TRUST Report| Chapter 01 | Introduction| 1 Integrated Water Management Solutions in the Lurín Catchment, Lima, Peru Supporting United Nations’ Sustainable Development Goal 6 Cover: Ururí reservoir in San Andrés de Tupicocha, Peru. Picture: C. D. León. Integrated Water Management Solutions in the Lurín Catchment, Lima, Peru Supporting United Nations’ Sustainable Development Goal 6 Final report of the joint project TRUST “Sustainable, fair and environmentally sound drinking water supply for prosperous regions with water shortage: Developing solutions and planning tools for achieving the Sustai nable Development Goals using the river catchments of the region Lima/Peru as an example”, funded by the German Federal Ministry of Education and Research (BMBF) within the funding measure “Global Resource Water (GRoW)”. 4 | TRUST Report| Editorial EDITORS: Christian D. León, Friederike Brauer, Michael Hügler, Sina Keller, Hannah Kosow, Manuel Krauss, Stephan Wasielewski, Jan Wienhöfer AUTHORS: (in alphabetical order) Jan Bondy, Friederike Brauer, Jaime Cardona, Johannes Chamorro, Thilo Fischer, Lucia Hahne, Stefan Hinz, Michael Hügler, Christian D. León, Sina Keller, Hannah Kosow, Hanna Kramer, Manuel Krauss, Ralf Minke, Fabienne Minn, Felix Riese, Samuel Schroers, Stefan Stauder, Sebas- tian Sturm, Stephan Wasielewski, Jan Wienhöfer, Yvonne Zahumensky CITATION: León, C. D., Brauer, F., Hügler, M., Keller, S., Kosow, H., Krauss, M., Wasielewski, S., & Wienhöfer, J. (Eds.) (2021): Integrated Water Management Solutions in the Lurín Catchment, Lima, Peru – Supporting United Nations’ Sustainable Development Goal 6. Final report of the joint project TRUST. University of Stuttgart, ISBN 978-3-00-068498-2. KEYWORDS: SDG 6, sustainable water management, drinking water, wastewater, interdisciplinary concepts, transdisciplinary concepts, water scarcity, water reuse, water use conflict, water safety plan PROJECT WEBSITE: www.trust-grow.de ISBN 978-3-00-068498-2 Stuttgart and Karlsruhe, Germany, March 2021 The underlying project of this report was funded by the German Federal Ministry of Education and Research under the grant number 02WGR1426A-G. The responsibility for the content of this publication lies with the authors. TRUST Report Editorial TRUST Report| Project Participants | 5 University of Stuttgart - 02WGR1426A Center for Interdisciplinary Risk and Innovation Studies (ZIRIUS) Christian D. León Dr. Hannah Kosow Fabienne Minn Yvonne Zahumensky Institute for Sanitary Engineering, Water Quality and Solid Waste Management (ISWA) Ralf Minke Manuel Krauss Stephan Wasielewski Hanna Kramer Philipp Richter Karlsruhe Institute of Technology (KIT) - 02WGR1426B Institute of Water and River Basin Management – Hydrology (IWG) Dr. Jan Wienhöfer Jan Bondy Samuel Schroers Institute of Photogrammetry and Remote Sensing (IPF) Prof. Dr. Stefan Hinz Dr. Sina Keller Dr. Felix M. Riese TZW: DVGW-Technologiezentrum Wasser - 02WGR1426C Sebastian Sturm Dr. Michael Hügler Friederike Brauer Thilo Fischer Dr. Stefan Stauder decon international GmbH - 02WGR1426D Heinrich Meindl Jaime Cardona Johannes Chamorro Disy Informationssysteme GmbH - 02WGR1426E Dr. Andreas Abecker Lucia Hahne Jonas Gottwalt Vanessa Rojas Ingenieurbüro Pabsch & Partner Ingenieurgesellschaft mbH - Dr. Holger Pabsch OTT Hydromet GmbH - 02WGR1426G Mario Keil TRUST joint research project participants, grant number and main contributions: Project coordination Conflict analysis Policy mixes Participatory assessment Multi-stakeholder dialogues Wastewater and reuse concepts SDG assessment Capacity development Monitoring water balance Hydrological modelling Remote sensing Monitoring water quality Risk management & water safety planning Artificial groundwater recharge Drinking water concepts Capacity development Data management Decision support tool Web information platform 02WGR1426F Feasibility study wastewater treatment Provision of measuring equipment and software Project participant - grant no. Main contributions TRUST Report Project Participants 6 | TRUST Report | Table of Contents TRUST Report Table of Contents Summary 8 Zusammenfassung 10 Resumen 12 1. Introduction 14 Zooming in: Perú, Lima, Lurín 18 2. Data and Assessment Tools 20 2.1 Overview of the TRUST Data and Data Management 22 InfoBox: The TRUST GIS-Portal for Data Management and Visualization 24 2.2 Hydro-Meteorological Monitoring 24 2.2.1 Overview on Hydro-Meteorological Data in the CHIRILU Region 25 2.2.2 Setting Up Monitoring Stations and Collecting Data in the Lurín Catchment 26 2.2.3 Stream Gauges and Rating Curves 28 2.3 Remote Sensing Data Acquisition and Data-Driven Estimations in Peru and Germany 29 Infobox: Exemplary Acquisition of Reference Data for Remote Sensing Applications 32 2.4 Stakeholder Analysis and Participation for Conflict Transformation 32 2.5 Monitoring of Water and Wastewater Quality 36 Infobox: Monitoring of Microbiological and Physical-Chemical Water Quality at Klingenberg Reservoir 37 2.6 Water Safety Plan-Tool 38 2.7 Sustainable Development Goal 6 - Targets and Indicators 2.7.1 Sustainable Development Goal 6 - Targets 40 2.7.2 Sustainable Development Goal 6 - Indicators 41 2.7.3 TRUST Concept Evaluation 41 Infobox: Data Gaps - What can be done 43 3. The Lurín Catchment: Geography, Hydrology, Governance, and Water Use Conflicts 44 3.1 Overview on Geography, Demography and Water Management of the Entire Catchment 46 3.2 Water Balance and Hydrology of the Lurín Catchment 48 3.2.1 Water Balance Estimations 48 3.2.2 Precipitation Interpolation 49 3.2.3 Hydrological Modelling 50 3.2.4 Analyses of Monitoring Data 52 3.3 Actors and Governance 57 3.4 Consistent Policy Mixes to Overcome Water Use Conflicts 61 3.5 Interim Conclusions Entire Lurín Catchment 68 4. Upper Lurín Catchment: Rural Highland Community San Andrés de Tupicocha 70 4.1 General Overview 72 4.2 Water Governance Actors 73 4.2.1 Mapping of Water-Related Actors 73 4.2.2 Description of the Key Actors 75 TRUST Report | Table of Contents | 7 TRUST Report Table of Contents 4.3 Water Supply Situation 77 4.3.1 Drinking Water Supply 77 4.3.2 Local Perception of Water Services 81 4.3.3 Hazard Analysis and Protective Effect of the Catchment Areas 82 4.3.4 Risk Assessment 89 4.3.5 Water Quality 91 4.4 Wastewater Situation 94 4.5 Integrated Concepts for Sustainable Drinking Water Supply and Wastewater Disposal 95 4.5.1 Technical Concept for Improved Drinking Water Supply 95 4.5.2 Technical Concept for Safe Wastewater Treatment and Reuse 100 4.5.3 Participatory Assessment of Technical Concepts 104 4.5.4 Evaluation of Developed Concepts with Regard to SDG 6 110 4.6 Interim Conclusions Upper Lurín Catchment 112 5. Lower Lurín Catchment: Urban Areas 114 5.1 General Overview 116 5.2 Water Supply Situation 116 5.2.1 Hazard Analysis and Risk Assessment 118 5.2.2 Water Quality of Ground and Surface Water 121 5.3 Wastewater Situation 127 5.4 Concepts for Sustainable Wastewater Treatment and Groundwater Recharge 130 5.4.1 Groundwater Recharge 130 5.4.2 Technical Concept for Wastewater Treatment (Retrofit of WWTP Cieneguilla) 134 5.4.3 Technical Concept for Wastewater Treatment (Retrofit of WWTP José Gálvez) 136 5.4.4 Summary of Concepts 142 5.4.5 Evaluation of the Concepts with Regard to SDG 6 142 5.5 Interim Conclusions Lower Catchment Area 143 6. Central Results and Lessons Learned 146 6.1 Central Results and Recommendations to the Lurín Catchment 148 6.1.1 Water Resources and Water Quality 148 6.1.2 Integrated Water Use and Water Management Concepts 150 Infobox: Capacity Building 154 6.2 Added Value, Challenges and Transfer of TRUST Approaches 156 6.2.1 Approaches for Generating a Sound Multidisciplinary Information Base 156 6.2.2 Approaches for Developing Integrated Water Use and Management Concepts 158 6.3 General Recommendations and Lessons Learned 164 References 168 Abbreviations 172 Authors 176 Acknowledgements 178 8 | TRUST Report | Summary With the 2030 Agenda for Sustainable Development, the United Nations have established a catalog of 17 Sustainable Development Goals (SDGs) to achieve a better and more sustainable future for all by 2030. One important aspect, formulated as Goal 6, is ensuring the availability and sustainable management of water and sanitation for all. Achieving SDG 6 represents a chal- lenge for planning, governance, and water management, especially in prosperous water-scarce regions, where water demand rises steadily and outgrows sustainable supply. Over a 3,5-year period, the joint project TRUST (“Sustainable, fair and environmentally sound drinking water supply for prosperous regions with water shortage: Developing solutions and plan- ning tools for achieving the Sustainable Development Goals using the river catchments of the region Lima/Peru as an example”) has developed inter- and transdisciplinary concepts for drin- king water use, safe wastewater disposal and water reuse to support achieving SDG 6 in water scarce regions, using the example of the catchment area of the Río Lurín, Peru. This report presents the approaches and results developed in the TRUST project. The approaches combine natural, engineering and social science expertise from research and practice, starting at the local level and scaled up to the level of catchment areas. They are structured along the do- mains of “water resources”, “water use” and “water management”. The domains are closely inter- linked and support working towards the development of integrated water management concepts. Each of these domains begins with the set-up of an information base, followed by the conduction of analysis and the development of concepts. It concludes with the derivation of lessons learned and recommendations for each of the domains. Generating a sound multidisciplinary information base is key for water resources planning and conduction of analysis. Installing a small number of monitoring stations at the right locations allows to get important insights on water quantity. Establishing and maintaining a monitoring network for water quantity is a challenging task in remote and mountainous areas, requiring long-term efforts and commitment. However, long time series are important to run hydrological models and even more if trends regarding climate change or land use change are to be considered in management decisions. Evaluating water quality and associated risks still requires conventio- nal lab analyses, both physical-chemical and microbiological. Test kits that allow simple water analyses to be carried out by specifically trained local actors can provide an additional means to acquire water quality data. Remote sensing techniques can be applied for the classification of land use, the detection of land cover changes, and the estimation of soil moisture. They provide a basis for methodological solutions by establishing a land cover change detection approach with deep learning methods on multi-temporal satellite data. A newly developed decision support system based on the WHO Water Safety Plan concept (WSP) enables the recording and evaluation of risks in the catchment area as well as the documentation of measures for risk control. As an online application with Web-GIS geodata processing, it is usa- ble for users without own GIS access. The tool visualizes the results quickly, thereby supporting the communication about the aims of risk analysis and helping to achieve a common understan- ding of which information is relevant. Summary English TRUST Report | Summary | 9 Regarding governance issues and conflict analysis, stakeholder analysis is important to obtain an overview on roles and relations. It allows to develop a participation strategy that identifies whom to involve during what project activity and in what intensity. Repeated field trips and interviews with key actors are required to achieve a full understanding and detailed overview of the