ClimateBench: A benchmark dataset for data-driven climate projections

AbstractFuture climate projections require Earth system models to simulate conditions outside their calibration range. It is therefore crucial to understand the applicability of such models and their modules under transient conditions. This study assesses the robustness of different types of models in terms of rainfall–runoff modeling under changing conditions. In particular, two process-based models and one data-driven model are considered: 1) the physically based land surface model of the European Centre for Medium-Range Weather Forecasts, 2) the conceptual Simple Water Balance Model, and 3) the Long Short-Term Memory-Based Runoff model. Using streamflow data from 161 catchments across Europe, a differential split-sample test is performed, i.e., models are calibrated within a reference period (e.g., wet years) and then evaluated during a climatically contrasting period (e.g., drier years). Models show overall performance loss, which generally increases the more conditions deviate from the reference climate. Further analysis reveals that the models have difficulties in capturing temporal shifts in the hydroclimate of the catchments, e.g., between energy- and water-limited conditions. Overall, relatively high robustness is demonstrated by the physically based model. This suggests that improvements of physics-based parameterizations can be a promising avenue toward reliable climate change simulations. Further, our study illustrates that comparison across process-based and data-driven models is challenging due to their different nature. While we find rather low robustness of the data-driven model in our particular split-sample setup, this must not apply generally; by contrast, such model schemes have great potential as they can learn diverse conditions from observed spatial and temporal variability both at the same time to yield robust performance.

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Enhanced Similarity for Spectral Clustering using Local Steering Features

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3073.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 3231-3235

Keyword(s):

Image Segmentation ◽

Covariance Matrix ◽

Spectral Clustering ◽

Local Features ◽

Benchmark Dataset ◽

Data Driven ◽

Spatial Coordinates ◽

Data Points

In the field of clustering, spectral clustering (SC) has become an effective tool to analyze complex non-convex data using only pairwise affinity between the data points. Many novel affinity metrics have been proposed in the literature which use local features such as color, spatial coordinates, and texture. Some of these methods used SC for image segmentation [1, 2]. In this work, we have used the covariance matrix of the pixels in a patch and proposed an orientation based feature of a pixel called steering feature. This feature is robust and data-driven. The steering feature is used to enhance the construction of affinity metric for spectral clustering proposed by Shi and Malik [1]. Using the Nystrom framework [2] on images from BSD500 benchmark dataset, we have shown that the proposed affinity metric gives better result than Shi and Malik [1]

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WaterBench: A Large-scale Benchmark Dataset for Data-Driven Streamflow Forecasting

10.31223/x57w6t ◽

2021 ◽

Author(s):

Ibrahim Demir ◽

Zhongrun Xiang ◽

Bekir Zahit Demiray ◽

Muhammed Sit

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Large Scale ◽

Short Term Memory ◽

Earth Science ◽

Science Research ◽

Benchmark Dataset ◽

Data Driven ◽

Streamflow Forecasting ◽

Learning Research

This study proposes a comprehensive benchmark dataset for streamflow forecasting, WaterBench, that follows FAIR data principles that is prepared with a focus on convenience for utilizing in data-driven and machine learning studies, and provides benchmark performance for state-of-art deep learning architectures on the dataset for comparative analysis. By aggregating the datasets of streamflow, precipitation, watershed area, slope, soil types, and evapotranspiration from federal agencies and state organizations (i.e., NASA, NOAA, USGS, and Iowa Flood Center), we provided the WaterBench for hourly streamflow forecast studies. This dataset has a high temporal and spatial resolution with rich metadata and relational information, which can be used for varieties of deep learning and machine learning research. We defined a sample streamflow forecasting task for the next 120 hours and provided performance benchmarks on this task with sample linear regression and deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and S2S (Sequence-to-sequence). To some extent, WaterBench makes up for the lack of a unified benchmark in earth science research. We highly encourage researchers to use the WaterBench for deep learning research in hydrology.

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