Aerial River Management for Future Water in the Context of Land Use Change in Amazonia

Humboldt-Universität zu Berlin – Geographisches Institut

Aerial river management for future water in the context of land use change in Amazonia

Dissertation

zur Erlangung des akademischen Grades Doctor rerum naturalium (Dr. rer. nat.) im Fach Geographie

eingereicht an der Mathematisch-Naturwissenschaftlichen Fakultät der Humboldt-Universität zu Berlin

von M.Sc., Wei Weng

Präsidentin der Humboldt-Universität zu Berlin Prof. Dr.-Ing. Dr. Sabine Kunst

Dekan der Mathematisch-Naturwissenschaftlichen Fakultät Prof. Dr. Elmar Kulke

Eingereicht am: 02.09.2019 Tag der mündlichen Disputation: 10.12.2019 Gutachter/innen: 1. Prof. Dr. Tobia Lakes 2. Prof. Dr. Fernando Jaramillo 3. Dr. habil. Ilona Otto

Dedicated to the lost Siaolin Village. (Typhoon Morakot, 8. 8. 2009). Abstract

This thesis focuses on aerial rivers, the preferential pathways of moisture flows in the atmosphere. It aims to provide knowledge for their integration into current paradigms of water management. Recharged by evapotranspiration and discharged by precipitation, aerial rivers connect the atmosphere, the water system and the land system. This work focuses on Amazonia and adjacent areas, which collectively experience some of the most rapid land use change on the planet. This thesis further develops three key aspects (theoretical, technical, and societal) of knowledge concerning aerial rivers. From a theoretical aspect, it advances the knowledge of connection between aerial rivers and surface rivers. Using a moisture tracking algorithm, the impact from upwind land use change via aerial rivers on target regions’ runoff reception is quantified. Spatial heterogeneity in the influence of the precipitationshed on runoff reception of the target region is found, implying a need to determine the most influential precipitationshed (MIP) for management purposes. From a technical aspect, the work demonstrates an aerial river management example for a rapidly growing city. It is shown that strategic reforestation in the MIP can increase both rainfall and runoff reception and secure 22%-59% of a rapidly growing city’s future water needs. Finally, the work explores the societal aspect of aerial river management. Socio-technical regimes along aerial rivers contributing to extreme events of mega-drought were traced through the social scientific method of multi-level perspective. It reveals that the source regimes such as land policy and market interventions in Brazil and Bolivia govern remote Colombian energy regimes and their transitions through aerial rivers. These findings show that aerial rivers are relevant and viable options for the development of future water resources - including hydropower - but their management will require a holistic consideration of the various societal interfaces as they cross jurisdictional boundaries and sectors.

Zusammenfassung

Diese Arbeit beschäftigt sich mit Aerial Rivers („luftgetragenen Flüssen“), welche die bevorzugten Wege des Flusses von Feuchte in der Atmosphäre darstellen. Ziel ist es, die Voraussetzung für deren Integration in aktuelle Paradigmen der Wasserwirtschaft zu schaffen. Aerial Rivers, die durch Evapotranspiration mit Feuchtigkeit aufgeladen und durch Niederschlag entladen werden, verbinden die Atmosphäre mit den aquatischen sowie den terrestrischen Systemen. Im Mittelpunkt der Arbeit stehen Amazonien und die angrenzenden Gebiete, Regionen der Erde, in denen sich derzeit der Landnutzungswandel mit am schnellsten vollzieht.

Bei der Erforschung von Aerial Rivers werden in dieser Arbeit im Wesentlichen drei Aspekte behandelt. Aus theoretischer Sicht wird das Wissen über die Verbindung zwischen Aerial Rivers und Oberflächenflüssen erweitert. Mit Hilfe eines Algorithmus zur Verfolgung des atmosphärischen Feuchtigkeitstransports werden die Auswirkungen von entferntem Landnutzungswandel in Windrichtung auf die Niederschlagsmenge einer Zielregion quantifiziert. Die räumliche Heterogenität des Einflusses der gesamten Quellevapotranspirationsfläche (precipitationsehed) auf die/den empfangene/n Niederschlagsmenge/Oberflächenabfluss der Zielregion wird untersucht und führt zur Identifizierung der „Most Influential Precipitationshed“ (MIP), der für Managementzwecke relevantesten Teilfläche. Der zweite, technische Aspekt verdeutlicht ein Aerial River-Managementbeispiel für eine schnell wachsende Stadt. Es wird gezeigt, dass die strategische Wiederaufforstung im MIP sowohl die Niederschlagsmenge als auch den empgangenen Oberflächenabfluss erhöht und 22%-59% des zukünftigen Wasserbedarfzuwachses einer schnell wachsenden Stadt sichern kann. Drittens untersucht die Arbeit den gesellschaftlichen Aspekt der Aerial Rivers. Sozio-technische Regime entlang der Aerial Rivers, die zu Extremereignissen wie Megadürren beitragen können, wurden durch die sozialwissenschaftliche Methode der Multi-Level-Perspektive (MLP) zurückverfolgt. Es zeigt sich, dass Ursachen wie Bodenpolitik und Marktinterventionen in Brasilien und Bolivien entfernte kolumbianische Energieregime und deren Wandel steuern. Diese Ergebnisse zeigen, dass Aerial Rivers relevant die Optionen für zukünftiges Gewässermangement einschließlich Wasserkraft berühren; ihr Management erfordert jedoch eine ganzheitliche Betrachtung der gesellschaftlichen Schnittstellen über administrative Grenzen und Sektoren hinweg. 2

Contents Abstract ...... 1

Zusammenfassung ...... 2

Contents ...... 3

List of Figures ...... 7

List of Tables ...... 8

Acknowledgements ...... 9

Chapter 1 Introduction ...... 11

1.1 The intangible aerial rivers ...... 12

1.2 Aerial rivers in Amazonia ...... 14

1.3 Moisture tracking tools for investigating the aerial rivers...... 16

1.4 Moisture recycling and aerial rivers ...... 18

1.5 Identifying upwind-downwind pairs along the aerial rivers ...... 22

1.5.1 Tracking the aerial rivers ...... 22

1.5.2 Identifying upwind-downwind pairs using the Most Influential Precipitationshed (MIP) framework ...... 24

1.6 Land use change impact on water availability via aerial rivers ...... 27

1.6.1 Inconclusive impact from upwind land use change on rainfall through aerial rivers ...... 27

1.6.2 Unknown impacts from land use change through the aerial rivers on surface rivers ...... 28

1.6.3 Reforestation competing water?...... 30

1.6.4 Aerial river relevance for land and water management ...... 32

1.7 Aerial river relevance for extreme events ...... 35

1.8 Social components influencing and influenced by the aerial rivers ...... 36

1.9 Dissertation road map and research questions ...... 39

Chapter 2 Aerial and surface rivers: downwind impacts on water availability from land use changes in Amazonia ...... 42

Abstract ...... 43

2.1 Introduction ...... 43

2.2 Methods ...... 47

2.2.1 Outlining aerial rivers...... 47

2.2.1.1 Tracing moisture flow in Amazonia ...... 47

2.2.1.2 Input data ...... 47

2.2.1.3 Structuring the precipitationsheds: the MIPs ...... 49

2.2.2 Modified downwind precipitation by land use change ...... 50

2.2.3 Sensitive pairs of sink and source regions ...... 52

2.3 Results ...... 52

2.3.1 Sensitive sinks and influential sources: water regime shifts by upwind land use change ... 52

2.3.2 Upper bounds for the influences of Amazonian land use change ...... 56

2.4 Discussion ...... 59

2.4.1 Sensitive sinks under pressure ...... 59

2.4.2 Interconnected aerial and surface rivers – spatially different response to land use change . 60

2.4.3 Water conservation hotspots outside of watersheds ...... 61

2.4.4 Managing interconnected surface and aerial rivers crossing boundaries ...... 63

2.4.5 Limitations ...... 64

2.5 Conclusion and outlook ...... 65

Acknowledgements ...... 67

Chapter 3 Aerial river management by smart cross-border reforestation ...... 69

Abstract ...... 70

3.1 Introduction ...... 70

3.2 Material and methods ...... 72

3.3 Study case...... 73

3.4 Results and discussion ...... 74

3.5 A more holistic practice of land-water management ...... 77

3.6 Conclusions ...... 79

3.7 Appendices ...... 80

Appendix A. Selection of the MIP ...... 80

Appendix B. Reforestation potential in the MIP ...... 82

Appendix C. Operational scheme for smart reforestation ...... 84

Acknowledgements ...... 85

Chapter 4 Sustainable transitions following mega-droughts: on landscape development and regime interaction from the multi-level perspective ...... 86

Abstract ...... 87

4.1 Introduction ...... 87

4.2 Tracking trajectories of transition under landscape development ...... 91

4.3 Study event: the 2015-2016 mega-drought ...... 93

4.4 Energy transition in Colombia along with the developing drought landscape ...... 94

4.4.1 Landscape development-Predictive shock ...... 97

4.4.2 Institutional interplay under landscape development ...... 98

4.5 Drought landscape developments ...... 99

4.6 Remote regime influencing Colombian energy transition...... 101

4.6.1 Source regime shapes connected landscapes ...... 102

4.6.2 Conceptualization of remote regime influence ...... 103

4.7 Repercussions for managing power generation in Colombia ...... 104

4.8 Theoretical insights ...... 106

4.8.1 Pre-drought landscape shock ...... 106

4.8.2 Uninformed decisions away from sustainability: remote regime influences ...... 106

4.8.3 Power of the regime influence ...... 107

4.9 Conclusion...... 108

Acknowledgements ...... 109

Chapter 5 Conclusions ...... 110

5.1 General achievements ...... 111

5.2 Answers to research questions ...... 112

5.2.1 Relevance of aerial rivers for current and future land and water practices in Amazonia .. 112

5.2.2 The approach to aerial river management ...... 115

5.3 Repercussions for further developments ...... 119

5.3.1 Recommendations for future decision-making and research ...... 119

5.3.2 Addtional application: a new tool for investigating interactions and feedbacks in coupled human-environmental systems ...... 122

5.3.3 Additional application: cross-border reforestation ...... 122

5.4 Perspective ...... 124

References...... 125

Supplementary 1...... 157

Declarations ...... 166

List of Figures

Figure 1.1 Average atmospheric moisture storage around the globe ...... 13 Figure 1.2 Aerial rivers...... 14 Figure 1.3 Moisture budget of a single box...... 19 Figure 1.4 Aerial rivers and moisture recycling...... 21 Figure 1.5 Rio Piraí catchment...... 34 Figure 1.6 An outreach to the governmental officials...... 34 Figure 2.1 The precipitationshed of the Yurimaguas area...... 50 Figure 2.2 Rainfall dependency on the Amazon basin...... 53 Figure 2.3 The precipitationshed of the sensitive areas...... 54 Figure 2.4 Different land use scenario domains for exploring rainfall and runoff susceptibility of the Ucayali River outlet...... 54 Figure 2.5 Influence of land use (LU) in different spatial domains on runoff and precipitation reception of the target sink ...... 55 Figure 2.6 Spatial patterns in local runoff changes compared to the control state for land use scenarios applied in the Amazon basin...... 59 Figure 3.1 Smart reforestation for water supply in Santa Cruz (conceptual representation)...... 75 Figure 3.A1 The terrestrial precipitationshed of Santa Cruz de la Sierra...... 81 Figure 3.A2 MIP size-contribution relationship for Santa Cruz de la Sierra...... 82 Figure 3.B Reforestation potential in the MIP...... 83 Figure 3.C Operational flow for smart reforestation...... 85 Figure 4.1 Multi-level Perspective on transitions...... 89 Figure 4.2 Typology of landscape development ...... 92 Figure 4.3 The share of Colombian power mix from different sources in 2015...... 94 Figure 4.4 Conceptualized drought related landscape shocks starting with the 2015-2016 mega-drought...... 98 Figure 4.5 Dry season simulated impact of deforestation on forward trajectory precipitation ...... 101

List of Tables

Table 2.1 The specification of the MOD experiments...... 48 Table 2.2 LBA-ECO evaporation data...... 51 Table 2.3 Estimated changes in annual rainfall and runoff over the Ucayali River basin outlet following land use scenarios in different spatial domains...... 56 Table 2.4 Estimated changes in annual rainfall over different regions when applying various land use scenarios in the Amazon basin...... 57 Table 2.5 Runoff estimates in different regions under different land use scenarios...... 58 Table 2.6 Estimated changes annual rainfall and runoff over the Ucayali River basin outlet following land use scenarios...... 63 Table 3.1 Estimated rainfall and runoff changes to the baseline due to smart reforestation ...... 76

Acknowledgements

This work has been made possible under the support and encouragement of many people. Firstly, I am deeply grateful to my supervisor Tobia Lakes. Her strong support with research, career, and life helped me in many critical moments. I owe my deepest gratitude to my mentor Matthias Lüdeke for his sustained support, helpful guidance, and invaluable contributions which were key for the development of this thesis. Special thanks to Ilona Otto and Fernando Jaramillo for undertaking the evaluation of this work. I would like to express my gratitude to Patrick Hostert for being the chair and Cecilie Friis for being a member of my thesis committee. My research was mostly carried out in Potsdam Institute for Climate Impact Research (PIK) at the urban transformation group. I would like to thank the group leader and my second supervisor Jürgen Kropp for advice and support. Discussions with Luís Costa have always been cheerful and insightful. His big ideas have inspired many parts of this thesis. Advice and help from David Milan Landholm Haight and Stefanie Lyn Becker were important throughout the thesis work. I would also like to thank other members of the group and the climate media factory for all the support as well as PIK colleagues in other groups. Without Delphine Clara Zemp’s advice and support, the thesis would not be possible. I would also like to thank Sara Minoli and Oscar Garcia for encouragement and friendship. My research is also hugely benefited from being a member of both the Applied Geoinformation Science group in the Geography department and the Integrative Research Institute for Transformation of Human Environment Systems (IRITHESys). I appreciate the helpful inputs and support from Saskia, Hannah, Dagmar, Florian, Blake, Lisa and other members of the GIScience group. I would also like to express gratitude to the interdisciplinary members of the IRITHESys for enriching my PhD experience tremendously. Special thanks to Kathrin Klementz for her generous help throughout my PhD study. Her great work for keeping IRITHESys a sparkling experience is very appreciated. Discussions with Özge Can Doğmuş have always been illuminating and her advice has been a great help for Chapter 4. I would like to express my special thanks to colleagues on the Agroecosystems and Sustainable Landscapes (ASL) team in the International Center for Tropical Agriculture (CIAT) for a wonderful and inspiring research stay in Colombia, in particular to the group leader Marcela Quintero. Special thanks to Marcela Beltran, Martha Vanegas, Erwan Sachet and Fanny Howland for offering helpful advice on the field work and on the empirical

contents of the thesis. I am grateful for Miguel Romero Sanchez and Leidi Sierra for their support during the field work and I would like to thank other ASL and CIAT colleagues for a great working environment. I would also like to express my greatest gratitude to Marissa Castro Magnani and Giulio Castelli. Without their help, the Bolivian field work will not be possible. Thank you also to my friends and family for love and patience. I have had mental and technical support from Min-Hung, Rachel, Jun-Yi, Yu-Kai, Hoseung, Bo, Adam, Alex, Edwin, Romão, Luis Miguel, Kerstin, and my Taiwanese friends throughout my thesis work. I am particularly grateful for the assistance given by Brendan at the final stage of the thesis. Special thanks to Giorgio for constructive comments and warm encouragement throughout my study. I owe my deepest appreciation to Martin Daniel who enriched the final part of my thesis work with passion. I am thankful for K, who inspired my research career in Environmental Science. Finally, I thank my parents and my brother Jiun the Pineapple for their love, patience and belief in me. Kám-un

Wei Weng Berlin, August 2019

Chapter 1

Introduction

“I believe that the river embraces these people and carries them away.

A river of humanity. The sorrows of this deep river of humanity. And I am part of it.”

-Shusaku Endo, Deep River, translated by Van C. Gessel

1 Introduction

1.1 The intangible aerial rivers

It is estimated that the total amount of water available on the Earth is about 1.5 × 109 km3 (Bengtsson, 2010). The ocean preserves the majority of 1.4 × 109 km3, while ground water composes 1.5 × 107 km3, and rivers discharge 40 × 103 km3 (Trenberth et al., 2007). Compared with other storages, the atmospheric moisture storage is rather small, estimated to be 12.7 × 103 km3 (Trenberth et al., 2007). However, the content of atmospheric moisture storage is more dynamic. Exchanges between it and other storages, namely the precipitation and evaporation flows, are about forty times than the storage volume itself. Every year, exchanges between the ocean surface and the atmosphere in the form of precipitation and evaporation flows reach 386 × 103 km3 and 426 × 103 km3. As for land, exchanges through precipitation and evapotranspiration total 114 × 103 km3 yr−1 and 74 × 103 km3 yr−1, respectively (Trenberth et al., 2011). The considerable amount of moisture provided from atmospheric storage (e.g., rainfall) is the major input of the surface rivers and sub-surface groundwater available for human use.

The atmospheric storage of moisture is not evenly distributed on the planet, nor is it static (Dominguez and Kumar, 2006). There is a clear geographical difference in atmospheric moisture storage. In general, low latitudes hold more moisture than high latitudes and the atmosphere above the ocean has more moisture than that above land (see Figure 1.1; van der Ent and Savenije, 2011). Through the advection by winds, moisture can be transported and redistributed. For example, there is a net transport of moisture from ocean to land globally, estimated at 38 × 103 km3 yr−1, or close to the amount of global river discharge to the ocean, at 40 × 103 km3 (Bengtsson, 2010).

Figure 1.1 Average atmospheric moisture storage around the globe from the ERA-Interim reanalysis (1999–

2008) (van der Ent and Savenije, 2011).

At long climatic time scales (e.g., seasonal or annual), patterns of predominant large-scale moisture transport can be observed (Arraut and Satyamurty, 2009). These preferential pathways of moisture flows in the atmosphere, filamentary or broad, are termed in Arraut et al., (2012) as aerial rivers because of their good symmetry to surface rivers. Aerial rivers collect moisture by way of being symmetrically positioned to surface rivers, and are thus recharged by evapotranspiration and discharged by precipitation. Moisture exchange between the surface and the atmosphere takes place along the aerial river flows. When flowing inland, aerial rivers are sustained by land-surface evapotranspiration. Thus, upwind evapotranspiration input can be influential for downwind precipitation via aerial rivers (Zemp et al., 2017a). Land use change (e.g. deforestation) in upwind regions alters moisture flux into the atmosphere and therefore governs downwind water availability (Bosch and Hewlett, 1982; Farley et al., 2005; Gordon et al., 2005; Sivério et al., 2015). Though they connect systems of land, atmosphere, and water, aerial rivers have not yet been integrated in management of those same systems so far.

Different from their surface counterparts, aerial rivers are normally invisible. The intangible nature of them has resulted in a delayed understanding of aerial rivers compared to the directly observable surface rivers. Since the late 20 century, the development of numerical computation tools in atmospheric studies has enabled more detailed investigation into moisture flows. To date, moisture tracking tools (see section 1.3) can be used to represent these invisible aerial rivers. Despite the ability to trace aerial rivers, there remain knowledge gaps preventing the integration of aerial rivers into management of land,

atmosphere and water systems. Foremost, the relevance of aerial rivers for operation of those systems while accounting for current and future human withdrawal is unclear. This thesis aims to provide a scientific framework to bridge this gap. The thesis will use the rapidly changing Amazonian landscape as the target region to investigate aerial rivers’ role in practices of land and water management in the region. Two overarching questions lead the investigation: how relevant are aerial rivers for current and future land and water practices in Amazonia and what can be the approach of aerial river management when it is relevant?

Figure 1.2 Aerial rivers. They are normally invisible, and can only be seen in certain circumstances. This picture shows the foothills at the East of the Andes, where the aerial rivers veer southward (the left of the picture). (Shot

in Caquetá, Colombia, Wei Weng)

1.2 Aerial rivers in Amazonia

The Amazon River basin hosts the most extensive tropical rainforest in the world. Being one of the most biodiverse ecosystems on the planet, the Amazon rainforest also regulates the regional and global climate through bio-geophysical and bio-geochemical cycles (Nobre et al., 1991; Gedney and Valdes, 2000). For example, the abundant evapotranspiration from the Amazon rainforest contributes up to 48-54% of the regional rainfall (van der Ent et al., 2010; Zemp et al., 2014). In recent decades, the Amazon forest has been under pressure of forest clearing. Brazil, the country with the largest share (60%) of the Amazon rainforest, has

lost over 20% of its rainforest in the last fifty years (INPE, 2017). Among these cleared lands, 80% are used for growing pastures (Veiga et al., 2002). Land use change has become one of the highest priority issues for current and future land management in the region.

Through aerial rivers, land use change at upwind regions influences downwind water availability. However, the relevance of this influence for downwind water availability remains inconclusive. Existing coupled land-atmosphere modelling experiments investigate the impact of past or projected land use change in a designated region (e.g. the whole Amazon basin or a smaller sub-basin) on regional climate and catchment budgets (D’Almeida et al., 2007; Lima et al., 2014; Swann et al., 2015). As land use change modelled in these experiments was not located upwind of the regions where rainfall or runoff change was examined, aerial river relevance regarding the impact of upwind land use change on downwind water availability can hardly be derived from their results. The lack of recognition of upwind-downwind dynamics in previous experiments is possibly due to a lack of scientific tools to efficiently identify upwind-downwind pairs. This gap will be closed in the early stage of this thesis work (see 2. 2. 1) to enable recognition of upwind-downwind roles throughout the thesis’ investigation over the aerial river relevance.

Despite the lack of tools to explicitly identify upwind-downwind pairs, studies have described the course of aerial rivers over Amazonia through observation-based climatic data that includes temperature, specific humidity, wind fields, and surface pressure. The prominent moisture flows in South America are enabled by the trade winds which advect moisture into the continent from the tropical Atlantic. These aerial rivers then veer to the East of the Andes and travel southward to the subtropics (Arraut and Satyamurty, 2009). All year round, Amazonia receives abundant moisture from the tropical ocean through the aerial rivers formed by trade winds. Downwind from the trade wind confluence, the wind speed decreases and the moisture flows broaden and ‘deepen’ into aerial lakes which are richer in precipitable water (Arraut et al., 2012). Of these, the aerial lakes located in the Western Amazon is especially deep. There is a seasonality in the moisture flows through the aerial lakes in this region. From November to March, the moisture flows travel southward and join the year-round moisture flow in the East Andes to subtropics, while in the dry season of July and August, the moisture flows travel northward toward Central America (Arraut et al., 2012). A 15

further identification of upwind-downwind pairs along the aerial rivers requires moisture tracking in which moisture can be tagged and the moisture source can be explicitly traced.

1.3 Moisture tracking tools for investigating the aerial rivers

Since late nineteenth century, analytical methods were developed to investigate moisture content in the atmosphere. In these early studies, the preliminary question was focused on the contribution of local evaporation to precipitation, namely the moisture recycling process. The algorithms, while concentrating on describing moisture recycling, allowed only a limited portrait of moisture flows (e.g. the bulk models; Budyko, 1974, Brubaker et al., 1993; Eltahir and Bras, 1994). Since the end of last century, the development of numerical computation tools to simulate the climatic environment has enabled the description of moisture flows. Given climate variables including wind field, humidity and temperature in different atmospheric pressure levels, forward (the moisture from evaporation to precipitation) and backward tracking (from precipitation to evaporation) of tagged moisture can be performed to illustrate the transportation of moisture in the atmosphere.

Moisture tracking has two major approaches: the Lagrangian (e.g. Dirmeyer and Brubaker, 1999; Dirmeyer et al., 2009; Tuinenburg, 2012) approach and the Eulerian (e.g. Knoche and Kunstmann, 2013; van der Ent et al., 2014) approach. The main difference between the two approaches is the control volume whose moisture budget is calculated during the tracing procedure, thereby associating to either Lagrangian or Eulerian reference of conservation laws. In the Lagrangian frame, moisture budgets of an infinitesimal air parcel are traced following its trajectory in the atmosphere (e.g. quasi-isentropic trajectory, see Dirmeyer and Brubaker, 1999). Changes in an air parcel’s moisture content over space and time throughout its trajectory reflect the evapotranspiration uptake and the precipitation output of the air parcel. The Lagrangian reference applied in moisture studies has the major challenges of poor kinematic and boundary layer process representation which have been gradually improved (Stohl and James, 2004; Sodemann et al., 2008; Sprenger and Wernli, 2015). In the Eulerian frame, moisture budgets of parallelepipeds relative to the coordinating system axes are calculated. Local changes in moisture content are net results of flux through the boundary of the control volumes via advection, precipitation and evapotranspiration.

Moisture tagging in Eulerian algorithms is facilitated by tracers to label moisture (Sodemann and Stohl, 2013) accompanied by limitation of predefined moisture sources (Winschall et al., 2014). Or, it can be enabled by the “well-mixed” assumption (Yoshimura et al, 2004; Keys et al., 2012; van der Ent et al., 2014), which can generate errors in areas with highly sheared wind systems (Bosilovich, 2002; Goessling and Reick, 2013). Recently, this shortcoming has been largely improved by introducing vertical structure of horizontal winds in the algorithm (van der Ent et al., 2013). In this thesis, moisture tracking results of a Eulerian algorithm equipped with improved wind shear representation was used for describing the aerial rivers among the South American continent. Lagrangian algorithms may be applicable to produce results for describing the preferential pathways of moisture flows at the same spatial and time scale, but a considerable amount of tracing parcel runs and computation time is required.

The moisture tracking can be operated online coupled with climate model runs (Bosilovich and Chern, 2006; Goessling and Reick, 2013; Knoche and Kunstmann, 2013) or offline a posteriori by climate input data such as reanalysis data (Dominguez et al., 2006; Dirmeyer and Brubaker, 2007; Sodemann et al., 2008; van der Ent et al., 2010; Zemp et al., 2014) or climate model output (Goessling and Reick, 2011). Online tracking is useful for future projection while coupled with climate scenarios. Offline tracking is more flexible in the input data and may be preferable for historical and present-day studies (van der Ent et al., 2013). Online operation generally performs better in regional and local scale tracking. However, some offline algorithms are able to reproduce the results of online tracking to good extent while saving considerable computation costs. This thesis uses the results from an a posteriori moisture tracking algorithm (WAM-2layers) to describe the long-term preferential pathways of moisture flows in the past decade. The WAM-2layers algorithm was shown in previous studies to agree with complex online tracking results with a high degree of similarity (van der Ent et al., 2013). Moisture tracking results from an experiment (MOD experiment, Zemp et al., 2014) where the WAM-2layers was driven by observation-based data were employed in the thesis to describe the aerial rivers.

1.4 Moisture recycling and aerial rivers

Moisture recycling describes the process in which moisture is recycled during evapotranspiration and precipitation within a domain of interest (Trenberth, 1999). In other words, the moisture recycling process describes that the moisture which is evapotranspirated again becomes precipitation, and that the moisture which is precipitated again becomes evapotranspiration within one given domain. The estimation of moisture recycling can be realized by moisture recycling ratios (Eq. 1.1) that count the extent of precipitation (evapotranspiration) coming from previous evapotranspiration (precipitation). This ratio is scale-dependent on the reference domain (van der Ent and Savenije, 2010). It can be zero when the reference domain only represents a point (no precipitation and evapotranspiration). As the reference area increases, the ratio also grows. Ultimately, when the domain includes the whole globe, the recycling ratio is 1 (global precipitation equals evapotranspiration). The recycling ratio ρvaries between 0 and 1 and is calculated as in (Trenberth, 1999):

ρ = (1.1) 𝑃𝑃𝐸𝐸 𝑃𝑃

where P represents the precipitation in a given area and PE stands for precipitation that sources from evapotranspiration in the same area.

In a designated area, moisture for precipitation can be sourced from (1) previous evapotranspiration moisture recycling, (2) advection from other areas, or (3) original moisture storage. In a long time scale, moisture storage (3) is not relevant. The moisture source for precipitation in a region can then be simplified into moisture recycling and the advection (see Figure 1.3). 𝛺𝛺

= + (1.2)

𝑃𝑃 𝑃𝑃𝐴𝐴 𝑃𝑃𝐸𝐸 ’ + + = + (1.3)

𝐴𝐴 𝑃𝑃𝐴𝐴 𝑃𝑃𝐸𝐸 𝐸𝐸 𝐴𝐴 where means the precipitation that sources from the moisture advected into the region ,

E represents𝑃𝑃𝐴𝐴 the evapotranspiration in the region , and and ’ stand for moisture𝛺𝛺 advected into the region and out of the region , respectively.𝛺𝛺 𝐴𝐴 𝐴𝐴 18 𝛺𝛺 𝛺𝛺

Figure 1.3 Moisture budget of a single box. From a single box model, a region’s moisture input can be either

from moisture recycling (the moisture evapotransipirated from the land or ocean surface of the same location) or

from advection (the moisture transported into the box).

There is contradiction in the usage of the term ‘moisture recycling’ in the literature. In van der Ent and Savenije (2010), the term ‘continental recycling’ was introduced to describe the evapotranspiration of the continent contributing to precipitation in a local area. The ‘continental recycling’ investigated in their study was mostly advected from other places on the continent rather than ‘recycled’ locally. Some later research continued the investigation on ‘continental recycling’ (van der Ent et al., 2014) or used the term ‘moisture recycling’ (Keys et al., 2014; Keys et al., 2017; Keys et al., 2018; Wang-Erlandsson et al., 2018) to refer to moisture transportation that was defined in Trenberth (1999) as advection. For example, moisture recycling was defined in Keys et al. (2014) as “the phenomenon of evaporation traveling through the atmosphere and returning as precipitation downwind”. ‘Downwind’ here is generally understood as the moisture carried by advection in atmospheric physics. The mixed usage of this term did not limit the contribution of the literature to furthering our understanding of the moisture flows. However, the term should be more strictly defined in the

future in order to integrate with other physics-based disciplines such as the atmospheric dynamics community.

Aerial rivers are related to the moisture recycling process regardless of the contradicting definitions in the literature. Aerial rivers are defined in Arraut et al. (2012) as the preferential pathways of moisture flows, which can also be understood as major moisture advection (or ‘moisture recycling’ following definition in Keys et al., 2014) routes in the long-term. The aerial rivers are recharged by evapotranspiration and discharged by precipitation, both of which may be followed by moisture recycling processes (following the definition in Trenberth, 1999). While evaporation recycled from precipitation (evaporation recycling) brings moisture to the atmosphere, precipitation recycling (precipitation recycled from evapotranspiration) brings moisture to the land. Evaporation recycling reloads the aerial rivers with moisture after precipitation. Precipitation recycling takes away the moisture loaded by evapotranspiration. Both may be followed by other rounds of moisture recycling reversing their effects to a smaller extent (Figure 1.4).

The relationship between aerial rivers and moisture recycling defined in Trenberth (1999) can also be explained at a particle-scale perspective. One water particle enters the aerial rivers through evapotranspiration and is advected to the place where it precipitates. More moisture particles join the aerial rivers through evapotranspiration along its way. These particles precipitate throughout the aerial rivers. Some precipitated moisture particles remain at the land becoming runoff while others are evapotranspirated again to the atmosphere through moisture recycling. These recycled moisture particles may locally precipitate again, forming another round of moisture recycling, or they may enter the aerial rivers, being advected to other places (Figure 1.4).

In sum, aerial rivers can be considered as the aggregation of advective flows at a large scale in the long-term. This aggregation of advective flows dynamically exchange moisture with the land surface via evapotranspiration and precipitation, which may be involved with moisture recycling.

Figure 1.4 Aerial rivers and moisture recycling. (a) Evapotranspiration (E) from the land surface adds moisture to the aerial river (A1). A part of its moisture comes back through the first round of moisture recycling where precipitation recycling (PE) takes a part of the moisture back to the land surface. Another moisture recycling round sees evapotranspiration recycled from PE (EPE) bringing moisture back to the aerial river. (b) Precipitation

(P) takes the load away from the aerial river (A2). A part of this load goes back to the aerial river through first moisture recycling round: evaporation recycling (EP). The second moisture recycling round sees precipitation

(PEP) bringing the load again to the land surface from EP.

Throughout the thesis, moisture recycling follows the definition of Trenberth (1999). Only in Chapter 2, moisture recycling was mentioned to describe moisture recycled within the modelling domain of the South American continent which might be considered as advection in other studies targeting smaller areas over the South American continent.

1.5 Identifying upwind-downwind pairs along the aerial rivers

1.5.1 Tracking the aerial rivers

An Eulerian moisture tracking algorithm fed with observation data is used to describe the aerial rivers over South America and to track upwind-downwind pairs for analysis. The WAM-2layers algorithm (see 2.2.1) was used for moisture tracking given its low computation cost and good agreement with other computation tools over South America (Zemp et al., 2014). It operates under the mass balance principle and the well-mixed assumption.

The underlying principle of the WAM-2layers model is the conservation of mass applied to moisture. The continuity equation for moisture at layer k can be written in an Eulerian frame of:

( ) ( ) + + = + (1.4) t x ∂𝑆𝑆𝑘𝑘 ∂ 𝑆𝑆𝑘𝑘𝑢𝑢 𝜕𝜕 𝑆𝑆𝑘𝑘𝑣𝑣 ∂ ∂ 𝜕𝜕𝜕𝜕 𝐸𝐸𝑘𝑘 − 𝑃𝑃𝑘𝑘 𝐹𝐹𝑣𝑣 where represents the atmospheric moisture storage, u and v are the wind speeds in the x

𝑘𝑘 ( ) (zonal) 𝑆𝑆and in the y (meridional) direction respectively. The term is the vertically x ∂ 𝑆𝑆𝑘𝑘𝑢𝑢 ∂ ( ) integrated moisture flux in the x direction, is the vertically integrated moisture flux in 𝜕𝜕 𝑆𝑆𝑘𝑘𝑣𝑣 the y direction. is evapotranspiration and 𝜕𝜕𝜕𝜕 is precipitation at layer k. is the vertical

𝑘𝑘 𝑘𝑘 𝑣𝑣 transport between𝐸𝐸 layers. The term denotes𝑃𝑃 the local change in moisture content.𝐹𝐹 t ∂𝑆𝑆𝑘𝑘 ∂ For a certain region Ω, a well-mixed atmosphere implies that (van der Ent, 2011):

( ) ( ) x y ∂ 𝑆𝑆 𝑣𝑣 = ∂ 𝑆𝑆(𝛺𝛺𝑢𝑢) = (𝛺𝛺) = (1.5) 𝑆𝑆𝛺𝛺 ∂x ∂y 𝑃𝑃𝛺𝛺 ∂ 𝑆𝑆𝑆𝑆 ∂ 𝑆𝑆𝑆𝑆 𝑆𝑆 ∂ ∂ 𝑃𝑃 where S represents the total atmospheric moisture storage, and P is the total precipitation.

is the atmospheric moisture that originates from the region Ω, is the part of the𝑆𝑆𝛺𝛺 precipitation that is originated from Ω. Following the well-mixed assumption,𝑃𝑃𝛺𝛺 moisture can be tagged and traced along the moisture flows.

The WAM-2layers algorithm has a two-layer resolution of the vertical structure. The upper and lower layers are divided by a boundary roughly at the height of 800hPa, which was found to best capture the major wind shear between the upper and the lower atmosphere (van der Ent et al., 2014). The two layers of atmosphere in the model are assumed well-mixed by:

= (1.6) 𝑆𝑆𝑘𝑘 𝑃𝑃𝑘𝑘 𝑃𝑃 𝑆𝑆 where P is the vertically total precipitation and S is the total moisture storage in the vertical. Exchange of precipitation between layers is not allowed in the algorithm. Evapotranspiration only applies to the lower layer; i.e. = 0 at the upper layer.

𝐸𝐸𝑘𝑘 The two-layer approach of the WAM-2layers algorithm was proven to significantly improve the common errors arising from poor wind shear representation in traditional offline moisture tracking studies constrained by the well-mixed atmosphere assumption (Goessling and Reick, 2013; van der Ent et al., 2013). The algorithm, by improving the wind shear representation, is known to reproduce results of three dimensional tracking tools to good extent of similarity while operating at low computation costs (van der Ent et al., 2013; Zemp et al., 2014).