of stakeholders’ positions regarding the project’s goals, and of the interrelations between stakeholder groups. The newly developed policy- and conflict analysis allows identifying central (latent) water use related conflicts as well as developing and assessing possible integrated policy-solutions for an improved and sustainable water management. The approach makes diverging goals and policy alternatives of different water user groups explicit. It reveals how non-intended side-effects of policies affect the effectiveness of other policies and allows identifying consistent, synergetic and sustainable policy mixes. For the development of integrated concepts for water supply and sanitation, an inter- and trans- disciplinary process, where scientists, water and sanitation engineers, social scientists as well as local actors and stakeholders collaborate closely, proves to be very helpful. Participatory assess- ment allows identifying the evaluation criteria that are relevant for local stakeholders and taking them into account in the further development of the concepts. Bringing actors from the upper and lower catchment together in joint multi-stakeholder workshops leads to more dialogue and fosters cooperation between catchment parts. Overall, the integrated approach allows to take the social-embeddedness of technological concepts into account and to co-construct concepts together with local stakeholders. Furthermore, involving stakeholders with different perspectives still requires ensuring the same level of information and knowledge. Stakeholders need to be enabled and empowered regularly to participate in the integrated planning processes – to this end, capacity building workshops are valuable. Using SDG 6 as a point of reference assured that our results are linkable to international debates and standards through the comparability of indicators. Central conditions to transfer our integrated approaches include the active interest of stakehol- ders, a continuous and/or repeated collaboration between local actors, researchers and NGOs, as well as sufficient data, a common problem awareness, and comparable boundary conditions (regarding, e.g., hydrology, geochemistry, sociology, culture, education, urban water manage- ment, etc.). Local contexts, however, can be very specific, so the approaches need to be carefully contextualized. Summary English 10 | TRUST Report | Zusammenfassung Mit der Agenda 2030 für nachhaltige Entwicklung haben die Vereinten Nationen einen Katalog von 17 Zielen für nachhaltige Entwicklung (Sustainable Development Goals, SDGs) aufgestellt, um bis 2030 eine bessere und nachhaltigere Zukunft für alle zu erreichen. Ein wichtiges Ziel ist im SDG 6 zusammengefasst und betrifft die Verfügbarkeit und nachhaltige Bewirtschaftung von Wasser und Sanitärversorgung für alle. Das Erreichen des SDG 6 stellt insbesondere eine Herausforderung für Planung, Governance und Wasserwirtschaft in prosperierenden Wasserman- gelregionen dar, in denen der stetig steigende Wasserbedarf deutlich über der nachhaltigen Was- sernutzung liegt. Über einen Zeitraum von 3,5 Jahren wurden im Verbundprojekt TRUST („Trinkwasserversorgung in prosperierenden Wassermangelregionen nachhaltig, gerecht und ökologisch verträglich – Ent- wicklung von Lösungs- und Planungswerkzeugen zur Erreichung der nachhaltigen Entwicklungs- ziele am Beispiel des Wassereinzugsgebiets der Region Lima/Peru.“) am Beispiel des Einzugs- gebiets des Río Lurín in Peru inter- und transdisziplinäre Konzepte zur Trinkwassernutzung, sicheren Abwasserentsorgung und Wasserwiederverwendung entwickelt, um zum Erreichen des SDG 6 in wasserarmen Regionen beizutragen. Dieser Bericht stellt die im TRUST-Projekt entwickelten Ansätze und Ergebnisse vor. Die Ansätze kombinieren natur-, ingenieur- und sozialwissenschaftliches Fachwissen aus Forschung und Praxis, beginnend auf lokaler Ebene und skalierbar bis auf die Ebene von Einzugsgebieten. Die Ansätze orientieren sich an den Bereichen „Wasserressourcen“, „Wassernutzung“ und „Wasser- management“, wobei diese Bereiche eng miteinander verknüpft sind und somit die Entwicklung integrierter Wassermanagementkonzepte erlauben. In jedem Bereich wird zunächst eine Infor- mationsbasis aufgebaut, woran die Durchführung von Analysen sowie die Konzeptentwicklung anknüpft. Schließlich werden für jeden Bereich die zentralen Lessons Learned und Empfehlungen abgeleitet. Es zeigt sich, dass eine solide multidisziplinäre Informationsbasis eine Schlüsselrolle für die Wasserressourcenplanung und die Durchführung der Analysen spielt. Bereits die Installation we- niger Messstationen an den richtigen Stellen ermöglicht es, wichtige Erkenntnisse über die Was- sermengen zu gewinnen. Der Aufbau und die Instandhaltung eines Messnetzes zur Bestimmung der Wassermengen in abgelegenen Bergregionen ist eine anspruchsvolle Aufgabe, die langfristige Anstrengungen und Einsatz erfordert. Lange Zeitreihen sind jedoch umso wichtiger, um hydrologi- sche Modellierung zu betreiben und Trends in Bezug auf den Klimawandel oder Landnutzungsän- derungen in Managemententscheidungen zu berücksichtigen. Die Auswertung der Wasserqualität und der damit verbundenen Risiken erfordert nach wie vor konventionelle, physikalisch-chemi- sche und mikrobiologische Laboranalysen. Testkits, mit denen speziell geschulte lokale Akteure einfache Wasseranalysen durchführen können, bieten eine zusätzliche Möglichkeit zur Erfassung von Wasserqualitätsdaten. Fernerkundungstechniken können eingesetzt werden, um die Land- nutzung zu klassifizieren, Änderungen der Landbedeckung zu erkennen und die Bodenfeuchte abzuschätzen. Sie liefern eine Grundlage für methodische Lösungen, indem sie einen Ansatz zur Erkennung von Veränderungen der Landbedeckung mit Deep Learning Methoden auf multitempo- ralen Satellitendaten kombinieren. Ein neu entwickeltes Entscheidungsunterstützungssystem auf Basis des WHO Water Safety Plan-Konzepts (WSP) ermöglicht die Erfassung und Bewertung von Risiken im Einzugsgebiet Zusammenfassung Deutsch TRUST Report | Zusammenfassung | 11 sowie die Dokumentation von Maßnahmen zur Risikokontrolle. Als Online-Anwendung mit Web- GIS-Geodatenaufbereitung ist es auch für Anwender ohne eigenen GIS-Zugang nutzbar. Das Tool visualisiert die Ergebnisse und unterstützt damit die Kommunikation über die Ziele der Risiko- analyse und hilft, ein gemeinsames Verständnis über relevante Informationen zu schaffen. Im Hinblick auf Governance-Themen und Konfliktanalyse ist die Stakeholder-Analyse wichtig, um einen Überblick über Rollen und Beziehungen zu erhalten. Sie ermöglicht zudem, eine Partizipationsstrategie zu entwickeln, die festlegt, welche Akteure in welcher Projektphase zu welchem Grad einbezogen werden sollen. Wiederholte Feldaufenthalte und Interviews mit Schlüsselakteuren sind erforderlich, um ein umfassendes Verständnis und einen detaillierten Überblick über die Haltung der Stakeholder hinsichtlich der Projektziele sowie der Beziehungen zwischen den Stakeholdergruppen zu gewinnen. Die neu entwickelte Politik- und Konfliktanalyse ermöglicht, zentrale (latente) Wassernutzungs- konflikte zu identifizieren und mögliche integrierte Politiklösungen für ein verbessertes und nach- haltiges Wassermanagement zu entwickeln und zu bewerten. Der Ansatz macht divergierende Ziele und Policy-Alternativen der verschiedenen Wassernutzergruppen explizit. Er deckt nicht-in- tendierte Nebeneffekte von Policies (Maßnahmen und Instrumente) auf die Effektivität anderer Policies auf und ermöglicht, konsistente, synergetische und nachhaltige Policy-Mixes zu identi- fizieren. Für die Entwicklung von integrierten Konzepten für die Wasserver- und Abwasserentsorgung erweist sich ein inter- und transdisziplinärer Ansatz als sehr hilfreich, bei dem Forscherinnen und Forscher aus Naturwissenschaften, Wasser- und Abwassertechnik sowie Sozialwissenschaften eng mit lokalen Akteuren und Interessengruppen zusammenarbeiten. Mithilfe von partizipativen Bewertungsformaten werden die für lokale Akteure relevanten Entscheidungskriterien identi- fiziert und fließen in die weitere Konzeptentwicklung ein. Gemeinsame Multi-Stakeholder-Work- shops bringen die Akteure aus dem oberen und unteren Einzugsgebiet zusammen, was den Dia- log und die Zusammenarbeit im Einzugsgebiet fördert. Insgesamt ermöglicht es der integrierte Ansatz, technologische Konzepte sozial einzubetten und die Konzepte gemeinsam mit lokalen Akteuren zu entwickeln. Um die diversen Stakeholder mit ihren unterschiedlichen Perspektiven erfolgreich zu beteiligen, ist es erforderlich, einen gleichen Informations- und Wissensstand si- cherzustellen. Die Stakeholder sollten unterstützt und befähigt werden, sich regelmäßig an den integrierten Planungsprozessen zu beteiligen - zu diesem Zweck sind auch Veranstaltungen zum Capacity Building hilfreich. Die Verwendung von SDG 6 als Bezugspunkt stellte sicher, dass unsere Ergebnisse durch die Vergleichbarkeit der Indikatoren mit internationalen Debatten und Standards anschlussfähig sind. Zentrale Voraussetzungen für die Übertragbarkeit unserer integrierten Ansätze sind unter ande- rem die Mitarbeit aktiver Stakeholder, die kontinuierliche und/oder wiederholte Zusammenarbeit von lokalen Akteuren, Forschern und NGOs, eine ausreichende Datengrundlage, ein gemeinsa- mes Problembewusstsein sowie vergleichbarer Randbedingungen (z. B. hinsichtlich Hydrologie, Geochemie, Soziologie, Kultur, Bildung, Siedlungswasserwirtschaft, usw.). Lokale Kontexte kön- nen jedoch äußerst spezifisch sein, so dass die Ansätze sorgfältig auf die lokalen Bedingungen abgestimmt werden müssen. Zusammenfassung Deutsch 12 | TRUST Report | Resumen Con la Agenda 2030 para el Desarrollo Sostenible, las Naciones Unidas han establecido un catá- logo de 17 Objetivos de Desarrollo Sostenible (ODS) para lograr un futuro mejor y más sostenible para todos en 2030. Un aspecto importante, formulado como Objetivo 6, es garantizar la dispo- nibilidad y la gestión sostenible del agua y el saneamiento para todos. La consecución del ODS 6 representa un reto para la planificación, la gobernanza y la gestión del agua, especialmente en las regiones prósperas con escasez de agua, donde la demanda de agua aumenta constantemente y supera el suministro sostenible. A lo largo de un periodo de 3,5 años, el proyecto TRUST („Suministro de agua potable soste- nible, equitativo y ecológico en regiones prósperas con déficit hídrico – Desarrollo de soluciones y herramientas de planificación para lograr los Objetivos de Desarrollo Sostenible, utilizando el ejemplo de la cuenca hidrográfica de la región Lima / Perú“) ha desarrollado conceptos inter- y transdisciplinarios para el uso del agua potable, la eliminación segura de las aguas residuales y la reutilización del agua para apoyar la consecución del ODS 6 en regiones con escasez de agua, utilizando el ejemplo de la cuenca del Río Lurín, Perú. Este informe presenta los enfoques y resultados desarrollados en el proyecto TRUST. Los enfo- ques combinan los conocimientos de las ciencias naturales, la ingeniería y las ciencias sociales procedentes de la investigación y la práctica, comenzando en el ámbito local y ampliando hasta el nivel de las cuencas hidrográficas. Están estructurados en los ámbitos de „recursos hídricos“, „uso del agua“ y „gestión del agua“. Estos ámbitos están estrechamente interrelacionados y permiten trabajar en el desarrollo de conceptos de gestión integrada del agua. Cada uno de estos ámbitos comienza con la creación de una base de información, seguida de la realización de análisis y el desarrollo de conceptos. Concluye con la derivación de lecciones aprendidas y recomendaciones para cada uno de los dominios. La generación de una sólida base de información multidisciplinar es fundamental para la planifi- cación de los recursos hídricos y la realización de análisis. La instalación de un