetation affects state of the land-surface through the change of its structural attributes (such as leaf area index and height) and vegetation fraction. The latter determines the relative contribution of a given vegetation type to the element-scale fluxes.


Ivanov, V. Y., S. Fatichi, G. D. Jenerette, J. F. Espeleta, P. A. Troch, and T. E. Huxman (2010), Hysteresis of soil moisture spatial heterogeneity and the “homogenizing” effect of vegetation, Water Resour. Res., 46, W09521, doi:10.1029/2009WR008611.

Ivanov, V.Y., Bras, R.L., and Vivoni, E.R. (2008a). Vegetation-Hydrology Dynamics in Complex Terrain of Semiarid Areas: I. A mechanistic Approach to Modeling Dynamic Feedbacks, Water Resour. Res., 44, W03429, doi:10.1029/2006WR005588

Ivanov, V.Y., Bras, R.L., and Vivoni, E.R. (2008b). Vegetation-Hydrology Dynamics in Complex Terrain of Semiarid Areas: II. Energy-Water Controls of Vegetation Spatio-Temporal Dynamics and Topographic Niches of Favorability, Water Resour. Res., 44, W03430, doi:10.1029/2006WR005595.

Ivanov, V.Y. (2006). Effects of Dynamic Vegetation and Topography on Hydrological Processes in Semi-Arid Areas. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA.

Kim, J., V. Y. Ivanov, A. Warnock, and N. Katopodes (2011). Coupled modeling of hydrologic and hydrodynamic processes including overland and channel flow. Advances in Water Resources, doi:10.1016/j.advwatres.2011.11.009.