This thesis uses the output of the MOD experiment (Zemp et al., 2014, see 2.2.1) to construct the aerial rivers over Amazonia. In the MOD experiment, observation-based data was served as an input into offline operation of WAM-2layers that produced output matrices listing moisture flows between grid cells (Zemp et al., 2014), which were iteratively used as inputs to describe the aerial rivers in the thesis.

The residence time span of one moisture particle in aerial rivers being evapotranspirated and precipitated differs. On average, one cycle takes eight days (Trenberth 1999, van der Ent and Tuinenburg, 2017). The residence time differs geographically, when the moisture approaching the mountains (e.g. the Andes) tends to have shorter residence time.

A decadal average (2000-2010) of annual/seasonal means of the MOD experiment outputs (with 3-hour time step, see 2.2.1) was used to approximate the preferential pathways

of moisture flows over the long-term, i.e. the aerial rivers. Given a grid cell, the preferential pathways of moisture flows providing moisture from source cells (including the given cell itself from local recycling) along the aerial rivers can be traced. It is worth noting that moisture flows that experienced one precipitation-evapotranspiration cycle are used in this thesis to represent the aerial rivers over the South American continent. Moisture flows that experience two or more precipitation-evapotranspiration cycles (cascading recycling, Zemp et al., 2014) also contribute to the aerial rivers, but with minor influence regarding contribution to a given place’s precipitation. Zemp et al. (2014) estimated that cascading recycling contributes about 9-10% total moisture inflows in the South American continent. Given its an-order-smaller influence, the provision from cascading recycling will not much affect the identification of upwind-downwind pairs through representation of aerial rivers by moisture flows experiencing one evapotranspiration-precipitation cycle. However, the aerial river relevance investigated through this representation will be a slight underestimation.

1.5.2 Identifying upwind-downwind pairs using the Most Influential Precipitationshed (MIP) framework

Keys et al. (2012) introduced the concept of precipitationshed which can be used to describe upwind regions of a given region. Defined as the upwind surface areas providing evapotranspiration to a specific area’s precipitation (Keys et al., 2012), the ‘precipitationshed’ concept however, provides limited indication for the identification of upwind regions in practice. This is due to both the ‘probabilistic’ and ‘hitched size-importance’ nature of the precipitationshed. For identifying upwind regions for quantitative analysis of land use change influences delivered downwind through the aerial rivers, a method for elaborating the precipitationshed is necessary.

The nature of aerial rivers, apart from surface rivers, is probabilistic instead of deterministic (Keys et al., 2012). Due to the fact that the moisture flows change direction according to often-changing wind direction, the upwind regions providing moisture to the aerial rivers are more dynamic than the upstream catchments along the surface rivers. Unlike upstream catchments that can be outlined by the land topography, upwind regions cannot be determined following any obvious division. Similar to the identification of the aerial rivers,

by tracing preferential pathways of moisture flows a ‘core-precipitationshed’ (Keys et al., 2014) representing the upwind regions predominantly providing moisture for precipitation in a given downwind region, over the long term, can be identified. This aspect will be addressed by using averaged decadal climatic data as means to illustrate the preferential pathways of moisture flows at a climatic time scale (annual and seasonal).

In addition, there is a hitch between size and importance within upwind regions. This makes difficult a straightforward selection of precipitationshed for investigating land use change influences through the aerial rivers even when the core-precipitationshed is represented. A dilemma is that when selecting a smaller area for analysis, the collective contribution from these areas in downwind precipitation is often too low to be considered. At the resolution of current moisture tracking tools, individual upwind point source has normally too little contribution to a given downwind region’s precipitation to be considered a significant contributor. Take the Caquetá region for example, where the averaged individual grid cell influence on the South American continent was smaller than 0.1% of its precipitation. On the other hand, a larger influence on a given area’s precipitation usually requires a selection of an extensive area, which would limit effective management. For example, the precipitationshed which controls 70% of Northern Pakistan’s precipitation can be traced spanning Scandinavia to Madagascar (Keys et al, 2012). This expansive precipitationshed, though contributing a considerable amount of moisture, represents a management effort for a downwind region that is hardly realistic, as land and water management often takes place at a finer (e.g., national or local) scale.

A scientific method to select a meaningful size precipitationshed will be a prerequisite to quantitatively investigating upwind influence, and is crucial for furthering the understanding of aerial river relevance. The approach taken in this work was inspired by an actual heterogeneity in upwind-downwind connection (Koster et al., 2004; Seneviratne et al., 2006; van der Ent et al., 2010; Keys et al., 2014) which implies some upwind regions are more important contributors than others for a given downwind region. It also means that representative influence does not necessarily mean selecting large areas, but rather selecting the ‘hotspots’. In the thesis, the concept of precipitationshed will be elaborated by exploring

the heterogeneity in the extensive upwind regions’ influence. A management-focused framework of the “Most Influential Precipitationshed (MIP)” will be introduced.

The MIP framework is the selection of important upwind moisture contributors. By inclusion of upwind hotspot areas, the MIP represents the smallest area providing moisture for an extent of precipitation in a given downwind region. The MIP framework is management-oriented while the inclusion of the most important contributors implies a ‘smaller, better’ principle, regarding the fact that managing larger areas usually means a rise in costs. However, the MIP framework enabling selection of a rather small area with enough importance also provides basis for investigation over the significance of land use change significance in a given downwind region’s water availability through the aerial rivers. This will be carried out in Chapter 2.

Threshold selection facilitates the determination of the MIP. Two approaches for obtaining the MIP threshold are shown in this thesis. The usage of either should correspond to different management purposes. Firstly, a threshold can be a given extent of moisture contribution to the target downwind region. This is similar to the threshold selection of precipitationshed (Keys et al., 2014), but the MIP framework selects for the smallest area to achieve this given extent of contribution (see 2.2.1). This approach is ideal for determining the MIP for moisture conservation. On the other hand, the threshold of the MIP can be derived from the pattern of moisture contribution among the upwind regions for the inclusion of the most important contributors in the MIP. For example, the threshold can be a mathematical breaking point where moisture contributors’ importance decreases rapidly (see 3.7). This approach can be useful for land planning to manage downwind water. In any case, thresholds for determining the MIPs should rule out unimportant contributors, minimize the actual size of the MIPs, and enable identification of remotely strong contributors. Management relevance of the MIP will be further explored in Chapter 3, where a case of aerial river management for a city by land planning in the MIP is introduced and discussed.

1.6 Land use change impact on water availability via aerial rivers

With the tools to describe the aerial rivers and the upwind-downwind roles, this thesis will address the question of aerial river relevance for land and water management in Amazonia. The primary question it asks will be: what is the impact from rapidly changing land use on water availability in the downwind regions through the aerial rivers? The moisture tracking algorithm and the MIP framework will allow a quantitative investigation of it.

Targeting the impacts on water availability, this study will analyze the changes in annual rainfall and runoff in downwind regions corresponding to upwind land use change. The work will select one of the regions most dependent on moisture flows in Amazonia as the target region to determine the degree of influence from aerial rivers. Land use change scenarios in upwind regions of the target region will be applied to quantify the changes in both precipitation and runoff reception in the target region. A range of scenarios will be used to give a spectrum of land use change influence on downwind water availability.

1.6.1 Inconclusive impact from upwind land use change on rainfall through aerial rivers

Impacts from land use change on precipitation in downwind regions, though widely recognized, had not yet been explicitly quantified in moisture flow research. Studies have identified regions that rely on moisture flows for precipitation (Trenberth 1999, van der Ent et al., 2014), or conditions of moisture input dependency between countries (Dirmeyer et al., 2009; Keys et al., 2017). Their results imply that land use change which alters moisture provision in the upwind regions might have considerable influence on downwind regions’ precipitation. However, it was not quantitatively determined how much upwind land use change could influence downwind precipitation. This is likely because of a lack of tools for identifying suitable size of precipitationshed for analysis (as discussed in 1.5.2).

Though not considering upwind-downwind roles, modelling and empirical studies have acknowledged deforestation’s influence on Amazonia rainfall through atmospheric circulation (Nobre et al., 1991; Costa and Foley, 2000; D’Almeida et al., 2007; Spracklen and Garcia-Carreras, 2015). A majority of the literature found that deforestation generally leads to 27

precipitation decrease, while relatively fewer studies found rainfall increased after deforestation. The increase in these studies were attributed to newly increased land surface heterogeneity enhancing local convection (Pielke, 2002; D’Almeida et al., 2006; D’Almeida et al., 2007). Lawrence and Vandecar (2015) have further explored a tipping point dynamic, in which the precipitation decrease results from deforestation’s lower moisture provision surpassing the effect of increased convective rain due to new land surface heterogeneity from deforestation. The latter ‘deforestation breeze’ is known to be decreasing according to deforestation patch size (Saad et al., 2010). Other processes, e.g. changed albedo (Dirmeyer and Shukla, 1994), root depth (Kleidon and Heimann, 2000), and surface roughness (Khanna and Medvigy, 2014; Khanna et al., 2017) that would also influence climate response to deforestation will be discussed in 2.4.5.

Though most studies agree on decreased rainfall from deforestation beyond the tipping point (Lawrence and Vandecar, 2015), it remains inconclusive how precipitation decreases corresponding to the scale of deforestation. Some studies find a non-linear relationship, while others find a linear relationship (Spracklen and Garcia-Carreras, 2015). It is worth noting that most of this research does not recognize the varying strength of upwind-downwind connections along the aerial rivers. Through quantitatively investigating the influences from upwind land use change on precipitation through the aerial rivers explicitly, this thesis will also explore how the heterogeneity in precipitationshed contribution to downwind precipitation can contribute to the still-debated relationship between precipitation response to deforestation in empirical and model studies.

1.6.2 Unknown impacts from land use change through the aerial rivers on surface rivers

The impacts on runoff reception in the downwind regions from land use change in upwind regions via the aerial rivers will also be quantitatively investigated.

Generally, land use change is known to change runoff. Deforestation into pastureland and cropland decreases the evapotranspiration uptake and lowers root depth and density resulting in lower biological water demand (Bosch and Hewlett, 1982; Gordon et al., 2005; D’Almeida et al., 2007; Lathuillière et al., 2012; Coe et al., 2013; Silvério et al., 2015;

Panday et al., 2015). In a catchment budget, the lower water uptake of moisture from the catchment leads to an increase in runoff received by the downstream region (D’Almeida et al., 2007; Coe et al., 2009; Hayhoe et al., 2011; Panday et al., 2015). However, through moisture circulation, the decrease in evapotranspiration also results in precipitation decrease and can compensate the effect of lower moisture uptake (D’Almeida et al., 2007; Davidson et al., 2012; Stickler et al., 2013). The offsets between these two effects with climate variability (Malhi et al., 2008, Coe et al., 2013) has led to diverse results of runoff changes in modelling studies and empirical research examining deforestation impacts on runoff in Amazonia. Some studies report increases after land use change (Dirmeyer and Shukla, 1994; Lean and Rowntree, 1997; Kleidon and Heimann, 2000, Hayhoe et al., 2011, Panday et al., 2015) while others find decreases (Nobre et al., 1991; Henderson-Sellers et al., 1993; Hahmann and Dickinson, 1997; Voldoire and Royer, 2004; Stickler et al., 2013). A partitioning of the different effects will be crucial to understand the hydrological impact that land use change can cause. However, the previous studies mostly investigate runoff changes from land use change within catchments that generate runoff. Thus, their results usually include catchment responses, which can be geographically different, and can hardly give specific information over how and how much upwind land use change can influence downwind runoff through aerial rivers.

The runoff responses to upwind land use change have not been investigated explicitly. This could stem from the difficulties with selection of upwind areas as discussed in 1.5.2. Another problem for investigating aerial river influence on runoff is that while deforestation areas include upstream catchments, the runoff response can be from the catchment rather than from the aerial river, meaning further partition will be necessary.

To quantitatively investigate aerial river influence on the downwind runoff, the MIP framework will be applied for identifying the upwind area. To explicitly investigate runoff response to aerial rivers, a river outlet of a sub-catchment of the Amazon River (the Ucayali River catchment) which has an MIP that is separated from its upstream catchment will be chosen as the target area. This allows investigation over impacts from the land use change in the MIP on the runoff received by the target region through the aerial rivers. Another reason for the selection of the river outlet of Ucayali catchment as the target region was that this 29

sub-catchment contains the area most dependent on aerial rivers for its precipitation. This will enable the examination on the aerial river’s relevance for water availability through those areas that are potentially most impacted. Furthermore, impacts from land use change in the target area’s upstream catchments, as well as from the MIP of the catchments (upwind regions that provide moisture to the upstream catchments), on the target region’s runoff reception will be investigated and compared to explore the interrelation between the aerial rivers and the surface rivers for a more holistic understanding and management of the hydrological cycle.

1.6.3 Reforestation competing water?

Restoring degraded forests and cleared lands is a prominent issue for land planning and management in Amazonia. Planned reforestation’s influence through the aerial rivers on downwind water availability will be explored following a management-oriented perspective.

Reforestation improves biodiversity and ecosystem integrity (Jackson et al., 2005; Bremer and Farley, 2010). Presenting multi-benefits, plantations including reforestation and afforestation have become one of the most active natural-based (Cohen-Shacham et al., 2016) and economically viable solutions to mitigate global climate change from rising atmospheric carbon concentration (Don et al., 2011; Post and Kwon, 2000; Bradshaw et al., 2013; CBD, 2010; UNFCCC, 2013). Under the framework of the Bonn Challenge (http://www.bonnchallenge.org/content/challenge), there is a global aim of restoring 350 million hectares of forests by 2030 to combat climate change and preserve biodiversity. The Initiative 20×20 (https://www.wri.org/our-work/project/initiative-20×20) further downscales this global aim into Latin American countries’ shared target of reforesting 20 million hectares by 2020. Despite the top-down target for reforestation, local implementation can be difficult due to the undesirable effects of the reforestation projects. A major issue arising from reforestation is the compromise of streamflow received in downstream areas (Jackson et al., 2005; Egginton et al., 2014; Bryan et al., 2015; Connor et al., 2015).

Plantations increase water uptake from the soil to the atmosphere compared with grasslands, croplands or pasturelands (Le Maitre et al., 1999; Zhang et al., 1999). Altering

leaf area, root density and depth, and stomatal responses, tree plantations enhance interception, evaporation and transpiration processes, and thus reduce moisture released to runoff processes in the catchment budget (Trimble et al., 1987; Duncan, 1995; Vertessy, 1999; Farley et al., 2005; Brown et al., 2007; Ouyang et al., 2013). In dry areas, semi-arid areas or areas facing water scarcity, the runoff decrease from the reforestation project can sometimes become a considerable externality (Jackson et al., 2005; Schrobback et al., 2011, Nordblom et al., 2012; Connor et al., 2015), but in large catchments or regions rich in fresh water the consequences are relatively small (van Dijk et al., 2007). Nevertheless, the water supply trade-off of reforestation has limited the implementation of previous reforestation projects (Kruger et al., 2008) and even triggered political tensions (Cao and Zhang, 2015). It is true that reforestation brings both desirable effects, such as carbon sequestration (Bradshow et al., 2013; Bryan et al., 2014), flood regulation (Dittrich et al., 2019), stream purification and slope stabilization (Jackson et al., 2005), as well as undesirable effects such as competing lands for food supply (Bryan et al., 2014; Connor et al., 2015) and reduction in water and sediments supply (Egginton et al., 2014; Bryan et al., 2015; Ouyang et al., 2013). Thus, the strategic selection of reforestation sites “at the right place for a clear function”, where benefits outweigh costs (Calder, 2007; Creed and van Noordwijk, 2018), is crucial.

Among the effects brought by reforestation, one often disregarded effect is that increasing evapotranspiration, which is considered water loss in the catchment budget, is an important input for the aerial rivers. An increase in the moisture loading of aerial rivers is likely to contribute to more precipitation in the downwind regions. This aspect has been so far neglected, as previous studies mostly focused on the impact of reforestation on runoff within the catchment where the reforestation happened or was simulated. Using moisture tracking and the MIP framework, reforestation’s impact on downwind water availability via the aerial rivers will be quantitatively investigated. In Chapter 3, the MIP framework will be used to trace the most influential upwind regions of a rapidly growing city - Santa Cruz de la Sierra, the economic capital of Bolivia. Based on an existing reforestation potential assessment (Potapov et al., 2011), reforestation sites where climate condition and socio-economic environment allows for reforestation will be identified in the influential upwind regions. The influence of reforesting those sites on Santa Cruz de la Sierra’s water

availability will be quantitatively examined. The finding of how reforestation influences water availability through the aerial rivers will contribute to the existing debate of carbon-water tradeoff (Jackson et al., 2005; Egginton et al., 2014; Bryan et al., 2015; Connor et al., 2016) and will enable a more integrative perspective for strategic selection of reforestation sites in order to bring the largest benefits which also outweigh costs (Calder, 2007).

1.6.4 Aerial river relevance for land and water management

In order to explore the aerial river relevance on water management, the impacts of feasible reforestation in the influential upwind region (smart reforestation, see Chapter 3) on downwind water availability will be compared with the city’s growing freshwater demand. Santa Cruz de la Sierra is an inland city with rapidly growing population, having an annual growth rate of 3.7% between 1992 and 2012 (Trohanis et al., 2015). The city currently relies on groundwater resources which were found to be degrading in both quality and quantity (Morris et al., 2003). In addition, more frequent occurrence of severe droughts in the region is projected by modelling studies (Marengo et al., 2011; Jimenez-Munoz et al., 2016; Erfanian et al., 2017). These factors have intensified the water stress of the economic capital of Bolivia (Castelli et al., 2017).

Additional water needed to secure current levels of water consumption per capita will be calculated using recent data on water consumption, population statistics (INE, 2017a; INE, 2017b), and population projections implied by different Shared Socioeconomic Pathways (“SSP”; Jones and O’Neill, 2016). By assuming constant withdrawal relative to total available water resources, the investigated changes in annual precipitation and runoff from smart reforestation will be compared with Santa Cruz’s growing demand in order to explore the implications of the change for the city’s water management.

Field work was carried out in July 2018 to further explore the aerial river’s integration into local land and water management and practices. The field work included surveying the water use of the city of Santa Cruz de la Sierra and its peri-urban areas, and reaching out to the city’s water and land governing bodies to discuss research results of smart reforestation

projects in the area. Surveys on local usage of water helped to interpret quantified water increase from smart reforestation and to understand vulnerabilities that were not documented. For example, the upstream catchment of the city situates poor villages where the withdrawal of the untreated water is not documented in official statistics. Unlike the core part of the city receiving full coverage of piped water, the poor villages along the Rio Piraí withdraw water directly from the stream (Figure 1.5), which exposes them to the threat of pollution and fluctuations from climate change which may bring more frequent droughts.

An outreach to the city’s water governance bodies was conducted to explore smart reforestation’s integration into current land and water management. Aspects from local practitioners were gained. During a visit to the Authority for the Survey and Social Control of Forests and Land, (Autoridad de Fiscalización y Control Social de Bosques y Tierra, ABT, Figure 1.6), the officers gave an account of the different types of reforestation projects currently taking place in Bolivia and pointed out the failures of previous ones caused by different conflicts of interest (competition between forest and agricultural land, for example). This has inspired discussion over the trade-offs of smart reforestation in this thesis work and will be detailed further in Chapter 3. After consulting the practitioners regarding avoiding sites with potential conflicts of interest, and examining the legal basis for the implementation of the reforestation measures (see 3.5), a multifaceted proposal on smart reforestation was organized into a policy brief and delivered to the authority of Santa Cruz de la Sierra (see Supplementary 1). This policy brief on smart reforestation to enhance the future water supply of Santa Cruz de la Sierra was approved by the director of Natural Resources of the Santa Cruz government and is currently pending implementation.

Figure 1.5 Rio Piraí catchment. This is the upstream catchment of Santa Cruz de la Sierra which collects water from the aerial river to the city after smart reforestation. (Shot in Santa Cruz, Bolivia, Wei Weng)

Figure 1.6 An outreach to the governmental officials. Meeting with the General Management Director of Forests and Land, Marco Antonio Condarco Iglesias (middle), and the Director of Integral Development of Forests and

Land, Thelmo Muñoz Rodriguez (left) in the Authority for the Survey and Social Control of Forests and Land,

ABT, Santa Cruz de la Sierra 5/7/2018. (Shot in Santa Cruz, Bolivia, Marissa Castro Magnani)

1.7 Aerial river relevance for extreme events

In addition to water availability, modelling studies have suggested aerial rivers are linked to extreme phenomenon such as droughts and floods (Eltahir and Bras, 1996; Dirmeyer and Brubaker, 1999; D’Odorico et al., 2013; Bagley et al., 2014; Wei et al., 2016; Miralles et al., 2019; Herrera-Estrada et al., 2019). Among those extreme events, droughts cause damages to livelihoods as well as ecosystems dependent on land and water. For example, the mega-droughts in South America (2005, 2010, 2015-2016) had left communities without basic water access and led to substantial agricultural losses (Marengo and Espinoza, 2016; FAO, 2015; OCHA, 2016).

The understanding of mechanisms that drive droughts is still being developed (Wood et al., 2015; Berg and Sheffield, 2018; Herrera-Estrada et al., 2019), but some mechanisms are known more certainly to contribute to droughts. In Amazonia, droughts are known to be regulated by the natural variability of El Niño South Oscillation (ENSO), where the positive ENSO conditions are associated with negative rainfall anomalies (Grimm et al., 1998; Marengo, 1992; Marengo, 2004; Marengo et al., 2008). Additionally, the warmer-than-normal Tropical North Atlantic Ocean is cited as having driven the 2010 mega-drought in the region (Marengo et al., 2011). More recently, several studies have suggested that anthropogenic forcing, including human-induced warming and deforestation, has contributed to drought events (Moore et al., 2007; Grimm 2010; Lee et al., 2011; Bagley et al., 2014; Alves et al., 2017; Erfanian et al., 2017). Aerial rivers, including both moisture recycling and moisture transport, are known to be the mechanism by which deforestation (or previous droughts) that reduces upwind moisture can result in drought conditions in downwind areas (Sheffield et al., 2014; Bagley et al., 2014; Erfanian et al., 2017; Herrera-Estrada et al., 2017; Roy et al., 2019; Herrera-Estrada et al., 2019).

Potentially because that contribution of deforestation to drought via the aerial rivers has only been identified more recently, there have not been any mitigation-oriented strategy to manage the intensification of drought through the aerial rivers, despite Amazonian countries’ common interest (UNCCD, 2017) to combat more severe and frequent droughts (Marengo et al., 2011; Jimenez-Munoz et al., 2016; Erfanian et al., 2017). To develop a more

comprehensive management of deforestation with regard to its influence on drought via aerial rivers, this thesis will use an inter-disciplinary perspective to analyze the socio-technical components interlinked with this mechanism (Chapter 4).

1.8 Social components influencing and influenced by the aerial rivers

The aerial rivers connect systems that foster changes in the upwind regions and that receive changes in the downwind regions. For effective aerial river management, capturing the social components of the systems that send and receive changes is essential and strategic.

From a coupled-human environmental system (Turner et al., 2003; Folke et al., 2005) perspective of land system science (LSS), land use change is an overall result of various socio-economic drivers’ influences (Ringfuss et al., 2004; Turner et al., 2007; Harbel et al., 2007; Meyfroidt et al., 2010), e.g. land use policies, or global or regional markets of agricultural products. To manage water availability and extreme events influenced by upwind land use change, the drivers that govern the upwind land use change are key. The dynamic of these drivers indicates development of upwind land use change that will affect downwind regions. For strategic aerial river management, they are the fundamental objects for intervention (Nepstad et al., 2006; Laurance, 2007; Barona et al., 2010; Soares-Filho et al., 2016).

Through the aerial rivers, the influence from the upwind systems is delivered and experienced by the systems downwind. Here, this can be understood as the sender (upwind systems) and the recipient (downwind systems) of the impact flows. The societal factors of the recipient systems such as demographic composition of the community, governance quality, gender (in)equality, and access to education, can contribute to the vulnerability of the systems and determine the reaction of the system to the changes delivered by the aerial rivers (Thomalla et al., 2006). Furthermore, the human-environment systems receiving changes are not static, their response to the change can alter subsequent responses to change, and how change is received. More importantly, through socio-economic coupling (Folke, 2005; Liu et al., 2007; Eakin, et al., 2014; Friis et al., 2016), the response of the recipient systems may provide feedback to the sender systems. This is especially important for managing the

recurring changes received by downwind systems from upwind systems following a stable land use change trend. The agency of the recipient systems can therefore be crucial for managing the aerial river influences with regards to the feedback mechanisms.

The governance of various drivers of land use change in Amazonia has been explord previously in the literature, and relevant findings from this literature will be incorporated into the thesis’ discussion on the interlinked social components of upwind and downwind systems (See 4.6.1). However, the qustion of downwind systems’ agency under aerial river influence has not yet been investigated.

Various inter-disciplinary frameworks consider the response of recipient systems to impact flows, such as the heuristic approach of the telecoupling framework (Liu et al., 2013; Eakin, et al., 2014; Friis et al., 2016), which stresses the process in which an initial change impacts recipient systems and is mediated by existing interactions between sender and recipient systems, forming feedback. The social ecological system (SES, Folke, 2005; Ostrom, 2009) approach describes impact loops between social and ecological subsystems and allows narratives on the feedback within systems. Keys and Wang-Erlandsson (2018) used the SES approach to analyze social components linked to moisture flows, but the loops from downwind systems to upwind systems selected in their analysis were weakly linked to the initial impact flows sent from the upwind (decreasing water availability in their case). This limited the description of emergent outcomes and the function of feedback mechanism in the SES approach they attempted to establish. Application of telecoupling heuristic approaches and that of SES approaches have one common challenge, namely, the need to define system division spatially (for telecoupling) or functionally (Friis et al., 2016; as ‘tier’ in McGinnis and Ostrom, 2014). A sharp separation of systems is not usually applicable in the interconnected human-environmental systems and can be merely an analytical choice that generates ambiguity in other temporal and spatial scales (Friis et al., 2016). Furthermore, a pre-definition of systems can limit the chance to detect emergent components of the system during the analytical process.

In order to understand the social components interlinked with the aerial river process and the interactions among them, a framework that allows for the structural conceptualization 37

of coupled human-environment systems that enables analysis of interaction will be essential. Inspired by the aim to examine relatively unexplored downwind reactions to influence from aerial rivers, this thesis will utilize the multi-level perspective developed from transition studies as a framework. ‘Transition’ describes the structural changes altering configurational systems of a society such as agri-food, energy, or other sectors. These structural changes are referred as socio-technical transitions or simply transitions (Rip and Kemp, 1998; Geels, 2004). The multi-level perspective (MLP) is designed to explain transition under three analytical levels of society from the extent of structuration (Geels, 2002). These levels are: landscape, regime, and niche (from most to least structuration). The landscape level describes “deep structural trends” (Geels, 2002). It is the broad backdrop of society, including historic, environmental and cultural settings in which the transitions take place (Rotmans et al., 2001; Geels 2011). The regime level consists of “a coherent configuration of technological, institutional, economic, social, cognitive and physical elements and actors with individual goals, beliefs or values” (Holtz et al., 2008). The niche level is where new innovations arise to challenge the regime (Kemp et al., 1998; Geels and Schot, 2007; Markard and Truffer 2008). A transition happens when a new regime replaces an old regime through the interactions between levels, such as landscape development that pressures a regime, creating a ‘window of opportunity’ (Geels, 2002; Geels and Schot, 2007; Geels, 2011). The levels of the MLP allow tracing of the sender systems leading to recipient transitions through operation of level interactions. Categorized by structuration extent, analysis following the MLP can avoid defining a division of systems that rules out potential interlinked components whose detection is also the target of the study. Thus the MLP will be utilized as the framework to enable the thesis’ investigation on coupled human-environment systems along the aerial rivers and the interactions within the systems.

The thesis will use the 2015-2016 mega-drought in Amazonia the intensity of which was known to be a consequence of the aerial rivers (Marengo and Espinoza, 2016; Erfanian et al., 2017; Marengo et al., 2018) - as the target case. This thesis will trace the downwind transition corresponding to the mega-drought and explore the interactions between upwind and downwind human-environmental systems using the MLP framework, which has been proven to be a valid process for explaining disaster-related transitions (Becker and Reusser,

2016). The analysis will be conducted by tracing the upwind regime and other regimes governing landscape developments (including drought hazard) that trigger and influence downwind transitions. In addition, the new downwind regime’s interaction with the regimes governing its transition, including the upwind regimes, will be examined to understand the feedback mechanisms. Via the analysis, the social components interlinked with the aerial rivers will be detected and their function in the systems will also be understood following the analysis enabled by the MLP framework. The traced example will be conceptualized for a general understanding of upwind-downwind interactions. The repercussions for managing both the transition and the aerial rivers will also be discussed (Chapter 4).

1.9 Dissertation road map and research questions

This thesis aims to provide a scientific framework to bridge the knowledge gap of unclear relevance of aerial rivers for land and water management. The rapidly changing Amazonian landscape will be used as the empirical research area for the investigation throughout the thesis. The thesis will focus on solving two overarching questions: how relevant are aerial rivers for current and future land and water practices in Amazonia, and what should be the approach of aerial river management?

To answer these two questions, the thesis will begin by addressing the quantification of aerial river influences following research questions of:

Q1 What is the extent that upwind land use change can influence downwind precipitation?

Q2 To what extent does land use change influence downwind runoff through the aerial rivers?

Q3 What is the influence of reforestation projects on water availability in the downwind regions through the aerial rivers?

Q4 What does the influence of land use change on downwind water availability imply for land and water management?

To address the research questions, in Chapter 2, moisture tracking results from the WAM-2layers algorithm will be used to describe the aerial rivers over Amazonia. Then, a management-focused framework of the “Most Influential Precipitationshed (MIP)” will be introduced to facilitate quantitative analysis of the influence of upwind land use change delivered downwind through the aerial rivers. Because that the MIP collects the most influential upwind regions, it will allow the estimation of ultimate land use change impact through the aerial rivers, which will then be used to discuss the relevance of aerial rivers for downwind water availability. Different land use scenarios associated with dominant land use types in Amazonia will be used in the analysis, to provide a spectrum of quantitative potential land use change influence on downwind precipitation (Q1) and runoff (Q2).

In Chapter 3, the MIP will be further used to indicate strategic sites of reforestation for optimizing moisture delivery to a given downwind area, as well as to enable quantification of the increase in water availability in the downwind area. Hypothetical reforestation in a rapidly growing city’s MIP, and in sites where the feasibility has been confirmed in literature, will be used to examine the benefits of land management to provide the city’s water availability via the aerial river (Q3). The rapidly growing city (Santa Cruz de la Sierra)’s current and future water challenge will be estimated and compared with the water availability increase from strategic reforestation, to understand the relevance of aerial rivers for managing water resources of one of the fastest growing city in the world (Q4).

This thesis will then explore the potential of integrating aerial rivers into land and water management following the research questions:

Q5 How can the implications of Q4 be considered in practice?

Q6 How can aerial river management be used as an instrument for mitigating the damage to land and water practices from extreme events?

Q7 What social components are linked with aerial river management?

Q8 How do social components function and interact with other components, and what do the interactions imply for aerial river management?

Also in Chapter 3, the trade-off of strategic reforestation along with chances and challenges for its implementation, regarding the current and planned land and water management in the region, will be discussed (Q4/Q5). An aerial river management policy brief discussing effects and providing guidance for the realization of the strategic reforestation for increasing Santa Cruz de la Sierra’s water supply is detailed in Supplement 1 (Q5).

Chapter 4 will use the known contribution to drought from the impact of land use change as channeled by the aerial rivers to explore aerial rivers’ potential in mitigating future droughts in Amazonia. The multi-level perspective (MLP) will be used as the analytical framework to examine the past mega-drought events in Amazonia. It will be used to trace the socio-technical regime governing the drought landscape development, and the recipient regime being impacted (Q7). The interactions between the sender regime and the recipient regime will also be analyzed under the framework (Q8). The detection of the social components, their function and their interactions with other components in the coupled human-environment systems will hold implications for more effective and holistic aerial river management (Q6/Q7/Q8). The repercussion of the analyzed example for managing both the extreme events and the aerial rivers will be provided (Q6/Q8).

The contents of Chapter 2 have been published in the journal Hydrology and Earth System Science. Chapter 3 has been published in the journal Land Use Policy. Chapter 4 has been submitted to the journal Environmental Innovation and Societal Transitions. Supplement 1 has been organized into a policy brief delivered to the authority of Santa Cruz de la Sierra and approved for implementation. I am the first author in all publications and the submitted manuscripts.

Chapter 2 Aerial and surface rivers: downwind impacts on water availability from land use changes in Amazonia

The contents of this chapter have been published in the journal Hydrology and Earth System Sciences:

Weng, W., Luedeke, M. K. B., Zemp, D. C., Lakes, T., Kropp, J. P., 2018. Aerial and surface rivers: downwind impacts on water availability from land use changes in Amazonia. Hydrol. Earth Syst. Sci. 22, 911-927. https://doi.org/10.5194/hess-22-911-2018

Abstract

The abundant evapotranspiration provided by the Amazon forests is an important component of the hydrological cycle, both regionally and globally. Since the last century, deforestation and expanding agricultural activities have been changing the ecosystem and its provision of moisture to the atmosphere. However, it remains uncertain how the ongoing land use change will influence rainfall, runoff, and water availability as findings from previous studies differ. Using moisture tracking experiments based on observational data, we provide a spatially detailed analysis recognizing potential teleconnection between source and sink regions of atmospheric moisture. We apply land use scenarios in upwind moisture sources and quantify the corresponding rainfall and runoff changes in downwind moisture sinks. We find spatially varying responses of water regimes to land use changes, which may explain the diverse results from previous studies. Parts of the Peruvian Amazon and western Bolivia are identified as the sink areas most sensitive to land use change in the Amazon and we highlight the current water stress by Amazonian land use change on these areas in terms of the water availability. Furthermore, we also identify the influential source areas where land use change may considerably reduce a given target sink’s water reception (from our example of the Ucayali River basin outlet, rainfall by 5–12% and runoff by 19–50% according to scenarios). Sensitive sinks and influential sources are therefore suggested as hotspots for achieving sustainable land–water management.

2.1 Introduction

The Amazon basin, draining an area of about 7 million km2, is the largest river basin in the world. It hosts the most extensive tropical rainforest ecosystem, covering about 5.3 million km2, which represents 40% of the global tropical forest area (Laurance et al., 2001; Aragão et al., 2014). The substantial transpiration from the canopy in addition to the evaporation contributes to abundant water fluxes to the atmosphere (Fisher et al., 2009). This atmospheric moisture eventually returns to the land and contributes about 25–35% of the basin’s rainfall and 48–54% of the regional rainfall (Salati and Nobre, 1991; Eltahir and Bras,

1994; Trenberth, 1999; Bosilovich and Chern, 2006; van der Ent et al., 2010; Zemp et al., 2014). When regulating the water cycle in the region, the Amazon forests are a key component of the regional but also global climate system (Foley et al., 2003, 2005; Meir et al., 2006; Snyder, 2010; Anderson-Teixeira et al., 2012).

It is uncertain how the ongoing land use change influences the operation of this ecosystem and its climate regulations (Pielke et al., 2002; Foley et al., 2007; Chapin et al., 2008; Soares-Filho et al., 2014). Since the 1960s, there has been substantial clearing of the Amazon forest for agricultural purposes; about 15% of the Brazilian Amazon rainforest has been cleared (INPE, 2017). Deforested areas are most often (80 %; Veiga et al., 2002) used as pastureland. Rice, cassava, maize, and soybean cropping have also driven deforestation (Nepstad et al., 2006; Barona et al., 2010). Soarse-Filho et al. (2006) have projected a loss of 47% Brazilian rain forest cover by 2050 under a business as usual scenario compared to the situation in 2004. Although this fast Brazilian deforestation trend has decelerated since 2004, a rebound of the deforestation rate has been observed since 2013 (Hansen et al., 2013; INPE, 2017). Moreover, a more recent Brazilian forest policy shift may allow for further deforestation in the country (Soares-Filho et al., 2014; Aguiar et al., 2016) in addition to observed increases in deforestation rates in other Amazonian countries (Hansen et al., 2013).

