ABSTRACT:

Geospatial data, such as street/building/city Arcgis layout, bathymetry DEM and corresponding meteorological variables for simulation of Mashville 2010 flood event.

ABSTRACT:

Hourly meteorological data, such as precipitation, temperature, radiation, wind speed, pressure, relative humidity, cloud cover, etc., are downloaded from WebMET (www.webmet.com) and cleaned. The hourly data cover the period of 1961 to 1990 over the whole US. All the data are prepared in matlab format.

ABSTRACT:

Accurate assessment of erosion rates remains an elusive problem because soil loss is strongly nonunique with respect to the main drivers. In addressing the mechanistic causes of erosion responses, we discriminate between macroscale effects of external factors—long studied and referred to as “geomorphic external variability”, and microscale effects, introduced as “geomorphic internal variability.” The latter source of erosion variations represents the knowledge gap, an overlooked but vital element of geomorphic response, significantly impacting the low predictability skill of deterministic models at field-catchment scales. This is corroborated with experiments using a comprehensive physical model that dynamically updates the soil mass and particle composition. As complete knowledge of microscale conditions for arbitrary location and time is infeasible, we propose that new predictive frameworks of soil erosion should embed stochastic components in deterministic assessments of external and internal types of geomorphic variability.

ABSTRACT:

Climate change will affect global temperatures and the distribution and amount of precipitation, which are expected to impact regional hydrology and water resources in many parts of the world. It is therefore vital to quantify characteristics of the change and the corresponding uncertainty. A substantial amount of recent research has relied on climate projections obtained with General Circulation Models (GCMs) to assess climate change. However, such modeling results typically carry biases that must be reduced in some optimal fashion before any conclusions about robustness of climate change can be drawn. To minimize model- and scenario-specific biases, we combined information provided by the 3rd phase of the Coupled Model Intercomparison Project database with a Bayesian Weighted Averaging method. Specifically, the results of 12 GCMs for three emission scenarios B1, A1B, and A2 were downscaled for mid- (2046–2065) and end-century (2081–2100) intervals, at six WebMET locations that represent a hydroclimatic transect of Michigan. Furthermore, hourly results of future climate are generated by an advanced weather generator using the information from the combine GCMs ensemble.

ABSTRACT:

Climate change will affect global temperatures and the distribution and amount of precipitation, which are expected to impact regional hydrology and water resources in many parts of the world. It is therefore vital to quantify characteristics of the change and the corresponding uncertainty. A substantial amount of recent research has relied on climate projections obtained with General Circulation Models (GCMs) to assess climate change. However, such modeling results typically carry biases that must be reduced in some optimal fashion before any conclusions about robustness of climate change can be drawn. To minimize model- and scenario-specific biases, we combined information provided by the 5th phase of the Coupled Model Intercomparison Project database with a Bayesian Weighted Averaging method. Specifically, the results of 18 GCMs for two emission scenarios RCP45 and RCP85 were downscaled for mid- (2041–2070) and end-century (2071–2100) intervals, at six WebMET locations that represent a hydroclimatic transect of Michigan. Furthermore, hourly results of future climate are generated by an advanced weather generator using the information from the combine GCMs ensemble.