pequeño número de estaciones de control en los lugares adecuados permite obtener información importante sobre la cantidad de agua. Establecer y mantener una red de monitoreo de la cantidad de agua es una tarea difícil en zonas remotas y montañosas, que requiere esfuerzos y compromisos a largo plazo. Sin embargo, las series temporales largas son importantes para ejecutar modelos hidrológicos y aún más si las tendencias relacionadas con el cambio climático o el cambio en el uso de la tierra deben tenerse en cuenta en las decisiones de gestión. La evaluación de la calidad del agua y los riesgos asociados sigue requiriendo análisis de laboratorio convencionales, tanto físico-quí- micos como microbiológicos. Los kits de pruebas que permiten la realización de análisis sencillos del agua por parte de actores locales con formación específica pueden proporcionar un medio adicional para adquirir datos sobre la calidad del agua. Las técnicas de teledetección pueden aplicarse para la clasificación de los usos del suelo, la detección de cambios en la cubierta vegetal y la estimación de la humedad del suelo. Proporcionan una base para soluciones metodológicas estableciendo un enfoque de detección de cambios en la cobertura del suelo con métodos de aprendizaje profundo sobre datos satelitales multitemporales. Resumen Español TRUST Report | Resumen | 13 Un sistema de apoyo a la toma de decisiones recientemente desarrollado, basado en el con- cepto de Plan de Seguridad del Agua (PSA) de la OMS, permite registrar y evaluar los riesgos en la zona de captación, así como documentar las medidas de control de riesgos. Al ser una aplicación en línea con procesamiento de geodatos Web- GIS, puede ser utilizada por usuarios sin acceso propio a los SIG. La herramienta visualiza rápidamente los resultados, apoyando así la comunicación sobre los objetivos del análisis de riesgos y ayudando a lograr una comprensión común de qué información es relevante. En cuanto a los temas de gobernanza y el análisis de conflictos, el análisis de las partes in- teresadas es importante para obtener una visión general de los roles y las relaciones. Permite desarrollar una estrategia de participación que identifique a quién hay que involucrar durante qué actividad del proyecto y en qué intensidad. Es necesario realizar repetidos viajes de campo y entrevistas con los actores clave para lograr una comprensión completa y una visión detallada de las posiciones de las partes interesadas con respecto a los objetivos del proyecto, y de las interrelaciones entre los grupos interesados. El nuevo análisis de políticas y conflictos permite identificar conflictos centrales (latentes) rela- cionados con el uso del agua, así como desarrollar y evaluar posibles soluciones políticas integ- radas para una gestión del agua mejorada y sostenible. El enfoque hace explícitos los objetivos divergentes y las alternativas políticas de los diferentes grupos de usuarios del agua. Revela cómo los efectos secundarios no intencionados de las políticas afectan a la eficacia de otras políticas y permite identificar combinaciones de políticas coherentes, sinérgicas y sostenibles. Para el desarrollo de conceptos integrados de abastecimiento de agua y saneamiento, resulta muy útil un proceso inter y transdisciplinar en el que colaboren estrechamente científicos, inge- nieros especializados en agua y saneamiento, científicos sociales y actores y partes interesadas locales. La evaluación participativa permite identificar los criterios de evaluación que son rele- vantes para los actores locales y tenerlos en cuenta en el desarrollo posterior de los conceptos. Reunir a los actores de la cuenca alta y baja en talleres conjuntos de múltiples partes interes- adas conduce a un mayor diálogo y fomenta la cooperación entre las partes de la cuenca. En general, el enfoque integrado permite tener en cuenta la integración social de los conceptos tecnológicos y construir los conceptos junto con las partes interesadas locales. Además, la par- ticipación de las partes interesadas con diferentes perspectivas sigue exigiendo que se garantice el mismo nivel de información y conocimiento. Es necesario capacitar y empoderar a las partes interesadas para que participen regularmente en los procesos de planificación integrada; para ello, son valiosos los talleres de desarrollo de capacidades. El uso del ODS 6 como punto de referencia garantizó que nuestros resultados fueran vinculables a los debates y normas interna- cionales gracias a la comparabilidad de los indicadores. Las condiciones centrales para transferir nuestros enfoques integrados incluyen el interés activo de las partes interesadas, una colaboración continua y/o repetida entre los actores locales, los investigadores y las ONG, así como datos suficientes, una conciencia común del problema y con- diciones de contorno comparables (en relación, por ejemplo, con la hidrología, la geoquímica, la sociología, la cultura, la educación, la gestión del agua urbana, etc.). Sin embargo, los con- textos locales pueden ser muy específicos, por lo que los enfoques deben ser cuidadosamente contextualizados. Resumen Español Picture: F. Riese 14 | TRUST Report | Chapter 01 | Introduction 1. Introduction Christian D. León Picture: C. D. León TRUST Report | Chapter 01 | Introduction| 15 16 | TRUST Report | Chapter 01 | Introduction Many regions of the world are characterised by natural water scarcity. Their ecosystems are adap- ted to this water shortage and specifically adapted animal and plant species have settled here. Similarly, humans have been adapting their farming and agricultural methods to cope with water shortage. Only by building reservoirs, wells, irrigation systems and diverting water from one cat- chment area to the other, men ensured that stronger economic development became possible in these water-scarce regions than originally thought. In the last decades, this development, accom- panied by population growth, has increased the pressure on water resources. The consequences are severely stressed or disappearing water-bound local ecosystems on the one hand and lack of access to sufficient drinking water to meet human needs on the other hand. Ensuring economic development and the protection of ecosystems at the same time is a challen- ge, which many regions of the world are facing today. This holds especially true for those located in water scarce areas. In addition, there are further goals to be accomplished, such as reducing poverty and securing energy supplies. With the adoption of the 2030 Agenda for Sustainable De- velopment, the United Nations have established a catalogue of 17 goals aimed to eradicate pover- ty in all its forms and dimensions worldwide (UN, 2015). A central aspect is the “availability and sustainable management of water and sanitation for all”, which is formulated as goal No. 6 of the UN Sustainable Development Goals (SDGs) and is to be achieved by 2030. This goal, which is specified by six targets, addresses both people’s needs for equitable access to safe drinking water and sanitation, and the protection of water-related ecosystems (Krauss et al., 2019). Achieving the SDGs in the water sector represents a challenge to planning, governance and water manage- ment, especially in prosperous water-scarce regions, where water demand is rising steadily. The research project TRUST was entitled „Sustainable, fair and environmentally sound drinking water supply for prosperous regions with water shortage: Developing solutions and planning tools for achieving the Sustainable Development Goals using the river catchments of the region Lima/ Peru as an example“ (León et al., 2019). The project was funded over a 3 ½ -year period by the German Federal Ministry of Education and Research (BMBF) within the funding measure “Wa- ter as a global Resource (GRoW)”. GRoW aimed to support achieving SDG 6 by funding twelve research projects in the thematic fields of “global water resources”, “global water demand” and “good governance in the water sector”. Using the example of the catchment area of the Río Lurín, Peru, the TRUST project demonstrated how interdisciplinary approaches can contribute to meet water management challenges and contribute to SDG 6 “Ensure availability and sustainable ma- nagement of water and sanitation for all” in prosperous regions with water scarcity. In addition to the Río Lurín catchment in Peru, the Klingenberg catchment area in the Federal State of Saxony, Germany, served as a test area for the application of specific tools and procedures. The Río Lurín catchment area (1 670 km²) is one of three water catchment areas relevant for the water supply of Lima, the capital of Peru. It is an area combining typical characteristics of prospe- rous regions of the world, where fast growing urban centres and competing domestic, agricultural and industrial use of water resources are exacerbating water shortage. Although the contribution of the Río Lurín to the total water resources of the three catchment areas is low (11 % of total surface water and 10 % of groundwater resources), the Río Lurín is becoming increasingly import- ant for the water supply in the lower catchment area. However, its increasing importance poses major challenges to water governance due to a weak institutional framework in combination with an insufficient data basis. TRUST Report | Chapter 01 | Introduction| 17 The research in TRUST was structured along the domains of “water resources”, “water use” and “water management” (see Figure 1.1). The domains are closely interlinked and thus this clear structure supported the work towards the development of integrated water management concepts. The objective of the first domain (water resources) is to characterize the catchment and to assess the availability and quality of water resources in the catchment area. The second domain (water use) aims at describing the different water users, assessing their respective water demand and analyzing potential conflicts. The third domain (water management) describes po- licy options as a function of available water resources and water demand. The work in each of these domains began with the set-up of an information base, followed by the conduction of analysis and development of concepts. In this context, the TRUST project developed concepts for drinking water use, safe wastewater disposal and water reuse in close cooperation with local actors and national authorities. » Figure 1.1: Structure of the TRUST research approach along the three domains “water resources”, “water use” and “water manage- ment”. This report presents the results and findings of the TRUST project. Inter- and transdisciplinary approaches are shown that combine natural, engineering and social science expertise from re- search and practice. The approaches start at the local level and can be scaled up to the level of catchment areas. The TRUST report is intended as a manual to help decision-makers and people professionally en- gaged in water management to develop and implement locally adapted solutions for sustainable water management. The chapters are organised according to the structure shown in Figure 1.1. Chapter 2 provides an overview of the information base and the different data collected during the project. The ana- lysis and concepts for the Lurín catchment are described in Chapter 3 and divided between the upper part (Chapter 4) and the lower part (Chapter 5). Chapter 6 summarizes the main findings and recommendations regarding results, lessons learned and transfer of the TRUST approach. 