Through land–atmosphere coupling mechanisms, deforestation and other land use changes in the Amazon affect climate both regionally and globally (Dickinson and Henderson-Sellers, 1988; Dirmeyer and Shukla, 1994; Gedney and Valdes, 2000; Costa and Foley, 2000; Snyder, 2010). Among those changes, modified moisture fluxes to the atmosphere (Gordon et al., 2005; Silvério et al., 2015) introduce shifts in rainfall pattern and runoff regime and influence water availability (Henderson-Sellers et al., 1993; D’Almeida et al., 2007; Coe et al., 2011; Bagley et al., 2014; Lima et al., 2014; Swann et al., 2015; Spracklen and Garcia-Carreras, 2015). Given the spatial differences found in land– atmosphere coupling strength (Koster et al., 2004; Seneviratne et al., 2006) and continental moisture recycling (van der Ent et al., 2010), the water regime in some areas can be more sensitive to land use change than others. However, this spatially different sensitivity in the hydrological responses to land use change is not well understood. Indeed, water regime changes are also experienced by the downwind regions that are spatially distanced from 44

where the land use change is taking place (Pires and Costa, 2013; Bagley et al., 2014; Badger and Dirmeyer, 2015; Keys et al., 2016; Pitman and Lorenz, 2016; Zemp et al., 2017b). Thus, it requires investigation into both the sinks and the sources of the moisture flows to understand this spatial difference. Such an investigation will advance the understanding of land use change impacts on the water cycle and is necessary in order to identify hotspots for conservation policy targets fulfilling the Sustainable Development Goals (the SDGs), goal 6 (“Ensure access to water and sanitation for all”) and goal 15 (“Sustainably manage forests, combat desertification, halt and reverse land degradation, halt biodiversity loss”), for example.

The most direct way of portraying the airborne moisture flows is using diagnostic models driven by observation data (or observation-based climatic data for data-scarce regions). In the present study, we utilize a moisture recycling tracking algorithm to structure the moisture flow for exploring spatial heterogeneity in land use change impacts on the rainfall and runoff in Amazonia. Moisture recycling describes the contribution of local evaporation to local precipitation and was investigated in earlier studies by utilizing bulk models to partition moisture recycling in the water cycle within an area of interest (Brubaker et al., 1993; Eltahir and Bras, 1996; Trenberth, 1999). Moisture tracking tools have been further developed to describe the course in which evapotranspirated moisture travels through the atmosphere and precipitates in downwind regions within the area of interest, thus making the architecture of “aerial rivers” perceivable, aerial rivers being the preferential pathways of moisture flow, termed in Arraut et al. (2012) as an analogy to surface rivers. Moisture tracking recognizes teleconnection between moisture sources and sinks, which are not limited to administrative and topographical boundaries. These moisture tracking tools include isotopic tracers (Salati et al., 1979; Victoria et al., 1991; Henderson-Sellers et al., 2002; Tian et al., 2007), numerical algorithms online coupled with an atmospheric circulation model (Koster et al., 1986; Bosilovich and Chern, 2006), or offline a posteriori with reanalysis or operational data (Yoshimura et al., 2004; Dirmeyer and Brubaker, 2007; van der Ent et al., 2010; Tuinenburg et al., 2012; Spracklen et al., 2012; Bagley et al., 2014). Here we use an offline Eulerian numerical tracking algorithm (WAM-2layers, van der Ent et al., 2014; see also section 2.2.1.1) driven by observation-based data to approach moisture flows for its

relatively low computation cost but robustness in identifying the spatial pattern of moisture flow in a certain region (Keys et al., 2012).

Our objectives are (1) to explore how land use change impacts on rainfall and runoff in Amazonia can differ spatially, (2) to quantify this spatial variation, and (3) to identify the regions sensitive to Amazonian land use change.

To address these objectives, spatially different rainfall and runoff responses at moisture sinks are quantified when land use change occurs in Amazonia. Different hydrological influences that result from land use change in various moisture source areas are also calculated. Furthermore, we identify the sensitive sinks (defined here as land surface areas where the water regime is most impacted by land use change in a given upwind area via moisture recycling) and the influential sources (defined here as land surface areas where land use change exerts the strongest impacts through moisture recycling on the water regime of a given area downwind).

In the following section we describe the moisture tracking experiments and the scenarios that were utilized to analyze land use change impacts on water regimes. We also introduce the concept of the most influential precipitationsheds (MIPs), which is used for highlighting the influential sources of moisture. In section 2.3, we present the results of identification of sensitive pairs of sinks and sources to Amazonian land use change. Then, we present the quantified impacts on rainfall and runoff by land use change in terms of sensitive sinks and influential sources. Additionally, calculation of upper bound water regime changes from hypothetical land use changes from the whole of the Amazon is also shown for further comparison. We discuss implications of our results in section 2.4. These include the contribution of the interconnection between surface and aerial rivers to the spatial heterogeneity and the importance of aerial river conservation hotspots when compared with the upper bound. We highlight the current pressure on the sensitive regions’ water availability by land use change. The uncertainties and limitations of our results are also discussed in this section. In section 2.5, we conclude our findings and show how they resonate with the current discussion in the field. We then provide suggestions for managing land use change impacts on water availability for sustainable land–water use in Amazonia. 46

2.2 Methods

2.2.1 Outlining aerial rivers

2.2.1.1 Tracing moisture flow in Amazonia

The moisture flow is traced by the Water Accounting Model – two layers, WAM-2layers version 2.3.01 (van der Ent et al., 2014), for the South American continent. With a Eulerian specification of the field, the WAM-2layers model backtracks the moisture origin of precipitation that occurs over a given area following water balance. The backtracking is based on given input data while assuming that the water reservoirs of the lower atmospheric layer and the land surface are well mixed.

The WAM-2layers distinguishes between the bottom and top atmospheric layers (separately by approximately 800 hPa for a standard surface pressure) in the calculation of moisture flux across grid cell boundaries (van der Ent et al., 2014). This allows for a better capturing of the wind shear system that resulted in errors in traditional offline 2-D tracking studies with a well mixed atmosphere assumption (Goessling and Reick, 2013; van der Ent et al., 2013).

We use simulations from WAM-2layers from a previous moisture backtrack modelling experiment (MOD experiment; see Zemp et al., 2014). The WAM-2layers model run for the MOD experiment was on a 1.5° latitude–longitude grid and the time coverage was 2000– 2010. The input data of the first year were used for spin-up runs. The MOD experiment result further used in this study is the moisture transport matrix m. Its elements mij describe the amount of moisture evapotranspirated from grid cell i which is precipitated in grid cell j.

2.2.1.2 Input data

The input data for evapotranspiration (ET) and precipitation (P) of the MOD experiments are based on global satellite products (see Table 2.1). The evapotranspiration input was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) evapotranspiration product MOD16ET (Mu et al., 2013). Based on the Penman–Monteith equation and the algorithm from Cleugh et al. (2007), global evapotranspiration is calculated 47

as the sum of evaporation (from different soil types and interception by the canopy) and transpiration from the vegetation while environmental constraints and diurnal cycles are recognized. The calculation is based on MODIS Earth observation data inputs (land cover, albedo, and enhanced vegetation index) in conjunction with the Global Modeling and Assimilation Office (GMAO, v.4.0.0) daily meteorology data. Loarie et al. (2011) validated MOD16ET’s estimation with eddy flux tower data and reported its good performance (differences in annual average of evapotranspiration are less than 4% in savannas, 5% in tropical forests, and 13% in pasture agricultural lands). The precipitation input used in the MOD experiment was the product from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) algorithm 3B42 version 7, in which rainfall data are acquired from multiple satellite observations including passive microwave and infrared data, which were then calibrated by global rain gauge data (Huffman et al., 2007). These remote-sensing-based rainfall data are widely used in regions that lack ground observations such as the Amazon (Wagner et al., 2009; Su et al., 2008; Awadallah and Awadallah, 2013). This dataset has been described as robust in precipitation estimations over the Amazon region, especially at a monthly timescale (Su et al., 2008; Collischonn et al., 2008). Humidity and wind speeds were taken from the ERA-Interim reanalysis product (Dee et al., 2011). Input data have been upscaled to the spatial resolution of the WAM-2layers model and downscaled to a temporal resolution of 3 hours using the temporal variability in the corresponding ERA-Interim products.

Table 2.1 The specification of the MOD experiments which were used in our analysis to trace the moisture. Specification of the MOD experiments Precipitation input Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) Evapotranspiration input Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD16ET Humidity and wind speeds ERA-Interim reanalysis Temporal resolution 3 hours Spatial resolution 1.5°×1.5° Experiment time span 2000–2010 Spatial domain South American continent (land part of 30° W–85.5° W, 15° N–49.5° S)

2.2.1.3 Structuring the precipitationsheds: the MIPs

In our analysis, we utilize the concept of precipitationsheds and outline them for our target areas according to mij, the amount of moisture evapotranspirated from grid cell i which is precipitated in grid cell j, derived from the MOD experiment as described in section 2.2.1.1. The concept of precipitationsheds was introduced by Keys et al. (2012) as the upwind surface areas providing evapotranspiration to a specific area’s precipitation. In the present study we focus on the terrestrial component of precipitationsheds because of their relevance to land use change. Intercontinental moisture transports are neglected as they have little influence in our study region (van der Ent et al., 2010). Recognizing the spatial heterogeneity of the contribution in the precipitationshed (Keys et al., 2014), we further extract the most influential precipitationsheds (MIPs) for our analysis. The MIP is defined here as the collection of the most important source areas of a given region’s rainfall. Since it includes the most prominent contributing source areas of the evapotranspiration, the MIP governs a given proportion of a given region’s precipitation with minimum land surface areas. An example of a MIP for a grid element located in the Yurimaguas area is depicted in Figure 2.1. The area delimited by the 0.2 contour line is the smallest land surface contributing to 20% of precipitation in the Yurimaguas grid element from continental evapotranspiration. Outside of this area, a wider land surface area collectively contributes to the same amount, the area between 0.2 and 0.4 contour lines or the area between 0.4 and 0.6 contour lines, for example. The area governs 20% of continental moisture and is defined here as the 20% threshold MIP for the Yurimaguas grid element. Likewise, the 40% threshold MIP and the 60% threshold MIP are the areas delimited by the 0.4 contour line and the 0.6 contour line in Figure 2.1. The larger the threshold value, the more insignificant contributing source areas are included. The selection of the threshold determines the MIP size and the representativeness of the most important source areas; therefore it should be chosen according to study purposes. Previous studies have suggested and discussed different thresholds to delineate a precipitationshed boundary, e.g. 70% (Keys et al., 2012) or 1% (Keys et al., 2017) threshold of continental recycled precipitation. In the present study, we propose a threshold that is a trade-off between the relative influence on the sink’s rainfall and the size of the area delimited where land use change could occur homogeneously.

Under the modelling resolution of the present study, a 40% threshold is the minimum contour value to delimit precipitationshed areas for some regions (e.g. the Andes regions). Aiming to approximate the MIP by a standard that can apply to all the grid elements, the smallest valid 40% threshold has been applied throughout our analysis.

Figure 2.1 The precipitationshed of the Yurimaguas area. The contour value represents the cumulative fraction of Yurimaguas’ rainfall that comes from the source region delimited by the contour, over the precipitation originating from the South American continent. Thus, the contour line delimiting the South American continent has the value 1.

2.2.2 Modified downwind precipitation by land use change

We employed different land use scenarios to investigate evapotranspiration shifts introduced by various land activities and their impacts on rainfall and runoff. The proportion

of precipitation changes for the grid cell j in a land use scenario that occurs in the region 1

can be described as 𝛺𝛺

´ = (1 )

𝑗𝑗 𝑖𝑖𝑖𝑖 𝐸𝐸𝐸𝐸 ∆𝑃𝑃 � 𝑚𝑚 − 𝑖𝑖 𝑖𝑖∈ 𝛺𝛺1 𝐸𝐸𝐸𝐸 (2.1)

where stands for the changes in precipitation in sink grid cell j, ETi is the original

𝑗𝑗 evapotranspiration∆𝑃𝑃 in source grid cell i which is located in the domain 1, and ´ is the corresponding evaporation of different land use types. This description𝛺𝛺 is a 𝐸𝐸𝐸𝐸first-order 50

approximation implying that major wind patterns remain similar when land use change occurs and feedback mechanisms such as altered energy balance, surface roughness, and aerosols (Bonan, 2008; Mahmood et al., 2014) have not yet been triggered or are of minor importance (Bagley et al., 2014). Empirical evaporation measurements of different land uses in the Amazon were derived from Large-Scale Biosphere-Atmosphere Experiment (LBA-ECO) flux tower data (see Table 2.2) (Sakai et al., 2004). The LBA-ECO flux tower observation was established in 2000 in the Santarém region in the Brazilian Amazon. The field has been converted into different land uses including old-growth forest, selective logging, bare soil, pasture land, and rice cropping during the flux tower’s operation period. The evaporation was estimated by the eddy covariance (EC) method, corrected by the nocturnal boundary layer budget method for night-time respiration underestimates, and validated by Acevedo et al. (2004). Changes in the annual surface runoff regime by altered moisture recycling under land use change are investigated as well. By assuming that ET and P are in equilibrium (i.e. mean annual evapotranspiration does not exceed mean annual precipitation) and steady groundwater storages, we use precipitation minus evaporation (P – ET) to estimate annual surface runoff. We calculate the control state of P – ET throughout catchments using the 10-year average of the respective input data from the MOD experiment (2000–2010).

The P – ET changes under different land use scenarios are obtained by calculating the altered precipitation in the catchment grid cells and subtracting altered evaporation ( ´) according to each land use scenario. The P – ET values under different land use scenarios𝐸𝐸𝐸𝐸 are then compared with the control state.

Table 2.2 LBA-ECO evaporation data. Land use type Bare soil Dry pastureland Wet pastureland Rice cropping Evaporation rate (mm day–1) 1.2±0.7 1.9±0.6 2.2±0.9 2.7±1.2 Indicated uncertainties are standard errors.

2.2.3 Sensitive pairs of sink and source regions

High precipitation sensitivity of a sink region regarding land use changes in its source regions combines two aspects: firstly, the precipitation in the sink region must depend strongly on aerial moisture transport from terrestrial sources (i.e. high dependency on the aerial rivers) and secondly, the areal extent of the relevant source regions has to be rather small. The latter results in strong effects with even spatially limited land use changes. Given the importance of the Amazonian provision of moisture on the regional climate, we first calculate the precipitation recycled from the basin for each continental grid element. In the following, we identify the grid elements with the highest ratios (defined by the 98% percentile) of precipitation contributed by the moisture from the Amazon basin as sensitive sink areas. Next we determine the MIP (40% threshold MIP; see section 2.2.1.3) for the sensitive sink areas to examine their precipitation sensitivity to Amazonian land use changes.

2.3 Results

2.3.1 Sensitive sinks and influential sources: water regime shifts by upwind land use change

The most sensitive sinks regarding the evapotranspiration of the Amazon basin are situated in the eastern foothills of the Andes, a geographical region in southern Peru and western Bolivia, where over 70% of the precipitation originates from the Amazon, according to our results. The sensitivity to potential Amazonian land use change is shown in Figure 2.2. The sensitivity increases westward throughout the Amazon forest and reaches its peak at its south-western fringe. We identified regions that have more than 50% of rainfall coming from Amazonian evapotranspiration (98% percentile of the highest sensitivity to Amazonian land use change, hereafter called “sensitive areas”) and tracked back the location of the most influential sources for them as the second step in the procedure described in section 2.2.3. It turns out that the southwestern part of the Amazon forest exerts the strongest influence. As demonstrated by Figure 2.3, the most influential precipitationshed (MIP; the area delimited by the first contour line in Figure 2.3) of the sensitive areas is located in the region of Ucayali, Peru. This particular part of the Amazon forest governs the rainfall of the sensitive areas with high spatial efficiency (high control per unit area) compared to the rest of the moisture 52

sources. While covering 3.5% of Amazonia, the MIP accounts for 50% of the Amazonian evapotranspiration’s contribution (80 %) to the sensitive areas’ continentally sourced rainfall.

Figure 2.2 Rainfall dependency on the Amazon basin. The number shows the fraction of local rainfall recycled from Amazonian evapotranspiration. The yellow areas are among those regions having the greatest sensitivity to Amazonian land use change.

The above result on the most sensitive source and sink regions leads to the choice of interesting areas to quantify the influence of defined land use scenarios on precipitation and runoff regimes. As we are interested in the relationship of land use effects with both precipitation and surface runoff availability, we investigate them at the outlet of the Ucayali River basin (referred to as the target sink hereafter), a sub-basin where half of the sensitive areas are located (see Figure 2.3). Accordingly, we applied land use scenarios in different spatial domains including the Ucayali River basin (the watershed of the target sink) and the MIP of the target sink. In addition, land use scenarios are also employed to the MIP of the Ucayali River basin (the MIP of the watershed) but excluding the basin component cells in order to understand land use change influences outside of the watershed boundary, which is traditionally not covered in depth in water availability studies. Figure 2.4 shows the location of different land use scenario domains.

Figure 2.3 The precipitationshed of the sensitive areas. The contour value stands for the fraction of rainfall from continental evapotranspiration in sensitive areas that is evapotranspirated from the delimited region collectively. The first contour delimits areas (shown in dark blue) corresponding to the most influential precipitationshed (MIP) for the sensitive regions (represented by yellow cells). 74.7 % of the sensitive areas’ total rainfall comes from continental evapotranspiration. Of this, 40 % originates from the MIP.

Figure 2.4 Different land use scenario domains for exploring rainfall and runoff susceptibility of the target sink (Ucayali River outlet). These domains include the Ucayali River basin (the watershed of the target sink), the MIP of the target sink and the MIP of the Ucayali River basin (the MIP of the watershed) but excluding the basin component cells, in order to understand land use change influences outside of the watershed boundary. In addition, land use scenarios are also applied in the domains of the Amazon basin and the Amazon basin without the Ucayali river basin for upper bound investigation.

Different land use scenarios including the conversion of the areas to bare soil, dry and wet pastureland, and rice cropping are applied in each domain depicted in Figure 2.4. For each domain and each scenario, we investigate changes in the rainfall and runoff reception of the Ucayali River outlet, the target sink, as described in section 2.2.2. Figure 2.5 shows the interactions which are considered: changes in evapotranspiration when applying land use scenarios influence the rainfall downwind in both the target sink (here the Ucayali River basin outlet) and the target sink’s upstream watershed (here the Ucayali basin) through moisture recycling (the light blue arrows in Figure 2.5), thus altering the rainfall and runoff reception in the target sink. We note that the runoff changes measured in the target sink are also influenced by the land use scenario applied in the domain of the target sink’s upstream watershed (here the Ucayali basin) as shown by the black arrows in Figure 2.5.

Figure 2.5 Influence of land use (LU) in different spatial domains (orange ellipses) on runoff and precipitation (cyan blue ellipses) of the outlet of the basin. Light blue arrows show influences via “aerial rivers”, black arrows represent surface-bound relations. ET denotes the annual evapotranspiration of the basin and Prc stands for precipitation.

Changes in the rainfall and runoff reception of the target sink vary in direction and magnitude when land use change occurs in different spatial domains (Table 2.3). The bare soil land use scenario leads to more considerable alteration than the pastureland and rice cropland scenarios, which have the least impact. Generally, the rainfall decreases when land use changes, but the extent depends on the location of such a change. Land use change in the

MIP of the target sink leads to a reduction in the target sink’s rainfall ranging from 5% (rice cropping) to 12% (bare soil). On the other hand, when land use change occurs in the Ucayali River basin, the rainfall in the target sink experiences a mild reduction of less than 5% in all scenarios. Runoff shifts differ in sign when land use change occurs in different locations. An increase in runoff received by the target sink is found when applying land use scenarios in the Ucayali basin: the runoff is intensified by adding a quarter (27 %, rice cropping) to more than doubling (103 %, bare soil) the original flow. However, we found that applying land use scenarios outside of the watershed boundary has negative influences on the runoff of the target sink. Land use change in the MIP of the watershed results in a 19% (rice cropping) to 50% (bare soil) reduction in the target sink’s runoff. The heterogeneous hydrological response due to the location of land use change is discussed in section 2.4.

Table 2.3 Estimated changes in annual rainfall (ΔP) and runoff (ΔR) over the Ucayali River basin outlet following land use scenarios in different spatial domains. Land use change domain MIP of Ucayali MIP of the river basin Ucayali basin river outlet excluding the Ucayali basin Ucayali river outlet’s water regime ΔP ΔR ΔP ΔR ΔP ΔR Bare soil –3 % +103 % –12 % –9 % –16 % –50 % Dry pastureland –2 % +67 % –8 % –7 % –12 % –36 % Wet pastureland –2 % +52 % –7 % –6 % –10 % –30 % Rice cropping –1 % +27 % –5 % –4 % –7 % –19 %

2.3.2 Upper bounds for the influences of Amazonian land use change

So far we have investigated the most sensitive source-sink pairs and have chosen the considered land use change areas accordingly. However, land use change may occur in various parts of the Amazon basin. Therefore, we estimated rainfall and runoff changes considering land use change Amazon-wide to describe the upper bounds of land use change impacts on water availability. For that, in the following we apply different hypothetical homogeneous land uses to the whole Amazon basin and calculate their effects on precipitation and runoff at different locations.

Table 2.4 shows the results for the reduction of rainfall in different Amazonian land use scenarios. Sensitive areas can experience 11.3–38.5% (according to scenarios) annual rainfall reduction via moisture recycling when all of the Amazon forest is cultivated. The reduction in sensitive areas almost doubles the reduction of rainfall in the Amazon basin average (6.5– 18.2 %, according to scenarios) and it also greatly surpasses the average South American continent decrease in rainfall (4.0–12.9 %, according to scenarios). The bare soil land use scenario results in the greatest reduction in rainfall while the rice cropping scenario exerts the least influence on rain reception in the sensitive areas. The same pattern appears in the continental and the Amazon basin average.

Table 2.4 Estimated changes in annual rainfall over different regions when applying various land use scenarios in the Amazon basin. Note that annual rainfall is reduced continental-wise, but the sensitive areas experience greater reductions. Rainfall Rainfall change for different land uses (%) Area dependency on the Bare Dry Wet Rice (km2) Amazon basin (%) soil pastureland pastureland cropping Sensitive areas 3.25×105 60.3 –38.5 –25.8 –20.4 –11.3 Amazon basin 7.77×106 27.5 –18.2 –12.7 –10.4 –6.5 South American continent 1.70×107 20.0 –12.9 –8.8 –7.0 –4.0

Conversely, runoff estimates rise in all land use scenarios but to a different extent across sub-basins. As shown in Table 2.5, the bare soil land use scenario introduces the greatest increase (by 32.7 %) among all scenarios in the runoff of the Ucayali River basin, a sub-basin where half of the sensitive areas are located. Rice cropping has a milder impact, resulting in nearly a 1% increase in the Ucayali runoff. The extent of the runoff increase is different across the basins. Runoff estimates of the Madeira basin, the largest sub-basin in the Amazon (see Figure 2.3), increase in the range of 4.1% (rice cropping) to 40.3% (bare soil). The spatial pattern of P – ET change in different Amazonian land use scenarios (bare soil, dry pastureland, wet pastureland, and rice cropping) can be seen in Figure 2.6. As this figure shows, generally, land use scenarios for almost the entire Amazon basin result in a surface runoff increase across the Amazon basin but a decrease outside of it. Runoff increase within the Amazon basin also shows spatial differences as it is more pronounced in the north-eastern part of the Amazon and less significant in the western part. 57

Table 2.5 Runoff (P–ET) estimates in different regions under different land use scenarios.

Bare Dry Wet Rice Control soil pastureland pastureland cropping P–ET in the Ucayali basin Ucayali basin 23.285 30.891 27.444 25.966 23.504 (10 km3 yr–1) (3.1 % of the Comparison with the Amazon) – +32.7 % +17.9 % +11.5 % +0.9 % control group Madeira basin P–ET in the Madeira basin 103.15 144.68 127.39 119.84 107.42 (13.9 % (10 km3 yr–1) of the Comparison with the – +40.3 % +23.4 % +16.2 % +4.1 % Amazon) control group

Similarly to in our investigation on smaller domains such as the MIP of the target sink, we apply different land use scenarios in the domains of Amazon basin and the Amazon basin without the Ucayali River basin to investigate the upper bounds of the rainfall and runoff reception changes in the target sink, the Ucayali River basin outlet (see Table 2.6). The comparison of these upper bounds with the impacts from the influential sources hotspots is presented in section 2.4.3. Rainfall in the target sink decreases in the range of 10% (rice cropping) to 26% (bare soil) in all cultivated Amazon basin scenarios, but runoff in the target sink increases by 11% (wet pastureland) to 33% (bare soil). Converting the whole Amazon basin into rice cropping has in fact a very small influence on the runoff received by the target sink (–1 %). Contrary to the results from applying scenarios to the Amazon basin, runoff decreases in the range of 27% (rice cropping) to 69% (bare soil) when applying land use scenarios in the domain of the Amazon basin without the Ucayali River basin. This resonates with the findings in section 2.3.1 that applying land use scenarios outside of the watershed boundary has negative influences on the runoff of the target sink and is discussed in the following section.

Figure 2.6 Spatial patterns in local runoff (P–ET) changes compared to the control state for land use scenarios applied in the Amazon basin. Runoff generally increases in all scenarios (especially in the north-eastern part of the Amazon basin) but the rise is less pronounced in the rice cropping scenario over Amazonia.

2.4 Discussion

2.4.1 Sensitive sinks under pressure

The sensitive areas most dependent on the moisture recycled from the Amazon forest have been identified as being situated in the Peruvian Amazon and its transition to the Andes, such as the Junín, Cusco, and Puno regions, and a part of western Bolivia. Given that the average annual rainfall of the sensitive areas is 997 mmyr-1 (on average 74.7% from terrestrial recycling), the 11.3–38.5% rainfall reduction from the upper bound of our investigation has considerable impacts on the ecosystems and agriculture in these areas, especially during dry seasons (Bagley et al., 2014; Alves et al., 2017). Though this upper limit in which land use change takes place in the whole Amazon is hypothetical, land use change within the MIP, covering 3.5% of the Amazon, is possible (Aguiar et al., 2016). As it controls half the Amazonian provision of evapotranspiration in the sensitive areas, the land use change taking place in the MIP has a greater ability to alter the rainfall of the sensitive 59

regions compared with that which occurs in the rest of the Amazon basin. The location of the MIP for the sensitive areas is identified here in the Ucayali and Madre de Dios region of Peru, as shown in Figure 2.3. About 2.76% of the forests were cleared in the Ucayali region in the period between 2001 and 2014 (MINAM, 2017) but the deforestation rate is expected to increase because of continuing migration into these regions and increasing investment in roads and transportation (Piu and Menton, 2014).

Our results on the spatial pattern of rainfall dependency in the Amazon basin (Figure 2.2) agree with maps produced in studies on other aspects of moisture recycling (see the Figure 4 and the Figure 6 in Eltahir and Bras, 1994; Burde et al., 2006), though the rainfall dependency may be slightly overestimated along the Andes because of the imbalance between the input precipitation TRMM product and the evapotranspiration product MOD16ET (Zemp et al., 2014). Nevertheless, the overestimation is small when the MOD experiment reports general agreement with other studies using other datasets and other moisture tracking approaches (see the Table 2 in Zemp et al., 2014).

2.4.2 Interconnected aerial and surface rivers – spatially different response to land use change

Our investigation suggests that the sensitive areas’ rainfall reacts more significantly to land use change in the Amazon basin, by doubling the average rainfall reduction of the Amazon basin and tripling that of the South American continent average, and this propagates to the runoff responses in the sensitive areas. Taking the upper bound investigation for instance, significant drops in evapotranspiration due to land use scenarios applied within the Amazon basin lead to higher runoff estimates (P – ET surpluses) throughout the basin. However, these runoff rises are more compensated for in sensitive catchments which experience more significant rainfall reduction by land use change. This is reflected in the spatial heterogeneity in the extent of runoff response across basins (Figure 2.6; also see Table 2.5 for the comparison between the Ucayali River basin and the Madeira River basin runoff responses). As shown in Figure 2.6, the rise in P – ET in each scenario becomes less prominent towards the western Amazon, corresponding to growing sensitivity of the rainfall to Amazonian land use change (see Figure 2.2). The north-eastern part of the Amazon, where

rainfall is the least dependent on Amazonian evapotranspiration, shows the greatest growth in the P – ET surplus in all scenarios. We estimated altered rainfall as a result of land use change through the moisture recycling process while neglecting the moisture pathway dynamic resulting from the altered energy balance (Shukla et al., 1990; Bonan, 2008; Mahmood et al., 2014; Lejeune et al., 2015), the deepening convective boundary layer (Fisch et al., 2004), and the reduction in surface roughness (Khanna and Medvigy, 2014) after land use change. Nevertheless, our estimate of shifts in rainfall caused by land use change is in line with results from studies considering such effects. Our calculation of an annual rainfall reduction of 10.4–12.7% in both wet and dry pastureland Amazon scenarios falls in the range of a mean 16.5±13% reduction in annual rainfall in the Amazon basin, reported from 44 global general circulation model (GCM) and regional climate model (RCM) studies that hypothetically convert 100% of the Amazon into soybean or pastureland use in Spracklen and Garcia-Carreras’s (2015) meta-analysis. A total of 18 out of the 44 studies also considered roughness and albedo changes through coupled runs with land surface models or biosphere models. Our estimates are still in agreement with their results, reporting an average 15.3±8% reduction in annual Amazon rainfall (Spracklen and Garcia-Carreras, 2015). In this case, the neglected processes have minor influences on our overall results. As for runoff discharges, modelling outputs from previous studies applying Amazon deforestation scenarios have diverse predictions. Some report increases after land use change (Dirmeyer and Shukla, 1994; Lean and Rowntree, 1997; Kleidon and Heimann, 2000) and some found a decrease (Henderson-Sellers et al., 1993; Hahmann and Dickinson, 1997; Voldoire and Royer, 2004). Our results show that runoff response differs from basin to basin and depends on alternative land use practices. This spatial heterogeneity in the P – ET response (as shown in Figure 2.6) may contribute to the diversity of the findings from previous studies.

2.4.3 Water conservation hotspots outside of watersheds

Our results suggest that a given region’s water availability is not only related to land activities in its upstream watershed but is also highly controlled by those in its MIP and its watershed’s MIP. These are the areas not necessarily located in the upstream watershed, which is traditionally considered in land use assessments for water conservation. The importance of land use change in the MIP on the target sink’s rainfall is shown by comparing 61

it with impacts on rainfall by land use change in the whole Amazon (the upper bound investigation in section 2.3.2). In our exploration for the Ucayali River basin outlet as a target sink, land use change in its MIP results in a 5–12% drop of the target sink’s rainfall. This is considerable compared with a 10–26% decrease in the target sink’s rainfall by land use change in the whole Amazon basin, 9 times the size of the target sink’s MIP. In contrast, when land use change occurs in the Ucayali River basin, the reduction in the target sink’s rainfall is considerably lower (by less than 5 %).

The interconnection between surface rivers and aerial rivers implies that the land use changes taking place outside of the watershed can be crucial to the runoff reception, as also found in Wang-Erlandsson et al.’s (2017) global analysis. In fact, in our investigation, land use change that happens in the target sink’s upstream watershed brings converse impacts on runoff compared with land use change taking place outside of the target sink’s upstream watershed. We found an abundant increase in the runoff received in the Ucayali River outlet, the target sink, when land use scenarios are applied in the Ucayali basin. This is consistent with modelling and observational studies that investigate runoff response to land use change in a specific sub-basin or catchment (Costa et al., 2003; Coe et al., 2011; Panday et al., 2015). However, the runoff reduces by 27–69% when employing land use scenarios in the domain of the Amazon basin excluding the Ucayali River basin (see Table 2.6).Within this area, land use change in the MIP of the watershed is more influential on the target sink’s runoff. The result is a 19–50% reduction, even though its areal content is less than half that of the Amazon basin excluding the Ucayali basin. These results also reflect that when applying land use scenarios at a pan-Amazon scale, runoff estimates of a specific watershed yield contradicting responses to land use change in different moisture source areas (within the watershed a positive response, outside of the watershed a negative response).

Table 2.6 Estimated changes annual rainfall (ΔP) and runoff (ΔR) over the Ucayali River basin outlet following land use scenarios in the Amazon basin and the Amazon basin excluding the Ucayali basin.

Amazon basin excluding Land use change domain Amazon basin the Ucayali basin Ucayali River outlet’s water regime ΔP ΔR ΔP ΔR Bare soil –23 % –69 % –26 % +33 % Dry pastureland –17 % –49 % –19 % +18 % Wet pastureland –14 % –41 % –16 % +11 % Rice cropping –9 % –27 % –10 % –1 %

2.4.4 Managing interconnected surface and aerial rivers crossing boundaries

Our results suggest that sensitive sinks (e.g. the sensitive areas; see section 2.3.1) and influential sources such as the MIP of the given region and the MIP of its watershed are the areas crucial for managing water availability under interconnected aerial and surface river regimes. In order to do this, transboundary involvement crossing regions, municipalities, provinces, or countries is necessary. For example, our results of the sensitive pairs reflect that as they are located in the downstream area of the aerial river, the Bolivian sensitive areas should recognize the importance of the land activities in the neighbouring Peruvian Amazon. For another example of the target sink in the Ucayali basin outlet, though its watershed area is located completely in Peru, its MIP has Peruvian, Brazilian, and Colombian components. Therefore, for the Amazon countries’ sustainable use and management of the fresh water, understanding the roles in the aerial river regime within and across individual countries and initiation of co-management are crucial. Previously, Dirmeyer et al. (2009) have investigated the imports and exports of the moisture per country globally. Though these moisture budgets can be useful for understanding each country’s dependency, they provide limited spatial information for conservation targets. Keys et al. (2012) introduced the concept of precipitationsheds to identify areas providing moisture for precipitation in downwind areas. We extended the discussion on precipitationshed boundaries (Keys et al., 2014, 2017) by showing that a particular component of the precipitationshed with small areal extent can be especially influential (MIP) for rainfall and that the interlinkage between the aerial and 63

surface rivers marks the importance of the MIP of the watershed for runoff. The identification of such hotspots and quantification of potential hydrological influences by land use change within them provide conservation targets for the sustainable management of interconnected surface and aerial river regimes crossing boundaries.

2.4.5 Limitations

Our analysis based on the average output of the period 2000–2010 from the MOD experiment has not accounted for the interannual variation of moisture recycling, though it is generally reported as small in the Amazon basin (Bosivolich and Chern, 2006). However, we note that the two major droughts (2005 and 2010) in the simulated period of the MOD experiment may lead to an overestimation of the moisture recycling influence (Bagley et al., 2014). The seasonal variation was also masked despite the slight difference (3–5 %) reported by Zemp et al. (2014) between dry and wet seasons in the precipitation recycling ratio in Amazonia over the investigation period. We are aware that the spatial patterns of recycling vary through the seasons (Arraut et al., 2012; Zemp et al., 2014) and that this can influence the identification of the MIP location. However, Keys et al. (2014) concluded that the core part of precipitationsheds can be suggestive for the analysis of terrestrial precipitation recycling, which may be reflected by our decadal average results. Still, further studies that focus on seasonal specific purposes such as rain-fed agriculture should take the growing season’s precipitationshed shift into account.

