Call for Chapters: AI-Enhanced Hydrological Modeling for Sustainable Water Systems in Semi-Arid Regions

Editors

Mohammed ACHITE, University Hassiba Benbouali of Chlef, Algeria

Call for Chapters

Proposals Submission Deadline: May 28, 2026
 Full Chapters Due: July 30, 2026
 Submission Date: July 30, 2026

Introduction

Semi-arid regions constitute some of the most water-stressed environments on Earth, where limited precipitation, high evapotranspiration rates, and pronounced climate variability impose significant constraints on water availability and ecosystem sustainability. These regions are particularly vulnerable to the impacts of climate change, population growth, and increasing water demand, which collectively exacerbate the pressure on already fragile hydrological systems. Effective water resource management in such contexts requires robust, accurate, and adaptive modeling approaches capable of capturing the complex interactions between climatic, hydrological, and anthropogenic processes. Traditional hydrological modeling techniques, while valuable, often face limitations in semi-arid environments due to data scarcity, non-linearity of hydrological processes, and spatial-temporal heterogeneity. In recent years, advancements in artificial intelligence (AI), including machine learning and deep learning methods, have opened new avenues for improving hydrological predictions and decision-making processes. AI-enhanced models offer the capacity to integrate multi-source datasets—such as remote sensing observations, reanalysis products, and in-situ measurements—while effectively handling non-linear relationships and uncertainties inherent in hydrological systems. The integration of AI into hydrological modeling represents a transformative shift toward more resilient and sustainable water management strategies. AI-driven approaches can enhance the accuracy of rainfall–runoff simulations, drought forecasting, groundwater estimation, and water quality assessment. Moreover, these methods facilitate real-time monitoring and predictive analytics, enabling proactive responses to hydrological extremes such as droughts and floods, which are increasingly frequent in semi-arid regions. This book aims to explore the theoretical foundations, methodological advancements, and practical applications of AI-enhanced hydrological modeling in semi-arid regions. It seeks to bring together interdisciplinary perspectives from hydrology, climatology, data science, and environmental management to address current challenges and identify future research directions. By presenting case studies, innovative modeling frameworks, and applied solutions, the book intends to contribute to the development of sustainable water systems that are resilient to environmental change and capable of supporting socio-economic development in water-limited regions.

Objective

This book aims to advance the scientific and practical understanding of hydrological processes in semi-arid regions through the integration of artificial intelligence (AI) techniques with conventional modeling approaches. Its primary objective is to provide a comprehensive framework that bridges the gap between data-driven methods and physically based hydrological models, thereby enhancing the accuracy, scalability, and applicability of water resource assessments. A key objective is to synthesize recent methodological developments in AI—such as machine learning, deep learning, and hybrid modeling—and evaluate their effectiveness in addressing the inherent challenges of semi-arid hydrology, including data scarcity, non-linearity, and high spatial-temporal variability. By systematically examining these approaches, the book seeks to establish best practices for model development, validation, and deployment in water-limited environments. Another important goal is to demonstrate how AI-enhanced hydrological models can support sustainable water management. This includes improving the prediction of critical variables such as precipitation, evapotranspiration, soil moisture, groundwater dynamics, and streamflow, as well as enhancing early warning systems for droughts and floods. The book also aims to highlight the role of AI in integrating diverse data sources, including remote sensing products, climate model outputs, and ground-based observations, to produce more reliable and actionable insights. In addition, the book intends to contribute to interdisciplinary research by connecting hydrology with fields such as climate science, geospatial analysis, and environmental policy. Through applied case studies and comparative analyses, it will provide evidence-based insights into how AI-driven approaches can inform decision-making processes and policy development in semi-arid regions. Finally, this volume seeks to identify current research gaps and propose future directions for AI applications in hydrology. By doing so, it will not only consolidate existing knowledge but also stimulate innovation, foster collaboration, and guide researchers and practitioners toward the development of more resilient and sustainable water systems in semi-arid environments.