18 | TRUST Report | Chapter 01 | Introduction Zooming in: Perú, Lima, Lurín Jan Wienhöfer Perú with an area of about 1.3 million km² is the third largest country in South America, and covers three major biomes: the arid pacific coastal region in the west (12 %), the highlands of the Andes mountains (28 %), and the tropical rainforest of the Amazon basin in the east (60 %). Renewable water resources sum up to about 1 880 × 109 m3/year, or 59 782 m3 per capita and year (Aquastat, 2017), which makes Perú the country with the eighth largest renewable water resources worldwide. These water resources, however, are mainly (97.3 %) available in the Atlan- tic drainage divide east of the Andes, while the more populated Pacific divide west of the Andes receives significant less rainfall, and only has a share of 2.2 % of the nation’s water resources (the remaining 0.5 % are found in the Titicaca divide). Population estimates for Perú surpass 33 million people in 2020 (UNDESA, 2019), of which two- thirds live in the arid coastal region, mainly in fast-growing cities like the nation’s capital Lima with 10 million inhabitants (INEI, 2018). Generally, the access to drinking water in urban areas is higher than in rural areas: In 2020, 23.7 % of the rural population had no access to the public drinking water network, in contrast to 5.2 % of the population in urban areas of Peru (INEI, 2020). This difference is even bigger regarding the connection to the sewage system: 80.5 % of the population in rural areas and 10.3 % of the population in urban areas had no access to a public sewage system (INEI, 2020). Community- managed organizations such as the Juntas Administradoras de Servicios de Saneamiento (JASS) are managing most of the water services in rural areas, while (public, private or public-private) companies and local governments are supplying the majority of urban areas (Calzada et al., 2017). Lima, situated in the coastal desert, receives less than 10 mm per year of rainfall and thus de- pends on the water brought from the Andes by three river catchments, namely Chillón, Rímac and Lurín, often taken together as CHIRILU (Figure 1.2). The catchments are typical for the Peruvian coast, in that they extend in an elongated shape from the sea to the top of the Andes at 4 700 to 5 500 m asl, featuring deep valleys and a steep topography. Water from the Mantaro catchment east of the watershed divide (belonging to the Amazon basin) is brought through tunnels into the Rímac catchment to augment the available water resources for Lima. Water availability in CHIRILU reduces to 125 m³ per capita and year, a tremendous difference to the national average of 64 000 m³ per capita and year (Aquafondo, 2016). Water resources in the CHIRILU catchment mainly come from surface waters (83 %), of which the most is taken from the Río Rímac (69 %), and to a lesser extent from the Río Chillón (20 %) and Río Lurín (11 %). Groundwater sources contribute to 17 % to the water supply, of which 90 % are taken from the Chillón-Rímac aquifer and 10 % from the Lurín aquifer (Aquafondo, 2016). TRUST Report | Chapter 01 | Introduction| 19 The Río Lurín catchment is located south of Lima and covers an area of 1 670 km² (Figure 1.2). The Lurín is about 111 km long und has its source in the Andes at about 5 300 m asl. Although the contribution of the Río Lurín to the total water resources of the three catchment areas is low (11 % of total surface water and 10 % of groundwater resources), it is becoming increasingly important for the water supply, not only for the population of Lima, but also for industry and agriculture in the lower catchment area. This is of particular importance for Lima, where the population is continuously growing. In the search for water sources for the increasing water de- mand of population and industry, the Río Lurín is therefore gaining more and more importance. » Figure 1.2: Location of the Lurín catchment near Lima, Peru. 20 | TRUST Report | Chapter 02 | Data and Assessment Tools 2. Data and Assessment Tools Organizing author: Sina Keller TRUST Report | Chapter 02 | Data and Assessment Tools | 21 In this chapter, we introduce different types of data that researchers of various disciplines mea- sured and collected within the TRUST project at different locations in Peru and Germany. First, we provide a brief overview of the different data types (Table 2.1), followed by a short de- scription of the data storage and the possibility to access selected data via a GIS portal. To combi- ne our heterogeneous data, we structure the data according to the research discipline responsible for the acquisition, the study region to which the data relate, and briefly describe the data source and the specification. In the subsequent sections, selected examples of the listed data types are pre- sented in more detail. Section 2.2 gives an overview of the hydro-me- teorological monitoring of the Chillón-Rímac-Lu- rín (CHIRILU) catchment in Peru and illustrates some details of the acquisition of hydrological and meteorological data in the Lurín catchment. In addition to hydro-meteorological data, remote sensing data were acquired as a basis for data- driven machine learning (ML) approaches aiming at the estimation of different physical parameters such as soil moisture or chlorophyll a concentra- tion (see Section 2.3). To develop ML approaches for the estimation of such parameters, the respec- tive reference data are crucial and are exemplarily described. Furthermore, we collected information on stakeholders and water policies to design par- ticipatory approaches for conflict transformation (see Section 2.4). The last part of this chapter (Section 2.5) deals with the monitoring of water quality parameters in general, and gives an exam- ple of monitoring of microbiological and physico- chemical water quality at Klingenberg Reservoir in Saxony, Germany. Picture: F. M. Riese 22 | TRUST Report | Chapter 02 | Data and Assessment Tools Table 2.1 summarizes the different data that we measured or collected in the context of the TRUST project. The data cover several areas: the Lurín catchment in Peru (Lurín) and its adjacent catchments (Chillón, Rímac, Lurín - CHIRILU), the district of San Andrés de Tupicocha within the upper Lurín catchment, and the catchment area of the Klingenberg reservoir in Saxony (Germany). DATA TYPE STUDY REGION SPECIFICATION SOURCE REFERENCE Actors & governance structure Lurín (PER) • Actor types • Networks • Positions and influence • Desk research • Stakeholder interviews • Stakeholder mapping Chapter 3.3, 4.2 Administrative & demography Lurín (PER) • Administrative units • Cities, urban and rural regions • Population • OSM • INEI (PER) Chapter 3.1, 4.1, 5.1 Hydro-meteorological CHIRILU (PER) • Air pressure & temperature • Precipitation • Relative humidity • Soil moisture • Solar radiation • Own measurements • SEDAPAL (PER) • SENAMHI (PER) Chapter 2.2, 2.3, 3.2 Land use & land cover Lurín (PER) & Klingenberg (DEU) • Classes of land cover & land use including vegetation & agriculture • GeoSN (DEU) • UNALM (PER) • MINAM (PER) Chapter 2.3, 3.2 Pedological & geological Lurín (PER) & Klingenberg (DEU) • Rock type • Soil infiltration rate • Soil texture • Soil type • Own measurements • INGEMMET (PER) • LfULG (DEU) Chapter 2.3 Perceptions & practices Tupicocha (PER) • People`s perceptions, attitudes and practices regarding water use • Focus group workshop • Transect walk Chapter 4.3 Water policies and objectives Lurín (PER) • Central water objectives and alternative policies in the upper and lower catchment • Desk research • Workshops with local experts and stake holders Chapter 2.4, 3.4 Remote sensing Lurín (PER),Klingenberg (DEU) & inland waters around Karlsruhe (DEU) • Hyperspectral image data • Multispectral satellite data • Hyperspectral spectrometer data • Own measurements • ESA Sentinel-2 mission Chapter 2.3 » Table 2.1: Overview of the data measured and collected within the TRUST project. The data are related to the Lurín catchment in Peru (Lurín), the district of San Andrés de Tupicocha in the Lurín catchment, the CHIRILU catchments in Peru, or the catchment area of the Klingenberg reservoir in Saxony (Germany). For full designations, see list of abbreviations in the Annex. 2.1 Overview of the TRUST Data and Data Management TRUST Report | Chapter 02 | Data and Assessment Tools | 23 DATA TYPE STUDY REGION SPECIFICATION SOURCE REFERENCE Actors & governance structure Lurín (PER) • Actor types • Networks • Positions and influence • Desk research • Stakeholder interviews • Stakeholder mapping Chapter 3.3, 4.2 Administrative & demography Lurín (PER) • Administrative units • Cities, urban and rural regions • Population • OSM • INEI (PER) Chapter 3.1, 4.1, 5.1 Hydro-meteorological CHIRILU (PER) • Air pressure & temperature • Precipitation • Relative humidity • Soil moisture • Solar radiation • Own measurements • SEDAPAL (PER) • SENAMHI (PER) Chapter 2.2, 2.3, 3.2 Land use & land cover Lurín (PER) & Klingenberg (DEU) • Classes of land cover & land use including vegetation & agriculture • GeoSN (DEU) • UNALM (PER) • MINAM (PER) Chapter 2.3, 3.2 Pedological & geological Lurín (PER) & Klingenberg (DEU) • Rock type • Soil infiltration rate • Soil texture • Soil type • Own measurements • INGEMMET (PER) • LfULG (DEU) Chapter 2.3 Perceptions & practices Tupicocha (PER) • People`s perceptions, attitudes and practices regarding water use • Focus group workshop • Transect walk Chapter 4.3 Water policies and objectives Lurín (PER) • Central water objectives and alternative policies in the upper and lower catchment • Desk research • Workshops with local experts and stake holders Chapter 2.4, 3.4 Remote sensing Lurín (PER),Klingenberg (DEU) & inland waters around Karlsruhe (DEU) • Hyperspectral image data • Multispectral satellite data • Hyperspectral spectrometer data • Own measurements • ESA Sentinel-2 mission Chapter 2.3 DATA TYPE STUDY REGION SPECIFICATION SOURCE REFERENCE Stakeholder assessments Tupicocha (PER) & Lurín (PER) • Participatory stakeholder assessment regarding technical concepts and policy mixes •Multi-stakeholder dialogues and expert workshops Chapter 3.4, 4.5 Topographical Lurín (PER) & Klingenberg (DEU) • Digital elevation model (DEM) • GeoSN (DEU) • NASA Aster (PER) • TanDEM-X mission (PER) Chapter 3.1 Wastewater quality Tupicocha (PER) & Lurín (PER) Tupicocha, wastewater treatment plants Cieneguilla and José Gálvez: • Chemical parameters such as COD, BOD, nitrogen and phosphorus compounds • Physical parameters such as temperature, pH, conductivity • Own measurement • SEDAPAL (PER) Chapter 4.4, 5.3 Water management CHIRILU (PER) • Channel and piping networks • Dams • Irrigation units • Sanitation • Wastewater discharges • Water extraction • Water supply • Wells • Supplied volumes and quantities • Own observations • ANA (PER) • LTV (DEU) • OA CHIRILU (PER) • SEDAPAL (PER) • INEI (PER) Chapter 4.3, 4.4, 4.5, 5.2, 5.3, 5.4 Water quality (surface, raw, and drinking water) Lurín (PER) & Klingenberg (DEU) • Chlorophyll a concentration • Heavy metals • Microbial community analysis • Phycocyanine • Physical-chemical parameters such as oxygen, temperature, turbidity, conductivity • Trace-organic compound • Microbiological parameters such as bac- terial and viral indicators, total cell counts, bacterial specification • Own measurements • OA CHIRILU (PER) • INGEMMET (PER) • SEDAPAL (PER) • DIRESA (PER) Chapter 2.5, 4.3, 5.2 24 | TRUST Report | Chapter 02 | Data and Assessment Tools The TRUST GIS-Portal for Data Management and Visualization Lucia Hahne Some of the collected and recorded data from the TRUST project are stored and can be accessed via a GIS-Portal. Thus, we know what data are available and have information about the data‘s current status. In addition, we jointly developed metadata to facilitate searching the database. The search is supported by attributes such as keywords on the subject, scope, and time frame of the data. The metadata are designed with a focus on collaborative work. Fortunately, the GIS portal established good cooperation and sustainable data handling. As a unique feature of our GIS portal, the data are presented in attribute tables and map representation for visualization purposes (Figure 2.1). Thus, the data can be viewed and analyzed without additional GIS tools. 