Scientific Programming

Scientific Programming Tools for Water Management

Publishing date
01 Jul 2020
Submission deadline
06 Mar 2020

1Geological Survey of Spain, Madrid, Spain

2Spanish National Research Council, Almería, Spain

3University of Murcia and Euro-Mediterranean Water Institute, Murcia, Spain

4Universidad Politécnica de Madrid, Madrid, Spain

5Universidad Católica de Murcia, Murcia, Spain

This issue is now closed for submissions.
More articles will be published in the near future.

Scientific Programming Tools for Water Management

This issue is now closed for submissions.
More articles will be published in the near future.


The integration of the latest breakthroughs in geology, hydrology, agronomy, and biotechnology from one side and high-performance computing, artificial intelligence, and computational modelling from the other have enabled remarkable advances in the field of water management. By merging these developments, scientists have started to create new strategies to cope with the consequences of global climatic forces and human action, such as water scarcity, ecosystem degradation, and the decreasing renewability rates of water-dependent resources.

These scientific applications typically require big datasets and complex simulations to adequately characterize the nonlinear processes that govern the dynamics of water. Highly intensive computational strategies could greatly benefit from increased scientific computational resources to reproduce the complex environmental and human interactions that occur in bodies of water and their associated ecosystems and dependent resources.

However, we are witnessing a steady transition towards heterogeneous architectures, in which traditional Central Processing Units (CPUs) and accelerators like Graphics Processing Units (GPUs), Intel Many Integrated Core (MIC)/Xeon Phi, and Field Programmable Gate Arrays (FPGAs) are being combined to maintain traditional performance increments. The underlying computational model of such architectures relies on massive vectorization to reduce the Energy per Instruction (EPI). Traditional algorithms are often tailored to sequential or modestly parallel-based architectures that may not fit within this landscape of computation. However, novel algorithms that are inspired by natural procedures, such as metaheuristics, machine learning algorithms, and artificial neural networks, are gaining particular interest within the community as they are massively parallel by definition.

This special issue therefore aims to explore how the intersections between algorithm designs, software platforms, and hardware architectures are used to deal with emerging challenges in the scientific field of water management. One of the main objectives of this special issue is to showcase the main trends in scientific parallel processing, algorithm definition, and problem-domain requirements in order to anticipate future solutions which could be translated into real societal benefits. Original research articles that describe a specific computational tool and/or compare several existing ones, as well as review articles that discuss the state of the art for a given computational tool and/or the sequential application of several of them over time, are particularly encouraged.

Potential topics include but are not limited to the following:

  • Parallel stochastic simulations for water management
  • Parallel and distributed architectures to enhance water management-related applications
  • Emerging processing architectures (e.g., GPUs, Intel Xeon Phi, FPGAs, mixed CPU-GPU, or CPU-FPGA) to accelerate water management kernels
  • Cluster, grid, and cloud deployment for water management applications
  • Soft computing algorithms applied to water management procedures
  • Decision-making tools based on intelligent algorithms for water management
  • Benchmarking of environmental software tools and packages
  • Big data treatment, analysis, and applications for water management
  • Visualization and geocomputational techniques for spatial water management procedures
Scientific Programming
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