Target Audience

This book is primarily intended for researchers, academics, and graduate students working in the fields of hydrology, climatology, environmental science, and geographic information systems (GIS). It is particularly relevant for those engaged in the study of water resources in semi-arid and arid regions, as well as scholars focusing on the application of artificial intelligence and data-driven methods in environmental systems. In addition, the book will be valuable for professionals and practitioners involved in water resource management, including hydrologists, environmental engineers, watershed managers, and policy analysts. These stakeholders will benefit from the practical insights and applied methodologies presented, which can support evidence-based decision-making and the development of sustainable water management strategies under conditions of uncertainty and climate stress. The content is also designed to appeal to data scientists and AI specialists interested in interdisciplinary applications, particularly those seeking to apply machine learning and deep learning techniques to geospatial and environmental datasets. By providing domain-specific challenges and case studies, the book offers opportunities for methodological innovation and cross-disciplinary collaboration. Furthermore, policymakers, governmental agencies, and non-governmental organizations (NGOs) working on water security, climate adaptation, and sustainable development will find the book useful. The integration of scientific findings with policy-relevant insights will help inform the design and implementation of adaptive water governance frameworks in semi-arid regions. Overall, the book targets a diverse but interconnected audience, aiming to foster collaboration between scientific research, technological development, and policy implementation in order to address the pressing challenges of water scarcity and sustainability.

Recommended Topics

AI-based rainfall–runoff modeling in semi-arid regions Machine learning approaches for drought detection and forecasting (SPI, SPEI, PDSI integration) Deep learning applications in streamflow prediction and hydrological extremes Hybrid hydrological models combining physical processes and AI techniques Remote sensing data integration (e.g., MODIS, Sentinel, SMAP) in AI-driven hydrological modeling Spatio-temporal analysis of soil moisture using AI methods Groundwater modeling and prediction using machine learning algorithms AI applications in evapotranspiration estimation and water balance modeling Climate change impacts on hydrological systems using AI-enhanced projections (e.g., CMIP6 integration) Data scarcity solutions: transfer learning, data augmentation, and synthetic data generation Geospatial AI and GIS-based hydrological modeling frameworks Flood prediction and early warning systems using artificial intelligence AI-driven watershed management and decision support systems Explainable AI (XAI) in hydrology: interpretability and model transparency Uncertainty analysis and model validation in AI-based hydrological studies Big data analytics for water resources management Internet of Things (IoT) and real-time hydrological monitoring systems AI applications in water quality assessment and pollution modeling Land use/land cover change impacts on hydrology using AI techniques Sustainable water resource management strategies supported by AI Policy implications of AI-driven hydrological modeling in semi-arid regions Case studies from semi-arid regions (e.g., Middle East, North Africa, Central Asia, Eastern Anatolia) Integration of cloud computing platforms (e.g., Google Earth Engine) with AI for hydrological analysis Multi-source data fusion (satellite, reanalysis, in-situ) for improved hydrological predictions Future directions and emerging trends in AI-enhanced hydrology

Submission Procedure

Researchers and practitioners are invited to submit on or before May 28, 2026, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by June 11, 2026 about the status of their proposals and sent chapter guidelines.Full chapters of a minimum of 10,000 words (word count includes references and related readings) are expected to be submitted by July 30, 2026, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, AI-Enhanced Hydrological Modeling for Sustainable Water Systems in Semi-Arid Regions. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.

Publisher

This book is scheduled to be published by IGI Global Scientific Publishing, an international academic publisher of the "Information Science Reference", "Medical Information Science Reference", "Business Science Reference", and "Engineering Science Reference" imprints. IGI Global Scientific Publishing specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2027.

Indexing Information for Prospective Authors

IGI Global Scientific Publishing meets the criteria for inclusion in major indexing services such as Scopus; however, it is important to note that all indexing decisions are made independently by these services. IGI Global Scientific Publishing books are selectively indexed by the indexing organization after publication. Indexing cannot be guaranteed for any book prior to publication, and the indexing organization has complete control over the final selection and timeline.

Important Dates

May 28, 2026: Proposal Submission Deadline
June 11, 2026: Notification of Acceptance
July 30, 2026: Full Chapter Submission
September 3, 2026: Review Results Returned
October 1, 2026: Final Acceptance Notification
October 8, 2026: Final Chapter Submission

Inquiries

Mohammed ACHITE University Hassiba Benbouali of Chlef achitemohammed@gmail.com
Back to Call for Papers List