Other uncertainties could remain in the extrapolation of LBA-ECO flux tower data measured in the Santarém region for the entire Amazon basin. The spatial variability in evapotranspiration that might arise from varying environmental conditions (Fisher et al., 2008) is not considered. However, the evapotranspiration approximation is still site- and ecoregion-based (Christoffersen et al., 2014), while the evapotranspiration modelling power over Amazon forest ecosystems is still poor (Karam and Bras, 2008; Werth and Avissar, 2004; Maeda et al., 2017). A similar limitation is shown in our estimation of surface runoff. Our assumption of steady groundwater storage is restrained by the fact that a lack of adequate soil hydraulic information (Miguez-Macho and Fan, 2012) leads to a modelling challenge of groundwater dynamics across the Amazon basin in addition to the lack of groundwater

observation data due to remoteness. The development of remote monitoring tools such as the Gravity Recovery And Climate Experiment (GRACE) satellite mission (Tapley, 2004) allows the examination of the terrestrial water storage (TWS) and can be potentially used for estimating groundwater storage (Rodell and Famiglietti, 2002). However, the groundwater storage’s importance in regulating the TWS change still remains inconclusive in the Amazon basin. While some studies found river water storage to explain most of the TWS variation (Kim et al., 2009), some others found groundwater storage dominance (Niu et al., 2007; Pokhrel et al., 2013) or the equal importance of both (Alkama et al., 2010) in contributing to TWS changes due to process representation differences in the models.

In the present study, we focus on the effect of land use change on moisture availability through the moisture recycling process. Other processes are also known to be involved in shifting water regime when land use change occurs, for example, rising aerosols modifying cloud microphysics (Koren et al., 2012), altered infiltration and hydraulic redistribution (Lee et al., 2005; Yeh and Famiglietti, 2009), changed surface roughness (Khanna and Medvigy, 2014; Khanna et al., 2017), and its forcing on convective systems (Baidya Roy and Avissar, 2000; Baidya Roy, 2002; D’Almeida et al., 2006). Feedback mechanisms such as vegetation– atmosphere interaction intensifying droughts and driving large forest dieback (Nepstad et al., 2008; Malhi et al., 2009; Zemp et al., 2017a, 2017b) can also influence the rainfall and runoff regime. Since our study has suggested the sensitive sinks and influential sources’ importance for calculating the shifts in water regime, further studies on how these processes interact with moisture recycling spatial heterogeneity can further advance our insights into the water regime shifts caused by land use change.

2.5 Conclusion and outlook

From our analysis of the moisture recycling process, we conclude that the impacts of Amazonian land use change on the water regime have spatial heterogeneity in two ways. First, hydrological responses in moisture sinks vary spatially. Second, land use change in different locations exerts varying influences. This spatial difference implies sensitive sinks and influential sources where land use change could have strong downwind impacts on water availability. Using a moisture tracking experiment of a water balance model (WAM-2layers),

we have identified the areas sensitive to Amazonian land use change in the area of semi-arid southern Peru and eastern Bolivia. We quantified changes in rainfall and runoff by various land use scenarios in the Amazon and found that sensitive areas experience a more significant rainfall reduction (11.3–38.5 %, depending on scenarios) and a lower runoff increase (0.9– 32.7% in the Ucayali River, depending on scenarios). In addition, we add to recent discussions on precipitationshed boundaries by introducing the concept of MIPs (most influential precipitationsheds), where the most important source areas of moisture for a given region are collectively situated (within a relatively small area) and backtracked the MIP of the sensitive areas, which is located in the Ucayali and Madre de Dios region in the Peruvian Amazon. We further explored the varying influences of land use change on a target sink’s water availability from different source areas and found that land use change in the upstream watershed of the target sink leads to a runoff rise, while land use change occurring outside of the target sink’s upstream watershed leads to a reduction in runoff. We also identified the MIP of the target sink’s upstream watershed as the hotspot for conserving runoff (19–50% reduction, depending on scenarios) and the MIP of the target sink as the hotspot for conserving rainfall (5–12% reduction, depending on scenarios) for land use assessment. Our results also show that the 40% threshold MIP utilized in the present study is plausible in reflecting important regions of moisture contribution to a given sink. However, the MIP threshold for further studies should be decided depending on different study purposes, tools, and focus regions.

The importance of spatially different land use change impacts on the water regime found in our analysis can explain the diversity of other modelling experiments’ findings. Macro-scale experiments reflect aggregated influences and responses from different spatial components; thus they do not contradict different findings from mesoscale experiments, in which estimates are geographically specific. Nevertheless, for conservation targets, these aggregated results are rarely suggestive. For future meso-scale analysis, we suggest a shift of spatial focus from a pure watershed study because land use changes outside of a target area’s watershed can also be very influential. Our results also reflect that the deforestation tipping point, beyond which rainfall changes will lead to strong rainfall reductions with drastic

ecological impact on the forest (Lawrence and Vandecar, 2015; Zemp et al., 2017b), can be lower when the deforestation takes place in influential source areas, such as MIPs.

At a national level, we suggest that a crucial step towards the Amazon countries’ sustainable usage of water (resonating the fulfilment of SDGs 6 and 15) is to include the influence of land activity in water management. However, as opposed to only traditionally recognizing upstream watershed regions in water management, land use in the precipitationsheds, especially the MIPs, is of importance in both the rainfall and runoff regime sustaining the ecosystem (Coe et al., 2013) and agriculture (Bagley et al., 2012; Keys et al., 2014). Our results also highlight the importance of transboundary cooperation along both the surface and the aerial river for managing water regime shift caused by land use change. Top-down international laws and regulation offer an opportunity (Keys et al., 2017) but bottom-up national efforts should focus on understanding each country’s role in the aerial river regime crossing boundaries and the places in need of action. This can be done by recognizing the moisture sinks sensitive to land use change and locating influential sources (MIPs) that exert strong controls on the rainfall and runoff regime and water availability of the sensitive regions, as demonstrated in the present study.

Code availability. The WAM-2layers model code is available at https://github.com/ruudvdent/WAM2layersPython under the GNU General Public License.

Data availability. The LBA-ECO flux tower data are available online at https://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=11#surf_hydro_and_water_chem_anchor and in Sakai et al. (2004).

Acknowledgements

This research was supported by the German International Climate Protection Initiative (project: Sustainable Latin America, reference number 42206-6157). Delphine C. Zemp acknowledges funding from the DFG (project IRT1740). We sincerely thank both referees, Patrick W. Keys and Rogier S. Westerhoff, for constructive comments that helped to improve

the manuscript. We are grateful to Florian Gollnow, Stefanie L. Becker, and David M. Landholm Haight for their comments on an earlier version of this manuscript. We also thank Ruud van der Ent, Kirsten Thonicke, and Anja Rammig for discussions on the WAM-2layers used in this study.

Chapter 3 Aerial river management by smart cross-border reforestation

The contents of this chapter have been published in the journal Land Use Policy:

Weng, W., Costa, L., Luedeke, M. K. B., Zemp, D. C., 2019. Aerial river management by smart cross-border reforestation. Land Use Policy 84, 105-113. https://doi.org/10.1016/j.landusepol.2019.03.010

Abstract

In the face of increasing socio-economic and climatic pressures in growing cities, it is rational for managers to consider multiple approaches for securing water availability. One often disregarded option is the promotion of reforestation in source regions supplying important quantities of atmospheric moisture transported over long distances through aerial rivers, affecting water resources of a city via precipitation and runoff (‘smart reforestation’). Here we present a case demonstrating smart reforestation’s potential as a water management option. Using numerical moisture back-tracking models, we identify important upwind regions contributing to the aerial river of Santa Cruz de la Sierra (Bolivia). Simulating the effect of reforestation in the identified regions, annual precipitation and runoff reception in the city was found to increase by 1.25% and 2.30% respectively, while runoff gain during the dry season reached 26.93%. Given the city’s population growth scenarios, the increase of the renewable water resource by smart reforestation could cover 22 - 59% of the additional demand by 2030. Building on the findings, we argue for a more systematic consideration of aerial river connections between regions in reforestation and land planning for future challenges.

3.1 Introduction

Reforestation has been one of the most active initiatives to mitigate global climate change impact. Being potentially a useful tool to sequestrate atmospheric carbon, it also presents co-benefits such as improving status of biodiversity loss and enhancing ecosystem integrity (UNFCCC, 2013). These co-benefits have been included as objectives of several international agreements addressing those issues, e.g. the Aichi Targets (CBD, 2010) and the Bonn Challenge (Bonn Challenge, 2019). However, an undesirable effect of such intervention is the trade-off with downstream water availability (Farley et al., 2005; Cunningham et al., 2015; Connor et al., 2016). A dramatic decrease in river runoff is normally observed downstream of the reforestation sites compromising water supply and other ecosystem services from the river (Jackson et al., 2005; Filoso et al., 2017). This has become a major

factor of low societal acceptance impeding reforestation projects and invoking conflicts (Cao, 2011; Cao and Zhang, 2015) which poses challenges for local implementation of such an intervention despite the top-down forces.

However, a usually neglected aspect of reforestation is that it can also enhance water availability through invisible aerial river connections (van Noordwijk et al., 2014; Ellison et al., 2017, 2018). Similar to surface river networks, aerial rivers (preferential pathways of moisture flows in the atmosphere; Arraut et al., 2012) connect regions, often across administrative borders and topographic watersheds. Upwind land activities govern evapotranspiration (Gordon et al., 2005; Silvério et al., 2015), the moisture input to terrestrial aerial rivers, and influence precipitation downwind via atmospheric circulation (D’Almeida et al., 2007; Ellison et al., 2012; Spracklen and Garcia-Carreras, 2015; Pitman and Lorenz, 2016). Through the hydrological cycle, this influence propagates to rivers and groundwater, thus impacting water availability (Coe et al., 2011; Bagley et al., 2012; Lima et al., 2014; Swann et al., 2015; Ramírez et al., 2017; Weng et al., 2018). Reforestation in general enhances evapotranspiration resulting in more water loss at the catchment scale compared with non-forested land cover (Brown et al., 2005; Farley et al., 2005; Dean et al., 2015). However, released from the land surface, these moisture fluxes to the atmosphere are important inputs of continental aerial rivers (Gordon et al., 2005). Through this mechanism, reforestation strengthens the delivery of water to downwind regions. A prerequisite of integrating this concept in land and water management is an assessment on how relevant it is for downwind water availability. Since aerial river connections are not directly observable, utilization of scientific tools are required to recognize connections between upwind and downwind regions (Ellison et al., 2018). Via numerical modelling or isotopic tracing it is possible to reveal the aerial river network connecting regions. Utilizing numerical modelling as a tool, we quantify the effect of strategic reforestation in the upwind regions on those downwind via aerial rivers, thus exploring the potential of the latter as a water resource management option.

3.2 Material and methods

Expanding on a previous study’s methods of structuring aerial rivers (Weng et al., 2018), we use the results of the WAM-2layers moisture back-tracking algorithm (van der Ent et al., 2014) to identify the most important upwind regions influencing precipitation in our study region, Santa Cruz de la Sierra. Based on a Eulerian approach, the model traces the origin of rainfall using the water balance principle and an assumption of well-mixed atmosphere in two vertical layers according to given input data. The WAM-2layers was shown to agree well with other moisture tracking approaches in the Amazon region while having lower computation cost (van der Ent et al., 2014; Zemp et al., 2014). For our analysis, we use a simulation experiment (MOD experiment, described in Zemp et al., 2014) that utilizes observation-based climatic input data (precipitation, evaporation, humidity and wind field) spanning 2000-2010. By averaging annual means of the simulations, we get a network quantifying the contribution of evapotranspiration from each grid cell (1.5° x 1.5° longitude and latitude) to rainfall at the Bolivian economic capital, Santa Cruz de la Sierra. This network can therefore be used to determine the precipitationshed (upwind surface area providing evapotranspiration to a specific sink area’s precipitation; Keys et al., 2012, 2018) of the city.

It is known that for a given location, its upwind regions can have different influences on its water resource through the aerial rivers (Keys et al., 2014; Weng et al., 2018). For planning purposes, it is actually possible to identify influential upwind regions which have greatest impact influencing a given region’s water availability. We investigated a reforestation intervention in these important upwind regions of Santa Cruz de la Sierra to estimate the optimal potential of such an intervention as a water management option for the city. In order to do this for our example location, we rank the regions’ importance in contributing to rainfall of Santa Cruz de la Sierra and outline the Most Influential part of Precipitationshed (MIP) of the city (see the blue region in Figure 3.1) by a boundary designating the smallest area which contributes 40% of the total continentally recycled precipitation in the city. This threshold was proven applicable in reflecting the most important moisture source regions for assessing land use change impacts on aerial rivers (Weng et al., 2018); also see 3.7 Appendix A for more details outlining the MIP. 72

3.3 Study case

Santa Cruz de la Sierra in the Plurinational State of Bolivia, is one of the world’s most rapidly growing cities (annual population growth rate 3.7% between 1992 and 2012; Trohanis et al., 2015) and the home of 1.4 million residents (INE, 2017). Migration flows, the main reason of the city’s growth in the past decades, are expected to persist. Though the city has the highest coverage of potable water in the country, the current groundwater resources that the city relies heavily on are under stress with a continuous deepening of modern recharge front and deterioration in quality (Morris et al., 2003). Water availability for both the growing population and peri-urban agriculture is becoming uncertain (Castelli et al., 2017). In addition, more frequently occurring severe droughts in the region also intensify the water challenge faced by the city (Marengo et al., 2011; Jiménez-Muñoz et al., 2016; Erfanian et al., 2017).

Following the moisture tracking and the identification of the city’s MIP, we tested the potential of managing aerial rivers by smart reforestation as an option to ease the city’s water stress. Here “smart” refers to the selective decision of reforestation sites (in the MIP) considering their impacts on aerial rivers and thereby on the water reception of Santa Cruz de la Sierra. Our smart reforestation scenario in each MIP component grid cell was built according to the restoration opportunities map of the International Union for Conservation of Nature (IUCN)/World Resources Institute (WRI) for the Bonn challenge (Potapov et al., 2011; Maginnis et al., 2014; see 3.7 Appendix B) where reforestation potential was assessed by ecological conditions and local land use culture. The additional evapotranspiration input from smart reforestation in each MIP cell was determined using the measured evapotranspiration per area of a neighboring forest reference cell (with forest fraction > 95%). We then subtracted the evapotranspiration typical of pasture-land (Sakai et al., 2004), which is the major current land-use type in areas selected for smart reforestation, from the reference forest evapotranspiration per area before multiplying the area reforested. The measured evapotranspiration used for forest evapotranspiration reference was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) evapotranspiration product MOD16ET (Mu et al., 2013). Thus the additional evapotranspiration input considered is corresponding to the forest structure and age during the observational period of the data (2000-2010). We then calculate the newly added evapotranspiration’s influence on the city’s 73 water availability including annual rainfall and runoff reception. Changes in annual rainfall could be directly quantified from our networks assuming that changes in atmospheric moisture flow is proportional to changes in the contribution of this moisture flow to local precipitation (Zemp et al., 2017a). This assumption is justified by a positive relationship between atmospheric moisture and rainfall in the region (Boers et al., 2014) which also implies sufficient supply of condensation nuclei in the region (van Noordwijk et al., 2015). In turn, the new runoff budget of the city’s upstream basin (belonging to the larger Madeira River basin) was evaluated by balancing the changed annual basin precipitation and annual evapotranspiration. We also analyzed changes in seasonal rainfall and runoff budgets in the smart reforestation scenario based on dry (June, July, August and September) and wet (December, January, February and March) months’ basin precipitation and evapotranspiration, in line with previous studies showing significant seasonal differences in the influence of aerial rivers on hydrological cycle (Trenberth, 1999; Zemp et al., 2014). For an operational scheme of the study case, we refer the reader to 3.7 Appendix C.

3.4 Results and discussion

We found that smart reforestation of 7.1 million ha in the MIP region of Santa Cruz de la Sierra lead to an increase of 1.25% in annual rainfall reception of Santa Cruz de la Sierra (absolute rainfall gain 5.86×108m3). This scenario further leads to a rise of 2.23% (absolute runoff gain 2.00×109m3) in the city’s annual runoff enabled by the spatial relationship between its upwind aerial river (more specifically the MIP) and upstream surface river basin. As shown in Figure 3.1, the MIP region of Santa Cruz de la Sierra is located in Brazil (states of Acre and Rondônia), Peru (departments of Madre de Dios), and Bolivia (departments of Pando and El Beni). The increase in runoff in Santa Cruz de la Sierra is due to the fact that smart reforestation in the MIP of the city also enhances rainfall in the city’s upstream surface river basin. Even more interestingly, the MIP region of Santa Cruz de la Sierra is largely separated from the upstream surface river basin of the city. Thus the expected local runoff decrease resulting from increasing evapotranspiration of reforestation in the MIP is not fully experienced by the city’s upstream surface river basin. In fact, the precipitation increase in the basin (through the aerial rivers) results in a marked gain in runoff reception of the city. The runoff increase is more prominent in the dry season (June, July, August and September) 74

reaching a 26.93% increase. In the wet season (December, January, February and March), gains are moderate (1.85%). Slight seasonal variation in rainfall increase following smart reforestation is also observed (dry season 1.27% and wet season 1.24%). The rainfall and runoff gain of the study region from smart reforestation are listed in Table 3.1.

Figure 3.1 Smart reforestation for water supply in Santa Cruz (conceptual representation). The Most Influential part of the Precipitationshed (MIP) is highlighted in blue and the upstream surface river basin of Santa Cruz de la Sierra is shown in purple. The blue arrows represent aerial river flows whereas the purple arrows represent surface river flows. The city Santa Cruz de la Sierra, is shown with an orange dot. See 3.7 Appendix B for an actual map of the MIP and the reforestation potential sites.

Table 3.1 Estimated rainfall and runoff changes to the baseline due to smart reforestation Dry season Wet season Annual (June, July, August, September) (December, January, February, March) Baseline rainfall 6.79×109m3 2.54×1010m3 4.69×1010m3 Baseline runoff 7.56×108m3 6.21×1010m3 8.68×1010m3 Rainfall change +1.27% +1.24% +1.25% Runoff change +26.93% +1.85% +2.23%

Uncertainties in our estimates may stem from the moisture tracking model, but moisture recycling ratios in the Amazon region estimated from the MOD experiment agree well with other studies’ estimation in the same region using other datasets and other moisture tracking approaches (see the table 2 in Zemp et al., 2014). We have also validated our runoff estimation in the Madeira river basin with the historical river observation data (Molinier et al., 1996) and found a slight (8%) overestimation. This has only a small impact on our estimation of the runoff gain by smart reforestation (-8.6% of the annual runoff gain). Our calculation of the reforestation impact on aerial rivers was based on the condition of the transferability of measured forest evapotranspiration from neighboring sites and minor wind field changes after changing land cover (Bagley et al., 2014). Local convection that may be decreased by a lower local land cover heterogeneity (Baidya Roy and Avissar, 2000; D’Almeida et al., 2006) after reforestation is not considered in our study. However, this effect is minor compared to changes in aerial river flows after land-use change in the Amazon (Bagley et al., 2014). Potential increase of soil infiltration (Bruijnzeel, 2004) after reforestation are not considered in the runoff calculation, but this is unlikely to affect our results since most of the reforestation sites are located remotely (in the downstream areas or out of the upstream catchment) of the city.

Our results show that smart reforestation is an option to enhance water supply especially during dry seasons. An increase of 26.93% in dry season runoff can be beneficial for sustaining ecosystem (Anderson-Teixeira et al., 2012; Brienen et al., 2015) and livelihood (Castelli et al., 2017) given the past (Fu et al., 2013) and projected (Marengo and Espinoza, 2016) lengthening and intensification of the dry season. The amount of water added to the region by smart reforestation can be used to ease the growing water stress brought about by fast urbanization (Castelli et al., 2017). Using the population growth projections implied by 76

different Shared Socioeconomic Pathways (SSP) (Jones and O’Neill, 2016), we calculated the additional water resources needed to secure the current water consumption per capita (INE, 2017) in the city of Santa Cruz by 2030. In the case of withdrawal being proportional to available water resources, our results imply that an early implementation (2020) of smart reforestation can gradually increase the renewable water resource of the city and ultimately cover between 22% and 59% of the additional water demand by 2030. Though the city’s current water system does not extract directly from runoff, the already confirmed large dam projects (Ministerio de Hidrocarburos y Energía, 2012) will allow to benefit from our proposed management option. Such benefits might be particularly relevant given the fact that the glaciers currently sustaining runoff are retreating under climate change (Rabatel et al., 2013; Rangecroft et al., 2016; Vuille et al., 2018). In addition, induced increases in both rainfall and runoff reception will have a positive impact on groundwater recharge which the city currently relies heavily on. Smart reforestation therefore has the potential to increase water supply being beneficial in mitigating the stress on the existing water system under both population growth and climate change. Additional benefits of smart reforestation include sequestering atmospheric carbon at the reforestation sites (Post and Kwon, 2000; Don et al., 2011) and sustaining ecosystem integrity (Coe et al., 2013). Our results add new insights into trade-off between carbon sequestration and fresh water supply (Farley et al., 2005; Gao et al., 2014; Connor et al., 2016), while a win-win situation between those is presented in our case.

3.5 A more holistic practice of land-water management

Nationally in Bolivia, under the commitment of Ley 1333, reforestation has been also one of the priority land management targets to preserve ecosystems and their services. Under the commitment of Decreto Supremo Nº 2912, there is a Bolivian national target of reforesting 4.5 million hectares by 2030. The smart reforestation scenario includes reforestation areas of 7.1 million hectares and is therefore in line with the national target while the Bolivian part covers 45% of these areas. A full implementation of smart reforestation will require cross-border cooperation. Current implementation of reforestation is mainly planned at the upstream catchments (the Piraí river banks) aiming at the improvement of water quality downstream. Nevertheless, following this strategy, tension usually arises from a reduction of water quantity in downstream areas. In addition, the feasible sites in 77 upstream catchments are usually limited while most of these areas are not easily accessible for development or are protected. Thus, a gap for fulfilling the national reforestation target hectares can be expected if prioritizing this traditional strategy. In this context, smart reforestation might be a good alternative to fill the gap, even more, it might be worth considered before the traditional strategy when aiming for a more water – resilient city of Santa Cruz in the future. In order to implement smart reforestation, it will be necessary to negotiate with other departments out of the Santa Cruz department, which the city belongs to. It is true that blue water and sediments losses can be expected from the reforestation sites locally and downstream. Nevertheless, the friction stemming from compromise in downstream water supply is likely small while those departments (El Beni and Pando) and their downstream regions have relatively rich runoff resources. The concept of “right tree at the right place for a clear function” (Creed and van Noordwijk, 2018) can be a national strategy prioritizing those areas facing challenges. A full implementation of smart reforestation will require international cooperation because a part of the city’s MIP is located in Peru and Brazil. This could be feasible when included in the context of both the Bonn Challenge (http://www.bonnchallenge.org/content/challenge) and the Initiative 20x20 (https://www.wri.org/our-work/project/initiative-20x20), where the Latin American countries have a common target of reforesting 20 million hectares by 2020. In addition, smart reforestation is in line with the Intended Nationally Determined Contributions (INDC) targeting atmospheric carbon reduction which increases individual country’s will to participate. A recent call for the regional joint effort mitigating drought may also impose momentum for the joint management on the aerial rivers (UNCCD, 2017).

Our case implies that, similar to integrated surface river basin management requiring collaboration between upstream and downstream entities, joint management between the upwind and downwind regions is necessary for the implementation of aerial river management and smart reforestation. This requires improved understanding of spatial connections by the aerial rivers (Dirmeyer et al., 2009; Keys et al., 2017) and the effect of land use practices at upwind regions propagating to downwind regions through hydrological cycle (Ellison et al., 2017, 2018; Weng et al., 2018). Nationally, an assessment that outlines critical regions (e.g. the MIPs) to preserve aerial rivers will be a precondition of involving

78 relevant regions for cooperation. By the time the present study was written, there is no inter-regional or international agreement explicitly governing aerial river connections. The Convention on Long-Range Transboundary Air Pollution (UNECE, 1979; Sliggers and Kakebeeke, 2004) might provide a feasible framework to develop on (Ellison et al., 2017). Moreover, establishment of bridging organizations can facilitate co-production of knowledge and collaborative decision making between actors (Cash et al., 2006; Olsson et al., 2007; Crona and Parker, 2012). Interestingly, different roles in aerial river regime and surface water regime may also foster positive policy environment for cooperation between aerial river sharing entities. Take our case for instance, the upwind regions of Santa Cruz de la Sierra are located downstream of the Madeira River, receiving impacts from the city through the surface rivers, but they can exert influences on the city through the aerial rivers since they are located upwind. The reverse roles in this case imply that the shared aerial rivers might challenge current relationships between entities established from surface water regime and the according paradigms of land-water management. Nevertheless, the aerial rivers are indeed the key to bring out a more holistic one of those.

3.6 Conclusions

To sum up, by taking advantage of a model recognizing teleconnections through the aerial rivers, we have exposed the potential of smart cross-border reforestation as a water management option mitigating challenges of future population growth and climate change. Through transportation by aerial rivers, atmospheric water added by smart reforestation is collected and delivered to the downwind region, presenting potential in increasing both rainfall and runoff (especially in the dry season) in our study region, and certainly beyond. Different from traditional upstream catchment approaches, smart reforestation projects attest the possibility of breaking the usual trade-off in reforestation projects between carbon sequestration and fresh water supply by enhancing both for specific target regions. Prioritization of smart reforestation projects in the important upwind regions of those experiencing or expecting water stress can present significant benefits but be also in line with national and global efforts in reducing atmospheric carbon. For example, the smart reforestation project can provide preferable results watering Bolivia’s fastest growing city and at the same time fulfilling the Bolivian national INDC. Beyond the study region, a more 79

systematic consideration of the interconnection between the land and water system while planning reforestation projects should be taken. Further studies should focus on exploring smart reforestation sites that optimizes the aerial river impacts downwind for such regions. The relevance of other land use types e.g. wetlands in aerial river management should be explored as well. However, full reception of aerial river benefits from smart reforestation or other approaches will, in most cases, require cross-border cooperation, which is arguably the key to sustainably managing the interrelated systems that underlie a livable planet.

3.7 Appendices

Appendix A. Selection of the MIP

Apart from surface river upstream basins having clear boundary, the aerial river source regions for a given target sink, the precipitationshed, does not have fixed and deterministic boundaries (Keys et al., 2012) and can be very broad. However, due to the fact that aerial river connections between sources and sinks are spatially different (Keys et al., 2014), there exist prominent contributing source regions governing a given sink’s rainfall more efficiently. The collection of the most important source regions is defined as the Most Influential part of Precipitationshed (MIP) (Weng et al., 2018). By including the most important components of the source areas, the MIP governs a given proportion of a target sink’s precipitation within the smallest land surface area. Thus for managing the aerial river, identification of the MIP can be considered a budget-efficient approach.

The selection of the MIP from the precipitationshed of a given sink requires a threshold which depends on the study purpose. Previous studies applied different standards as thresholds. e.g. 70% of the precipitation (Keys et al., 2012) or 1% of the precipitation from continental sources (Keys et al., 2017). In the present study, we are interested in the terrestrial source areas since we aim to analyze reforestation within the MIP. Figure 3.A1 shows the terrestrial component of the precipitationshed for Santa Cruz de la Sierra. We further use a boundary of 40% terrestrial contribution (the 0.4 contour on Figure A1.) to determine the MIP in our study. This threshold was a trade-off between enough aerial river influences (terrestrial contribution represents 53% of the precipitation received in Santa Cruz de la Sierra) and the fast growing size of the MIP when the threshold value goes up. As Figure 80

3.A2 shows, there is a change in the characteristics of the increase of the aggregated area around aggregated 40% contribution. Up to 40%, we observe an almost linear increase while for larger contributions the area increases super-linearly (approximately exponential). Other breakpoints deliver significantly worse R2 - values. We arrive at the 40% continental contribution as threshold applied in our study since thresholds above imply the inclusion of less efficient areas. Furthermore, the MIP identified here designates reforestation high potential areas close to the national reforestation target in Bolivia. While the chosen threshold was more policy target-oriented in our study, the selection of the MIP threshold for future studies, however, will have to follow their study purposes.

Figure 3.A1 The terrestrial precipitationshed of Santa Cruz de la Sierra. The contour value represents the delimited area’s contribution to Santa Cruz de la Sierra’s rainfall that is from the continental recycling. The 0.4 contour was the threshold used for selecting the MIP in our study (the blue region in Figure 3.1). Note that the delimited areas grow quickly as the contour value grows.

Figure 3.A2 MIP size-contribution relationship for Santa Cruz de la Sierra. Black solid line: size of the delimited area and its corresponding collective contribution to Santa Cruz de la Sierra’s rainfall from continental source (x-axis). Blue dashed line: up to 40% contribution the aggregated area increases almost linearly (R2 = 0.991). Red dashed line: above 40% the area increases super-linearly, very close to exponential growth (R2 = 0.997).

Appendix B. Reforestation potential in the MIP

The reforestation potential considered in our analysis for smart reforestation was based on the restoration opportunities map of the IUCN/WRI in which the forest growing potential was assessed by climate and current land use conditions (Minnemeyer et al., 2011; Potapov et al., 2011), shown in Figure 3.B. Intact forests and fragmented/managed natural forests were considered no potential for restoration. The restoration opportunities were constrained by human pressure taking into account population density and land use practices. Restoration opportunities were then categorized into four groups for degraded forestlands. These groups include wide-scale restoration (low human pressure; with potential to support closed forest), mosaic restoration (moderate human pressure), remote restoration (very low human pressure) and agricultural lands (intensive human pressure). In our MIP area, remote restoration areas are not presented. We used wide-scale restoration category as potential areas for smart

82 reforestation because it refers to areas where closed forests can possibly grow back on a large scale (Minnemeyer et al., 2011). Note that the restoration opportunities map used in the present study was an assessment at a global scale aiming to give indication for capability of lands to support forests. Identification of local reforestation sites should be complemented by other socio-economic investigations for interventions to begin with (Maginnis et al., 2014).

Figure 3.B Reforestation potential in the MIP. The MIP of Santa Cruz de la Sierra outlined by our network is the skin color area (conceptualized as the blue area in Figure 3.1). In the present study, the wide-scale restoration category (highlighted by dark green areas) is selected to describe smart reforestation areas. Source: Global map of forest landscape restoration opportunities (Potapov et al., 2011).

83 Appendix C. Operational scheme for smart reforestation

84 Figure 3.C Operational flow for smart reforestation. This operational scheme describes how we apply smart reforestation and estimate its potential as a water resource management option. TRMM TMPA is an abbreviation for Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis product (Huffman et al., 2007). MOD16ET is a Moderate Resolution Imaging Spectroradiometer (MODIS) evapotranspiration product using algorithm MOD16ET (Mu et al., 2013). Humidity and wind speeds input of the MOD experiment were taken from the ERA-Interim reanalysis product (Dee et al., 2011).

Acknowledgements

We are grateful for two anonymous reviewers for their constructive comments on this manuscript. This research was supported by the German International Climate Protection Initiative (project: Sustainable Latin America, reference number 42206-6157). We thank B. B. Walker, J. P. Kropp, and D. M. Landholm Haight for helpful comments on this manuscript and we appreciate S. L. Becker’s help on language. We also acknowledge R. van der Ent, K. Thonicke and A. Rammig for discussions on the WAM-2layers used in this study. The WAM-2layers model code is available on https://github.com/ruudvdent/WAM2layersPython under the GNU General Public License.

85 Chapter 4 Sustainable transitions following mega-droughts: on landscape development and regime interaction from the multi-level perspective

The contents of this chapter have been submitted to the journal Environmental Innovation and Societal Transitions:

Weng, W., Becker, S. L., Luedeke, M. K. B., Lakes, T., 2019. Sustainable transitions following mega-droughts: on landscape development and regime interaction from the multi-level perspective. Under Review. The peer-reviewed version may be published under a different copyright policy.

86 Abstract Mega-droughts can cause disruption to the affected society sparking a transition. We explore the causes and effects of the 2015-2016 mega-drought in Colombia. Using the multi-level perspective as a framework, we found that the mega-drought sparked an energy transition in Colombia whose dynamics were impacted both by the institutionalization of niches as well as the ability to predict the next drought. We were able to trace, using the current understanding of anthropogenic forces, the cause of the mega-drought to socio-technical landscape development influenced by human-induced warming and land use change. We found that the regimes in Bolivia and Brazil were able to influence the landscape through deforestation, and hence contribute to the intensity of a mega-drought in Colombia. The knowledge that a regime can cause disruption in regimes in other geographies and sectors has implications for transition research as well as decision-making for coping with droughts and other disasters.

4.1 Introduction Beginning in 2005, three once-per-century droughts (‘mega-droughts’ as described in Marengo and Espinoza, 2016) have occurred in South America in a period of ten years. These mega-droughts cause extreme climate conditions with impacts on ecosystems and livelihoods. They left communities without basic water access and resulted in substantial agricultural and hydroelectric losses (FAO, 2015; OCHA, 2016; UNGRD, 2016a). The more frequent occurrence of mega-droughts increases the need for a societal move towards sustainability1 in the face of future disasters2. Often, these changes include altering the socio-technical system composed of agri-food, energy, transport, and other sectors. These profound changes are referred as socio-technical transitions (here after transitions) in the literature (Rip and Kemp, 1998; Geels, 2004).

1 As defined in 1987 the United Nations Brundtland Commission “meeting the needs of the present without compromising the ability of future generations to meet their own needs.” (UNAI, 2019)

2 Disaster in the present study follows the United Nation definition: “ [a] serious disruption of the functioning of a community or a society causing widespread human, material, economic or environmental losses which exceed the ability of the affected community or society to cope using its own resources” (UN, 2004)

87 Transitions towards sustainability (hereafter ‘sustainable transitions’) are ‘goal-oriented’ in addressing future environmental problems (Smith et al., 2005). Since they do not necessarily offer obvious and immediate actor benefits, it often requires new policies introducing extra economic and regulatory frameworks, e.g. subsidies, taxes, and rules to facilitate sustainable transitions (Geels, 2011). These policies are however, constrained by a larger physical and socio-economic environmental setting (van Driel and Schot, 2005). By restricting possibilities for action and responses, the larger social, political, economic, and natural environment influences the policy changes and their approach to sustainability (Thelen, 2014, Geels et al., 2016). For instance, prolonged heatwaves limit the ability of leisure firms to respond considerably to electricity saving subsidies. Elections and macro-economic trends influencing decision-making are also examples. Disasters play a dual role in influencing a sustainable transition. Firstly, it may shock the systems and provide an opportunity initiating or accelerating a transition (Birkmann et al., 2009; Becker and Reusser, 2016). Secondly, the nature of the disaster also shapes action and responses involved in the transition. Therefore, it can be a factor in determining whether a transition occurs and leads to the overall target of sustainability. So far, the role of disasters in providing opportunity for changes have been rather explored (Pelling and Dill, 2010; Becker and Reusser, 2016; Brundiers and Eakin, 2018) while the trajectory of transition following the developments within the disaster is seldom investigated. The present study will focus on studying both aspects utilizing the multi-level perspective (MLP; see Figure 4.1), one of the leading frameworks for understanding transitions (Markard and Truffer, 2008). Designed to explain social-technical transitions (Geels, 2002), the MLP enables the conceptualization and the investigation of multi-dimensional and configurational changes of sustainable transitions following disaster events (Becker and Reusser, 2016). It operates under three analytical levels, listed here from highest to lowest structuration: landscape, regime, and niche (Geels, 2004; Verbong and Geels, 2007). The landscape level consists of slowly changing “deep structural trends" (Geels 2002) and includes such things as the environmental setting in which the transitions take place (Rotmans et al., 2001; Geels 2011). The regime level, is "a coherent configuration of technological, institutional, economic, social, cognitive, and physical elements and actors with individual goals beliefs or values" (Holtz et 88

al., 2008). In this article, it will be used to explore societal rules, regulations, and power dynamics. A disaster event is a result of hazard (here in this study: drought) at the landscape level and its interaction with the regime. The niche is where innovations are experimented with and shielded until they can gain enough momentum to challenge the regime (Kemp et al., 1998; Markard and Truffer 2008; Smith and Raven 2012). Niche-innovations can be seen as the innovative policies developed in response to disaster (Becker and Reusser, 2016) as a contrast to the preexisting arsenal of policy measures used by the regime in disaster responses. A transition happens when a new regime replaces an old regime through the interaction of the different levels (Geels and Schot, 2007).

