We explore a strategy for long-range hydrologic forecasting that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts at spatial and temporal scales appropriate for water management. Coarse-scale monthly ensemble climate model forecasts produced by the NCEP/CPC Global Spectral Model (GSM) are bias corrected, downscaled to 1/8...

Analysis of inconsistencies in multi-year gridded quantitative precipitation estimate over complex terrain and its impact on hydrologic modeling" (2012). s u m m a r y It is common that bias trends of long term precipitation data change over time due to various factors such as gauge relocation and changes in data processing methods. Temporal consistency of this error characteristic of precipita...

A mixed-model moving-average approach to geostatistical modeling in stream networks. Ecology 91:644–651. A distance matrix that contains the hydrologic distance between any two sites in a study area is needed to fit a geostatistical model using the tail-up and tail-down autocovariance functions. However, the hydrologic distance information needed to model the covariance between flow-connected a...

A methodology is presented for extracting topographic, topologic, and hydrologic information, from the digital spatial data of a hydrologic system, for hydrologic modeling with HMS. Methods for stream and watershed delineation from digital elevation models are presented. After vectorizing the raster-based stream segments and watersheds, navigation fields are added to their tables to support top...

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[1] We explore a strategy for long-range hydrologic forecasting that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts at spatial and temporal scales appropriate for water management. Monthly ensemble climate model forecasts produced by the National Centers for Environmental Prediction/Climate Prediction Center global spec...

Soil depth and texture exert strong controls on the spatial distribution of water and energy fluxes and states in semiarid watersheds. As a result, realistic representations of the spatial soil characteristics within watersheds are important for the improvement of process-based distributed hydrologic modeling applications. In this study, we evaluated the effects of combinations of soil thicknes...

[1] Distributed hydrological models have the potential to provide improved streamflow forecasts along the entire channel network, while also simulating the spatial dynamics of evapotranspiration, soil moisture content, water quality, soil erosion, and land use change impacts. However, they are perceived as being difficult to parameterize and evaluate, thus translating into significant predictiv...

An important goal of spatially distributed hydrologic modeling is to provide estimates of streamflow (and river levels) at any point along the river system. To encourage collaborative research into appropriate levels of model complexity, the value of spatially distributed data, and methods suitable for model development and calibration, the US National Weather Service Hydrology Laboratory (NWSH...