Bernardo de Miera y Pacheco (1713–1785) is remembered today not only as colonial New Mexico’s preeminent religious artist, but also as the cartographer who drew some of the most important early maps of the American West. His “Plano Geographico” of the Colorado Plateau and Great Basin, revised by his hand in 1778, influenced other mapmakers for almost a century. This book places the man and the map in historical context, reminding readers of the enduring significance of Miera y Pacheco. Later Spanish cartographers, as well as Baron Alexander von Humboldt, Captain Zebulon Montgomery Pike, and Henry Schenck Tanner, projected or expanded upon the Santa Fe cartographer’s imagery. By so doing, they perpetuated Miera y Pacheco’s most notable hydrographic misinterpretations. Not until almost seventy years after Miera did John Charles Frémont take the field and see for himself whither the waters ran and whither they didn’t.

After modern science turns every human into a genetic time bomb with men dying at age twenty-five and women dying at age twenty, girls are kidnapped and married off in order to repopulate the world.

The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.