Figure 4.1 Multi-level Perspective on transitions introduced by Geels (2002), the different filling patterns of the circles represent multiple socio-technical dimensions, e.g. culture, policy, industry, and technology dimensions.

To further analyze the trajectory of a transition following disaster events, we will use the typology of transition pathways entailed from the MLP (for more detailed explanation on the pathways see section 4.2) which help the examination of interactions between regime and landscape (Geels and Schot, 2007). Observations on actors and institutional interplay within a sustainable transition will be included in our analysis of the pathway since their importance in 89 understanding the typology of transition is highlighted by the literature (Geels et al., 2016). A particular focus will be on how these were influenced by landscape development. We examine the case of hydropower dependent Colombia following the mega-drought 2015-2016 while the energy sector under drought pressure remains to be documented in discussions of sustainable transition (Geels, 2011; Turnheim et al., 2015; Köhler et al., 2019). To do this, we use outputs of scientific study to qualitatively analyze the dynamic physical climate (drought hazard) landscape, which is often conceptualized as nearly unchanging in the literature (van Driel and Schot, 2005). This allows an insight into the developing landscape, a rather unexplored ‘residual category’ in the MLP literature, and its influences on transition typology (Geels, 2011; Geels et al., 2016). Moreover, we will explicitly elaborate on the landscape development by also asking the reverse causality where regime shifts contribute to drought hazard landscape development to further contribute to the discussion on transitions in the context of landscape-regime interactions (Geels, 2006; Geels, 2011). This chapter will explore the following research questions, 1. Was there a transition as a result of the 2015-2016 mega-drought in Colombia? 2. How did the nature of the shock influence regime institutions and transition development? 3. What landscape and regime developments can be traced as the cause the 2015-2016 mega-drought in Colombia? 4. What does this new insight mean for the energy transition in Colombia and for the multi-level perspective? In the following section, we first introduce the theoretical framework that enables analysis of the transition typologies responding to landscape development and the evidence selecting method used to track the transition. We then briefly present the study event of the South American mega-droughts. Next, we describe the conceptualized Colombian energy transition pathways along with landscape development. This includes our observation on the institutional interplay and landscape dynamics influencing the transition pathway. It then leads to our discussion on interrelated landscape and regime development shown from the pathway analysis of our case. We then summarize new theoretical insights and recommendations for policy before we conclude from the findings.

90 4.2 Tracking trajectories of transition under landscape development To understand transition following landscape development, we used the typology of transition pathways constructed by Geels and Schot (2007) describing the multi-level (between landscape, regime and niche) interactions for our analysis. However while researchers often focus on the bottom-up, niche-driven aspects of transitions (Berkhout et al., 2004; Geels 2011), we will use this typology of transition pathways to enable insight into landscape changes and their interaction with regime and niche levels following evolutionary and social-institutional aspects. The typology of pathways is based on the timing and nature of multi-level interactions. Timing refers to that of landscape pressure on regimes generating ‘windows of opportunity’ for transition. It also relates to the state of the niche at the moment, whether it is matured to take advantage of this window for transitions. The nature of the interactions refers to (1) a reinforcing (stabilizing existing regime, not driving transitions) or disruptive landscape (pressure on regime, opportunity for transitions) development and to (2) competitive niche-innovations (with an aim to replace existing regime) or symbiotic niche-innovations (as enhancement to the regime). In the context of landscape development, Geels and Schot (2007) employed Suarez and Oliva’s (2005) characterization of environmental changes (see Figure 4.2) to describe different natures of landscape developments. Building from above points, Geels and Schot (2007) proposed the following transition pathways: (1) Transformation: Moderate disruptive landscape pressure on the regime when niche– innovations have not been sufficiently developed. Changes enacted by regime actors modifying existing development paths. Changes are moderate and gradual. New regime grows out from the old regime with gradual adjustments and add-ons of symbiotic niche-innovations. (2) Reconfiguration: Symbiotic innovations initially adopted in regime to solve local problems trigger architectural changes creating space for new adoptions of niche-innovations. Sequences of component innovations add up to major changes in guiding techniques, beliefs, and practices. When a new regime in this path also grows out of an old regime, it differs from the transformation path via changes in the basic architecture enabled by cumulative adoption of niche components (3) Technological substitution: Competitive niche-innovations are mature when windows 91 of opportunity from disruptive landscape pressure open. The new niche will use the momentum and replace the existing regime entailing power struggles. (4) De-alignment and re-alignment: Landscapes changes (avalanche changes) lead to regime problems. The regime erodes, experiences collapse, and eventually de-align. Regime actors lose faith and no longer defend the regime. A vacuum is built if no specific niche-innovation is sufficiently developed. At this moment, a lack of stable rules leads to the emergence and co-existence of multiple embryonic niche-innovations. There is a prolonged experimentation/competition period for attention and resources until eventually one niche-innovation becomes dominant and it re-aligns a new regime. (5) A sequence of transition pathways: If landscape pressure has the form of ‘disruptive change’ which develops gradually while becoming disruptive, regime changes might experience a particular sequence of pathways beginning with transformation or reconfiguration and followed by substitution or de-alignment and re-alignment.

Figure 4.2 Typology of landscape development (Geels and Schot, 2007) using characterization of environmental changes from Suarez and Oliva (2005). Frequency: number of disturbances per unit time. Amplitude: the deviation of a disturbance from initial conditions. Speed: rate of change of disturbance. Scope: dimensions being affected by the disturbance.

92 4.3 Study event: the 2015-2016 mega-drought The more frequently occurrence of mega-droughts in South America has motivated our exploration of the most recent (to the time when this chapter is written) mega-drought event, that occurred in 2015 to 2016, and the related transition. During the 2015-2016 drought phase, deficient rainfall led to a low river level in the Amazon River accompanied by the strongest warming of the Amazon forest in the last century. The 2015-2016 drought surpassed the severity of previous strong El Niño events (e.g., 1982/1983 and 1997/1998) and mega-droughts (e.g., droughts that happened in 2005 and 2010) (Jiménez-Muñoz et al., 2016; Marengo et al., 2018). Substantial damages were reported from the Amazonian and adjacent countries (WFP, 2016). In Colombia, a red alert was declared starting in December 2015 because of deficient rainfall in the wet season of October and November which resulted in extreme low levels of both the Magdalena and Cauca rivers, the major rivers of the country. By January 2016, over 124 municipalities were affected by the drought. People in Valencía, Córdoba were without potable water. Agricultural losses were reported from 45,000 producers in the Cesar region. Extreme dry conditions exacerbated the lack of access to food and drinking water in the poor department of La Guajira where malnutrition killed 93 children in 2016. As a result, various governmental actions were taken to recover from damages of the particularly severe drought disaster (UNGRD, 2016b). Furthermore, as a country with over 70% of its power mix from hydropower plants (see Figure 4.3), Colombia experienced a nation-wide energy crisis resulting from the drying of rivers during the record-breaking 2015-2016 mega drought. This has led to significant national policy change aiming for a more sustainable power mix3. Therefore we choose this case and trace the Colombian energy transition along with the drought landscape development to explore trajectories of a sustainable transition under landscape pressure and their implications for regime-landscape interactions. We use a variety of sources to confirm the presence of evidence for each step in the conceptualized relationship between the regime and 2015-2016 drought disaster. In particular, evidence was located by examining

3 “This government was, is and will be the government that promoted the energy transition towards a sustainable energy mix, adaptable to climate change, and that provides security in the power supply.-- Germán Arce Zapata, then minister of Mines and Energy (PV magazine, 2018)

93 peer-reviewed papers, working papers, book chapters, government reports and speeches, and media reports.

Figure 4.3 The share of Colombian power mix from different sources in 2015. Data Source: Unit for Mining and

Energy Planning (UPME, 2015).

4.4 Energy transition in Colombia along with the developing drought landscape Actors began to perceive changes within the regime when, starting October 2015, the level of the Cauca and the Magdalena Rivers gradually lowered and in December, a red alert of drought was issued. This beginning phase of changes had seen a moderate response among the actors. The XM, the company in charge of the country’s energy system management, had originally projected to the public that the reservoir level was acceptable, but then declared an amendment in their projection when the drought crisis worsened later in March. Meanwhile, there was generally insufficient effort to save energy within the country before the drought worsened while energy consumption was higher in January and February than the same period in the previous year (neutral El Niño-Southern Oscillation phase) (El País, 2016). Pressure on the regime continued to develop as the drying persisted. Accompanied by an unexpected fire at the hydro facility in Guatapé in March 2016, the drought resulted in a deep crisis in the country’s hydropower generation. Though at a low speed, the drying condition developed and gradually became increasingly disruptive. Although normally an exporter of energy to neighboring countries, Colombia was on the edge of energy rationing and had to import energy from neighboring Ecuador (The Bogotá Post, 2016). The crisis resulted in the

resignation of the minister of Mines and Energy (El País, 2016). Landscape pressure from drought developing in a low speed gradually formed a disruptive change of energy crisis. This ‘window of opportunity’ however opened when niche-innovations (e.g. solar power and other alternative energies) were not sufficiently developed (El Tiempo, 2016) and a sequence of transition pathways (Geels and Schot, 2007) was observed. Various changes in the regime were introduced by the regime actors corresponding to the gradual development of drought landscape pressure. Initially actors appear to perceive a moderate landscape pressure. Starting from incremental rule changes, the government imposed incentives for households to save energy and punishment on those using more energy than the average (El País, 2016; Portafolio, 2016). In March 2016, the energy crisis becomes more prominent as a result of the length of time with deficient rainfall occluding hydropower generation. Then, newly approved guidelines enabling companies (cogenerators) to sell energy surpluses in stock price facilitated, for the first time, a considerable amount of cogenerators’ contribution in Sistema Interconectado Nacional (SIN, National Interconnected System) (El Tiempo, 2016). These local symbiotic innovations were adopted by the regime and triggered architectural changes creating space for new adoptions of niche-innovations (reconfiguration pathways). In May 2016, there was the first approval from Unidad de Planeación Minero Energética (UPME, the Mining and Energy planning unit) of an energy project that exempts companies from VAT by investing solar panels (El Tiempo, 2016). These cumulative changes, though triggering architectural adjustments in the regime were, however, considered insufficient by firms as viable energy model, according to Ser Colombia, the association of renewable energies (El Espectador, 2016). When the rainy season arrived in April of that year, the dry condition temporarily ceased. Before this window of opportunity closed, only a few developing innovations were observed (El Espectador, 2016). This was from the result of a lack of investment guarantees, to support niche developments, according to the Derivex, company specialized in the market of derivatives of energy commodities (El Tiempo, 2016). Then, after the 2015-2016 mega-drought and before the beginning of the next El Niño phase, there was a gradual increase in political pressure enabling the rise of niche-innovations. This pressure was not directly from the physical climate, but from the fear of next onset of El Niño (we call that a ‘predictive shock’, for details and definition see section 4.4.1). Aiming at 95

power mix diversification, the Ministry of Mines and Energy issued decree 2253 (MinMinas, 2017) and decree 0570 (MinMinas, 2018a). While decree 0570 aimed to encourage long-term contracting of renewable energy projects, decree 2253 addressed incentives on investments of gas and oil reserve exploration. The then minister of Mining and Energy, Germán Arce Zapata, commented that “the diversification makes us less vulnerable in cases such as the El Niño phenomenon” (MinMinas, 2018b). In this opportunity window provided by predictive shock, incumbent actors lost faith in hydropower dominance. Hydropower de-aligns while niche-innovations are not yet sufficiently developed, featuring the beginning of a de-alignment and re-alignment pathway (Verbong and Geels, 2010). Here a policy strategy stimulating niche-innovations and nurturing the emergence of a new system has been observed (e.g. the enactment of decree 0570 and decree 2253). The next El Niño phase occurred in late 2018 as forecasted and dry conditions again caused concerns over the water and energy supply (El País, 2019). When this window of opportunity presented, there was again no niche-innovation with the strength to outright challenge the regime. Meanwhile, multiple embryonic innovations popped up, competed for attention, and co-existed under the encouragement of the government (pre-realignment). The first renewable energy long-term contract auction was held in February 2019. These projects consist of solar, wind, and biomass source generation and distribution aiming to boost the development of the renewable source share in 2021 to 10% of the energy supply, according to María Fernanda Suárez, the minister of Mining and energy (Portafolio, 2019). On the other hand, the government was eyeing gas-fired power plants as the solution to the drought-induced brownout (Umwelt Bundesamt, 2018). The government seeks to enhance natural gas-fired capacity by adding an additional 3841 megawatts (24% of the current total supply) by 2028 (Oil Price, 2019). Consequently, gas-fired plants are currently gaining momentum. The growing consumption of power and the doubts about the newly introduced renewable energy projects have encouraged investment in both natural gas and oil reserves. Starting late 2017, Colombia began to regularly import natural gas despite a growing domestic gas production highlighting the gap between local supply and rising demand. This shortage saw energy companies such as Canacol (most of its natural gas production is from the Magdalena basin) expanding its investment in gas reserves and its operation in Colombia (Oil Price, 2019). 96

In sum, the trajectories of transition along with the gradual development of landscape pressure can be observed. Firstly, the regime actors addressed the problem by introducing incremental changes. Following the gradually worsening drought, a configuration pathway where cumulative adoption of component innovations together contributed to major changes in guiding techniques and practices. Though the dry condition was temporarily solved by the coming rainy season at that point, a gradually increasing pressure before the next onset of El Niño opened up a second window of opportunity. This predictive shock (see section 4.4.1) sees regime actors losing faith in the hydropower regime (de-alignment pathway). It was only at this point the investment guarantees and viable business models to support niche developments were introduced. Niche-innovations did not become institutionalized to the point where they could challenge the regime. After the 2018 El Niño phase started, drought pressure on hydropower again opened a window of opportunity, however, the regime has not yet re-aligned with a new configuration. Currently, multiple innovations are competing for attention and resources, a clear sign of an ongoing transition. As transitions, even shock induced ones, can take many years, it is not unexpected that the transition caused by the 2015-2016 drought has not yet completed.

4.4.1 Landscape development-Predictive shock The drought landscape shock has a form of disruptive change according to the categories in Geels and Schot (2007), with a high amplitude and low speed typology (see Figure 4.2). A distinct characteristic of droughts from other studied landscape shocks in the MLP, e.g. cyclones (Becker and Reusser, 2016), societal values (Geels and Verhees, 2011) and changing trade patterns (van Driel and Schot, 2005) is that drought recurrence is almost certain for the Andean country. This generates another type of drought related landscape pressure before drought actually occurs. Between the 2015-2016 mega drought and the onset of the El Niño phase in late 2018, climate model projection from the IDEAM (Institute of Hydrology, Meteorology and Environmental Studies) had already placed warnings for the then upcoming 2018-2019 drought phase and dry condition (Caracol Radio, 2018). This predictability at that time asserted pressure on the regime even before the drought phase began since it meant the hydropower crisis would almost certain reoccur (Hoy Diario del Magdalena, 2018; Wradio,

2019). This low amplitude, low speed pressure on the regime generated actual institutional changes guaranteeing contracts for niche developments, and we have given it the name ‘predictive shock’ (see Figure 4.4).

Figure 4.4 Conceptualized drought related landscape shocks starting with the 2015-2016 mega-drought.

4.4.2 Institutional interplay under landscape development Colombian’s power mix features a strong centrally planned infrastructure including the dominating hydropower. This welfare regime pocket (Wood and Gough, 2006) ensures Colombian’s energy operations during normal times, but its strong structuration can act as a barrier to transition. This structural barrier is especially revealed when the hydro-power lost its ability to function during the drought. During the opportunity window because of the 2016 drought (see Figure 4.4, the valley of the first disruptive drought pressure,), a decentralization of the power mix was observed. Local solutions, such as energy surplus generated from enterprises and energy saving incentives, were imposed to incrementally cover the gap of the hydropower brownout (following reconfiguration pathway). However, the highly centralized energy institution was able to suppress the niche developments that challenged the incumbent regime because the legal basis for niche developments was not established. E.g. because of a lack of legal support for long-term contracts and investments in clean energy, firms did not consider further investments a viable business approach. Clear institutional actions that removed caps for alternative energy development was only seen during the second opportunity window of late 2017 until the onset of the 2018 drought. The predictive shock has clearly displayed less welfare of the hydropower-dominant path and resulted in the de-alignment of the hydropower regime. Political tension from the

presidential election in 2018 also provided pressure to solve the problem. Long-term contracts on investing renewable energies were guaranteed by decree 0570 for the first time. Gas-firing capability was strengthened and encouraged then (Oil Price, 2018) while a lack of firing facilities has constrained the past development of gas energy sources. These were however, the beginning of the re-alignment process. During the 2018-2019 drought, the second disruptive shock opened another opportunity window but no prevailing solution was able to displace the hydropower regime. The reason may be the close timing of the two windows of opportunity following the predictive shock and following drought disruptive shock two, and path-dependency (Wood and Gough, 2006). A full re-alignment of a new regime will need time to develop, especially for the institutional structure that was beginning to change before the onset of the 2018 drought. For example, few contracts were awarded for alternative energy despite a large interest from firms because the newly introduced auction mechanism was not yet properly implemented (Reuter, 2019). However, it is possible the ongoing transition will experience an acceleration during the next El Niño phase. The realignment of niche-innovations and their survival, will depend on their structuration, how they fit (Ramos-Mejía et al., 2018), or are empowered to fit (Smith and Ravens, 2012) into the landscape pressures the country faces. One of those landscape pressures is the occurrence of more frequent droughts.

4.5 Drought landscape developments It is almost certain that droughts will generate future shocks to hydropower systems. Regulated by the natural variability of El Niño South Oscillation (ENSO), El Niño phases usually lead to droughts in Colombia. Along with the El Niño phase, the warmer than normal Tropical North Atlantic Ocean has been known to lead to past ‘once-per-century’ mega-droughts (Marengo et al., 2011). Despite the above mentioned sea surface temperature forcing, anthropogenic factors linked with the terrestrial process are known to have contributed to the development of more recent droughts (Moore et al., 2007; Lee et al., 2011; Bagley et al., 2014; Alves et al., 2017; Erfanian et al., 2017). Erfanian et al. (2017) found the severity of the record breaking 2015-2016 mega-drought which resulted in Colombian energy crisis, cannot be explained

99 alone by the oceanic forcing. They highlighted the contribution of human-induced warming and deforestation (Erfanian et al., 2017). These two anthropogenic factors with different governing regimes influence drought landscape development in Colombia via different atmospheric physical mechanisms. Human-induced warming is mostly a result of the growing atmospheric carbon concentration. Carbon dioxide, a major contributor to the past warming, accumulates in the atmosphere and distributes rather homogeneously around the globe (IPCC, 2013). The concentration of atmospheric carbon is thus determined by the collective global contribution, addressed by emitters around the world possessing equal individual effect. Human-induced warming has been regulated by global conventions limiting carbon emissions and worldwide temperature rise, such as the Paris agreement aiming at a 2 degree range above pre-industrial level (UNFCCC, 2015). The influence of the warming factor on development of the drought hazard landscape relies on the alliance of global entities in the conventions, including Colombia’s own regime (UNFCCC, 2016). On the other hand, deforestation, another anthropogenic factor known to govern drought severity has regionally specific contexts. Through aerial rivers 4, the drought landscape of Colombia (sink area5) is influenced by land users from a particular location (source area6) (Bagley et al., 2014; Keys et al., 2014; Weng et al., 2018). Furthermore, these source areas can locate remotely from the sink areas. Bagley et al. (2014) uncovered the remote contribution of deforestation to dry climate condition in South America using coupled land-climate modelling. Their simulation revealed that deforestation in a major moisture recycling7 area in South America (mostly out of the country Colombia, see pink box in Figure 4.5a) explains most of the land use induced drying in Colombia. It is worth noting that

4 Aerial rivers are the main pathways of moisture flow in the atmosphere, termed in Arraut et al. (2012) as an analogy to surface rivers. Opposite to surface rivers, aerial rivers gain water through evaporation and lose water from precipitation. Aerial rivers include the moisture recycling process in footnote 7.

5 Here we use the term ‘sink’ from the aerial river community to conceptualize the receivers of a certain flow, here land use change’s effect on dry climate condition.

6 Same to footnote 5, the term ‘source’ is used to conceptualize the providers of a certain flow.

7 The moisture recycling process describes that the moisture which is evapotranspirated again becomes precipitation, and that the moisture which is precipitated again becomes evapotranspiration within one given domain (Trenberth, 1999).

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the past land use changes within this area (pink box of Figure 4.5a) were concentrated in Brazil and Bolivia. Another forward trajectory simulation by the same authors which specifically focusing on Central-Western Brazil and Eastern Bolivia more clearly underscored the remote influence of deforestation in this area enhancing the drying condition in Colombia and Peru (see Figure 4.5b).

Figure 4.5 Dry season simulated impact of deforestation on forward trajectory precipitation in Bagley et al.

(2014). They show geography of precipitation reduction in the dry season resulting from land cover change in the pink boxes of: (a) major recycling area in South America, (b) Central-Western Brazil and Eastern Bolivia.

4.6 Remote regime influencing Colombian energy transition The aerial rivers connect the drought hazard in the sink area (Colombia) to the deforestation8 in the source area (Bolivia and Brazil for example) within the environmental landscape. This connection at the analytical landscape level has also bridged the regime in the source and the regime in the sink areas. In our example, the source regime shapes land use/land cover in the source Brazil and Bolivia, which is connected to the drought hazard in the sink Colombia in the landscape. Via this, it influences as well the resulting transitions from drought shocks in Colombia. In this section, we first trace the regime influencing drought landscape development in Colombia, then we provide a conceptualization of remote regime influence learned from our case.

8 Deforestation and forest losses in source area are conceptualized as part of the environmental landscape following its characteristic “deep structural trends" (Geels, 2002) as environmental setting of socio-technical regime.

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4.6.1 Source regime shapes connected landscapes The deforestation in the source area is known to be governed by various factors. Regions which had undergone deforestation in source area were mostly converted into pasturelands or croplands (De Sy et al., 2015) driven by the global demands for maize, soybeans, and beef (Nepstad et al., 2006; Laurance, 2007; Barona et al., 2010). Along with the global agricultural market influence, regional markets in wood, land, biofuel, and forest certificates (CRAs) are also known to have directly (on the production sites) and indirectly (elsewhere than the production sites) caused forest losses (Geist and Lambin et al., 2002; Lapola et al., 2010; Soarse-Filho et al., 2016). It is however not only the market regime that has shaped the deforestation landscape in the source area and influenced recent mega-droughts in Colombia. Since 1960s, population growth (mostly from immigration) and the related logging and mining activities along with the need for transportation network development have seen a constant increase in forest loss in the source region (Lawrance et al., 2001; Hansen et al., 2013). There was also a regime in the source area that decelerated forest loss. After deforestation rate peaked in 2004, a combination of strong legal forces managed to successfully slow down forest loss in the source area. This regime includes close monitoring, which enhances enforcement of laws, interventions on supply chains through soy and beef moratoria, restrictions on access to credit, and expansion of protected areas (Nepstad et al., 2014; Gibbs et al, 2015). However, a recent shift of this regime via a revision of the Forest Code (2012) has led to massive ongoing investments in road paving, and a reduction in established protected areas which have seen a rebound in the deforestation rate since 2013 (Soarse-Filho et al., 2014; Arima et al., 2014; Hansen et al., 2013). Intensification of deforestation in the Bolivian Santa Cruz department has also been observed since 2008, likely linked to the leakage from Brazilian soy moratoria (Kalamandeen, et al., 2018). Given that the Brazilian deforestation rate also hit a new high in 2018 (INPE, 2019), if no further changes or transition is to take place in the source area, the current source regime will continue to influence the landscape towards a furthering of the dry condition in the sink Colombia.

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4.6.2 Conceptualization of remote regime influence The above case inspired our conceptualization of how the regime in one locality can influence the regime in another through the landscape (Figure 4.6). Because this necessarily means referring to two regimes, we will refer to the regime doing the influencing as the source regime and the regime being influenced as the sink regime. Moreover, the source regime and sink regime need not be in the same sectors, as the case for our case study. Thus, the process begins in the source regime where the current regime is causing deforestation; this could be potentially through specific policies or specific actors. Through aerial rivers, the influence of this process crosses over administrative and international boundaries, meaning that the source area which has influence over this process does not necessarily feel the repercussions of their regimes practices through this path. The increasing deforestation in the environmental landscape intensifies the drought hazard in the sink area which asserts pressure on the sink regime and opens window of opportunity for transition. It is worth noting that the physical climate landscape here is in fact not ‘unchanged’ as in the literature (van Driel and Schot, 2005). Connecting regimes in different geographies, its development is a mechanism through which the source regime triggers the sink regime shift. A transition in the sink regime as a result of the landscape shock occurs then through different pathways depending on the timing and form of landscape changes as well as the strength of the niches. A sequence of transition pathways can be expected following a low speed, high amplitude disruptive landscape shock such as drought which would lead to a de-alignment of old regime and a re-alignment into a new regime (Geels and Schot, 2007) as in our specific Colombian case. Through this pathway, multiple niches may use the shock as an opportunity to develop and challenge the regime. Once through this process the old sink regime is replaced by a new sink regime, a transition has occurred.

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Figure 4.6 Conceptualization of how one regime influences another through the landscape. Arrows represent impact flows.

4.7 Repercussions for managing power generation in Colombia The current strategy of diversifying power generation was developed under the precondition that drought is influenced by the El Niño phenomena (PV magazine, 2018). Given the above mentioned anthropogenic influences on the intensity of drought, this strategy may need to be rethought. Investment in carbon related energy should be especially reassessed since it is not sustainable as releasing atmospheric carbon intensifies droughts. Though Colombia as one individual country encouraging carbon related energy may have limited influence on the warming of the planet, each country that does so makes it more acceptable for others to do the same thus lowers the willingness to comply with global conventions on reducing carbon emissions (a rotten apple can spoil the barrel). Should, as a result, more countries choose carbon related energy, there would be considerable effects on augmenting warming and an intensification of droughts can be expected. Therefore, a full re-alignment of renewable energies is the only approach to avoid domestic augmentation of the drought hazard as a result of the human-induced warming mechanism. Since currently carbon-related sources are competing with the renewable energies, policy interventions 104 should be introduced to phase out carbon-related sources in the power mix (Verbong and Geels, 2010). Efforts to lower the atmospheric carbon concentration along with global alliance should be recognized as a part of national security and sustainability targets because of their effects on reducing drought intensity. Raising awareness of the anthropogenic factors in the intensification of droughts among authorities will be crucial for facilitating these actions. However, even with a pure renewable energy approach, Colombia as a sink country is still passively influenced by the source regime through the landscape. Given that the source regime currently continues to develop in the direction of intensifying the dry condition in the sink Colombia, water and food security will continue to be threatened by the coming increasing mega-droughts. Furthermore, the country’s current path dependence on hydropower before re-alignment of new energy regime will continue to be prone to energy crisis. Mitigation measures not only reducing carbon emissions, but also slowing source countries’ deforestation should be taken. This can be realized through negotiation between source and sink countries directing source regimes toward land conservation and land trading measures that had successfully decelerated forest loss in source areas in the past (Arima et al., 2014; Gibbs et al., 2015; Nepstad et al., 2014). It is worth noting that, according to the results of Bagley et al. (2014), the landscape as influenced by source area’s deforestation is also able to affect drought severity received by source areas itself. Thus it is in the interest of source countries to change the regime as well, especially as these countries also reported serious damage from the record breaking 2015-2016 drought. Additionally, the 2017 regional call for combating drought already offers a regional platform for co-management (UNCCD, 2017). Yet there still may be a lack of awareness of the anthropogenic factors causing the intensification of the droughts among the stakeholders since the current regional combat against droughts focuses on the preparedness rather than collaborative mitigation to halt intensification of drought through deforestation and carbon emissions.

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4.8 Theoretical insights

4.8.1 Pre-drought landscape shock In our case, two types of landscape shock from droughts were observed triggering the Colombian energy transition (see section 4), they can be distinguished from the character of the shock. The first type is the landscape shocks during the drought of 2015-2016 and 2018-2019, featuring a slow but large amplitude change which resembles the disruptive landscape shock in Geels and Schot (2007). Another type of the landscape shock features small scale and mild speed triggers, observed before the onset of the 2018-2019 drought. This type of landscape shock originates from the predictive capability of modern science (modelling capability on the onset of El Niño phase) accompanied with the threat of upcoming drought phase. In our case a sequence of transition pathways (internal changes – configuration pathway – de-alignment and re-alignment pathway) was observed, triggered by both types of shocks. The mechanism triggering a sequence of transition pathways in our case is different from that described in Geels and Schot (2007) where the de-alignment was triggered by increasing level of disruptive changes. In our case, the de-alignment of the hydropower regime happened at a time when hydropower was stable and the 2015-2016 drought disruptive shock had passed. However, actors lost faith in a fear of the next, forecasted drought. The small but gradually growing pressure of the predictive shock resulted in the de-alignment and re-alignment phase of Colombian energy regime. This type of shock, can be useful in understanding other informed (forecasting equipped) pre-disaster transitions that takes place before low speed but high amplitude environmental hazards e.g. floods and heatwaves.

4.8.2 Uninformed decisions away from sustainability: remote regime influences The physical landscape as a backdrop is not ‘not changing’ (van Driel and Schot, 2005; Geels, 2011) but its dynamics link remote or cross-sector (in our case the land sector and the energy sector) regimes and these could be key to transitioning to sustainability. This ‘telecoupling’ (Liu et al., 2013) through both social (e.g. global market chains) and natural (e.g. atmospheric, hydrological or biological) processes is often not recognized by current regimes but it could be crucial for achieving sustainability. In our case, where Colombia is not informed about the remote regime influences (Bolivian and Brazilian land use

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policies/market influences) on the drought, and thus its adaptation-focused policy will not be sufficient regarding continuously growing droughts. Furthermore, without recognizing the effects of human-induced warming on the drought hazard, a decision to encourage more carbon related energy in the power mix will very likely contribute to stronger domestic droughts rather than mitigating them. This calls for more investigation into other telecoupled systems that can be strategic for approaching sustainability. The MLP can provide a good analytical structure to understand the relationship between telecoupled source and sink regimes and the causality of source regime (Geels, 2011) on sink regime changes. The geographical nature of these linkages should be in particular underscored for further managing these source-sink linkages, which usually requires transboundary and trans-sectoral cooperation.

4.8.3 Power of the regime influence The source and sink regime linkage through aerial rivers demonstrates a power structure of one-side influence. Similar to a rather well-understood upstream-downstream power hierarchy, the remote regime in the source countries of Bolivia and Brazil exerts influence on the sink regime in Colombia. However, another anthropogenic factor influencing Colombia drought hazard, the human-induced warming through atmospheric carbon forcing, has a different power structure configuration. Due to the nature of carbon dioxide (the dominant output gas that causes the warming effect), a source area located anywhere on the planet (including Colombia itself) has the same level of influence on the sink regime. This underscores the importance of understanding landscape development and the power structure residing in the landscape bridge between the source and sink regimes. Reverse causality investigations in transition studies asking how regime shifts contribute to landscape changes (Geels, 2006; Geels, 2011) can be useful to identify those sink-source regimes. The management of these different structures can be linked with international relation studies and political ecology. For example, the Convention on Long-Range Transboundary Air Pollution (United Nations Economic Commission for Europe (UNECE, 1979; Sliggers and Kakebeeke, 2004)) which facilitates co-management by upwind and downwind regions – which also has a clear power hierarchy – may provide a feasible framework for developing management of the source and sink regime linkage through deforestation.

107 4.9 Conclusion We examined a shock-induced transition in Colombia and explored how the dynamics of the landscape influenced regime institutional development and the trajectory of transition. We also traced the influence through the environmental landscape as well as the source regime affecting those landscape developments which is rarely done in transition research. In doing so, the origin of the disaster-induced transition in Colombia is in part traced to regime pressures on the landscape in Bolivia and Brazil. This has consequences for the management of both a transition responding to landscape shocks and the remote regime influencing it. Having done this, we are able to provide both recommendations for policymakers as well as future research. The knowledge that through influencing the landscape, regimes can influence other regimes across geographies and sectors has repercussions for how policymakers should respond to landscape events: 1. Policy-makers should be aware of how remote regimes influences affect the stabilization of their local regimes so that they can put pressure on appropriate source regimes. For example, using our case study, Colombia could attempt to pressure on Brazil and Bolivia to reduce deforestation. 2. There needs to be a growing awareness that by putting pressure on the landscape, decision makers may be causing destabilization in other geographies and sectors. This may change the cost-benefit analysis of policies as the ramifications of those policies may change if potential sink regime effects are considered. 3. Given that regimes are able to influence each other through the aerial rivers, consideration should be given to forming international cooperation to manage the moisture flows (Weng et al., 2019) similar to international cooperation for surface rivers and air pollution (Ellison et al., 2017; Keys et al, 2017). However, the knowledge gathered from understanding that landscapes can transmit influence from one regime to another leads to further questions which should be explored in future research. Firstly, other transitions beyond our case should be traced to examine the processes of different types of landscape dynamics and remote regime influences. This could provide further insight into the processes of transitions and their causes. Furthermore such knowledge 108

would also help policymakers in deciding where to put pressure to reduce destabilization in their districts. Future research from a natural science perspective on mechanisms and geography of teleconnection (Lima and AghaKouchak, 2017; Boers et al., 2019) will be essential for tracing those processes and the development of efficient management tools. Regimes and transitions being remotely related by landscape dynamics can provide analytical insights into discussions of telecoupling in the Geography and the Earth system science community (Liu et al., 2013, Friis and Nielsen, 2017). While iteratively, contribution from these fields will also enlarge the understanding of landscape and remote regimes, rather unexplored areas in transition studies. Secondly, future research could explore how the prediction of landscape event, not limiting to disaster, influences the chances and dynamics of a transition. That Colombia began acting before the landscape-induced shock means that the predictability of the landscape events, not simply the shock event itself, pressures the regime and influences the transition. This may be particularly interesting as much of the effects of climate change have not yet come to pass and knowing how the prediction of those effects influences transitions may be insightful for sustainable transitions. Lastly, this research further highlights that so called “natural” disasters are not necessarily natural in their origins (UN, 2004). While up to this point the non-natural aspect of natural disasters was often the vulnerability of the affected society (Cannon, 1994; Thomalla et al., 2006; IOM, 2010), this research implies that remote non-natural influences also affect the chances of a “natural” disaster. Further research could trace these remote causes of disasters and by increasing our knowledge of the causes of “natural” disasters will help to make headway in mitigating them.

Acknowledgements This research was supported by the German International Climate Protection Initiative (project: Sustainable Latin America, reference number 42206-6157). The authors thank J. P. Kropp, Y. K. Liao, and Ö. C. Doğmuş for helpful comments on the manuscript.

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Chapter 5

Conclusions

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5 Conclusions

5.1 General achievements

This thesis has advanced the knowledge about relevance of aerial rivers for societies’ reliance on connected land and water systems. Scientific frameworks that facilitate management of aerial rivers have been provided to approach a more sustainable practice of land-water systems.

This thesis has conducted the first quantitative estimation of land use change influence through the aerial river process to downwind water availability and explicitly exposed aerial rivers’ relevance in water management in the context of fast changing Amazonian landscape. The quantitative analysis was enabled by the “Most Influential Precipitationshed (MIP)” framework introduced in the thesis. The MIP as a management-oriented framework can be used as an instrument to identify upwind hotspots for moisture conservation, for a given region. In addition, its mathematical formulation enables the quantitative assessment of land use change influence on the water availability for a given downwind region. An application of the MIP framework has been shown in Chapter 3 where smart reforestation in the MIP can be an aerial river management tool to increase fresh-water resources in a downwind city.

