Model-Based Assessment of Preventive Drought Management Measures’ Effect on Droughts Severity

Preventive Drought Management Measures (PDMMs) aim to reduce the chance of droughts and minimize drought-associated damages. Selecting PDMMs is not a trivial task, and it can be asserted that actual contributions to drought alleviation still need to be adequately researched. This study evaluates the effects of three potential PDMMs, namely,...

Remote Sensed and/or Global Datasets for Distributed Hydrological Modelling: A Review

This research paper presents a systematic literature review on the use of remotely sensed and/or global datasets in distributed hydrological modelling. The study aims to investigate the most commonly used datasets in hydrological models and their performance across different geographical scales of catchments, including the micro-scale (<10...

How well can machine learning models perform without hydrologists?: Application of rational feature selection to improve hydrological forecasting

With more machine learning methods being involved in social and environmental research activities, we are addressing the role of available information for model training in model performance. We tested the abilities of several machine learning models for short-term hydrological forecasting by inferring linkages with all available predictors...

Impact of dataset size on the signature-based calibration of a hydrological model

Many calibrated hydrological models are inconsistent with the behavioral functions of catchments and do not fully represent the catchments’ underlying processes despite their seemingly adequate performance, if measured by traditional statistical error metrics. Using such metrics for calibration is hindered if only short-term data are...

Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning

Modeling dynamic geophysical phenomena is at the core of Earth and environmental studies. The geoscientific community relying mainly on physical representations may want to consider much deeper adoption of artificial intelligence (AI) instruments in the context of AI's global success and emergence of big Earth data. A new perspective of using...

Decomposing satellite-based rainfall errors in flood estimation: Hydrological responses using a spatiotemporal object-based verification method

A spatiotemporal object-based rainfall analysis method is used to evaluate the hydrological response of two systematic satellite error sources for storm estimation in the Capivari catchment, Brazil. This method is called Spatiotemporal Contiguous Object-based Rainfall Analysis (ST-CORA) specifically evaluates the error structure of satellite...

Practical experience of sensitivity analysis: Comparing six methods, on three hydrological models, with three performance criteria

Currently, practically no modeling study is expected to be carried out without some form of Sensitivity Analysis (SA). At the same time, there is a large number of various methods and it is not always easy for practitioners to choose one. The aim of this paper is to briefly review main classes of SA methods, and to present the results of the...

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

In the MLUE method (reported in Shrestha et al. [1, 2]) we run a hydrological model M for multiple realizations of parameters vectors (Monte Carlo simulations), and use this data to build a machine learning model V to predict uncertainty (quantiles) of the model M output. In this paper, for model V, we employ three machine learning techniques,...

Safety against flooding: Activity Report 2008-2009

This document reports the progress of Delft Cluster project CT04.30 "Safety against flooding" till June 2009. Fundamental knowledge from the project has resulted in a large number of scientific publications, PhD theses and MSc theses. Work package A 1 in particular has resulted in high-profile scientific publications, immediately awarded with...