2.2 Hydro-Meteorological Monitoring Jan Bondy, Samuel Schroers, Jan Wienhöfer To understand, quantify, and simulate the water balance and discharge dynamics within the Lurín catchment, we required data from hydro-meteorological monitoring stations and information ab- out soil parameters. The most important parameters are precipitation and streamflow (discharge). Furthermore, data describing atmospheric conditions such as air temperature, solar radiation, relative air humidity, and wind speed are relevant. Soil moisture measuring is often not part of ground-based monitoring networks, even though it is a crucial parameter influencing fluxes at the ground-atmosphere interface. Environmental monitoring designs should ideally reflect the nature of the investigated processes at the spatiotemporal resolution and extent of the corresponding measurement parameter. In an ideal hydro-meteorological monitoring setting, long monitoring time series are available at a high spatial resolution. However, for remote regions or regions with limited resources available for en- vironmental monitoring, usually less data are available. Quantifying and modeling hydrological systems with a small amount of spatiotemporal data presents a key challenge in predictions in ungauged basins. » Figure 2.1: Exemplarily visualization of the GIS tool user interface. i TRUST Report | Chapter 02 | Data and Assessment Tools | 25 The data used to analyze the hydrology in the Lurín catchment and set up hydrological models are summarized in Table 2.1. The following section provides more details on the data retrieved from Peruvian environmental institutions and data collected by setting up new monitoring stati- ons and field campaigns. 2.2.1 Overview on Hydro-Meteorological Data in the CHIRILU Region Only a few hydro-meteorological monitoring stations set up by the Peruvian weather service SENAMHI existed in the Lurín basin at the beginning of the TRUST project. These were mainly located in the central and southwestern parts of the catchment (Figure 2.2). We, therefore, in- stalled additional stations in the northern and northwestern parts (“KIT-IWG stations” in Figure 2.2): Stream gauges (Section 2.2.3), rainfall gauges (Aerocone tipping buckets, Davis Instru- ments, USA; with Hobo Pendant loggers, Onset Computer Corp., USA), soil moisture measure- ments (SMT100 and TrueLog100, Truebner GmbH, Germany), and an automatic weather station (ATMOS 41, Meter Group, USA/Germany). In addition to data from monitoring stations in the Lurín catchment, we also retrieved data from the two neighboring catchments, Rímac, and Chil- lón, from Peruvian sources, namely SENAMHI and SEDAPAL (Table 2.1). Table 2.2 summarizes the monitoring stations in the CHIRILU region. In general, the length of the available time series, the measuring interval, and the number and the kind of sensors vary from station to station. SENAMHI (see Table 2.2) has the most extended history of operating monitoring stations in the region. Their most recent weather stations acquire information about rainfall, temperature, relative humidity, solar radiation, air pressure, air tem- perature and automatically transmit data at an hourly time step. Other stations measure fewer » Figure 2.2: Map of hydro- meteorological monitoring stations in the Lurín catchment. 26 | TRUST Report | Chapter 02 | Data and Assessment Tools parameters at lower frequencies, for example, daily rainfall amounts. Most of SEDAPAL’s monito- ring stations (see Table 2.2) are combined weather stations and stream gauges that were set up recently during the term of the TRUST project. Five weather stations measure all of the mentioned parameters, while others only measure precipitation and temperature. All stations transmit the data automatically at an hourly interval. The monitoring stations set up by KIT-IWG for the TRUST project collect stream water levels and weather data, including soil moisture values (Table 2.2). All monitoring stations measure data at a high temporal frequency with an interval between 5 and 10 minutes, but only the weather station transmits data automatically. The monitoring data collected by the stations during the TRUST project have been published as LAMA dataset, and are freely available (Schroers et al., 2021). Daily precipitation amounts were also taken from the gridded precipitation data product PISCOp V2.1 (Peruvian Interpolation of the SENAMHI‘S Climatological and hydrological data Observati- ons - precipitation; Aybar et al., 2019). The dataset was derived by merging three different data sources: The national quality-controlled and infilled rain gauge dataset, satellite radar-derived climatologies for spatial patterns and seasonality (from TRMM data), as well as Climate Hazards Group Infrared Precipitation (CHIRPS) estimates. It covers Peru at a spatial resolution of 0.1° (about 10 km) from January 1981 to June 2018 (status as of June 2020). 2.2.2 Setting Up Monitoring Stations and Collecting Data in the Lurín Catchment Most of the monitoring stations used within the TRUST project were installed at altitudes of 3000 m asl (stream gauge Santa Rosa) or higher. The only exception is the stream gauge Man- chay at 229 m asl. In such an environment, the installation of the stations and the regular maintenance of the equipment was challenging. Most locations require a 4x4 off-road vehicle, but even with that, we could only access some sites during the low-flow period between May and November, when it is possible to cross the Lurín riverbed without bridges (Figure 2.3). The rainfall and meteorological stations were installed with the permission of the local farmers’ com- munities near agricultural fields or irrigation reservoirs. In this way, the measurement equipment was placed on safe grounds and out of reach for non-local passers-by (Figure 2.4). Not only the » Table 2.2: Monitoring stations operated in the CHIRILU region during the project term between 2017 and 2020. Stations of SEDAPAL and KIT-IWG were only set up during that time, while SENAMHI stations had been in operation before. The only SENAMHI stream gauge in the Lurín basin (* at Antapucro) was destroyed in a flood event in March 2017, and was replaced with a contactless sensor in June 2018. OPERATOR STATION TYPE NUMBER OF STATIONS INSTALLED LURÍN RÍMAC CHILLÓN SENAMHI Weather stations, rain gauges Stream gauges 4 1* 22 6 12 3 SEDAPAL Weather stations, rain gauges Stream gauges 1 1 6 6 5 2 KIT-IWG Weather stations, rain gauges Stream gauges Soil moisture monitoring 8 2 3 TRUST Report | Chapter 02 | Data and Assessment Tools | 27 » Figure 2.3: Crossing the Lurín River in the upper catchment is only possible with off- road vehicles during low-flow conditions. Picture: S. Schroers. » Figure 2.4: Assessing soil hydraulic conduc- tivity in the Lurín catchment using a hood infiltrometer (left). Rain gauge RSN05 in San Damián district at 4455 m asl (right). Pictures: J. Bondy. successful installation of the monitoring network but also the maintenance and data collection were time-consuming and resource-intensive. Regular visits to the stations for checking the equipment, collecting data, and making calibrations and reference measurements are essential. Moreover, in the case of stream gauges, the development of consistent rating curves relating water level to stream discharge was of particular importance (Section 2.2.3). We also conducted two measurement campaigns focussing on soil properties, for which we chose five sampling sites based on soil maps, topography, and land use information (see Table 2.1). The sites were each about 0.5 hectares in area. During the first campaign, we collected around 50 soil samples at each site, which were analysed for texture (the content of clay, silt, sand) and organic matter at the laboratory of Soils and Water at the Universidad Nacional Agraria La Molina, Lima. We also measured soil hydraulic conductivities with a hood infiltrometer (UGT GmbH, Germany; see Figure 2.4 left) and obtained additional soil samples, which were analysed at the soil laboratory of IWG. In a second campaign, we measured soil moisture using handheld sensors at these field sites, while IPF collected data with a hyperspectral camera mounted on an unmanned aerial vehicle (Section 2.3). 28 | TRUST Report | Chapter 02 | Data and Assessment Tools 2.2.3 Stream Gauges and Rating Curves The new stream gauges in the Lurín catchment (Manchay and Santa Rosa) were constructed at cross-sections defined by bridges. We attached flexible tubes to the rocks and foundations of the bridges (see Figure 2.5) and equipped these with water level loggers (Hobo U20L, Onset Com- puter Corp., USA). Limited funding did not allow for more sophisticated technical equipment and design; for example, building defined cross-sections, installing contactless sensors, or implemen- ting automatic data transmission was not feasible. For converting water levels to discharge, specific rating curves had to be defined for each station. We measured reference discharges using the tracer dilution method with salt or uranine (sodi- um fluorescein) on different dates. The observed discharges covered a range between 0.5 m³/s and 7.8 m³/s. Together with the corresponding water level observations, these were used to fit continuous rating curves for each station (Figure 2.6). The resulting rating curves‘ uncertainty is generally higher for high flows because obtaining independent discharge observations is less likely. The reference values - like every measurement - bear a relative measurement uncertainty. The river’s cross-section at Santa Rosa has an irregular shape due to the presence of large rocks and overhanging natural riverbanks, which further increases the uncertainty of the rating curve for both high and low flow conditions (Figure 2.6). In conclusion, it is essential for successful stream gauging and hydro-meteorological monitoring that maintenance and reference measurements continue regularly. Obtaining new reference dis- charge observations can help improve rating curves or check for possible changes after high-flow periods. Analogously, rain gauges and weather stations need regular calibrations and maintenance. » Figure 2.5: Stream gauging station at Santa Rosa de Quilquichaca (3 000 m asl): View on the installa- tion at the right river- bank from the bridge above (the river flows to the left); the rectangle highlights the installation tube, which has a length of about 10 m (left). Cross-section and part of the river bed under the bridge, looking upstream (right). Pictures: S. Schroers. TRUST Report | Chapter 02 | Data and Assessment Tools | 29 » Figure 2.6: Reference discharge observations and fitted continuous rating curves for stream gauging stations Santa Rosa (left) and Manchay (right). The 95 % confidence intervals were estimated from measurement uncertain- ty (right) and differential weighting of reference observations during non- linear fitting (left). Water levels are relative to the sensor position. 