Moreover, the thesis has enriched the scope of managing atmospheric water. Most literature stresses the need of managing atmospheric moisture because of its role in sustaining water availability (Dirmeyer et al., 2010; Bagley et al., 2012; Keys et al., 2017; Certini et al., 2019; van Noordwijk and Ellison, 2019) but few scholars extend this recommendation to management of moisture for extreme events. This thesis has not only provided quantification of aerial river influence on water availability, supporting existing literature, but also brings the influences of aerial rivers on extreme events into focus. Based on the emerging knowledge of aerial rivers’ influence on the intensity of droughts, the thesis explored the aerial rivers’ potential as a mitigation option for extreme climate events. This thesis also broadened the dimensions of aerial river management by investigating the social components intertacting with the aerial river process. Via a socio-technical perspective, the influence of the source regime on the sink regime through the aerial rivers has been traced. This has

111 enabled the analysis of feedback between source-sink regimes and interactions with other social, political, and natural processes. Our analysis in Chapter 4 has shown that decisions over the sink regime in Colombia, if uninformed about aerial river influence from source regimes outside of Colombia, could lead to unsustainable practices. This illustrates that identification of social components is crucial in determining who and what should be managed when approaching aerial river management for sustainable land-water practices.

5.2 Answers to research questions

5.2.1 Relevance of aerial rivers for current and future land and water practices in Amazonia

This thesis found prominent influences from both upwind deforestation and upwind reforestation through the aerial rivers on downwind water availability. From a theoretical approach, it quantifies deforestation’s general impacts on rainfall and runoff. Impacts from converting the forests into alternative land use types on annual rainfall and runoff have been provided. A spectrum of alternative land use types can be used to understand ranges of mixed land use change, e.g. the conversion of forests to bare lands and/or pasture, currently ongoing in Amazonia. As for reforestation influence, a planning-oriented investigation has shown that smart reforestation at the upwind MIP can increase a downwind city’s water supply as a management option for future water practices. From these quantitative results, the thesis has demonstrated that aerial rivers are relevant for managing current and future water in the context of both ongoing and planned land practices (e.g. deforestation and reforestation).

The investigation of aerial river relevance was led by the following questions:

Q1 What is the extent that upwind land use change can influence downwind precipitation?

Q2 To what extent does land use change influence downwind runoff through the aerial rivers?

Q3 What is the influence of reforestation projects on water availability in the downwind regions through the aerial rivers?

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Q4 What does the influence of land use change on downwind water availability imply for land and water management?

In Chapter 2, deforestation in the upwind hotspot (the MIP) has been found to reduce (depending on the alternative land use types) the annual precipitation received in the target region of the Ucayali River outlet from 5-12% (Q1). Converting forests into bare soil will see a more significant reduction in rainfall downwind than converting forests to pastureland and rice cropping. The investigation in Chapter 2 demonstrates that, in general, upwind deforestation reduces moisture provision to the aerial rivers and in turn decreases the precipitation received in the downwind areas. However, the MIP exerts the strongest aerial river impact by smallest area that collects the most important upwind moisture contributors. It represents the hotspot for moisture conservation. For instance, in the Ucayali River outlet investigated in Chapter 2, the MIP covers only 3.5% the Amazon basin but is responsible for 50% of the Amazonian moisture provision to the target region. The MIP framework has been used here to overcome the dilemma of choosing size or importance of precipitationshed that has previously led to large area selection of upwind regions for meaningful analysis (Keys et al., 2012, Keys et al., 2018). It is also worth noting that the changes in precipitation following land use change in the MIP can be larger for regions dependent on continental aerial rivers for precipitation, such as the sensitive areas identified in Chapter 2 (see yellow areas in Figure 2.3).

The response in the target region’s runoff reception following upwind deforestation has been found to be varied, with the difference in response depending on the location of the deforestation. For a given region, deforestation (in general) decreases moisture input into the aerial rivers, reducing both the precipitation received by its surface river catchment as well as the runoff it receives (Q2). For instance, the annual runoff reception of the target region in Chapter 2, the Ucayali River outlet, is found to decrease 4-9% (depending on the alternative land use types adopted) following deforestation in the MIP. The most prominent reduction in runoff is found when deforestation happens in the MIP of the catchment. In the example of the Ucayali River outlet, deforestation in the MIP of its catchment results in a 19-50% (depending on the alternative land use types adopted) reduction of its runoff reception. Conversely, when deforestation occurs in the given region’s upstream catchment, the 113

decrease in evapotranspiration provides less input into the aerial rivers, but at the same time retains more water in the catchment budget. The runoff reception will be the net response of the two effects. Normally, the ‘retain effect’ surpasses the aerial river effect and leads to runoff increase. The offsetting extent differs from catchment to catchment, subject to individual catchment characteristics and the importance of aerial river input for catchment precipitation (see Figure 2.6). In the case of Ucayali River outlet investigated in Chapter 2, deforestation in the Ucayali basin produces an increase in runoff reception by 27-103% (depending on the alternative land use types adopted).

This thesis has explained that the diversity of results from modelling studies and empirical research examining deforestation impacts on runoff is due to spatially different land use change impacts. Macro-scale estimates may reflect aggregated influences and responses from different spatial components, e.g. deforestation inside a catchment (resulting in runoff increase) and deforestation outside a catchment (resulting in runoff decrease). Meso-scale estimates may hold insights for catchment-specific responses, but their results may also differ depending on the location of deforestation included in the scope of investigation. The spatially explicit investigation on the impact of land use change on runoff has enabled the thesis’ exploration on the connection specifically between the aerial rivers and surface rivers, which, to the author’s knowledge, is the first of its kind. The advanced knowledge of the interlinked aerial and surface hydrology holds new implications for land-water management: strategic management of MIPs of the surface river catchments is critical for conserving moisture-sustaining runoff (Q4).

This thesis has also explored the potential of managing aerial rivers for downwind water availability through land planning. Reforestation has been employed in the investigation as it is one of the most active land planning processes in Amazonia, (Chapter 3). Based on the restoration opportunities map from the IUCN/WRI, this thesis has quantified that reforestation, where feasible, in a rapidly growing city’s (Santa Cruz de la Sierra) MIP can increase the city’s annual rainfall reception by 1.25 %, annual runoff reception by 2.23%, and dry season runoff by 26.93% (Q3). These results provide an answer also to Q1 and Q2 from an application perspective, as they are derived from the aerial influence of a specific upwind land use change plan (smart reforestation, see 3.2). 114

Using population growth projections, the increase in the fresh water supply from smart reforestation has ultimately been found to be able to cover between 22% and 59% of Santa Cruz de la Sierra’s growing water demand (depending on the scenarios used) by 2030 (Q4). This shows that planned reforestation which takes advantage of aerial rivers has the potential to be considered an effective water management option for the Bolivian economic capital.

Apart from water management, the findings on aerial river influences from smart reforestation also have implications for land planning in Amazonia and beyond. These results have added new insights to both the selection of strategic sites for fulfilling reforestation targets and the cost-benefit assessments of reforestation projects (Q4). Restoring forests in upwind hotspots, i.e. the MIP, of water-scarce regions can be a strategic way to optimize the benefits while executing national or regional reforestation commitments. It can be especially ideal when the MIP is located in places relatively rich in runoff, such as the El Beni and Pando regions discussed in Chapter 3. Thus, the impacts on downstream runoff from reforestation which have previously induced low societal acceptance in other reforestation projects (Cao, 2011; Cao and Zhang, 2015) can be minimized. After all, the aerial river benefits from reforestation attest to the possibility of breaking the usual trade-off between forests and water supply (Farley et al., 2005; Brown et al., 2007; Egginton et al., 2014; Connor et al., 2015). In the example of Santa Cruz de la Sierra, the increase in water supply from smart reforestation is considerable and can be beneficial for the rapidly growing city. This shows that benefits carried by aerial rivers can be impactful and should be assessed along with other reforestation benefits and costs (Jackson et al., 2005; Ouyang et al., 2013; Bryan et al., 2014; Connor et al., 2015; Dittrich et al, 2019). From this, a more holistic perspective can be brought to leverage the largest benefits while outweighing the costs (Calder, 2007) of reforestation and other land projects.

5.2.2 The approach to aerial river management

In addition to introducing the MIP framework to facilitate aerial river management, this thesis has also explored the social components interconnected with the aerial rivers, crucial for more comprehensive planning and process of aerial river management. The social environment and conditions key for bringing smart reforestation into force were investigated

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through field work in Santa Cruz de la Sierra. The findings have led to the development of a policy brief on smart reforestation which has been approved by the authority (also see 3.5). Moreover, in Chapter 4 the socio-technical regimes which interact with the aerial river process have been traced. Aerial river management for extreme climate event mitigation has also here been brought into focus, broadening the aerial river management discussion. The interactions and feedbacks between the traced socio-technical regimes and the aerial rivers have been analyzed and found to be critical for achieving sustainable land and water practices. This thesis has overviewed the case investigation and produced a theoretical framework for further identification of other social components interrelated with the aerial rivers, as their integration is key to achieving a more holistic aerial river management system.

Exploration on the approach of aerial river management was based on the questions.

Q5 How can the implications of Q4 be considered in practice?

Q6 How can aerial river management be used as an instrument for mitigating the damage to land and water practices from extreme events?

Q7 What social components are linked with aerial river management?

Q8 How do social components function and interact with other components and what do the interactions imply for aerial river management?

Following the discussion of the implications of smart reforestation for water management of the city Santa Cruz de la Sierra, in Chapter 3, the possibility of realizing smart reforestation in the research area has been explored. It has been found that the realization of smart reforestation is possible given the legal basis of the PNFR reforestation program (Programa Nacional de Forestación y Reforestación – PNFR, Decreto Supremo Nº 2912), in which Bolivia has a national commitment of reforesting 4.5 million hectares before 2030 (Q5). Smart reforestation is also in-line with other legal items such as Ley 300, Ley 1333 and Ley 1700, and the nationally defined NDC (Nationally Determined Contributions) goals presented for the Paris Agreement Framework (UNFCCC, 2015). Though the target of smart reforestation covers 7.1 million hectares, the Bolivian portion totals 3.2 million

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hectares, and can be fulfilled by executing existing commitments. In the region, the implementation of previous reforestation projects had been halted mainly because reforestation sites were planned where human pressure is present. This pressure arises from needs for agriculture/food and commercial profits of sediment mining that are challenged by reforestation projects. This social background obtained from the field work (see 1.4.4) has inspired the inclusion of an assessment of human pressures for smart reforestation. Developed with local practitioners, a policy brief has been delivered to and approved by the Santa Cruz government officials (the director of Natural Resources), which proposes smart reforestation to strategically execute national reforestation targets as well as to enhance Santa Cruz de la Sierra’s future water supply, all while avoiding sites with potential conflicts of interest. If implemented accordingly, it will be the first aerial river management process brought into effect (Q5, Supplementary 1).

The discussion of aerial river management has been extended to include a discussion of the influence of aerial rivers on extreme climate events in Chapter 4. The investigation was focused on the ‘once-per-century’ mega-droughts that have increasingly occurred in Amazonia in recent decades (Marengo and Espinoza, 2016) and how to approach aerial river management for mitigating these disasters. Using the multi-level perspective (MLP, see 4.1), this thesis has qualitatively investigated aerial rivers’ contribution to the 2015-2016 mega-drought and the energy transition in the electricity sector following it, in the hydro-power dependent country of Colombia. Interactions between social system components and the aerial rivers in the context of mega-drought influence have been traced and analyzed. Another anthropogenic mechanism known to contribute to mega-droughts, human-induced warming (Erfanian et al., 2017; Herrera-Estrada et al., 2019), has been included in the investigation for a more holistic view of linkages between components and processes.

This analysis has found that the remote source (sender) regime resulted in deforestation and, through the mechanism of aerial rivers, contributed to the drought development that in turn pressured on the sink (recipient) energy regime in Colombia. Changes in this remote regime are driven by transportation network development, population growth, land policy in Brazil and Bolivia, as well as global and regional markets of agricultural products, land and forest certificate (CRAs) (Q7). A conceptualization of remote regime influence, in which the 117

source regime (Brazil, Bolivia and beyond in the case of Chapter 4) influences the sink regime (Colombia) in different geographies through connections at the landscape level (deforestation-aerial rivers-drought), has been developed (see 4.6).

The agency of the sink regime following drought development (landscape level) has been observed from the institutional and actors’ interplay in the Colombian energy regime (4.4.2). This has facilitated the inspection of the sink regime’s responses to the drought pressure, where feedback from the sink regime intensifying droughts has been found.

An ongoing transition in the Colombian energy regime, in the electricity sector, has seen a clear trend of hydro-power de-alignment and subsequent uptick in the development of gas-fired power. Given human-induced warming’s contribution to drought, Colombia’s current decision to encourage gas-fired power in the power mix is not sustainable, as it increases atmospheric carbon intensifying drought. Additionally, this decision is also very likely to trigger a larger effect on intensification of drought if other countries follow the Colombian path of allowing more emissions (‘a rotten apple can spoil the barrel’, see 4.7) (Q8).

While focusing on securing power supply during drought by employing alternative energies, the adaptation-oriented decision of drought-pressured Colombia has neglected the fact that the source regime continues to intensify droughts through the aerial river mechanism. Given a recent shift of the source regime in Brazil leading to deforestation increase in both Brazil and Bolivia (Soares-Filho et al., 2014; Kalamandeen et al., 2018; INPE, 2019), further drying can be expected in in Colombia. If no changes or transition is to take place in the source area, the intensification of drought through the aerial river mechanism may accelerate the de-alignment of hydro-power and provide the opportunity for carbon-related power to re-align, consequently bringing stronger drought through the mechanism of human-induced warming. This shows that intensification of drought through aerial rivers can trigger a positive feedback through sink regime’s response (transition to realignment of carbon-related energy) that in turn reinforces drought intensification through another mechanism (human-induced warming) (Q8).

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In the case studied in Chapter 4, interventions should be taken with regards to the Colombian energy regime in order to phase out carbon-related energy sources in the power mix (Verbong and Geels, 2010), thus halting the feedback-reinforcing droughts through a human-induced warming mechanism. Even after doing so, the energy regime in Colombia will continue to be pressured by the remote regime intensifying drought through the aerial rivers, threatening also the water and food security of the region. This shows that the adaptation-oriented management of the drought-affected sink regime in Colombia is not sufficient. Mitigation measures reducing carbon emissions and slowing source countries’ deforestation should be taken (Q6). The latter will be more efficiently done by pressuring the source regime governing the deforestation traced in 4.6.

These results have implied that a more holistic management of the aerial rivers should be carried out, recognizing the social components interrelated with the process as well as their functions and interactions with other components in the coupled human-environmental system (Turner et al., 2003; Folke et al., 2005) (Q5). This thesis has proven that the MLP is applicable as an analytical framework for tracing socio-technical regimes related to the aerial river process. The remote regime influence found in the analysis of Chapter 4 has been conceptualized and can facilitate understanding of other remote regime influences through aerial rivers and similar landscape-level connections (4.6). For example, it can be used to detect the regimes related to smart reforestation in Chapter 3 for a more comprehensive cost-benefit analysis (see 5.3.1).

5.3 Repercussions for further developments

5.3.1 Recommendations for future decision-making and research

This thesis has laid out the scientific basis for the development of aerial river management. Here it provides recommendations for policy makers and future research in order to push further the development.

Firstly, for policy makers, land assessments should include the aerial river influence that has been confirmed significant in the thesis. Cost-benefit analysis of policies may change as well as the ramification of them if the influence from the aerial rivers is considered. For 119

example, the cost of forest clearing in water-scarce regions’ MIPs may be higher, and could possibly overweigh the benefits when aerial influences are included.

Secondly, cost-benefit analysis of policies may be more comprehensive when interactions and feedbacks in the coupled human-environmental systems are considered. This is also relevant for cost-benefit analysis of other policies beyond aerial river management. The identification of senders and receivers of the impact flow and the interactions between them can be helpful for finding potential feedback mechanisms. The analytical framework of the MLP can facilitate the detection of these interactions (see 5.3.2). For example, in the case of smart reforestation, the cost-benefit analysis (beyond the scope of this thesis focusing on aerial river relevance) can be more comprehensive when the regime governing reforestation in the MIP, the regime receiving smart reforestation impacts (on water, sediments and so on), and the interactions between these regimes are identified. It can also be a way to avoid potential conflicts of interest, the importance of which for smart reforestation has been highlighted in 3.1.

Thirdly, interventions of aerial river management should be introduced in a timely manner. The most urgent task will be determining influential sources and sensitive sinks to secure moisture for vulnerable areas. The methods of identifying these areas have been introduced in 2.3. Also, aerial river management for mitigating disasters should also be undertaken in a timely fashion. As demonstrated in Chapter 4, tracing the source regime that influences destabilized local regimes under disasters can help to identify the target to be addressed. From Chapter 4, for instance, this research finds that Colombia should attempt to put pressure on regimes governing deforestation in Brazil and Bolivia. Another crucial instrument to be developed for aerial river management is cross-border cooperation, which is discussed more in detail in 5.3.3. Furthermore, the urgency of aerial river management implies that ‘moisture security’ should be considered in the sustainable development process, preserving future water and mitigating damages from disasters.

For future research on this topic, two interesting avenues have arisen during the investigation of this thesis:

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Firstly, the thesis has exposed the connection between aerial and surface rivers, which should be further investigated to understand the hydrological cycle in a more complete way. The thesis has underscored catchments’ MIPs being the hotspots to preserve moisture for aerial rivers sustaining surface rivers. The opposite case, where surface rivers are key supports for aerial rivers, can be explored in more detail, e.g. the search of areas providing strong evapotranspiration that is sustained by surface rivers. Additionally, the role of aerial rivers as a water bridge between different catchments could be an interesting topic to investigate further. This could be done by investigating changes in budgets of catchments that share the same aerial rivers following the introduction of land use change in one of the catchments. Surface rivers that are highly dependent on aerial rivers’ precipitation input can be a good target, as a stronger linkage of aerial and surface components of the hydrological cycle is present.

Furthermore, groundwater’s connection with the aerial rivers is also worth considering for future research. The linkage of the atmospheric and subsurface component of the hydrological cycle has been rather unstudied, as the atmospheric moisture cycle operates on a much smaller time scale than that of groundwater. As aerial rivers are climatic-scale phenomena, the difference in time scale might be smaller enough to enable inquiry into the connection between the atmospheric and sub-surface components of hydrological cycle.

Secondly, the importance of feedback in coupled human-environmental systems for sustainable management of land and water practices has been highlighted in Chapter 4. For the development of sustainable management of rapidly changing environments, further exploration of other feedbacks between components within coupled human-environmental systems should be conducted. For example, the feedback between social and natural components related to aerial rivers’ influence on water availability or other extreme events such as floods may be topics to be investigated. As shown in Chapter 4, an inter-disciplinary frame of investigation is necessary for natural and social components and processes in coupled human-environmental systems to be recognized. The social scientific framework of the MLP combined with natural scientific results has been proven to be a viable frame of investigation (see 5.3.2). Future research can explore the potential of other possible tools,

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such as the telecoupling framework (Liu et al., 2013) or other combinations of tools to perform similar tasks.

5.3.2 Additional application: a new tool for investigating interactions and feedbacks in coupled human-environmental systems

The thesis has shown that when combined with institutional analysis to improve the resolution of agency within analytical levels (Geels, 2011; Geels et al., 2016), the MLP can be useful for investigating the interactions between components and processes related to aerial rivers. Since the analytical levels are defined by structuration extent, the use of the MLP avoids the problem of defining system divisions that can rule out emergent interactions and feedbacks during the analytical process (McGinnis and Ostrom, 2014; Friis et al., 2016), a common problem of other analytical frameworks considering recipient systems’ responses to impact flows such as SES (Folke et al., 2005; Ostrom, 2007) and telecoupling (Liu et al., 2013; Eakin et al., 2014) frameworks. Therefore, the MLP can be complementary to these frameworks, especially for future studies targeting the evolving interactions and feedbacks between components and processes crucial for informing aerial river management. It can also be useful for analyzing other feedbacks in coupled human-environmental systems. Still, there are challenges to overcome when using the MLP approach. For example, the dynamics of landscape level had been poorly explored in previous studies (Geels, 2011). In this regard, this thesis has enriched the discussions of landscape level in the MLP by analyzing the dynamics of physical climate, previously treated as ‘unchanged’ (van Driel and Schot, 2005), as well as by documenting the phenomena of aerial rivers in the MLP framework analysis for the first time, allowing for the detection of remote regime influences across geographies which is novel in the transition studies.

5.3.3 Additional application: cross-border reforestation

Aerial rivers connect regions across jurisdictional and topographic boundaries of watersheds. Thus, cross-border cooperation will be essential for managing them, as shown in the cases studied in the thesis. In Chapter 2, the MIP of the Ucayali River outlet (Peru) is composed of Peruvian, Colombian, and Brazilian parts. In Chapter 3, a full implementation

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of aerial river management by smart reforestation will rely on cross-border cooperation between Bolivia, Peru and Brazil. Even in the case of smart reforestation domestically in Bolivia, cooperation between departments is also necessary because the MIP of Santa Cruz de la Sierra is mainly in the department of El Beni and Pando, instead of the department of Santa Cruz (which the city belongs to). In Chapter 4, regimes able to influence each other via aerial rivers also cross the country boundary between Brazil, Bolivian, Colombia, and beyond.

A precondition for activating cross-border aerial river management is that the awareness of aerial rivers sharing needs to be raised among stakeholders. This thesis has clarified the (previously inconclusive) relevance of aerial rivers in sustainable land and water practices, which will be helpful for raising the awareness and willingness of stakeholders to manage aerial rivers cooperatively. The thesis has also provided the scientific framework for identification of regions dependent on aerial rivers and the upwind hotspots (MIP) sustaining these regions via aerial rivers (see 2.3.1). This will contribute to detecting the key regions for action, as well as the related parties to be included in cross-border aerial river management.

To foster cross-border cooperation for aerial river management, paradigms of shared resource governance, e.g. management of air pollution and surface rivers, can be informative (Ellison et al., 2017; Keys et al., 2017) for measures such as forming international laws (Keys et al., 2017) and establishing bridging organizations to enable collaborative decision making (Cash et al., 2006; Crona and Parker, 2012). In this way, using existing regional collaborative platforms may be a more direct approach to bring cross-border aerial river management into force. Chapter 3, for instance, details a case in which, to enforce smart reforestation in the Peruvian and Brazilian component of the MIP and to increase water in Santa Cruz de la Sierra, Bolivia may take advantage of the regional common target of reforesting 20 million hectares in Latin America per the Initiative 20 × 20 (https://www.wri.org/our-work/project/initiative-20 × 20) and the Bonn Challenge (http://www.bonnchallenge.org/content/challenge). Similarly, Chapter 4 details how Colombia may use the regional call for combating drought (UNCCD, 2017) as the platform for co-managing regime influence through the aerial rivers with Brazil and Bolivia. It is also worth noting that entities connected by aerial rivers may have reversed roles regarding 123

surface rivers (i.e., upwind senders in aerial rivers may be downstream receivers in surface rivers). Take the case of Chapter 3 for example, where the MIP of the city is located downstream from the city. In cases such as this, willingness for cross-border entities to co-manage the connected aerial and surface rivers may be higher. However, recognizing roles in the aerial rivers and the priority regions to be managed will be a prerequisite, which can be facilitated by the MIP framework introduced in this thesis.

5.4 Perspective

Overall, this thesis has demonstrated that, regarding their impacts on water availability, aerial rivers can and should be effectively managed. Additionally, the thesis has highlighted an urgency for the receiver/sink regimes to act to mitigate the aerial rivers’ contribution to extreme climate events. In both cases, targeting the regimes that generate influence or feedbacks is a more efficient way of managing aerial rivers.

The findings of this thesis have implied that dynamic interaction between components, natural or social, of coupled human-environmental systems are key to approaching sustainable livelihoods in fast-changing environments. This research calls for further exploration of these interactions, as well as the development of effective instruments to manage them. These tasks will not be easy to address, as both require interdisciplinary efforts. However, the examples outlined in this thesis leave me optimistic regarding further research and policy development that seeks to address and build upon the findings outlined here.

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References

Acevedo, O. C., Moraes, O. L., Da Silva, R., Fitzjarrald, D. R., Sakai, R. K., Staebler, R. M., Czikowsky, M. J., 2004. Inferring nocturnal surface fluxes from vertical profiles of scalars in an Amazon pasture. Global Change Biol. 10, 886–894.

Anderson-Teixeira, K. J., Snyder, P. K., Twine, T. E., Cuadra, S. V., Costa, M. H., de Lucia, E. H., 2012. Climate-regulation services of natural and agricultural ecoregions of the Americas. Nat. Clim. Chang. 2, 177–181.

Aguiar, A. P. D., Vieira, I. C. G., Assis, T. O., Dalla-Nora, E. L., Toledo, P. M., Santos Junior, R. A. O., et al., 2016. Land use change emission scenarios: anticipating a forest transition process in the Brazilian Amazon. Global Change Biol. 22, 1821–1840.

Alkama, R., Decharme, B., Douville, H., Becker, M., Cazenave, A., Sheffield, J., et al., 2010. Global Evaluation of the ISBA-TRIP Continental Hydrological System. Part I: Comparison to GRACE Terrestrial Water Storage Estimates and In Situ River Discharges. J. Hydrometeorol. 11, 583–600.

Alves, L. M., Marengo, J. A., Fu, R., Bombardi, R. J., 2017. Sensitivity of Amazon Regional Climate to Deforestation. Am. J. Clim. Change 6, 75–98.

Aragão, L., Poulter, B., Barlow, J. B., Anderson, L. O., Malhi, Y., Saatchi, S., et al., 2014. Environmental change and the carbon balance of Amazonian forests. Biol. Rev. 89, 913–931.

Arima, E. Y., Barreto, P., Araújo, E., Soares-Filho, B., 2014. Public policies can reduce tropical deforestation: Lessons and challenges from Brazil. Land use policy 41, 465–473.

Arraut, J. M. and Satyamurty, P., 2009. Precipitation and Water Vapor Transport in the Southern Hemisphere with Emphasis on the South American Region. J. Appl. Meteor. Climatol. 48, 1902–1912.

Arraut, J. M., Nobre, C., Barbosa, H. M. J., Obregon, G., Marengo, J., 2012. Aerial rivers and lakes: looking at large-scale moisture transport and its relation to Amazonia and to subtropical rainfall in South America. J. Clim. 25 (2), 543–556.

125

Awadallah, A. G. and Awadallah, N. A., 2013. A Novel Approach for the Joint Use of Rainfall Monthly and Daily Ground Station Data with TRMM Data to Generate IDF Estimates in a Poorly Gauged Arid Region. Open J. Mod. Hydrol. 3, 1–7.

Badger, A. M. and Dirmeyer, P. A., 2015. Climate response to Amazon forest replacement by heterogeneous crop cover. Hydrol. Earth Syst. Sci. 19, 4547–4557.

Bagley, J. E., Desai, A. R., Dirmeyer, P. A., Foley, J. A., 2012. Effects of land cover change on moisture availability and potential crop yield in the world’s breadbaskets. Environ. Res. Lett. 7 (1), 014009.

Bagley, J. E., Desai, A. R., Harding, K. J., Snyder, P. K., Foley, J. A., 2014. Drought and deforestation: Has land cover change influenced recent precipitation extremes in the Amazon? J. Clim. 27 (1), 345–361, https://doi.org/10.1175/JCLI-D-12-00369.1. Figure 4.5 here in the thesis is originated from the Figure 8 of this cited work (on page 358) under © American Meteorological Society. Used with permission.

Baidya Roy, S., 2002. Impact of land use/land cover change on regional hydrometeorology in Amazonia. J. Geophys. Res. 107, 8037.

Baidya Roy, S. and Avissar, R., 2000. Scales of response of the convective boundary layer to land-surface heterogeneity. Geophys. Res. Lett. 27 (4), 533–536.

Barona, E., Ramankutty, N., Hyman, G., Coomes, O. T., 2010. The role of pasture and soybean in deforestation of the Brazilian Amazon. Environ. Res. Lett. 5, 024002.

Becker, S. L. and Reusser, D. E., 2016. Disasters as opportunities for social change: Using the multi-level perspective to consider the barriers to disaster-related transitions. Int. J. Disaster Risk Reduct. 18, 75–88.

Bengtsson, L., 2010. The global atmospheric water cycle. Environ. Res. Lett. 5, 025202.

Berg, A. and Sheffield, J., 2018. Climate Change and Drought: the Soil Moisture Perspective. Curr. Clim. Chang. Reports. 4, 180-191.

126

Berkhout, F., Smith, A., Stirling, A., 2004. Socio-technological regimes and transition contexts. In: Elzen, B., Geels, F. W., Green, K. (Eds.), System Innovation and the Transition to Sustainability: Theory, Evidence and Policy. Edward Elgar, Cheltenham, pp. 48–75.

Birkmann, J., Buckle, P., Jaeger, J., Pelling, M., Setiadi, N., Garschagen, M., et al., 2010. Extreme events and disasters: A window of opportunity for change? Analysis of organizational, institutional and political changes, formal and informal responses after mega-disasters. Nat. Hazards 55, 637–655.

Boers, N., Bookhagen, B., Barbosa, H. M. J., Marwan, N., Kurths, J., Marengo, J. A., 2014. Prediction of extreme floods in the eastern Central Andes based on a complex networks approach. Nat. Commun. 5 (5199).

Boers, N., Goswami, B., Rheinwalt, A., Bookhagen, B., Hoskins, B., Kurths, J., 2019. Complex networks reveal global pattern of extreme-rainfall teleconnections. Nature. 566. 373-377.

Bonan, G. B., 2008. Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests. Science 320, 1444–1449.

Bonn Challenge, 2019. The Challenge. (accessed 25 Jan. 2019) http://www.bonnchallenge.org/content/challenge.

Bosch, J. M. and Hewlett, J.D., 1982. A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration. J. Hydrol. 55, 3-23.

Bosilovich, M. G., 2002. On the vertical distribution of local and remote sources of water for precipitation. Meteorol. Atmos. Phys. 80, 31–41.

Bosilovich, M. G. and Chern, J. D., 2006. Simulation of Water Sources and Precipitation Recycling for the MacKenzie, Mississippi, and Amazon River Basins. J. Hydrometeorol. 7, 312–329.

Bradshaw, C. J. A., Bowman, D. M. J. S., Bond, N. R., Murphy, B. P., Moore, A. D., Fordham, D. A., et al., 2013. Brave new green world – Consequences of a carbon economy for the conservation of Australian biodiversity. Biol. Conserv. 161, 71-90.

127

Brazilian Forest Code, 2012. Federal Law 12.727. www.planalto. Gov.br/ccivil_03/_Ato2011-2014/2012/Lei/L12727.htm/ 17 October (accessed 29 May 2019).

Bremer, L .L. and Farley, K. A., 2010. Does plantation forestry restore biodiversity or create green deserts? A synthesis of the effects of land-use transitions on plant species richness. Biodivers. Conserv. 19, 3893–3915.

Brienen, R. J. W., Phillips, O. L., Feldpausch, T. R., Gloor, E., Baker, T. R., Lloyd, J., Lopez-Gonzalez, G. et al. 2015. “Long-Term Decline of the Amazon Carbon Sink.” Nature 519, 344–48.

Brown, A. E., Zhang, L., McMahon, T. A., Western, A. W., Vertessy, R. A., 2005. A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation. J. Hydrol. (Amst) 310 (1-4), 28–61.

Brown, A. E., Podger, G. M., Davidson, A. J., Dowling, T. I., Zhang, L., 2007. Predicting the impact of plantation forestry on water users at local and regional scales. An example for the Murrumbidgee River Basin, Australia. For. Ecol. Manage. 251, 82–93.

Brubaker, K. L., Entekhabi, D., Eagleson, P. S., 1993. Estimation of continental precipitation recycling. J. Climate 6, 1077–1089.

Bruijnzeel, L. A., 2004. Hydrological functions of tropical forests: not seeing the soil for the trees? Agric. Ecosyst. Environ. 104 (1), 185–228.

Brundiers, K. and Eakin, H. C., 2018. Leveraging post-disaster windows of opportunities for change towards sustainability: A framework. Sustain. 10 (5). 1390.

Bryan, B. A., Nolan, M., Harwood, T. D., Connor, J. D., Navarro-Garcia, J., King, D., et al., 2014. Supply of carbon sequestration and biodiversity services from Australia’s agricultural land under global change. Glob. Environ. Chang. 28, 166–181.

Bryan, B. A., Crossman, N. D., Nolan, M., Li, J., Navarro, J., Connor, J. D., 2015. Land use efficiency: Anticipating future demand for land-sector greenhouse gas emissions abatement and managing trade-offs with agriculture, water, and biodiversity. Glob. Chang. Biol. 21, 4098–4114. 128

Budyko, M. I., 1974. Climate and Life. Academic Press, New York, 508 pp.

Burde, G. I., Gandush, C., Bayarjargal, Y., 2006. Bulk recycling models with incomplete vertical mixing. Part II: Precipitation recycling in the Amazon basin. J. Climate 19, 1473– 1489.

Calder, I. R., 2007. Forests and water-Ensuring forest benefits outweigh water costs. For. Ecol. Manage. 251, 110–120.

Cannon, T., 1994. Vulnerability analysis and the explanation of “natural” disasters, in: Varley, A. (Eds.), Disasters, Development and Environment. John Wiley and Sons, Chichester, pp. 13–30.

Cao, 2011. Impact of China’s large-scale ecological restoration program on the environment and society in arid and semiarid areas of China: achievements, problems, synthesis, and applications. Crit. Rev. Environ. Sci. Technol. 41 (4), 317–335.

Cao, S. and Zhang, J., 2015. Political risks arising from the impacts of large-scale afforestation on water resources of the Tibetan Plateau. Gondwana Res. 28 (2), 898–903.

Caracol Radio, 2018. Aumenta la amenaza de sequía en Colombia. https://caracol.com.co/programa/2018/08/09/6am_hoy_por_hoy/1533819120_719653.html/ 10 August (accessed 29 May 2019).

Cash, D. W., Adger, W., Berkes, F., Garden, P., Lebel, L., Olsson, P., et al., 2006. Scale and cross-scale dynamics: governance and information in a multilevel world. Ecol. Soc. 11 (2), 8.

Castelli, G., Foderi, C., Guzman, B. H., Ossoli, L., Kempff, Y., Bresci, E., Salbitano, F., 2017. Planting waterscapes: green infrastructures, landscape and hydrological modelling for the future of Santa Cruz de la Sierra. Bolivia. For. 8 (11).

CBD (Convention on Biological Diversity), 2010. Strategic Plan for Biodiversity 2011–2020 and the Aichi Biodiversity Targets. https://www.cbd.int/doc/strategicplan/2011-2020/Aichi-Targets-EN.pdf.

129 Certini, G., Castelli, G., Bresci, E., Calamini, G., Pierguidi, A., Villegas Paredes, L.N., Salbitano, F., 2019. Fog collection as a strategy to sequester carbon in drylands. Sci. Total Environ. 657, 391–400.

Chapin, F. S., Randerson, J. T., McGuire, A. D., Foley, J. A., and Field, C. B.: Changing feedbacks in the climatebiosphere system, Front. Ecol. Environ., 6, 313–320.

Christoffersen, B. O., Restrepo-Coupe, N., Arain, M. A., Baker, I. T., Cestaro, B. P., Ciais, P., et al., 2014. Mechanisms of water supply and vegetation demand govern the seasonality and magnitude of evapotranspiration in Amazonia and Cerrado. Agr. Forest Meteorol. 191, 33– 50.

Cleugh, H. A., Leuning, R., Mu, Q., Running, S. W., 2007. Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sens. Environ. 106, 285–304.

Coe, M. T., Costa, M. H., Soares-Filho, B. S., 2009. The influence of historical and potential future deforestation on the stream flow of the Amazon River – Land surface processes and atmospheric feedbacks. J. Hydrol. 369, 165–174.

Coe, M. T., Latrubesse, E. M., Ferreira, M. E., Amsler, M. L., 2011. The effects of deforestation and climate variability on the streamflow of the Araguaia River, Brazil. Biogeochemistry 105 (1), 119–131.

Coe, M. T., Marthews, T. R., Costa, M. H., Galbraith, D. R., Greenglass, N. L., Imbuzeiro, H. M. A., et al., 2013. Deforestation and climate feedbacks threaten the ecological integrity of south-southeastern Amazonia. Philos. Trans. Biol. Sci. 368 (1619).

