l5d1l5 Goto Github PK

se_veg

Examines the drivers of South Eastern Vegetation particularly along the coastal plain

sediment_load_calculation

Model chain to compute sediment loads using RUSLE and the SEDD model

seeking_connection

A proof-of-concept Agent-Based Model built in NetLogo 6.1.1., designed to explore how different forms of connectivity influence movements of individuals, species of different traits, and their populations within a fragmented hypothetical landscape.

segmentandclass

Segmentation and classification framework

sei-seir_arboviruses

Climate-based mechanistic model of arbovirus transmission in Ecuador and Kenya.

semi_automated_obia_processing_with_local_uspo

This repository contains the computer code of a semi-automated framework for land cover mapping using OBIA and local USPO

sensorpush

Use the SensorPush API to save temperature, humidity, dewpoint, barometric pressure, altitude and VPD data to a local InfluxDB database

sensorspecies

sentinel-evaluations-generator

Automatically generate raster evaluation maps for Geodesignhub using Sentinel earth obervation data.

sentinel-tree-cover

Image segmentations of trees outside forest

sergom

spatially explicit regional growth model (adapted for Arizona)

ses-resilience

Aula sobre resiliência de sistemas sócio-ecológicos.

sev_climatesens_spei

Research into climate sensitivity of ecosystems at the Sevilleta LTER

shapley-value-regression

Shapley Value Regression for calculating relative importance

sheepweather

An R data package for US Sheep Experiment Station soil moisture and weather data. These data are being used by the Adler lab to forecast plant response to annual weather.

sheng-xin-xiao-bai-yu

历史文档备份（只包含2019年和2020年）

sheng-xin-xiao-bai-yu-2021

历史文档备份（2021年）

shifts-between-boreal-alternative-states

Drivers of shifts in boreal understory vegetation between coniferous and broadleaf deciduous alternative states

sif-downscaling-essd

Code associated with the paper on downscaling sun-induced fluorescence (SIF) data by G. Duveiller et al in ESSD (https://doi.org/10.5194/essd-2019-121)

signalling-reciprocity

Study of the co-evolution of signalling and reciprocity and their influence in the emergence of cooperation

silicone

Automated filling of detail in reported emission scenarios

simmod

Python Simple Climate Model

simplelrc

The light response curves, coded based on equations in Lasslop 2010

skillnets

Studying the skill networks as proxy of adaptive capacity in Arctic communities

slcp_consolidation

Code for my paper "Socio-economic implications of scaling back a massive payments for ecosystem services program: Evidence from China"

smart-surface-sensing-system

smires

Science and Management of Intermittent Rivers and Ephemeral Streams

smos-l-vod-seasonality

smsc_deep_residual_network

Soil moisture storage capacity (SMSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models, but there are currently no available parameter datasets of SMSC on a global scale especially for hydrological models. Here, we produce a dataset of SMSC parameter for global hydrological models. Parameter calibration of three commonly used monthly water balance models provides the labels for the deep residual network. Calibration on the global grids can significantly reduce parameter discontinuities compared to calibration on individual catchments. The global SMSC is reconstructed at 0.5° resolution by integrating 15 types of meteorological, topographic, and runoff data based on a deep residual network. SMSC products are validated with spatial distribution against root zone depth datasets and validated in terms of simulation efficiency on global grids and 20 catchments from different climatic regions, respectively. We provide the global SMSC parameter dataset as a benchmark for geoscientific modelling by users.

smsc_monthly_water_balance_models

Soil moisture storage capacity (SMSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models, but there are currently no available parameter datasets of SMSC on a global scale especially for hydrological models. Here, we produce a dataset of SMSC parameter for global hydrological models. Parameter calibration of three commonly used monthly water balance models provides the labels for the deep residual network. Calibration on the global grids can significantly reduce parameter discontinuities compared to calibration on individual catchments. The global SMSC is reconstructed at 0.5° resolution by integrating 15 types of meteorological, topographic, and runoff data based on a deep residual network. SMSC products are validated with spatial distribution against root zone depth datasets and validated in terms of simulation efficiency on global grids and 20 catchments from different climatic regions, respectively. We provide the global SMSC parameter dataset as a benchmark for geoscientific modelling by users.