2.3 Remote Sensing Data Acquisition and Data-Driven Estimations in Peru and Germany Felix M. Riese, Sina Keller, Stefan Hinz Remote sensing of the earth includes the acquisition of imagery, for example, based on mobile platforms such as satellites and unmanned aerial vehicles (UAVs), the complex processing and analysis of the acquired remote sensing data, and the support of area-wide decisions with the processed data. Within the TRUST project‘s scope, the Institute of Photogrammetry and Remote Sensing (IPF) contributed various results based on different remote sensing data. These contri- butions are organized into six parts (see Table 2.1): 1. the delivery of a digital elevation model (DEM), 2. the development of machine learning (ML), met hods for the soil moisture estimation with hyperspectral imagery, 3. the development of classification systems for the automatic detection of land-use changes with deep learning methods, 4. methods for the soil-texture classification with hyperspectral imagery, 5. the acquisition of large datasets from inland waters for the estimation of water quality parameters, and 6. the upscaling of the estimation to satellite scale. In the following, these six contributions are briefly described and summarized. The applied methodology is described in detail by Riese & Keller (2020) and Riese (2020) . Our methodological approaches mainly rely on data-driven ML as a part of artificial intelligence. In general, the estimation of a variable with ML approaches follows four levels: the sensor level, the data level, the feature level, and the model level. In the case of our estimations in TRUST, the variables are land use classes, soil moisture values, soil texture classes, and water quality para- meter values. The sensor level includes measurement campaigns with several sensor systems to record the remote sensing data and corresponding reference data. For example, we calculated a digital elevation model of an area at the Lurín catchment based on RGB images captured by a camera mounted on a UAV (see Figure 2.7). 30 | TRUST Report | Chapter 02 | Data and Assessment Tools » Figure 2.7: Digital elevation model (DEM) of an area at the Lurín catchment (see Riese et al., 2020b) generated from an UAV, which is equipped with an RGB camera. » Figure 2.8: Aerial image of a measurement area in Peru with a red-green-blue came- ra and a hyperspectral camera, both mounted on UAVs. A white reference marked by an arrow is placed in- side the measurement area for the camera calibrations. Additionally, soil-moisture reference data (black circles) was acquired. (Taken from Riese, 2020) For the estimation of soil moisture, soil moisture measurements were conducted in several re- gions of the Lurín catchment (Section 2.2.2). The data level includes the pre-processing of the acquired data and the dataset splitting, which is, for example, necessary to evaluate the model performance and the generalization capabilities of an applied model. Within the TRUST project, we generated and published three datasets combining soil moisture probe measurements and hyperspectral data: the KarLy dataset, the HydReSGeo dataset, and the ALPACA dataset (Riese & Keller, 2018; Keller et al., 2020; Riese et al., 2020b). Besides, our SpecWa dataset containing chlorophyll a values and spectral data, is also freely available (Maier & Keller, 2020). In Figure 2.8, a measurement area applied for the ALPACA dataset is shown. For the area-wide estimation of selected variables with the developed methodological approaches, freely available data of the ESA Sentinel-2 mission were used for study areas located in Peru and Germany. Exemplary estimations of soil texture and land use based on Sentinel-2 data are shown in Figure 2.9. Further, the openly available European soil database LUCAS was included in the estimation of soil texture At the feature level, relevant features were extracted from the hyperspectral data with unsuper- vised clustering approaches or approaches to reduce the dimensionality of the high-dimensional data. Based on these approaches, we were able to deepen the understanding of the hyperspectral input data and the estimation task itself. At the model level, either supervised or semi-supervised TRUST Report | Chapter 02 | Data and Assessment Tools | 31 » Figure 2.9: Machine learning classification of (a) soil text ure and (b) land use and cover based on ESA Senti- nel-2 data, both for the catchment of the Klingenberg reservoir in Saxony, Germany. (Left: Taken from Rothfuß, 2019) » Figure 2.10: Example for the visualization of the true values (left) and estimated values (right) of chlorophyll a along the river Elbe. (Taken from Keller et al., 2018a) Outside Area of Interest Forest / Wood Grassland Settlement Area Farmland Water Body Excluded Categories 4.1. Comparison of Machine Learning Models and Datasets Silt Sand Loam (a) Silt Sand Loam (b) Figure 9: Comparison of the soil texture between (a) the reference data and (b) the classifier with the highest overall accuracy based on the complete L1C dataset. To visualize the classification performance, two maps of the study area are generated. In Figure 9a, the soil texture according to the reference data is shown to allow a comparison with the classification. The second map (Figure 9b) is generated with the prediction of the classifier with the best overall accuracy. In this case, this is the voting classifier of the L1C dataset. It can be seen that the prediction of the voting classifier reflects the actual state well. Despite some incorrect classifications, the patterns of the soil texture in the study area are visibly reproduced. As a note, it should be mentioned that the classification was trained with the training subset and predicted with the complete subset (including the training subset) and therefore gained a higher accuracy. The downloaded Sentinel-2 satellite data have not undergone any atmospheric correction. This means that disruptive factors such as cirrus clouds may affect the reflectance. Since the atmospheric correction is not automated at present time and has to be carried out manually with the help of the toolbox Sen2Cor (Louis et al., 2016), this is associated with some effort. Therefore, it was investigated whether this effort is worthwhile and the L2A dataset leads to a better classification performance than the L1C dataset. For all seven classification algorithms, the classification based on the L2A dataset shows a lower prediction accuracy than the L1C dataset. This can be seen exemplary with the k-NN classifier comparing Figure 7a and Figure 8a as well as regarding Table 4. With these results, the effort of creating the L2A dataset is not worthwhile. This result is only valid for this dataset and may depend on the subject of the study. 24 » a » b ML models were selected depending on the estimation task, which had to be solved. Mostly, the choice of which model was used is defined by the availability of reference data. To sum up the contributions at the feature and model level, we developed an estimation framework including (semi-)supervised self-organizing maps for distinct estimation tasks, especially when only limi- ted labeled data is available, as described in more detail by Riese et al. (2020a). We implemen- ted three innovative convolutional neural network architectures as methodological output for the classification of soil texture with hyperspectral spectrometer data, introduced by Riese & Keller (2019). These models function purely data-driven and include state-of-the-art ML research. Further, we presented an automatic detection of land-use changes with long short-term memory (LSTM) networks. These LSTM networks are characterized by learning from several satellite image sequences rather than from single images. Therefore, the developed LSTM networks can differentiate classes and processes that change during time, such as during different seasons of a year. Besides, the estimation of soil moisture and water quality parameters such as chlorophyll a concentration is realized with a developed and implemented ML framework. This framework is described in detail by Keller et al. (2018a, b), and its exemplary results for a measurement campaign along the river Elbe in Germany are illustrated in Figure 2.10. 32 | TRUST Report | Chapter 02 | Data and Assessment Tools Exemplary Acquisition of Reference Data for Remote Sensing Applications Friederike Brauer To develop a classification system for the automatic detection of land-use changes (see objective (3) of Section 2.3), reference data on land use and land cover is necessary for the deep learning methods. For the catchment of the Klingenberg reservoir (Germany), these reference data were obtained from the German Digital Landscape Model of the Official Topographic-Cartographic In- formation System (ATKIS Basis-DLM), which contains digital, object-structured vector data and serves as a basis for generating topographical maps. Information on land use and land cover were gathered from the various shapefiles and summarized in a standardized way. Thus, a detailed shapefile presenting the land use and land cover in the area was generated and used as reference data to train the deep learning methods. The results of the land use and land cover classification are shown in Figure 2.9b. Besides information on land use and land cover, information on the permeability of the topsoil was collected. Several possibilities exist to determine the permeability experimentally at a test site. At the Klingenberg test site in Germany, a double ring infiltrometer (Figure 2.11) was used to estimate the permeability at different locations. To obtain information for larger areas, the appro- ximate permeability was derived from data on the soil texture from the soil map 1:50 000. The soil map is generated from point information gathered in the field and collected by the Saxonian State Office for Environment, Agriculture, and Geology. In the first step, the information from the map was used as a reference. In a second step, the original point information was used to avoid difficulties arising from generalizations in the map. Results of the ML classification of the soil texture are presented in Figure 2.9a. 2.4 Stakeholder Analysis and Participation for Conflict Transformation Christian D. León & Hannah Kosow A decisive factor in water management at the river basin level is an in-depth analysis of the re- levant stakeholders, a so-called stakeholder mapping. We define stakeholder as a person, group, or organization that has an interest or concern in water management and who can affect (or is affected by) water management policies and actions. Relevant stakeholders include all water users located along a river or river basin as well as public or private actors, who define policies, provide financial resources, and make decisions that positively or negatively affect water users. » Figure 2.11: Permeability measurement with a double ring infiltrometer at the test site Klingenberg, Germany. Picture: F. Rees. i TRUST Report | Chapter 02 | Data and Assessment Tools | 33 Before conducting a stakeholder analysis, the first step is to analyze the political, cultural, and geographical conditions. This first analysis helps to better understand which stakeholders exert, either directly or indirectly, influence in the study area. For this purpose, the different political levels (national – regional – local) and their relationships of influence were analyzed. For exam- ple, actors outside of a watershed may need to be taken into account because they indirectly influence actors in the watershed through their activities (e.g., markets, production). As sources of information, we have referred to publications on the region and the topic, such as academic and popular science articles, newspaper articles, book contributions and policy reports, legal texts, and master/bachelor theses. Extensive internet research has enabled us to update and supplement information on the players involved (see Table 2.1 “actors and govern- ment structures”). To learn more about actors and their goals, interviews with locally active orga- nizations and key persons were vital information sources. This holds especially true for actors in the upper catchment area of the Lurín, as the information situation for this area is relatively low. The following classification has proven to be useful for categorizing the stakeholders: 1) state/ governmental actors, 2) non-governmental organizations (NGOs), 3) civil society actors, 