Cohen-Shacham, E., Walters, G., Janzen, C., Maginnis, S. (Eds.), 2016. Nature-based Solutions to address global societal challenges. The International Union for Conservation of Nature (IUCN), Gland, xiii + 97pp.

Collischonn, B., Collischonn, W., Tucci, C. E. M., 2008. Daily hydrological modelling in the Amazon basin using TRMM rainfall estimates. J. Hydrol. 360, 207–216.

Connor, J. D., Bryan, B. A., Nolan, M., 2016. Cap and trade policy for managing water competition from potential future carbon plantations. Environ. Sci. Policy 66, 11–22.

130

Costa, M. H. and Foley, J. A., 2000. Combined effects of deforestation and doubled atmospheric CO2 concentrations on the climate of Amazonia. J. Climate 13, 18–34.

Costa, M. H., Botta, A., Cardille, J. A., 2003. Effects of largescale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia. J. Hydrol. 283, 206–217.

Creed, I. F. and van Noordwijk, M., 2018. Forests, trees and water on a changing planet: a contemporary scientific perspective. In: In: Creed, I.F., van Noordwijk, M. (Eds.), Forest and Water on a Changing Planet: Vulnerability, Adaptation and Governance Opportunities: A Global Assessment Report Volume 38. International Union of Forest Research Organizations (IUFRO) World Series, Vienna, pp. 13–24.

Crona, B. I. and Parker, J. N., 2012. Learning in support of governance: theories, methods, and a framework to assess how bridging organizations contribute to adaptive resource governance. Ecol. Soc. 17 (1), 32.

Cunningham, S. C., Mac Nally, R., Baker, P. J., Cavagnaro, T. R., Beringer, J., Thomson, J. R., Thompson, R. M., 2015. Balancing the environmental benefits of reforestation in agricultural regions. Perspect. Plant Ecol. Evol. Syst. 17 (4), 301–317.

D’Almeida, C., Vörösmarty, C. J., Marengo, J. A., Hurtt, G. C., Dingman, S. L., Keim, B. D., 2006. A water balance model to study the hydrological response to different scenarios of deforestation in Amazonia. J. Hydrol. (Amst) 331 (1), 125–136.

D’Almeida, C., Vörösmarty, C. J., Hurtt, G. C., Marengo, J. A., Dingman, S .L., Keim, B. D., 2007. The effects of deforestation on the hydrological cycle in Amazonia: A review on scale and resolution. Int. J. Climatol. 27, 633–647.

Davidson, E. A., de Araújo, A. C., Artaxo, P., Balch, J. K., Brown, I. F., Bustamante, M. M. C., et al., 2012. The Amazon basin in transition. Nature 481, 321-328.

De Sy, V., Herold, M., Achard, F., Beuchle, R., Clevers, J. G. P. W., Lindquist, E., Verchot, L., 2015. Land use patterns and related carbon losses following deforestation in South America. Environ. Res. Lett. 10,124004.

131

Dean, J. F., Webb, J. A., Jacobsen, G. E., Chisari, R., Dresel, P. E., 2015. A groundwater recharge perspective on locating tree plantations within low-rainfall catchments to limit water resource losses. Hydrol. Earth Syst. Sci. 19 (2), 1107–1123.

Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al., 2011. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137 (656), 553–597.

Dickinson, R. E. and Henderson-Sellers, A., 1988. Modelling tropical deforestation: A study of GCM land-surface parametrizations. Q. J. Roy. Meteorol. Soc. 114, 439–462.

Dirmeyer, P. A. and Brubaker, K. L., 1999. Contrasting evaporative mois-ture sources during the drought of 1988 and the flood of 1993. J. Geophys. Res. 104, 19383–19397.

Dirmeyer, P. A. and Brubaker, K. L., 2007. Characterization of the Global Hydrologic Cycle from a Back-Trajectory Analysis of Atmospheric Water Vapor. J. Hydrometeorol. 8, 20–37.

Dirmeyer, P. A. and Shukla, J., 1994. Albedo as a modulator of climate response to tropical deforestation. J. Geophys. Res. 99, 20863.

Dirmeyer, P. A., Brubaker, K. L., DelSole, T., 2009. Import and export of atmospheric water vapor between nations. J. Hydrol. (Amst) 365 (1-2), 11–22.

Dittrich, R., Butler, A., Ball, T., Wreford, A., Moran, D., 2019. Making real options analysis more accessible for climate change adaptation. An application to afforestation as a flood management measure in the Scottish Borders. J. Environ. Manage. 245, 338–347.

D’Odorico, P., Bhattachan, A., Davis, K. F., Ravi, S., Runyan, C. W., 2013. Global desertification: Drivers and feedbacks. Adv. Water Resour. 51, 326– 344.

Dominguez, F., Kumar, P., Liang, X., Ting, M., 2006. Impact of Atmospheric Moisture Storage on Precipitation Recycling. J. Climate 19, 1513–1530.

Don, A., Schumacher, J., Freibauer, A., 2011. Impact of tropical land-use change on soil organic carbon stocks – a meta-analysis. Glob. Chang. Biol. 17 (4), 1658–1670.

Duncan, M. J., 1995. Hydrological impacts of converting pasture and gorse to pine plantation, and forest harvesting, Nelson, New Zealand. Journal of Hydrology New Zealand 34, 15–41. 132

Eakin, H., DeFries, R., Kerr, S., Lambin, E. F., Liu, J., Marcotullio, P. J., et al., 2014. Significance of telecoupling for exploration of land-use change. In: Seto, K. C. and Reenberg, A. (Eds) Rethinking Global Land Use in an Urban Era. The MIT Press, Massachusetts, USA, pp. 141–161.

Egginton, P., Beall, F., Buttle, J., 2014. Reforestation – Climate change and water resource implications. For. Chron. 90, 516–524.

El Espectador, 2016. ¿Está enredado el camino de las energías renovables en Colombia?. https://www.elespectador.com/noticias/medio-ambiente/esta-enredado-el-camino-de-energias -renovables-colombia-articulo-644422/ 20 July (accessed 27 May 2019).

El País, 2016. Renunció el ministro de Minas y Energía, Tomás González, ante crisis energética. https://www.elpais.com.co/elpais/economia/noticias/presidente-santos-acepto-renuncia-tomas -gonzalez-ministro-minas/ 07 March (accessed 27 May 2019).

El País, 2019. Fenómeno del Niño tiene a Colombia entre sequías e incendios forestales. https://www.elpais.com.co/colombia/fenomeno-del-nino-tiene-a-colombia-entre-sequias-e-in cendios-forestales.html/ 8 January (accessed 27 May 2019).

El Tiempo, 2016. El país empieza a pensar en las energías alternativas. https://www.eltiempo.com/archivo/documento/CMS-16586596/ 08 May (accessed 27 May 2019).

Ellison, D., Morris, C. E., Locatelli, B., Sheil, D., Cohen, J., Murdiyarso, D., et al., 2017. Trees, forests and water: cool insights for a hot world. Glob. Environ. Chang. Part A 43, 51– 61.

Ellision, D. N., Futter, M., Bishop, K., 2012. On the forest cover–water yield debate: from demand to supply side thinking. Glob Chang Biol 18 (3), 806–820.

Ellison, D., Claassen, M., van Noordwijk, M., Sullivan, C.A., Vira, B., Xu, J., et al., 2018. Governance options for addressing changing forest-water relations. In: In: Creed, I.F. and van Noordwijk, M. (Eds.), Forest and Water on a Changing Planet: Vulnerability, Adaptation and

133

Governance Opportunities: A Global Assessment Report Volume 38. International Union of Forest Research Organizations (IUFRO) World Series, Vienna, pp. 147–170.

Eltahir, E. A. B. and Bras, R. L., 1994. Precipitation recycling in the Amazon basin. Q. J. Roy. Meteorol. Soc. 120, 861–880.

Eltahir, E. A. B. and Bras, R. L., 1996. Precipitation recycling. Rev. Geophys. 34, 367–378.

Erfanian, A., Wang, G., Fomenko, L., 2017. Unprecedented drought over tropical South America in 2016: significantly under-predicted by tropical SST. Sci. Rep. 7 (1).

FAO (Food and Agriculture Organization of the United Nations), 2015. GIEWS Country Brief: Brazil. http://www.fao.org/giews/countrybrief/country.jsp?code=BRA 30 December, (accessed 29 May 2019).

Farley, K. A., Jobbagy, E. G., Jackson, R. B., 2005. Effects of afforestation on water yield: a global synthesis with implications for policy. Glob. Chang. Biol. 11 (10), 1565–1576.

Filoso, S., Bezerra, M. O., Weiss, K. C. B., Palmer, M. A., 2017. Impacts of forest restoration on water yield: a systematic review. PloS One 12 (8), e0183210.

Fisch, G., Tota, J., Machado, L. A. T., Silva Dias, M. A. F., da Lyra, R. F., Nobre, C. A., et al., 2004. The convective boundary layer over pasture and forest in Amazonia. Theor. Appl. Climatol. 78, 47–59.

Fisher, J. B., Malhi, Y., Bonal, D., Da Rocha, H. R., De Araújo, A. C., Gamo, M., et al., 2009. The land–atmosphere water flux in the tropics. Global Change Biol. 15, 2694–2714.

Fisher, R. A., Williams, M., de Lourdes Ruivo, M., de Costa, A. L., Meir, P., 2008. Evaluating climatic and soil water controls on evapotranspiration at two Amazonian rainforest sites. Agr. Forest Meteorol. 148, 850–861.

Foley, J. A., Costa, M. H., Delire, C., Ramankutty, N., Snyder, P., 2003. Green surprise? How terrestrial ecosystems could affect earth’s climate. Front. Ecol. Environ. 1, 38–44.

Foley, J. A., Defries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., et al., 2005. Global consequences of land use. Science 309, 570–574.

134

Foley, J. A., Asner, G. P., Costa, M. H., Coe, M. T., Defries, R., Gibbs, H. K., et al., 2007. Amazonia revealed: forest degradation and loss of ecosystem goods and services in the Amazon Basin. Front. Ecol. Environ. 5, 25–32.

Folke, C., Hahn, T., Olsson, P., Norberg, J., 2005. Adaptive governance of social-ecological systems. Annu. Rev. Environ. Resour. 30, 441–473.

Friis, C., Nielsen, J. Ø., Otero, I., Haberl, H., Niewöhner, J., Hostert, P., 2016. From teleconnection to telecoupling: taking stock of an emerging framework in land system science. J. Land Use Sci. 11, 131–153.

Fu, R., Yin, L., Li, W., Arias, P. A., Dickinson, R. E., Huang, L., et al., 2013. Increased Dry season Length Over Southern Amazonia in Recent Decades and Its Implication for Future Climate Projection. Proc. Natl. Acad. Sci. U.S.A. 110 (45), 18110–18115.

Gao, Y., Zhu, X., Yu, G., He, N., Wang, Q., Tian, J., 2014. Water use efficiency threshold for terrestrial ecosystem carbon sequestration in China under afforestation. Agric. For. Meteorol. 195-196, 32–37.

Gedney, N. and Valdes, P. J., 2000. The effect of Amazonian deforestation on the northern hemisphere circulation and climate. Geophys. Res. Lett. 27, 3053.

Geels, F. W., 2002. Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study. Res. Policy 31, 1257–1274.

Geels, F. W., 2004. From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory. Res. Policy 33, 897–920.

Geels, F. W., 2006. Multi-Level Perspective on System Innovation: Relevance for Industrial Transformation, in: Olsthoorn X., Wieczorek A. (Eds.), Understanding Industrial Transformation. Environment & Policy, vol 44. Springer, Dordrecht, pp. 163–186.

Geels, F. W., 2011. The multi-level perspective on sustainability transitions: Responses to seven criticisms. Environ. Innov. Soc. Transitions. 1, 1, 24-40.

Geels, F. W. and Schot, J., 2007. Typology of sociotechnical transition pathways. Res. Policy 36, 399–417.

135

Geels, F. W. and Verhees, B., 2011. Cultural legitimacy and framing struggles in innovation journeys: A cultural-performative perspective and a case study of Dutch nuclear energy (1945-1986). Technol. Forecast. Soc. Change 78, 910–930.

Geels, F. W., Kern, F., Fuchs, G., Hinderer, N., Kungl, G., Mylan, J., et al., 2016. The enactment of socio-technical transition pathways: A reformulated typology and a comparative multi-level analysis of the German and UK low-carbon electricity transitions (1990-2014). Res. Policy 45, 896–913.

Goessling, H. F. and Reick, C. H., 2011. What do moisture recycling estimates tell us? Exploring the extreme case of non-evaporating continents. Hydrol. Earth Syst. Sci. 15, 3217-3235.

Goessling, H. F. and Reick, C. H., 2013. On the “well-mixed” assumption and numerical 2-D tracing of atmospheric moisture. Atmos. Chem. Phys. 13, 5567–5585.

Gordon, L. J., Steffen, W., Jonsson, B. F., Folke, C., Falkenmark, M., Johannessen, A., 2005. Human modification of global water vapor flows from the land surface. Proc. Natl. Acad. Sci. U.S.A. 102 (21), 7612–7617.

Grimm, A. M., 2010. Interannual climate variability in South America: Impacts on seasonal precipitation, extreme events, and possible effects of climate change. Stochastic Environ. Res. Risk Assess. 25, 537–554.

Grimm, A. M., Ferraz, S. E., Gomes, J., 1998. Precipitation Anomalies in Southern Brazil Associated with El Niño and La Niña Events. J. Climate 11, 2863–2880.

Haberl, H., Erb, K. H., Krausmann, F., Gaube, V., Bondeau, A., Plutzar, C., et al., 2007. Quantifying and mapping the human appropriation of net primary production in earth’s terrestrial ecosystems. Proc. Natl. Acad. Sci. U. S. A. 104, 12942–12947.

Hahmann, A. N. and Dickinson, R. E., 1997. RCCM2 – BATS Model over Tropical South America: Applications to Tropical Deforestation. J. Climate 10, 1944–1964.

Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., et al., 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. 136

Hayhoe, S. J., Neill, C., Porder, S., Mchorney, R., Lefebvre, P., Coe, M. T., et al., 2011. Conversion to soy on the Amazonian agricultural frontier increases streamflow without affecting stormflow dynamics. Glob. Chang. Biol. 17, 1821–1833.

Henderson-Sellers, A., Dickinson, R. E., Durbidge, T. B., Kennedy, P. J., McGuffie, K., Pitman, A. J., 1993. Tropical deforestation: Modeling local- to regional-scale climate change. J. Geophys. Res.-Atmos. 98, 7289–7315.

Henderson-Sellers, A., McGuffie, K., and Zhang, H., 2002. Stable isotopes as validation tools for global climate model predictions of the impact of Amazonian deforestation. J. Climate 15, 2664–2677.

Herrera‐Estrada, J. E., Satoh, Y., Sheffield, J., 2017. Spatiotemporal dynamics of global drought. Geophys. Res. Lett. 44, 1– 25.

Herrera-Estrada, J. E., Martinez, J. A., Dominguez, F., Findell, K. L., Wood, E. F., Sheffield, J., 2019. Reduced Moisture Transport Linked to Drought Propagation Across North America. Geophys. Res. Lett. 46, 5243–5253.

Holtz, G., Brugnach, M., Pahl-Wostl, C., 2008. Specifying “regime” – A framework for defining and describing regimes in transition research. Technol. Forecast. Soc. Change 75, 623–643.

Hoy Diario del Magdalena, 2018. Gobierno hace llamado para consumo racional de energía por fenómeno de ‘El Niño’. http://www.hoydiariodelmagdalena.com.co/archivos/179864/ 21 December (accessed 29 May 2019).

Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., et al., 2007. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8 (1), 38–55.

INE (Instituto Nacional de Estadíticas de Bolivia), 2017ª. Ficha Resúmen Censo Población y Vivienda 2012. (accessed 04 Dec. 2017) http://censosbolivia.ine.gob.bo/censofichacomunidad/c_pdfm/generar_pdf/07/01/01/x.

INE (Instituto Nacional de Estadíticas de Bolivia), 2017b. Bolivia celebra el Día Nacional del Agua con 848.224 conexiones. (accessed 04 Dec. 2017) 137

https://www.ine.gob.bo/index.php/notas-de-prensa-y-monitoreo/item/2034-boliviacelebra-el- dia-nacional-del-agua-con-848-224-conexiones.

INPE (Instituto Nacional de Pesquisas Espaciais), 2017. http://www.obt.inpe.br/prodes/index.php (accessed 19 July 2017).

INPE (Instituto Nacional de Pesquisas Espaciais), 2019. Terrabrasilis database. http://terrabrasilis.dpi.inpe.br/app/dashboard/deforestation/biomes/amazon/increments/ (accessed 29 May 2019).

IOM (International Organization for Migration), 2010. Disaster Risk Reduction, Climate Change Adaptation and Environmental Migration: A Policy Perspective. https://publications.iom.int/system/files/pdf/ddr_cca_report.pdf/ (accessed 29 May 2019).

IPCC, 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., et al. (Eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.

Jackson, R. B., Jobbagy, E. G., Avissar, R., Roy, S. B., Barrett, D. J., Cook, C. W., et al., 2005. Trading water for carbon with biological sequestration. Science 310 (5756), 1944– 1947.

Jiménez-Muñoz, J. C., Mattar, C., Barichivich, J., Santamaría-Artigas, A., Takahashi, K., Malhi, Y., et al., 2016. Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015-2016. Sci. Rep. 6.

Jones, B., and O’Neill, B. C., 2016. Spatially explicit global population scenarios consistent with the shared socioeconomic pathways. Environ. Res. Lett. 11 (8), 084003.

Kalamandeen, M., Gloor, E., Mitchard, E., Quincey, D., Ziv, G., Spracklen, D., et al., 2018. Pervasive Rise of Small-scale Deforestation in Amazonia. Sci. Rep. 8.

Kemp, R., Schot, J., Hoogma, R., 1998. Regime shifts to sustainability through processes of niche formation. Technol. Anal. Strateg. Manag. 10, 2, 175-198.

138

Keys, P. W. and Wang-Erlandsson, L., 2018. On the social dynamics of moisture recycling. Earth Syst. Dyn. 9, 829–847.

Keys, P. W., van der Ent, R. J., Gordon, L. J., Hoff, H., Nikoli, R., Savenije, H .H. G., 2012. Analyzing precipitationsheds to understand the vulnerability of rainfall dependent regions. Biogeosciences 9 (2), 733–746.

Keys, P. W., Barnes, E. A., van der Ent, R. J., Gordon, L .J., 2014. Variability of moisture recycling using a precipitationshed framework. Hydrol. Earth Syst. Sci. 18 (10), 3937–3950.

Keys, P. W., Wang-Erlandsson, L., Gordon, L. J., Gemmill-Herren, B., LeBuhn, G., Minckley, R., 2016. Revealing Invisible Water: Moisture Recycling as an Ecosystem Service. PloS One 11, e0151993.

Keys, P. W., Wang-Erlandsson, L., Gordon, L. J., Galaz, V., Ebbesson, J., 2017. Approaching moisture recycling governance. Glob. Environ. Chang. Part A 45, 15–23.

Keys, P. W., Wang-Erlandsson, L., Gordon, L. J., 2018. Megacity precipitationsheds reveal tele-connected water security challenges. PloS One 13 (3).

Khanna, J. and Medvigy, D., 2014. Strong control of surface roughness variations on the simulated dry season regional atmospheric response to contemporary deforestation in Rondonia, Brazil. J. Geophys. Res.-Atmos. 119, 13067–13078.

Khanna, J., Medvigy, D., Fueglistaler, S., Walko, R., 2017. Regional dry-season climate changes due to three decades of Amazonian deforestation. Nat. Clim. Change 7, 200–204.

Kim, H., Yeh, P. J. F., Oki, T., Kanae, S., 2009. Role of rivers in the seasonal variations of terrestrial water storage over global basins. Geophys. Res. Lett. 36, L17402.

Kleidon, A. and Heimann, M., 2000. Assessing the role of deep rooted vegetation in the climate system with model simulations: mechanism, comparison to observations and implications for Amazonian deforestation. Clim. Dynam. 16, 183–199.

Knoche, H. R. and Kunstmann, H., 2013. Tracking atmospheric water pathways by direct evaporation tagging: A case study for West Africa. J. Geophys. Res. Atmos. 118, 12345– 12358.

139

Köhler, J., Geels, F.W., Kern, F., Markard, J., Wieczorek, A., Alkemade, F., et al., 2019. An agenda for sustainability transitions research: State of the art and future directions. Environ. Innov. Soc. Transitions 31, 1–32

Koren, I., Altaratz, O., Remer, L. A., Feingold, G., Martins, J. V., Heiblum, R. H., 2012. Aerosol-induced intensification of rain from the tropics to the mid-latitudes. Nat. Geosci. 5, 118–122.

Koster, R. D., Jouzel, J., Suozzo, R., Russell, G., Broecker, W., Rind, D., et al., 1986. Global sources of local precipitation as determined by the Nasa/Giss GCM. Geophys. Res. Lett. 13, 121–124.

Koster, R. D., Dirmeyer, P. A., Guo, Z., Bonan, G., Chan, E., Cox, P., et al., 2004. Regions of Strong Coupling Between Soil Moisture and Precipitation. Science 305, 1138–1140.

Lambin, E. and Geist, H., 2002. Proximate Causes and Underlying Driving Forces of Tropical Deforestation. Bioscience 52, 2, 143–150.

Lapola, D. M., Schaldach, R., Alcamo, J., Bondeau, A., Koch, J., Koelking, C., Priess, J. A., 2010. Indirect land-use changes can overcome carbon savings from biofuels in Brazil. Proc. Natl. Acad. Sci. 107, 3388–3393.

Lathuillière, M. J., Johnson, M. S., Donner, S. D., 2012. Water use by terrestrial ecosystems: temporal variability in rainforest and agricultural contributions to evapotranspiration in Mato Grosso, Brazil. Environ. Res. Lett. 7, 024024.

Lau, W. K. M. and Kim, K. M., 2011. The 2010 Pakistan Flood and Russian Heat Wave: Teleconnection of Hydrometeorological Extremes. J. Hydrometeorol. 13, 392–403.

Laurance, W. F., 2007. Switch to Corn Promotes Amazon Deforestation. Science 318, 1721b–1721b.

Laurance, W. F., Cochrane, M. A., Bergen, S., Fearnside, P. M., Delamônica, P., Barber, C., et al., 2001. The future of the Brazilian Amazon. Science 291, 438–439.

Lawrence, D. and Vandecar, K., 2015. Effects of tropical deforestation on climate and agriculture. Nat. Clim. Change 5, 27–34.

140

Le Maitre, D. C., Scott, D. F., Colvin, C., 1999. A review of information on interactions between vegetation and groundwater. Water SA 25, 137–152.

Lean, J. and Rowntree, P. R., 1997. Understanding the Sensitivity of a GCM Simulation of Amazonian Deforestation to the Specification of Vegetation and Soil Characteristics. J. Climate 10, 1216–1235.

Lee, J. E., Oliveira, R. S., Dawson, T. E., Fung, I., 2005. Root functioning modifies seasonal climate. P. Natl. Acad. Sci. USA 102, 17576–17581.

Lee, J. E., Lintner, B. R., Boyce, C. K., Lawrence, P. J., 2011. Land use change exacerbates tropical South American drought by sea surface temperature variability. Geophys. Res. Lett. 38.

Lejeune, Q., Davin, E. L., Guillod, B. P., Seneviratne, S. I., 2015. Influence of Amazonian deforestation on the future evolution of regional surface fluxes, circulation, surface temperature and precipitation. Clim. Dynam. 44, 2769–2786.

Lima, C. H. R. and AghaKouchak, A., 2017. Droughts in Amazonia: Spatiotemporal Variability, Teleconnections, and Seasonal Predictions. Water Resour. Res. 53, 10824– 10840.

Lima, L. S., Coe, M. T., Soares-Filho, B. S., Cuadra, S. V., Dias, L. C. P., Costa, M. H., et al., 2014. Feedbacks between deforestation, climate, and hydrology in the Southwestern Amazon: implications for the provision of ecosystem services. Landsc. Ecol. 29 (2), 261–274.

Liu, J., Dietz, T., Carpenter, S. R., Alberti, M., Folke, C., Moran, E., et al., 2007. Complexity of coupled human and natural systems. Science, 317, 1513-1516.

Liu, J., Hull, V., Batistella, M., deFries, R., Dietz, T., Fu, F., et al., 2013. Framing sustainability in a telecoupled world. Ecol. Soc. 18(2).

Loarie, S. R., Lobell, D. B., Asner, G. P., Mu, Q., Field, C. B., 2011. Direct impacts on local climate of sugar-cane expansion in Brazil. Nat. Clim. Change 1, 105–109.

Maeda, E. E., Ma, X., Wagner, F., Kim, H., Oki, T., Eamus, D., Heute, A., 2017. Evapotranspiration seasonality across the Amazon basin. Earth Syst. Dynam. 8, 439–454.

141

Maginnis, S., Laestadius, L., Verdone, M., DeWitt, S., Saint-Laurent, C., Rietbergen-McCracken, J., Shaw, D. M. P., 2014. A Guide to the Restoration Opportunities Assessment Methodology (ROAM): Assessing Forest Landscape Restoration Opportunities at the National or Sub-national Level. Gland, International Union for Conservation of Nature.

Mahmood, R., Pielke, R. A., Hubbard, K. G., Niyogi, D., Dirmeyer, P. A., Mcalpine, C., et al., 2014. Land cover changes and their biogeophysical effects on climate. Int. J. Climatol. 34, 929–953.

Malhi, Y., Aragao, L. E. O. C., Galbraith, D., Huntingford, C., Fisher, R., Zelazowski, P., et al., 2006. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. P. Natl. Acad. Sci. USA 106, 20610–20615.

Marengo, J. A., 1992. Interannual variability of surface climate in the Amazon basin. Int. J. Climatol. 12, 853–863.

Marengo, J. A., 2004. Inter-decadal variability and trends in rainfall in the Amazon basin. Theor. Appl. Climatol. 78, 79–96.

Marengo, J. A. and Espinoza, J. C., 2016. Extreme seasonal droughts and floods in Amazonia: Causes, trends and impacts. Int. J. Climatol. 36, 3, 1033-1050.

Marengo, J. A., Nobre, C. , Tomasella, J., Oyama, M., Sampaio, G., Camargo, H., Alves, L. M., 2008. The drought of Amazonia in 2005. J. Climate, 21, 495–516.

Marengo, J. A., Tomasella, J., Alves, L. M., Soares, W. R., Rodriguez, D. A., 2011. The drought of 2010 in the context of historical droughts in the Amazon region. Geophys. Res. Lett. 38 (12).

Marengo, J. A., Souza, C. M., Thonicke, K., Burton, C., Halladay, K., Betts, R. A., et al., 2018. Changes in Climate and Land Use Over the Amazon Region: Current and Future Variability and Trends. Front. Earth Sci. 6.

Markard, J., Truffer, B., 2008. Technological innovation systems and the multi-level perspective: Towards an integrated framework. Res. Policy 37, 596–615.

142

McGinnis, M. D. and Ostrom, E., 2014. Social-ecological system framework: initial changes and continuing challenges. Ecol. Soc. 19(2).

Meir, P., Cox, P., Grace, J., 2006. The influence of terrestrial ecosystems on climate. Trends Ecol. Evol. 21, 254–260.

Meyfroidt, P., Rudel, T. K., Lambin, E. F., 2010. Forest transitions, trade, and the global displacement of land use. Proc. Natl. Acad. Sci. U. S. A. 107, 20917–20922.

Miguez-Macho, G and Fan, Y., 2012. The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands. J. Geophys. Res.-Atmos. 117, D15113.

MINAM (Ministerio del Ambiente), 2017. http://geobosques.minam.gob.pe/ (accessed 19 July 2017).

Ministerio de Hidrocarburos y Energía, 2012. Plan Óptimo De Expansión Del Sistema Interconectado Nacional 2022. http://www.cndc.bo/media/archivos/boletines/pexpa_sin_1222.pdf (accessed 15 May 2018)

MinMinas (El Ministerio de Minas y Energía/Ministry of Mining and Energies), 2017. Decree 2253. https://www.minenergia.gov.co/documents/10180/23517/47645-dec_2253_291217.pdf (accessed 29 May 2019).

MinMinas (El Ministerio de Minas y Energía/Ministry of Mining and Energies), 2018a. Decree 0570. https://www.minenergia.gov.co/documents/10180/23517/47726-dec_0570_230318.pdf/ (accessed 29 May 2019).

MinMinas (El Ministerio de Minas y Energía/Ministry of Mining and Energies), 2018b. Gobierno expide decreto que diversifica matriz de generación con renovables y la contratación a largo plazo que traerá mejores precios para los usuarios. https://www.minenergia.gov.co/web/10180/1332?idNoticia=23987055/ (accessed 27 May 2019).

143

Minnemeyer, S., Laestadius, L., Sizer, N., Saint-Laurent, C., Potapov, P., 2011. A World of Opportunity-world Resources Report. World Resources Institute. http://pdf.wri.org/world_of_opportunity_brochure_2011-09.pdf.

Molinier, M., Guyot, J. L., De Oliveira, E., Guimarães, V., 1996. Les régimes hydrologiques de l’Amazone et de ses affluents. Iahs Publication, 238, pp. 209–222.

Moore, N., Arima, E., Walker, R., Ramos da Silva, R., 2007. Uncertainty and the changing hydroclimatology of the Amazon. Geophys. Res. Lett. 34.

Morris, B. L., Lawrence, A. R. L., Chilton, P. J. C., Adams, B., Calow, R. C., Klinck, B. A., 2003. Groundwater and Its Susceptibility to Degradation: a Global Assessment of the Problem and Options for Management. Early Warning and Assessment Report Series. (RS. 03-3). United Nations Environment Programme, Nairobi.

Mu, Q., Zhao, M., Running, S. W., 2013. MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3), Algorithm Theoretical Basis Document, Collection 5, NASA HQ, Missoula, MT: Numerical Terradynamic Simulation Group. University of Montana.

Nepstad, D. C., Stickler, C. M., and Almeida, O. T., 2006. Globalization of the Amazon soy and beef industries: Opportunities for conservation. Conserv. Biol. 20, 1595–1603.

Nepstad, D. C., Stickler, C. M., Filho, B. S., Merry, F., 2008. Interactions among Amazon land use, forests and climate: prospects for a near-term forest tipping point. Philos. T. Roy. Soc. B 363, 1737–1746.

Nepstad, D., McGrath, D., Stickler, C., Alencar, A., Azevedo, A., Swette, B., et al., 2014. Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains. Science 344, 6188, 1118-1123.

Niu, G. Y., Yang, Z. L., Dickinson, R. E., Gulden, L. E., Su, H., 2007. Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data. J. Geophys. Res. 112, D07103.

Nobre, C. A., Sellers, P. J., Shukla, J., 1991. Amazonian Deforestation and Regional Climate Change. J. Climate 4, 957–988. 144

Nordblom, T. L., Finlayson, J. D., Hume, I. H., 2012. Upstream demand for water use by new tree plantations imposes externalities on downstream irrigated agriculture and wetlands. Aust. J. Agric. Resour. Econ. 56, 455–474.

OCHA (UN Office for the Coordination of Humanitarian Affairs), 2016. Bolivia: Afectación y riesgo por sequía. https://reliefweb.int/sites/reliefweb.int/files/resources/Redhum-BO-Infografia_Sequia-201606 02-AM-18551.pdf 01 June, (accessed 29 May 2019).

Oil Price, 2018. A Natural Gas Crisis Looms Over Colombia. https://oilprice.com/Energy/Natural-Gas/A-Natural-Gas-Crisis-Looms-Over-Colombia.html/ 21 April (accessed 27 May 2019).

Olsson, P., Folke, C., Galaz, V., Hahn, T., Schultz, L., 2007. Enhancing the fit through adaptive co-management: creating and maintaining bridging functions for matching scales in the Kristianstads Vattenrike Biosphere Reserve, Sweden. Ecol. Soc. 12 (1), 28.

Ostrom, E., 2009. A general framework for 145ocio-pol sustainability of social-ecological systems. Science, 325, 419-422.

Ouyang, Y., Leininger, T. D., Moran, M., 2013. Impacts of reforestation upon sediment load and water outflow in the Lower Yazoo River Watershed, Mississippi. Ecol. Eng. 61, 394– 406.

Panday, P. K., Coe, M. T., Macedo, M. N., Lefebvre, P., de Castanho, A. D. A., 2015. Deforestation offsets water balance changes due to climate variability in the Xingu River in eastern Amazonia. J. Hydrol. 523, 822–829.

Pelling, M., Dill, K., 2010. Disaster politics: Tipping points for change in the adaptation of 145ocio-political regimes. Prog. Hum. Geogr. 34, 21–37.

Pielke, R. A., Marland, G., Betts, R. A., Chase, T. N., Eastman, J. L., Niles, J. O., et al., 2002. The influence of land-use change and landscape dynamics on the climate system: relevance to climate-change policy beyond the radiative effect of greenhouse gases. Philos. T. Roy. Soc. A 360, 1705–1719.

145

Pires, G. F. and Costa, M. H., 2013. Deforestation causes different subregional effects on the Amazon bioclimatic equilibrium. Geophys. Res. Lett. 40, 3618–3623.

Pitman, A. J. and Lorenz, R., 2016. Scale dependence of the simulated impact of Amazonian deforestation on regional climate. Environ. Res. Lett. 11 (9).

Piu, H. C. and Menton, M., 2014. The context of REDDC in Peru: Drivers, agents and institutions. Occasional Paper 106, Center for International Forestry Research, Bogor, Indonesia.

Pokhrel, Y. N., Fan, Y., Miguez-Macho, G., Yeh, P. J. F., Han, S. C., 2013. The role of groundwater in the Amazon water cycle: 3. Influence on terrestrial water storage computations and comparison with GRACE. J. Geophys. Res.-Atmos. 118, 3233–3244.

Portafolio, 2016. Se acabó la campaña ‘Apagar paga’. https://www.portafolio.co/economia/gobierno/finaliza-campana-apagar-paga-494701/ (accessed 27 May 2019).

Portafolio, 2019. Fuentes renovables serían el 10% de la matriz de energía. https://www.portafolio.co/economia/fuentes-renovables-serian-el-10-de-la-matriz-de-energia- 525457/ (accessed 27 May 2019).

Post, W. M. and Kwon, K. C., 2000. Soil carbon sequestration and land-use change: processes and potential. Glob. Change Biol. 6 (3), 317–327.

Potapov, P., Laestadius, L., Minnemeyer, S., 2011. Global Map of Forest Landscape Restoration Opportunities. World Resources Institute, Washington, DC. www.wri.org/forest-restoration-atlas.

Rabatel, A., Francou, B., Soruco, A., Gomez, J., Cáceres, B., Ceballos, J. L., et al., 2013. Current state of glaciers in the tropical Andes: a multi-century perspective on glacier evolution and climate change. Cryosphere 7 (1), 81–102.

Ramírez, B. H., Teuling, A. J., Ganzeveld, L., Hegger, Z., Leemans, R., 2017. Tropical Montane Cloud Forests: hydrometeorological variability in three neighbouring catchments with different forest cover. J. Hydrol. (Amst) 552, 151–167.

146

Ramos-Mejía, M., Franco-Garcia, M. L., Jauregui-Becker, J. M., 2018. Sustainability transitions in the developing world: Challenges of socio-technical transformations unfolding in contexts of poverty. Environ. Sci. Policy 84, 217–223.

Rangecroft, S., Suggitt, A. J., Anderson, K., Harrison, S., 2016. Future climate warming and changes to mountain permafrost in the Bolivian Andes. Clim. Change 137 (1-2), 231–243.

Reuters, 2019. Colombia fails to award renewable energy projects despite appetite. https://www.reuters.com/article/us-colombia-renewables/colombia-fails-to-award-renewable- energy-projects-despite-appetite-idUSKCN1QF2Z5/ (accessed 29 May 2019).

