文章目录
- 常见问题
- what is mesoscale hydrologic model (mHM)
- how does the mHm model upscale the parameters
- what is the multiscale parameter regionalization (MPR)
- how does MPR aggregating the parameters at a small scale to the parameters at the regional scale
- <3 what are the technical details of the parameter aggregation
- 安装Cygwin
常见问题
what is mesoscale hydrologic model (mHM)
The mesoscale hydrologic model (mHM) is a distributed hydrologic model that simulates the hydrological cycle at the catchment scale. It is designed to simulate the spatial and temporal variability of the water balance components, including precipitation, evapotranspiration, infiltration, surface runoff, subsurface flow, and groundwater recharge.
mHM is a physically-based model that uses a gridded representation of the landscape, which allows it to simulate hydrological processes at a high spatial resolution. The model incorporates a range of submodels to represent different processes, such as the soil moisture dynamics, vegetation dynamics, and land surface processes. mHM also includes a data assimilation module, which allows it to incorporate observed data into the model to improve its performance.
The mHM model has been widely used for various applications, including flood forecasting, water resources management, and climate change impact assessment. It has been applied in various regions of the world, including Europe, Asia, Africa, and South America.
how does the mHm model upscale the parameters
The mHM model uses a combination of top-down and bottom-up approaches to upscale parameters from small to large scales. At the small scale, mHM uses physically-based models to represent hydrologic processes, which require a large number of detailed input data, such as soil properties, land use, and topography. At the large scale, these data are often not available or are too expensive to collect. Therefore, mHM uses a range of methods to upscale the parameters from the small to the large scale:
Parameter transfer functions: These functions relate the parameters of a small-scale model to the corresponding parameters of a large-scale model. The transfer functions are typically derived from statistical relationships between the small-scale and large-scale parameters using regression analysis.
Remote sensing data: Remote sensing data, such as satellite images, can provide information on land use, vegetation, and topography, which can be used to estimate the parameters of a large-scale model.
Soil-vegetation-atmosphere transfer (SVAT) models: SVAT models are used to simulate the exchange of energy and water between the land surface and the atmosphere. These models can be used to estimate the parameters of a large-scale model, such as the surface roughness, the leaf area index, and the soil moisture content.
Overall, mHM uses a combination of methods to upscale the parameters from small to large scales, which allows it to simulate the hydrological cycle at different spatial scales with high accuracy.
what is the multiscale parameter regionalization (MPR)
Multiscale parameter regionalization (MPR) is a method used in hydrologic modeling to estimate model parameters at a regional scale by aggregating information from a set of representative small-scale models. It is a type of upscaling method that allows for the transfer of parameter values from the small-scale models to the regional scale.
The MPR method is based on the idea that hydrologic models can be decomposed into a set of submodels, each of which represents a different process or component of the hydrologic cycle. These submodels are assumed to be transferable across scales, meaning that their parameters can be estimated at a small scale and then transferred to a larger scale.
The MPR method uses a combination of clustering and regression techniques to estimate the model parameters at the regional scale. First, a set of small-scale models are developed and calibrated for a representative set of catchments. Next, the catchments are grouped into clusters based on their similarities in terms of climate, topography, and soil characteristics. Then, a regression model is developed to estimate the regional-scale parameter values based on the parameters of the small-scale models and the catchment characteristics.
The MPR method has been applied successfully to a range of hydrologic models, including the Variable Infiltration Capacity (VIC) model, the Soil and Water Assessment Tool (SWAT), and the mesoscale Hydrologic Model (mHM). It has been used to estimate model parameters in a variety of regions, including Europe, Africa, and North America, and has been shown to improve model performance compared to traditional parameter estimation methods.
how does MPR aggregating the parameters at a small scale to the parameters at the regional scale
The Multiscale Parameter Regionalization (MPR) method aggregates the parameters at a small scale to the parameters at the regional scale by using clustering and regression techniques. The basic steps involved in the MPR method are as follows:
Small-scale model development: A set of small-scale models is developed and calibrated for a representative set of catchments. These models represent the hydrological processes in each catchment and are calibrated using observed data.
Catchment grouping: The catchments are grouped into clusters based on their similarities in terms of climate, topography, and soil characteristics. The clustering is done to identify groups of catchments that have similar hydrological responses.
Parameter aggregation: The parameters of the small-scale models are aggregated to the regional scale using a regression model. The regression model estimates the regional-scale parameter values based on the parameters of the small-scale models and the catchment characteristics.
Model evaluation: The regional-scale model is evaluated using independent data to assess its performance.
The regression model used in the MPR method is typically a multiple linear regression model or a machine learning model such as a random forest. The model is trained using the parameters of the small-scale models and the catchment characteristics as predictors and the regional-scale parameters as the response variable.
The MPR method allows for the transfer of parameter values from the small-scale models to the regional scale, which can improve the accuracy of hydrological models at larger scales. The method has been successfully applied to a range of hydrological models and has been shown to improve model performance compared to traditional parameter estimation methods.
❤️ what are the technical details of the parameter aggregation
The technical details of parameter aggregation in the Multiscale Parameter Regionalization (MPR) method can vary depending on the specific implementation, but typically involves clustering and regression techniques.
Clustering: The first step in parameter aggregation is to group catchments into clusters based on their similarities in terms of climate, topography, and soil characteristics. The clustering algorithm used will depend on the specific implementation, but commonly used algorithms include K-means clustering and hierarchical clustering.
Regression: Once the catchments have been grouped into clusters, a regression model is used to estimate the regional-scale parameter values based on the parameters of the small-scale models and the catchment characteristics. The regression model can be a simple linear regression model or a more complex machine learning model, such as a random forest or support vector machine.
The regression model is trained on a set of catchments that have both small-scale model parameters and regional-scale parameter values. The catchment characteristics used as predictors in the regression model can include variables such as mean annual precipitation, mean annual temperature, soil texture, and topography.
Once the regression model is trained, it can be used to estimate the regional-scale parameter values for catchments that only have small-scale model parameters. These estimated regional-scale parameter values can then be used in hydrological models to simulate the water cycle at larger scales.
It should be noted that the parameter aggregation step in the MPR method is dependent on the quality and representativeness of the small-scale models used to estimate the catchment parameters. Therefore, it is important to carefully calibrate the small-scale models and select catchments that are representative of the region of interest.
安装Cygwin
默认镜像地址
C:\cygwin64
默认下载地址和edge下载地址一致
E:\Download
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