4) re- search organizations and universities, 5) private sector actors and 6) international actors. Hybrid forms are also possible. Within their respective categories, the actors were listed according to the degree of their involvement in the research topic. The key actors are those who are directly affected or involved. This classification is important since the aim of the stakeholder analyses is to assess which actors need to be involved to what degree and at what time in a participatory process. Even though participatory processes require considerable time and resources, they offer stake- holders an important opportunity to become familiar with management options and participate in decision-making processes Increased participation can ultimately lead to greater ownership of the outcome, increased credibility and acceptance, or recognition of the intended and necessary measures, policies and objectives (Behnke & Schwaiger, 2019). According to the International Association for Public Participation (2018), participation can occur in five different intensity levels. First, it can be limited to information communication and » Figure 2.12: Participants of the stakeholder work- shop in discussion. Picture: C. D. León. 34 | TRUST Report | Chapter 02 | Data and Assessment Tools the sharing of knowledge (Inform). On a second level, participation takes place by consulting for feedback (Consult). On a third level, there is regular and intensive involvement in the process (Involve). On the fourth level, participation takes place in the form of partnership in developing appropriate solutions (Collaborate). On the fifth and last level, participation is carried out to ena- ble the stakeholders to develop solutions and implement them independently (Empower). One focus of the TRUST project was the involvement of water users and stakeholders, as well as experts and decision-makers in the research process. Involvement was organized in different formats: Focus groups with specific user groups (e.g., women), multi-stakeholder dialogues, and assessment workshops to include relevant actors in developing innovative water management concepts. More specifically, these participatory assessment workshops with stakeholders contri- buted substantially to gaining a more socio-technical perspective (see Chapter 4). Managing water resources in water-scarce regions also means to manage potential conflicts bet- ween goals and interests of various water users in a catchment. Different actions (policies) can be chosen to reach different water-related goals. A range of policy tools (e.g., measures and instru- ments) needs to be combined to reach multiple goals. Such policy tool combinations - also called policy mixes - cannot be assembled freely or arbitrarily, as policy tools can contradict and support each other. We applied qualitative systems analysis to understand potential water use conflicts and design integrated, consistent, and sustainable policy mixes to prevent these. Cross-impact balance analysis (CIB, Weimer-Jehle 2006) was used to assess interactions between alternative policies to reach (potentially) conflicting objectives of different central water users (agriculture, households, tourism, industry, and ecosystems) in different parts of the Lurín catchment. The process comprised desk research, expert consultation, and stakeholder involvement. Table 2.3 summarizes 14 central objectives with 2 to 5 alternative policies each to achieve these objectives (in total, 47 policy options). We have identified the objectives as well as the policies through li- terature review and stakeholder consultation. We interviewed local actors (n = 19) and technical experts (n = 10) to learn about fostering and hindering interrelations between policy options and different objectives. All assessments taken together formed a conceptual yet formalized policy-in- teraction model (see Figure 3.12 in Chapter 3.4). The CIB balance algorithm allowed to identify alternative but consistent and synergetic policy mixes for the entire Lurín catchment and to assess their contribution to attain different targets of SDG 6 (cf. Kosow et al. 2019 and 2020 for more information on the methodology). The identified policy mixes served also as knowledge input for local participation and planning processes (Chapter 3.4). NO. WATER USER MAIN OBJECTIVE ALTERNATIVE POLICIES UPPER CATCHMENT AREA 1 Households and tourism Ensure suffi- cient drinking water (quantity) 1a Own resources (including reservoirs) 1b Supply from remote resources 1c Water metering and tariffs 1d Drinking water saving technology 2 Households and tourism Ensure the qua- lity of drinking water for health protection (quality) 2a Treatment in households 2b Central drinking water treatment 2c Preven- tion of con- tamination (local) 3 Households and commerce Safe treatment and disposal of domestic and commercial wastewater 3a Infiltration or direct disposal (status quo) 3b Central treatment 3c Treat- ment with material flow separa- tion » Table 2.3: Central water use related objectives and policies in the Lurín catchment TRUST Report | Chapter 02| Data and Assessment Tools | 35 NO. WATER USER MAIN OBJECTIVE ALTERNATIVE POLICIES 4 Agriculture Ensure suf- ficient water availability to expand agricul- tural areas 4a Reservoirs 4b Traditional means (ande- nes, cochas etc.) 4c Increa- sing water efficiency 4d Reuse of treated wastewater 5 Ecosystems Long-term conservation of water-related ecosystems in the upper cat- chment area. 5a Green infrastructure 5b Regulation of water re- sources (Pro- tected areas and near to nature outflow) LOWER CATCHMENT AREA 6 Households and tourism Ensure the access and the distribution of the drinking water for the growing popu- lation 6a Water trucks 6b Public drinking water and wastewater network 6c Local drinking water and wastewater network 7 Households and tourism Ensure suffi- cient drinking water to supply the growing population (quantity) 7a Ground- water 7b River water 7c River water transfer (from other catchment areas) 7d Uncon- ventional alternatives 7e Arti- ficial ground- water recharge 8 Households and tourism Ensure the qua- lity of the drin- king water for health protec- tion (quality). 8a Treatment in households 8b Central drinking water treat- ment (level of wells) 8c Preven- tion of con- tamination (local) 9 Households and tourism Water saving and efficient use of the drin- king water 9a Water metering and tariffs 9b Drinking water saving technology in households 9c Water culture and behavioral change 10 Households and tourism Safe treatment and disposal of municipal wastewater 10a Central primary treat- ment (» Pacific Ocean) 10b Central secondary treatment (»River) 10c Central tertiary treatment 10d Decen- tralized treat- ment with multiple use 11 Agriculture and green areas Ensure suffi- cient water for irrigation in agriculture and of green areas. 11a Ground- water 11b River water 11c Increa- sing water efficiency 11d Reuse of treated wastewater 12 Industry Ensure suffi- cient process water for (agro-) industrial activi- ties (quantity) 12a Private groundwater wells 12b Private desalinization of sea water 12c Treated wastewater (multiple use) 12d Public drinking water network 13 Industry Safe treatment and disposal of industrial wastewater 13a Disposal through muni- cipal treatment plants without pretreatment 13b Internal pretreatment and indirect discharge 13c Decen- tralized treatment and direct discharge 14 Ecosystems Long-term conservation of water-related ecosystems 14a Conserva- tion of green areas 14b Regula- tion of water resources (protected areas and regulation of extractions) 36 | TRUST Report | Chapter 02 | Data and Assessment Tools 2.5 Monitoring of Water and Wastewater Quality Michael Hügler & Stefan Stauder The usage of different water sources (rivers, lakes, reservoirs, springs and groundwater) for drin- king water production places a wide range of demands on water treatment. Due to the high vulnerability of surface water against environmental influences, there are potential health risks, both for the use as drinking water, as well as for other types of usages such as irriga- tion. Especially river water is subject to strongly fluctuating water qualities and can exhibit very high fecal loads. The same holds true for lakes and dams, although the hygienic load is gene- rally lower than in river waters. Besides hygienic risks, there are also risks through chemical substances such as heavy metals (e.g., from mining and industry), organic trace substances (e.g., biocides from agriculture), or toxins (e.g., from cyanobacteria blooms). Furthermore, waste- water poses a significant health risk if it is not treated properly when released to the river, or used for irrigation. The water use patterns in the Lurín catchment differ significantly between the upper and lower catchment areas. In the upper catchment, mainly surface water, i.e., rainwater stored in artificial reservoirs, and spring water is used as a drinking water resource and for irrigation. In contrast, in the lower catchment, groundwater is the primary water resource for drinking water, irrigation, and industry (see Chapters 4 & 5 for details). To evaluate the situation in the Lurín catchment with respect to the achievement of SDG 6 (clean water and sanitation), water quality needs to be monitored and evaluated. As minimal data was available, monitoring concepts were developed and applied in the Lurín valley (see Table 2.1 “water quality” and “wastewater quality”). Water quality analyses included reservoirs, springs, drinking water distribution networks, and waste- water discharges in the upper catchment area. Within the lower catchment area, groundwater wells (run by SEDAPAL and farmers), wastewater discharges of two wastewater treatment plants (Cieneguilla and José Gálvez), and the Lurín river itself were monitored. The water quality was analyzed comprehensively in the TZW laboratory, complemented by on-site measurements of sensitive parameters like O2, pH, and turbidity. The laboratory analyses included main and trace elements, as well as persistent organic pollutants (e.g., pharmaceuticals, industrial chemicals, and pesticides). In order to assess the microbial water quality, bacterial and viral indicators, like E. coli, coliform bacteria, enterococci, Clostridia, and somatic coliphages, as well as heterotrophic plate counts (HPC) and total cell counts (TCC), were analyzed. Selected samples were subjected to more detailed analyses with the bacterial specification or the measurement of antibiotic-re- sistant bacteria and index pathogens. Furthermore, as part of the wastewater sampling campaign, composite samples were taken at two-hour intervals (8 a.m. – 8 p.m.) from the influent of the wastewater treatment plants Ciene- guilla and Jose Galvez. Subsequently, they analyzed for their constituents (chemical oxygen de- mand (COD), biological oxygen demand (BOD5), total organic carbon (TOC), total suspended so- lids (TSS), total kjaeldahl nitrogen (TKN), ammonium (NH4-N), nitrate (NO3-N), nitrite (NO2-N), phosphate (PO4-P) and total phosphorus (Ptot)). In Tupicocha, wastewater from a discharge into a gully was sampled (6 a.m. – 6 p.m). TRUST Report | Chapter 02| Data and Assessment Tools | 37 iMonitoring of Microbiological and Physical-Chemical Water Quality at Klingenberg Reservoir Michael Hügler Especially in regions with water scarcity, reservoirs and dams are a significant source for drinking water production. Major problems concerning water quality arise from blooms of potentially toxic cyanobacteria, and possible mass proliferations of hygienically relevant bacteria like coliform bacteria. To investigate these water quality issues, we planned and performed a comprehensive monitoring program at the Klingenberg test site, a reservoir used for drinking water production in Saxony, Germany (see Table 2.1 “water quality”). In addition to the standard water quality ana- lyses performed by the Landestalsperrenverwaltung (LTV), we installed a multi-parameter sensor provided by the project partner OTT Hydromet for the online analyses of temperature, turbidity, oxygen content, salinity, chlorophyll a and phycocyanin (cyanobacterial pigment). Water samples from the reservoirs were taken on a two to four weeks basis and analyzed for microbiological parameters. Besides, two sampling campaigns were carried out during which the entire reservoir, the depth profile, and all inflows were sampled and tested for additional micro- biological parameters, including fecal marker genes (see Stange et al. 2019 for analytical details) and analyses of the microbial community. Coliform bacteria were further specified to see which species were present and which species can proliferate in the reservoir. Our studies showed that the mass proliferation of coliform bacteria is an autochthonic process in the water column and occurs during the summer months. A single strain of the genus Enterobacter was responsible for this “coliform bloom” (Figure 2.13; see Reitter et al., 2021, for further details). The multi-para- meter sensor proved to be useful for the detection of algal blooms. High chlorophyll concentra- tions were present in spring and could be assigned to diatoms. In contrast, we could not detect high chlorophyll concentrations during the two sampling campaigns. 