Rindfuss, R. R., Walsh, S. J., Turner, B. L., Fox, J., Mishra, V., 2004. Developing a science of land change: Challenges and methodological issues. Proc. Natl. Acad. Sci. U. S. A. 39, 13976-13981,

Rip, A. and Kemp, R., 1998. Chapter 6 Technological change, in: Rayner, S., Malone, E.L. (Eds.), Human Choice and Climate Change.Vol. II, Resources and Technology. Battelle Press, Columbus, Ohio, pp. 327–399. https://ris.utwente.nl/ws/portalfiles/portal/5604260/K356.pdf

Rodell, M. and Famiglietti, J. S., 2002. The potential for satellite-based monitoring of groundwater storage changes using GRACE: The High Plains aquifer, Central US. J. Hydrol. 263, 245–256.

Rotmans, J., Kemp, R., van Asselt, M., 2001. More evolution than revolution: Transition management in public policy. Foresight 3, 1, 15-31.

Roy, T., Martinez, J. A., Herrera‐Estrada, J. E., Zhang, Y., Dominguez, F., Berg, A., et al., 2019. Role of moisture transport and recycling in characterizing droughts: Perspectives from two recent US droughts and the CFSv2 system. J. Hydrometeorol. 20, 139– 154.

Saad, S. I., da Rocha, H. R., Silva Dias, M. A., Rosolem, R., 2010. Can the Deforestation Breeze Change the Rainfall in Amazonia? A Case Study for the BR-163 Highway Region. Earth Interact. 14, 1–25.

Sakai, R. K., Fitzjarrald, D. R., Moraes, O. L. L., Staebler, R. M., Acevedo, O. C., Czikowsky, M. J., et al., 2004. Land-use change effects on local energy, water, and carbon balances in an Amazonian agricultural field. Glob. Change Biol. 10 (5), 895–907. 147

Salati, E. and Nobre, C. A., 1991. Possible climatic impacts of tropical deforestation. Climatic Change 19, 177–196.

Salati, E., Dall’Olio, A., Matsui, E., Gat, J. R., 1979. Recycling of water in the Amazon Basin: An isotopic study. Water Resour. Res. 15, 1250–1258.

Schrobback, P., Adamson, D., Quiggin, J., 2011. Turning Water into Carbon: Carbon Sequestration and Water Flow in the Murray-Darling Basin. Environ. Resour. Econ. 49, 23– 45.

Seneviratne, S. I., Lüthi, D., Litschi, M., Schär, C., 2006. Land–atmosphere coupling and climate change in Europe. Nature 443, 205–209.

Sheffield, J., Wood, E. F., Chaney, N., Guan, K., Sadri, S., Yuan, X., et al., 2014. A Drought Monitoring and Forecasting System for Sub-Sahara African Water Resources and Food Security. Bull. Am. Met. Soc. 95, 861–882.

Shukla, J., Nobre, C., Sellers, P., 1990. Amazon Deforestation and Climate Change. Science 247, 1322–1325.

Silvério, D. V., Brando, P. M., Macedo, M. N., Beck, P. S. A., Bustamante, M., Coe, M. T., 2015. Agricultural expansion dominates climate changes in southeastern Amazonia: the overlooked non-GHG forcing. Environ. Res. Lett. 10 (10).

Sliggers, J. and Kakebeeke, W. (Eds.) 2004. CLEARING THE AIR 25 Years of the Convention on Long-range Transboundary Air Pollution. United Nations, Geneva.

Smith, A. and Raven, R., 2012. What is protective space? Reconsidering niches in transitions to sustainability. Res. Policy 41, 1025–1036.

Smith, A., Stirling, A., Berkhout, F., 2005. The governance of sustainable socio-technical transitions. Res. Policy 34, 1491–1510.

Snyder, P. K., 2010. The Influence of Tropical Deforestation on the Northern Hemisphere Climate by Atmospheric Teleconnections. Earth Interact. 14, 1–34.

Soares-Filho, B. S., Nepstad, D. C., Curran, L. M., Cerqueira, G. C., Garcia, R. A., Ramos, C. A., et al., 2006. Modelling conservation in the Amazon basin. Nature 440, 520–523. 148

Soares-Filho, B. S., Rajao, R., Macedo, M., Carneiro, A., Costa, W., Coe, M., et al., 2014. Cracking Brazil’s Forest Code. Science 344, 363–364.

Soares-Filho, B., Rajâo, R., Merry, F., Rodrigues, H., Davis, J., Lima, L., et al., 2016. Brazil’s market for trading forest certificates. PloS One 11, 6, e0157203.

Sodemann, H. and Stohl, A., 2013. Moisture origin and meridional transport in atmospheric rivers, and their association with multiple cyclones. Mon. Weather Rev. 141, 2850–2868.

Sodemann, H., Schwierz, C., Wernli, H., 2008. Interannual variability of Greenland winter precipitation sources: Lagrangian moisture diagnostic and North Atlantic Oscillation influence. J. Geophys. Res., 113, D03107.

Spracklen, D. V. and Garcia-Carreras, L., 2015. The impact of Amazonian deforestation on Amazon basin rainfall. Geophys. Res. Lett. 42 (21), 9546–9552.

Spracklen, D. V., Arnold, S. R., Taylor, C. M., 2012. Observations of increased tropical rainfall preceded by air passage over forests. Nature 489, 282–285.

Sprenger, M. and Wernli, H., 2015. The LAGRANTO Lagrangian analysis tool – version 2.0, Geosci. Model Dev. 8, 2569-2586.

Stickler, C. M., Coe, M. T., Costa, M. H., Nepstad, D. C., McGrath, D. G., Dias, L. C. P., et al., 2013. Dependence of hydropower energy generation on forests in the Amazon Basin at local and regional scales. Proc. Natl. Acad. Sci. U. S. A. 110, 9601–9606.

Stohl, A. and James, P., 2004. A Lagrangian analysis of the atmospheric branch of the global water cycle. Part I: Method description, validation, and demonstration for the August 2002 flooding in Central Europe. J. Hydrometeorol., 5(8), 656– 678.

Su, F., Hong, Y., Lettenmaier, D. P., 2008. Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and Its Utility in Hydrologic Prediction in the La Plata Basin. J. Hydrometeorol. 9, 622–640.

Suarez, F. F. and Oliva, R., 2005. Environmental change and organizational transformation. Ind. Corp. Chang. 14, 1017–1041.

149

Swann, A. L. S., Longo, M., Knox, R. G., Lee, E., Moorcroft, P. R., 2015. Future deforestation in the Amazon and consequences for South American climate. Agric. For. Meteorol. 214-215, 12–24.

Tapley, B. D., 2004. GRACE Measurements of Mass Variability in the Earth System. Science 305, 503–505.

The Bogotá Post, 2016. Weathering the drought. https://thebogotapost.com/weathering-the-drought/13782/ (accessed 27 May 2019).

Thelen, K., 2014. How institutions evolve: Insights from comparative historical analysis, in: Mahoney, J. and Rueschemeyer, D. (Eds.), Comparative Historical Analysis in the Social Sciences. Cambridge University Press, pp. 208–240. https://doi.org/10.1017/CBO9780511803963.007.

Thomalla, F., Downing, T., Spanger-Siegfried, E., Han, G., Rockström, J., 2006. Reducing hazard vulnerability: Towards a common approach between disaster risk reduction and climate adaptation. Disasters 30, 39–48.

Thornton, C. M., van der Wal, J., Williams, R. J., Keniger, L., Specht, A., 2013. Brave new green world – Consequences of a carbon economy for the conservation of Australian biodiversity. Biol. Conserv. 161, 71-90.

Tian, L., Yao, T., MacClune, K., White, J. W. C., Schilla, A., Vaughn, B., et al., 2007. Stable isotopic variations in west China: A consideration of moisture sources. J. Geophys. Res.-Atmos. 112, D10112.

Trimble, S.W., Weirich, F. H., Hoag, B. L., 1987. Reforestation and Reduction of Water Yield on the Southeastern Piedmont Since Circa 1940. Water Resour. Res. 23, 425-437.

Trenberth, K. E., 1999. Atmospheric moisture recycling: role of advection and local evaporation. J. Clim. 12 (5 II), 1368–1381.

Trenberth, K. E., Smith, L., Qian, T., Dai, A., Fasullo, J., 2007. Estimates of the Global Water Budget and Its Annual Cycle Using Observational and Model Data. J. Hydrometeor. 8, 758–769.

150

Trenberth, K. E., Fasullo, J. T., Mackaro, J., 2011. Atmospheric Moisture Transports from Ocean to Land and Global Energy Flows in Reanalyses. J. Climate 24, 4907–4924.

Trohanis, Z. E., Zaengerling, B. M., Sanchez-Reaza, J., 2015. Urbanization Trends in Bolivia : Opportunities and Challenges (English). Directions in Urban Development. World Bank Group, Washington, D.C. http://documents.worldbank.org/curated/en/997641468187732081/Urbanization-trends-in-Bo livia-opportunities-and-challenges.

Tuinenburg, O. A., Hutjes, R.W. A., Kabat, P., 2012. The fate of evaporated water from the Ganges basin. J. Geophys. Res.-Atmos. 117, D01107.

Turner, B. L., Matson, P. A., McCarthy, J. J., Corell, R. W., Christensen, L., Eckley, N., et al., 2003. Illustrating the coupled human-environment system for vulnerability analysis: Three case studies. Proc. Natl. Acad. Sci. U. S. A. 100, 8080–8085.

Turner, B. L., Lambin, E. F., Reenberg, A., 2007. The emergence of land change science for global environmental change and sustainability. Proc. Natl. Acad. Sci. U. S. A. 104, 20666-20671.

Turnheim, B., Geels, F., Nykvist, B., McMeekin, A., van Vuuren, D., Berkhout, F., Hof, A., 2015. Evaluating sustainability transitions pathways: Bridging analytical approaches to address governance challenges. Glob. Environ. Chang. 35, 239–253.

Umwelt Bundesamt, 2018. Implementation of Nationally Determined Contributions Colombia Country Report. https://www.umweltbundesamt.de/sites/default/files/medien/1410/publikationen/2018-11-01_ climate-change_25-2018_country-report-colombia.pdf/ (accessed 27 May 2019).

UN (United Nations), 2004. Living With Risk: A Global Review of Disaster Risk Reduction Initiatives (2004 Version – Volume II Annexes), https://www.unisdr.org/files/657_lwr21.pdf (accessed 29 May 2019).

UNAI (The United Nations Academic Impact), Sustainability hub. https://academicimpact.un.org/content/sustainability (accessed 29 May 2019).

151

UNCCD (United Nations Convention to Combat Desertification), 2017. Santa Cruz de la Sierra Drought Conf. Declaration (Santa Cruz de la Sierra). (accessed 04 Dec. 2017) http://www2.unccd.int/latin-america-and-caribbean-regionaldrought-conference.

UNECE (United Nations Economic Commission for Europe, 1979. 1979 Geneva Convention on Long-range Transboundary Air Pollution. http://www.unece.org/fileadmin//DAM/env/lrtap/full%20text/1979.CLRTAP.e.pdf.

UNFCCC (United Nations Framework Convention on Climate Change), 2013. Afforestation and Reforestation Projects Under the Clean Development Mechanism: a Reference Manual. https://cdm.unfccc.int/public_inputs/2013/arcdm_01/AR_CDM_Manual_Draft_01.pdf.

UNFCCC (United Nations Framework Convention on Climate Change), 2015. Paris Agreement. https://unfccc.int/files/meetings/paris_nov_2015/application/pdf/paris_agreement_english_.p df/ (accessed 27 May 2019).

UNFCCC (United Nations Framework Convention on Climate Change), 2016. Aggregate effect of the intended nationally determined contributions: an update/Synthesis report by the secretariat. https://unfccc.int/sites/default/files/resource/docs/2016/cop22/eng/02.pdf/ (accessed 29 May 2019)

UNGRD (Unidad Nacional para la Gestión del Riesgo de Desastres, Gobierno de Colombia), 2016ª. Gobierno nacional mantiene llamado al ahorro del agua. http://portal.gestiondelriesgo.gov.co/Paginas/Noticias/2016/Gobierno-nacional-mantiene-lla mado-al-ahorro-del-agua.aspx (accessed 29 May 2019).

UNGRD (Unidad Nacional para la Gestión del Riesgo de Desastres, Gobierno de Colombia), 2016b. Tras 15 meses se da por terminada la Temporada Seca en Colombia. http://portal.gestiondelriesgo.gov.co/Paginas/Noticias/2016/Tras-15-meses-se-da-por-termina da-la-Temporada-Seca-en-Colombia.aspx (accessed 27 May 2019).

UPME (Unidad de Planeación Minero Energética/Unit for Mining and Energy Planning, Government of Colombia), 2015. INFORME MENSUAL DE VARIABLES DE GENERACIÓN Y DEL MERCADO ELÉCTRICO COLOMBIANO.

152

http://www.siel.gov.co/portals/0/generacion/2015/Seguimiento_Variables_Junio_2015.pdf (accessed 27 May 2019).

van der Ent, R. J. and Savenije, H. H. G., 2011. Length and time scales of atmospheric moisture recycling. Atmos. Chem. Phys. 11, 1853-1863. Figure 1.1 here in the thesis is originated from the Figure 3 of this cited work (on page 1857), reformatted without

adaptation. This figure is under the Creative Commons Attribution 4.0 License. https://creativecommons.org/licenses/by/4.0/ with the copyright from the authors of this cited work. Used with permission.

van der Ent, R. J. and Tuinenburg, O. A., 2017. The residence time of water in the atmosphere revisited, Hydrol. Earth Syst. Sci. 21, 779-790.

van der Ent, R. J., Savenije, H. H. G., Schaefli, B., Steele-Dunne, S. C., 2010. Origin and fate of atmospheric moisture over continents. Water Resour. Res. 46, W09525.

van der Ent, R. J., Tuinenburg, O. A., Knoche, H. R., Kunstmann, H., Savenije, H. H. G., 2013. Should we use a simple or complex model for moisture recycling and atmospheric moisture tracking?. Hydrol. Earth Syst. Sci. 17, 4869–4884.

van der Ent, R. J., Wang-Erlandsson, L., Keys, P. W., Savenije, H. H. G., 2014. Contrasting roles of interception and transpiration in the hydrological cycle – part 2: moisture recycling. Earth Syst. Dyn. 5, 471–489. van Dijk, A. I. J. M., Hairsine, P. B., Arancibia, J. P., Dowling, T. I., 2007. Reforestation, water availability and stream salinity: A multi-scale analysis in the Murray-Darling Basin, Australia. For. Ecol. Manage. 251, 94–109. van Driel, H. and Schot, J., 2005. Radical innovation as a multilevel process: Introducing floating grain elevators in the port of Rotterdam. Technol. Cult. 46, 51–76. van Noordwijk, M. and Ellison, D., 2019. Rainfall recycling needs to be considered in defining limits to the world’s green water resources. Proc. Natl. Acad. Sci. U. S. A. 116, 8102-8103.

153

van Noordwijk, M., Namirembe, S., Catacutan, D., Williamson, D., Gebrekirstos, A., 2014. Pricing rainbow, green, blue and grey water: tree cover and geopolitics of climatic teleconnections. Curr. Opin. Environ. Sustain. 6, 41–47.

van Noordwijk, M., Bruijnzeel, S., Ellison, D., Sheil, D., Morris, C.E., Sands, D., et al., 2015. Ecological Rainfall Infrastructure: Investment in Trees for Sustainable Development. ASB Partnership for the Tropical Forest Margins.

Veiga, J. B., Tourrand, J. F., Piketty, M. G., 2002. Cattle ranching in the amazon rainforest. Proc. Aust. Soc. Anim. Prod. 24, 253–256.

Verbong, G. P. J. and Geels, F. W., 2007. The ongoing energy transition: Lessons from a socio-technical, multi-level analysis of the Dutch electricity system (1960-2004). Energy Policy 35, 1025–1037.

Verbong, G. P. J. and Geels, F. W., 2010. Exploring sustainability transitions in the electricity sector with socio-technical pathways. Technol. Forecast. Soc. Change 77, 1214– 1221.

Vertessy, R. A., 1999. The impacts of forestry on stream flows: a review. In: Croke, J. and Lane, P. (Eds), Forest Management for Water Quality and Quantity. Proceedings of the Second Forest Erosion Workshop, May 1999, Warburton, Australia. Report 99/6. Cooperative Research Centre for Catchment Hydrology, CSIRO Land and Water, Canberra. Pp. 93–109.

Victoria, R. L., Martinelli, L. A., Mortatti, J., Richey, J., 1991. Mechanisms of Water Recycling in the Amazon Basin – Isotopic Insights. Ambio 20, 384–387.

Voldoire, A. and Royer, J. F., 2004. Tropical deforestation and climate variability. Clim. Dynam. 22, 857–874.

Vuille, M., Carey, M., Huggel, C., Buytaert, W., Rabatel, A., Jacobsen, D., et al., 2018. Rapid decline of snow and ice in the tropical Andes – impacts, uncertainties and challenges ahead. Earth. Rev. 176, 195–213.

154

Wagner, S., Kunstmann, H., Bárdossy, A., Conrad, C., Colditz, R. R., 2009. Water balance estimation of a poorly gauged catchment in West Africa using dynamically downscaled meteorological fields and remote sensing information. Phys. Chem. Earth 34, 225–235.

Wang-Erlandsson, L., Fetzer, I., Keys, P. W., van der Ent, R. J., Savenije, H. H. G., Gordon, L. J., 2017. Remote land use impacts on river flows through atmospheric teleconnections. Hydrol. Earth Syst. Sci. Discuss.

Wang-Erlandsson, L., Fetzer, I., Keys, P. W., van der Ent, R. J., Savenije, H. H. G., Gordon, L. J., 2018. Remote land use impacts on river flows through atmospheric teleconnections. Hydrol. Earth Syst. Sci. 22, 4311–4328.

Wei, J., Jin, Q., Yang, Z. L., Dirmeyer, P. A., 2016. Role of ocean evaporation in California droughts and floods. Geophys. Res. Lett. 43, 6554– 6562.

Weng, W., Costa, L., Lüdeke, M. K. B., Zemp, D. C., 2019. Aerial river management by smart cross-border reforestation. Land Use Policy 84, 105-113.

Werth, D. and Avissar, R., 2004. The Regional Evapotranspiration of the Amazon. J. Hydrometeorol. 5, 100–109.

WFP (World Food Programme), 2016. El Niño in Latin America and the Caribbean External Situation Report #1. https://reliefweb.int/sites/reliefweb.int/files/resources/WFP%20El%20Nino%20in%20Latin% 20America%20and%20the%20Caribbean%20External%20Situation%20Report%20%231%2 030%20May%202016_0.pdf 30 May, (accessed 27 May 2019).

Winschall, A., Pfahl, S., Sodemann, H., Wernli, H., 2014. Comparison of Eulerian and Lagrangian moisture source diagnostics –the flood event in eastern Europe in May 2010. Atmos. Chem. Phys. 14, 6605–6619.

Wood, G. and Gough, I., 2006. A Comparative Welfare Regime Approach to Global Social Policy. World Dev. 34, 1696–1712. 155

Wood, E. F., Schubert, S. D., Wood, A. W., Peters‐Lidard, C. D., Mo, K. C., Mariotti, A., Pulwarty, R. S., 2015. Prospects for advancing drought understanding, monitoring, and prediction. J. Hydrometeorol. 16, 1636– 1657.

Wradio, 2019. MinMinas aseguró que está listo para proveer energía confiable durante El Niño. https://www.wradio.com.co/noticias/actualidad/minminas-aseguro-que-esta-listo-para-provee r-energia-confiable-durante-el-nino/20190206/nota/3860524.aspx/ 06 February (accessed 29 May 2019).

Yeh, P. J. F. and Famiglietti, J. S., 2009. Regional Groundwater Evapotranspiration in Illinoi. J. Hydrometeorol. 10, 464–478.

Yoshimura, K., Oki, T., Ohte, N., Kanae, S., 2004. Colored Moisture Analysis Estimates of Variations in 1998 Asian Monsoon Water Sources. J. Meteorol. Soc. Jpn. 82, 1315–1329.

Zemp, D. C., Schleussner, C. F., Barbosa, H. M. J., van der Ent, R. J., Donges, J. F., Heinke, J., et al., 2014. On the importance of cascading moisture recycling in South America. Atmos. Chem. Phys. 14, 13337–13359.

Zemp, D. C., Schleussner, C. F., Barbosa, H. M. J., Hirota, M., Montade, V., Sampaio, G., et al., 2017a. Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks. Nat. Commun. 8, 14681.

Zemp, D. C., Schleussner, C. F., Barbosa, H. M. J., Rammig, A., 2017b. Deforestation effects on Amazon forest resilience. Geophys. Res. Lett. 44, 6182–6190.

Zhang, L., Dawes, W.R., Walker, G.R., 1999. Predicting the Effect of Vegetation Changes on Catchment Average Water Balance. Cooperative Research Centre for Catchment Hydrology, CSIRO Land and Water, Canberra.

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Supplementary 1 A policy brief developed from Chapter 3 (plain text version)

A new strategy for planning reforestation sites

Collecting atmospheric water for the city’s future usage

Wei Weng, Luís Costa, Matthias Lüdeke, Delphine Clara Zemp, Marissa Castro Magnani, Boris Hinojosa Guzmán, Tobia Lakes, Marcela Quintero

Key messages

Planting trees adds moisture to the atmosphere and through circulation it can benefit downwind water availability in the long run.

In this brief, we show the application of this concept on planning strategic reforestation sites using the case of Santa Cruz de la Sierra and modelled strategic reforestation’s impact on the water availability of the city.

Our results show that a considerable increase in the city’s water can be expected following strategically planting trees. This increase can ultimately cover 22-59% of projected growth in the city’s future water demand.

Introduction

Smartly reforest can bring more water

Reforestation reduces atmospheric carbon while presenting co-benefits such as preserving biodiversity and ecosystem integrity. Internationally, reforestation has been one of the most active initiatives to mitigate global climate change impact, e.g. the Aichi target and the Bonn Challenge. In Bolivia, under the commitment of Ley 300, Ley 1333 and Ley 1700, reforestation has been also one of the priority land management target to preserve the national ecosystem and its services. Despite the top-down forces, there have been challenges in the local implementation of reforestation. A major factor drawing back past reforestation projects 157

is their low societal acceptance. Friction usually stems from a reduction of fresh water supply following reforestation. A dramatic decrease in runoff is normally observed downstream of the reforestation sites compromising water supply and other ecosystem services from the river.

However, a usually neglected aspect of reforestation is that it can also enhance water availability through invisible aerial river connection. Aerial rivers refer to the main pathways of moisture flow in the atmosphere. Their names arise from the analogy with surface rivers (Arraut et al., 2012). The aerial rivers are recharged by evapotranspiration in the upwind regions and discharged by precipitation in the downwind regions (see Figure 1 for explanation). Connecting regions across administrative and topographic boundaries, their great potential in influencing water availability is well known (Weng et al., 2018) but has not yet been integrated in water management. Reforestation generally intensifies evapotranspiration resulting in runoff decrease in the catchment, but this evapotranspiration enhances moisture input into the aerial rivers. While reforestation inevitably reduces surface runoff of the downstream regions, downwind regions, can however, benefit from the aerial rivers gain. It is known that for a given region, there are some upwind regions that are especially important on its water through the aerial river connections. In other words, it is actually possible to plan reforestation sites in those influential upwind regions to optimize a given region’s aerial river benefits following reforestation. Here we show a framework to identify strategic reforestation sites that can achieve optimal benefits for water availability from aerial river regime. Its application on the city of Santa Cruz de la Sierra (here after Santa Cruz) in eastern Bolivia is demonstrated.

Figure 1 Aerial Rivers. Preferential pathways of moisture flows in the atmosphere. They are recharged by evapotranspiration of upwind areas discharged by precipitation in downwind areas following major wind directions (credits: Wei Weng, Sicily, Italy 2016)

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Framework to identify optimal reforestation sites

In order to identify sites that are influential on the target region through aerial rivers, a moisture back-tracking algorithm was applied to trace the most important upwind regions providing moisture to Santa Cruz (WAM-2layers). Given precipitation, evapotranspiration and other climatic data, the algorithm can be used to track the sources of a target region’s rainfall based on the water balance principle. We used the mean of annual average of earth observation-based climatic data in the period of 2000 to 2010 to identify the aerial river connections with the city’s moisture source areas. From the results calculated, we can further identify hotspot areas that are most influential for the rainfall received in Santa Cruz. These hotspot regions are understood as the most influential precipitationshed (MIP) of Santa Cruz (see Figure 2, the blue area), being precipitationshed also an analogy to the surface river’s watershed (Keys et al., 2012). Then we quantify the influences of land use change in these important hotspots (MIP) through the aerial rivers downwind to Santa Cruz.

We then employed a feasible reforestation scenario in the MIP regions and calculated the moisture that can be added to Santa Cruz. This reforestation scenario was developed according to the IUCN Bonn challenge restoration potential map (Maginnis et al., 2014) where local reforestation potential was assessed by climate, soil and socio-economic conditions such as land use history and land use culture. We found that the moisture added by our reforestation scenario (named smart reforestation) can increase Santa Cruz’s annual rainfall by 1.25 % and annual runoff by 2.30%. Moreover, the dry season runoff gain can even reach 26.93% by reforesting where feasible in the MIP regions (See Figure 2).

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Figure 2 Smart reforestation for water supply in Santa Cruz. The Most Influential part of the Precipitationshed (MIP) is highlighted in blue and the upstream surface river basin of Santa Cruz de la Sierra is shown in purple. The blue arrows represent aerial river flows whereas the purple arrows represent surface river flows. The city Santa Cruz de la Sierra, is shown with an orange dot.

Future water for Santa Cruz

Our results show that reforesting smartly can enhance water supply and can be considered a water management option for the city of Santa Cruz. This can be especially beneficial for sustaining ecosystem and livelihood and for mitigating the impact of region’s projected longer and intensification of dry season (Seiler 2013, Marengo and Espinoza 2015). Furthermore, the water added by smart reforestation can be useful for easing the water stress brought about by fast urbanization. Santa Cruz is one of the most rapidly growing cities in the world, and migration input, the main reason of the city’s growth in the past decades, is expected to persist. Though the city enjoys the highest potable water coverage in the country, it is heavily relying on the groundwater resources which are under stress both for it quality

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and quantity (Morris et al., 2003). Securing water availability for the growing population and the peri-urban agriculture that sustains the citizens is urgent. We use the population growth projection implied by different Shared Socioeconomic Pathways (SSP) (Jones and O’Neal 2016), and calculated the additional water resources needed to secure the current water consumption per capita (INE, 2017) in the city of Santa Cruz by 2030. Our results show that an early implementation (by 2020) of smart reforestation will stably expand the renewable water resource of the city and ultimately cover between 22% and 59% of the additional water demand by 2030. Although the city currently does not have infrastructure to directly utilise runoff, the already confirmed large dam projects (Ministerio de Hidrocarburos y Energía, 2012) will allow benefits from the smart reforestation option. Furthermore, given the fact that the glaciers currently sustaining runoff are retreating under climate change (Rabatel et al 2013, Rangecroft et al 2016, Vuille et al. 2018), sustaining runoff by smart reforestation might be particularly relevant. In addition, increases in both rainfall and runoff by smart reforestation will also have a positive impact on groundwater recharge which the city currently relies heavily on. Thus, managing land and water by smart reforestation can increase water supply and mitigate the stress on the current system from both population growth and climate change.

Alignment of smart reforestation with current practices

At a national level, smart reforestation could be a beneficial tool to support the PNFR reforestation program (Programa Nacional de Forestación y Reforestación – PNFR, Decreto Supremo Nº 2912) and the ultimately defined NDC (Nationally Determined Contributions) goals in the forest chapter presented for the Paris Agreement Framework (UNFCCC, 2015) where a Bolivian national target of reforesting 4.5 million hectares by 2030 is established. The smart reforestation scenario includes 7.1 million hectares reforestation areas (Bolivian part 3.2 million hectares) is in line with this target while a full implementation of smart reforestation will require cross-border cooperation. Current implementation of the national target follows traditional strategy in which reforestation sites are planned at the upstream catchments with the aim to improve water quality. Nevertheless, this strategy usually rises the friction of the reforestation projects while downstream water quantity is compromised. In addition, the feasible sites in upstream catchments are also usually limited. A gap for fulfilling the national target of reforesting large areas can be expected if prioritizing this

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traditional strategy. In this context, smart reforestation can be a good option to fill the gap, and it might be even worth considered before the traditional strategy when aiming for a more resilient water city of Santa Cruz to the future challenge. In practice, it will be necessary to negotiate between the states especially the state of Beni and Pando, for the implementation of smart reforestation. Nevertheless, the friction stemming from reduction in downstream water supply is likely to be small as those states and their downstream regions have relatively rich runoff resources. Furthermore, feasibility of reforestation sites in smart reforestation scenario has already been evaluated previously by the IUCN according to socio-economic factors (Maginnis et al., 2014). Those sites with strong community frictions, therefore, can be very likely avoided.

Policy recommendations

• Desirable effects on downwind water availability can be expected through planning reforestation on the upwind hotspots. • Reforestation must be planned minimizing friction and maximizing the benefits. • Awareness on reforestation’s effect on water availability through aerial rivers should be increased among planners. • Planning reforestation in upwind hotspots (MIP) of the city Santa Cruz should be prioritized for the city’s resilience on water and for an easier realization of both Bolivia’s national restoration target and its international commitment (eg. INDC). • Assessments on local reforestation potential and on reforestation’s effects are helpful to increase reforestation projects’ viability.

Conclusion

In this brief we propose fulfillment of Bolivian national reforestation targets by a strategic reforestation planning that can bring more water to the fast growing Santa Cruz de la Sierra. By integrating aerial river aspects and identifying important sites for implementation, reforestation following this new strategy (smart reforestation) can effectively provide water to the city through the atmospheric circulation. Reforestation sites planned under this strategy are located relatively downstream invoking less friction from compromise of surface water supply. Thus, main obstacles for current reforestation practice following traditional reforestation strategy that plants priory in upstream catchments are avoided. Moreover, the 162

feasibility of the reforestation on suggested sites was already confirmed by assessments that take socio-economic factors into consideration, therefore smart reforestation allows a more efficient fulfillment of the national target. Presenting known benefits of reforestation, smart reforestation can also be a water management tool while optimally sustaining Santa Cruz’ growing water demands by 22-59%, if implemented early enough. An action realizing it will be a key approach for win-win situation between the country’s biggest city’s water resilience to future challenges and the effort to mitigate climate change challenges both nationally and globally.

References

Arraut, J. M., Nobre, C., Barbosa, H. M. J., Obregon, G., & Marengo, J. (2012). Aerial rivers and lakes: Looking at large-scale moisture transport and its relation to Amazonia and to subtropical rainfall in South America. Journal of Climate, 25(2), 543–556. https://doi.org/10.1175/2011JCLI4189.1 Instituto Nacional de Estadíticas de Bolivia (INE) (2017) Ficha Resúmen Censo Población y Vivienda 2012. Retrieved from http://censosbolivia.ine.gob.bo/censofichacomunidad/c_pdfm/generar_pdf/07/01/01/x [Accessed 04 Dec. 2017] Jones, B., & O’Neill, B. C. (2016). Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environmental Research Letters, 11(8), 084003. https://doi.org/10.1088/1748-9326/11/8/084003 Keys, P. W., van Der Ent, R. J., Gordon, L. J., Hoff, H., Nikoli, R., & Savenije, H. H. G. (2012). Analyzing precipitationsheds to understand the vulnerability of rainfall dependent regions. Biogeosciences, 9(2), 733–746. https://doi.org/10.5194/bg-9-733-2012 Maginnis, S., Laestadius. L., Verdone, M., DeWitt, S., Saint-Laurent, C., Rietbergen-McCracken, J. & Shaw, D. M. P. (2014). A guide to the Restoration Opportunities Assessment Methodology (ROAM): Assessing forest landscape restoration opportunities at the national or sub-national level. Gland: International Union for Conservation of Nature. Marengo, J. A., & Espinoza, J. C. (2016). Extreme seasonal droughts and floods in Amazonia: Causes, trends and impacts. International Journal of Climatology, 36, 1033-1050. https://doi.org/10.1002/joc.4420

163

Ministerio de Hidrocarburos y Energía (2012). Plan Óptimo de Expansión del Sistema Interconectado Nacional 2022. Retrieved from http://www.cndc.bo/media/archivos/boletines/pexpa_sin_1222.pdf [Accessed 15 May 2018] Morris, B. L., Lawrence, A. R. L., Chilton, P. J. C., Adams, B., Calow, R. C. & Klinck, B. A. (2003). Groundwater and its Susceptibility to Degradation: A Global Assessment of the Problem and Options for Management. Early Warning and Assessment Report Series. (RS. 03-3). Nairobi: United Nations Environment Programme. Rabatel, A., Francou, B., Soruco, A., Gomez, J., Cáceres, B., Ceballos, J. L., et al. (2013). Current state of glaciers in the tropical Andes: A multi-century perspective on glacier evolution and climate change. Cryosphere, 7(1), 81–102. https://doi.org/10.5194/tc-7-81-2013 Rangecroft, S., Suggitt, A. J., Anderson, K., & Harrison, S. (2016). Future climate warming and changes to mountain permafrost in the Bolivian Andes. Climatic Change, 137(1-2), 231–243. https://doi.org/10.1007/s10584-016-1655-8 Seiler, C., Hutjes, R. W. A., & Kabat, P. (2013). Climate variability and trends in Bolivia. Journal of Applied Meteorology and Climatology, 52(1). https://doi.org/10.1175/JAMC-D-12-0105.1 United Nations Framework Convention on Climate Change (UNFCCC) (2015) Adoption of the Paris Agreement, Report no. FCCC/CP/2015/L.9/Rev.1. Retrieved from http://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf [Accessed 25 Sep. 2018] Vuille, M., Carey, M., Huggel, C., Buytaert, W., Rabatel, A., Jacobsen, D., et al. (2018). Rapid decline of snow and ice in the tropical Andes – Impacts, uncertainties and challenges ahead. Earth-Science Reviews, 176, 195-213. https://doi.org/10.1016/j.earscirev.2017.09.019 Weng, W., Luedeke, M. K. B., Zemp, D. C., Lakes, T., & Kropp, J. P. (2018). Aerial and surface rivers: downwind impacts on water availability from land use changes in Amazonia. Hydrology and Earth System Sciences, 22(1), 911–927. http://doi.org/10.5194/hess-22-911-2018

About the authors Wei Weng is a doctoral researcher at the Potsdam Institute for Climate Impact Research, Humboldt Universität zu Berlin and the Ecosystem Services group, CIAT, Cali, Colombia. Luís Costa is an environmental engineer turned researcher at the Potsdam Institute for Climate Impact Research, Potsdam, Germany.

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Matthias Lüdeke is a senior scientist at the Potsdam Institute for Climate Impact Research, Potsdam, Germany. Delphine Clara Zemp is a postdoctoral researcher at the Potsdam Institute for Climate Impact Research and the Faculty of Forest Sciences and Forest Ecology, University of Goettingen, Goettingen, Germany. Marissa Castro Magnani is a geographer responsible of monitoring system of mother earth and climate change at the Autoridad Plurinacional de la Madre Tierra, La Paz, Bolivia. Boris Hinojosa Guzmán is a hydrologist responsible for the Hierotierra consultancy group, Santa Cruz, Bolivia. Tobia Lakes is a professor specializing Applied Geoinformation Science at the Geography department, Humboldt Universität zu Berlin, Berlin, Germany. Marcela Quintero is the leader of the Ecosystem Services group, CIAT, Cali, Colombia.

Correponding Author

Wei Weng [email protected]

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Declarations

I declare that I have completed the thesis independently using only the aids and tools specified. I have not applied for a doctor’s degree in the doctoral subject elsewhere and do not hold a corresponding doctor’s degree. I have taken due note of the Faculty of Mathematics and Natural Sciences PhD Regulations, published in the Official Gazette of Humboldt-Universität zu Berlin no. 42/2018 on 11/07/2018.

Berlin, 30/08/2019 Wei Weng

I declare that the written and electronic versions of my doctor’s thesis submitted to the University Library match the accepted thesis.

Berlin, 28/01/2020 Wei Weng

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