diff --git a/book/chapters/Overview 03.ipynb b/book/chapters/Overview 03.ipynb deleted file mode 100644 index fbdc222..0000000 --- a/book/chapters/Overview 03.ipynb +++ /dev/null @@ -1,127 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "78dbd86f", - "metadata": {}, - "source": [ - "# Overview: Enhancing Snow Water Equivalent Forecasting Efficiency with PyGeoweaver" - ] - }, - { - "cell_type": "markdown", - "id": "0db79bda", - "metadata": {}, - "source": [ - "Snow Water Equivalent (SWE) is a critical measure in hydrology and environmental science that quantifies the amount of water contained within a snowpack. Essentially, SWE represents the volume of water that would result from melting the snow in a specific area. This measure is crucial for a variety of applications, including water resource management, flood forecasting, agriculture, and climate studies. Understanding and accurately predicting SWE is vital for regions that depend on snowmelt as a key water source, influencing decisions related to water allocation, agricultural planning, and flood risk management." - ] - }, - { - "cell_type": "markdown", - "id": "94df52f1", - "metadata": {}, - "source": [ - "The importance of SWE lies in its role as a natural reservoir during the winter months, storing water that is gradually released as temperatures rise in the spring. This release is essential for maintaining river flows, replenishing groundwater, and supporting ecosystems. For many regions, especially those in mountainous areas, the timing and volume of snowmelt are critical for ensuring a consistent water supply throughout the year. Additionally, accurate SWE predictions are necessary for preparing for and mitigating the risks of spring floods, which can occur when rapid snowmelt combines with heavy rainfall." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "d3a59482", - "metadata": {}, - "source": [ - "![](../img/swe.png)" - ] - }, - { - "cell_type": "markdown", - "id": "3a502741", - "metadata": {}, - "source": [ - "

\n", - " Figure 1: SWE prediction workflow in Geoweaver.\n", - "

" - ] - }, - { - "cell_type": "markdown", - "id": "0a94514e", - "metadata": {}, - "source": [ - "However, forecasting SWE is a challenging task due to the complex and dynamic nature of snowpacks. Factors such as elevation, temperature, snow density, and the timing of snowfall all contribute to the variability of SWE across different regions. Traditional methods of measuring SWE, such as ground-based surveys and manual sampling, are often limited in scope and accuracy, especially in remote or difficult-to-access areas. Moreover, the spatial variability of snowpack adds another layer of complexity, making it difficult to create accurate and reliable forecasts using conventional approaches." - ] - }, - { - "cell_type": "markdown", - "id": "4b022409", - "metadata": {}, - "source": [ - "To address these challenges, advanced tools and technologies like Geoweaver are being developed to improve the accuracy and efficiency of SWE forecasting. Geoweaver is a comprehensive platform that facilitates the integration of diverse data sources, including satellite imagery, weather models, and in-situ measurements. By consolidating these datasets into a unified format, Geoweaver enables researchers to capture the full complexity of snowpack dynamics and produce more accurate SWE forecasts. This integration is crucial for overcoming the limitations of traditional methods and providing a more detailed understanding of how snowpack changes over time." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "420fe2da", - "metadata": {}, - "source": [ - "![](../img/features.png)" - ] - }, - { - "cell_type": "markdown", - "id": "b4984df3", - "metadata": {}, - "source": [ - "

\n", - " Figure 2: Coupling architecture of Geoweaver modules.\n", - "

" - ] - }, - { - "cell_type": "markdown", - "id": "ec39dd67", - "metadata": {}, - "source": [ - "A key feature of Geoweaver is its support for advanced machine learning models, which are particularly effective for handling the high variability and complexity of snowpack data. The platform’s computational capabilities allow for efficient model training, leveraging parallel processing and distributed computing resources to enhance forecast accuracy. By supporting both time series and cumulative forecasting methodologies, Geoweaver caters to a wide range of research needs, from monitoring seasonal snowpack changes to predicting total SWE accumulation over a specific period." - ] - }, - { - "cell_type": "markdown", - "id": "cd839b59", - "metadata": {}, - "source": [ - "Geoweaver facilitates the seamless integration of diverse datasets essential for precise SWE forecasting. Leveraging datasets such as Digital Elevation Model (DEM), GridMET, MODIS, GHCD, and AMSR, Geoweaver ensures comprehensive coverage of meteorological and snow cover information. This integration lays a solid foundation for accurate forecasting by capturing the intricate interplay of factors influencing SWE dynamics." - ] - }, - { - "cell_type": "markdown", - "id": "1faf7b21", - "metadata": {}, - "source": [ - "In addition to its data processing and modeling capabilities, Geoweaver also excels in visualization and real-time analysis of SWE forecasts. Overall, Geoweaver represents a significant advancement in SWE forecasting, offering a powerful tool for improving our understanding and management of snow-dependent water resources in the face of climate change." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}