Preface

Annually, the global hydrological cycle circulates a large amount (~0.6 Mkm3 ) of freshwater that is returned as precipitations (*blue and green water*) into the aquatic (~80%) and terrestrial (~20%) ecosystems, causing spatiotemporal distribution of readily available, fresh hydro-resources. The annual per capita availability of fresh water resources has sharply reduced during the last century, while over the same period water withdrawals and consumption has increased in the agri-/ domestic/industrial triangle. In the same period availability and quality of fresh (*blue/green*) water resources have been exposed to extreme pressures (pollution, population growth, urbanization, deruralization) and non-sustainable management (overexploitation, rising of *grey waters*), which, under global climate changes, further disturbs the water cycling/balance. As a consequence, global ecosystems are frequently experiencing negative water balance (-ΔW), that is, a scenario in which certain domain water inputs (precipitations, river/groundwater inflows) are substantially lower than water outputs (evapotranspiration, water abstraction, deep percolation). Although drought and aridity are not synonymous, both share –ΔW. However, while drought assumes a relatively short-term –ΔW (usually several weeks to months), that is, a temporary, recurring reduction of water level/volume/ flow in a certain domain (river, lake, catchment, aquifer), aridity implies long-term –ΔW, that is, permanent average climate condition with negative water balance. Irrespective of drought situations (hydrological, meteorological, agronomical), they jeopardize energy and food production, ecological value of certain domains, and limit *blue/green waters* for our basic needs. Thus, each particular drought type should be managed appropriately, often with costly integrative agro-hydrotechnical approaches. New and more sustainable approaches are being developed depending on environmental conditions. This book presents the most recent insights related to drought types, their detection, and their effects on food, energy, and municipal water supplies. It also examines some novel approaches to drought management, which is one of the most challenging tasks for humankind.

**II**

**Chapter 8 129**

**Chapter 9 147**

**Chapter 10 169**

**Chapter 11 195**

Understanding the Drought Phenomenon in the Iberian Peninsula *by Matilde García-Valdecasas Ojeda, Emilio Romero Jiménez,* 

*and Toshiyuki Kurino*

under Water Scarcity

Air Temperatures

*by Katalin Posta and Nguyen Hong Duc*

*Sonia R. Gámiz-Fortis, Yolanda Castro-Díez and María Jesús Esteban Parra*

Climate Risk and Early Warning Systems (CREWS) for Papua New Guinea *by Yuriy Kuleshov, Kasis Inape, Andrew B. Watkins, Adele Bear-Crozier,* 

Benefits of Arbuscular Mycorrhizal Fungi Application to Crop Production

Physiological Features of Red Currant Adaptation to Drought and High

*by Panfilova Olga Vitalevna, Golyaeva Olga Dmitrievna, Knyazev Sergey Dmitrievich and Kalinina Olga Vitalevna*

*Zhi-Weng Chua, Pingping Xie, Takuji Kubota, Tomoko Tashima, Robert Stefanski* 

**Gabrijel Ondrasek** University of Zagreb, Faculty of Agriculture, Zagreb, Croatia

Chapter 1

Abstract

1. Introduction

1

northern and central Europe [3].

Satellite Data and Supervised

Learning to Prevent Impact of

Drought on Crop Production:

Leonardo Ornella, Gideon Kruseman and Jose Crossa

Reiterated and extreme weather events pose challenges for the agricultural sector. The convergence of remote sensing and supervised learning (SL) can generate solutions for the problems arising from climate change. SL methods build from a training set a function that maps a set of variables to an output. This function can be used to predict new examples. Because they are nonparametric, these methods can mine large quantities of satellite data to capture the relationship between climate variables and crops, or successfully replace autoregressive integrated moving average (ARIMA) models to forecast the weather. Agricultural indices (AIs) reflecting the soil water conditions that influence crop conditions are costly to monitor in terms of time and resources. So, under certain circumstances, meteorological indices can be used as substitutes for AIs. We discuss meteorological indexes and review SL approaches that are suitable for predicting drought based on historical satellite data. We also include some illustrative case studies. Finally, we will survey rainfall products existing at the web and some alternatives to process the data: from high-performance computing systems able to process terabyte-scale datasets to open source software enabling the use of personal computers.

Keywords: remote sensing, supervised learning, meteorological index, wavelet

Climate change is shifting the rainfall patterns and increasing the severity of droughts and floods around the Earth. Australia [1], Europe, and the rest of the continents have been affected by a number of major drought events [2]. In 2018, drought and heat waves reduced harvests up to 40–50% in some countries of

Drought is by far the Earth's most costly natural disaster and can have widespread impacts [4]. Globally, it is responsible for 22% of the economic damage caused by natural disasters and 33% of the damage in terms of the number of people affected [5]. Though average yields rose steadily between 1947 and 2008, there is no evidence that relative stress tolerance has improved [6, 7]. Therefore, until breeding programs develop adapted germplasm, drought forecasting will be

Meteorological Drought