18/06 18/09 3 2 1 ≥ log10 (MPN/100 ml) Enterobacter Lelliottia Serratia others pre-reservoir depth profile raw water main inflow Rauschenbach raw water depth profile pre-reservoir main inflow A B » Figure 2.13: Quantification and identification of coliform bacteria at the Klingenberg reservoir during the sampling campaigns in June (left) and September 2018 (right). (Taken from Reitter et al., 2021). 38 | TRUST Report | Chapter 02 | Data and Assessment Tools 2.6 Water Safety Plan-Tool Friederike Brauer, Thilo Fischer, Lucia Hahne, Sebastian Sturm Assuring the long-term quality of drinking water is the principal objective of preventive resource protection. Establishing a risk management system, for example a Water Safety Plan (WSP), is an important instrument in this context. In the update to the WHO guidelines for drinking water qua- lity, the WHO has recommended implementing a WSP since 2003. The WSP concept is a risk- based approach to drinking-water quality management and is the international point of reference for safe management of drinking-water supply from catchment to tap. It is regarded by the WHO as an essential, globally applicable instrument for safely achieving the strategic development goal for clean drinking water at local level (WHO, 2003). Implementing a risk management system involves a great deal of effort. Especially a spatially resolved assessment of the catchment area is very time-consuming. As part of the TRUST pro- ject, we developed a prototype of a decision support system to facilitate the implementation of a risk management system. Its structure is based on the WSP-concept. Further information on the design of the application can be found in Gottwalt et al. (2018 a + b) and Brauer et al. (2019). The decision support system was implemented as a database-based specialist application. It focuses on risk analysis in the catchment area but also facilitates risk analysis for further process steps in water supply chains like water catchment, storage and distribution. It supports a targeted management of water resources and provides a basis for the development of monitoring systems and further measures to ensure water quality. The interactive application enables recording and assessing risks in the water supply system and documenting measures to control risks (Figure 2.14). For hazard analysis, existing data on land use or activities in the area may be used. The user can also create new features within the tool (Figure 2.15). In the next step, hazardous events can be assigned to those hazard carriers. For example, organic fertilization can be as- signed to the corresponding part of the arable land. The initial risk for every hazardous event is calculated within the tool based on severity of consequences and likelihood of occurren- ce, which the user can rate on a 5-level-scale from very low to very high. The reasons lea- ding to the rating are to be documented within the tool. The initial risk then is the same for » Figure 2.14: Homescreen of the Water Safety Plan-Tool. TRUST Report | Chapter 02 | Data and Assessment Tools | 39 » Figure 2.15: Recording of hazard carriers in the map (test area Klingenberg, Germany). » Figure 2.16: Initial risk in the catchment area shown in a map (test area Klingen- berg, Germany). » Figure 2.17: Initial raw water risk in the cat- chment area (test area Klingenberg, Germany). every object of one kind, regardless of the location within the catchment area. For example, every organically fertilized area of arable land is rated equally at this point (Figure 2.16). Both the initial risk arising from the uses and actions and the vulnerability of the areas are taken into account to assess the arising risk for the raw water. Thus, in addition to information on land use, data on area characteristics such as slope inclination and soil type as well as distance to the extraction point can be included into the evaluation. This results in a spatially differentiated risk assessment and shows clearly, which areas are most likely to lead to significant water quality issues (Figure 2.17). In the following step, risk management measures can be assigned to those hazard carriers, which lead to significant risks. The results show where action is required. The WSP-Tool supports uniform documentation and minimizes the effort required to maintain the WSP by automated calculations and by keeping all data in a single database and thus avoiding redundant entries. Adding the spatial component, which is indispensable for a targeted cat- chment area risk management, represents a significant benefit compared to previously available tools to create and maintain a WSP. 40 | TRUST Report | Chapter 02 | Data and Assessment Tools 2.7 Sustainable Development Goal 6 - Targets and Indicators Hanna Kramer 2.7.1 Sustainable Development Goal 6 - Targets Sustainable Development Goal 6 „Ensure availability and sustainable management of water and sanitation for all“ includes eight sub-goals, called targets. Each target has a different focus to address the current problems related to water, sanitation and the affected environment (Figure 2.18). The first two targets address the human right to safe drinking water (Target 6.1) and ade- quate sanitation (Target 6.2). Target 6.3 aims at improving water quality by reducing pollution and halving the proportion of untreated wastewater. Further targets tackle water use efficiency (Target 6.4), integrated water resources management Target (6.5), and the protection and rest- oration of water-related ecosystems (Target 6.6). Targets 6.A and 6.B address international co- operation and support through capacity building, and supporting the participation of local com- munities, respectively. 6.1: By 2030, achieve universal and equitable access to safe and affordable drinking water for all 6.2: By 2030, achieve access to adequate and equitable sanitation and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those in vulnerable situations 6.3: By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globally 6.B: Support and strengthen the participation of local communities in improving water and sanitation management 6.5: By 2030, implement integrated water resources management at all levels, including through transboundary cooperation as appropriate 6.6: By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakes 6.A: By 2030, expand international cooperation and capacity-building support to developing countries in water- and sanitation-related activities and programs, including water harvesting, desalination, water efficiency, wastewater treatment, recycling and reuse technologies 6.4: By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity » Figure 2.18: SDG Targets TRUST Report | Chapter 02 | Data and Assessment Tools | 41 2.7.2 Sustainable Development Goal 6 - Indicators SDG 6 and its associated targets are very ambitious as they attempt to broadly cover existing pro- blems. However, setting the targets is merely the beginning. In the best case this is followed by possible measures to achieve these targets. Finally, measures have to be evaluated to determine whether the achievement of the targets has moved closer. For this purpose, one or more indicators for each target record the status quo and measure the development achieved over time. In total, eleven indicators for progress on SDG 6 targets have been defined by the Inter-Agency and Expert Group on Sustainable Development Goal Indicators, considering issues of relevance, methodological soundness and measurability (ECOSOC, 2016). For monitoring of the eleven global SDG 6 indicators, three monitoring programs are in place, consisting in total of eight organizations of the United Nations. The first program JMP (Joint Mo- nitoring Program) is responsible for monitoring progress on drinking water, sanitation and hygiene (SDG targets 6.1 and 6.2) led by WHO and UNICEF. The Second GEMI (Global Environmental Management Initiative) program tracks progress on wastewater, water quality, water resources management, and water-related ecosystems (SDG targets 6.3-6.6) and is composed of FAO, UN- ECE, UNEP, UNESCO, UN-HABITAT, UNICEF, WHO and the World Meteorological Organization. The third monitoring program GLAAS (Global Analysis and Assessment of Sanitation and Drin- king-Water) is responsible for SDG targets 6.A and 6.B. It monitors finances, capacities and the enabling environment and is composed of WHO, UN environment and OECD (UN Water 2018). The monitoring organizations publish regular reports to provide global analysis for informed deci- sion-making. These reports include „Step by Step methodologies“ to provide consistent guidance on how to monitor, calculate, and implement each of the global SDG 6 indicators. The Step by Step methodologies can be accessed under https://unwater.org/publications/. 2.7.3 TRUST Concept Evaluation The TRUST project used SDG 6 indicators to assess the status quo situation in the Lurín cat- chment and to evaluate the anticipated effects of the integrated concepts on achieving SDG 6. For this purpose, the Step by Step methodologies were used. The TRUST concepts focus mainly on the provision of safe drinking water and adequate treatment of wastewater. Therefore, we pri- marily applied the indicators 6.1.1 and 6.3.1, which are the indicators for targets 6.1 and 6.3, respectively. The concepts have further positive indirect impacts on the achievement of other SDG 6 targets due to the strong linkages between the targets, nevertheless. The achievement of indicator 6.1.1 “proportion of population using safely managed drinking wa- ter services” is monitored using a service ladder, which was established by the JMP and classifies different access situations into five levels, ranging from safely managed drinking water to surface water use (Figure 2.19a). In the same way, the progress towards target 6.2 is monitored using the indicator 6.2.1 “pro- portion of population using safely managed sanitation services, including a handwashing facility with soap and water”. The sanitation service ladder classifies the different sanitation conditions 42 | TRUST Report | Chapter 02 | Data and Assessment Tools from safely managed sanitation to open defecation (Figure 2.19b). Safely managed is defined as “use of improved facilities which are not shared with other households and where excreta are safely disposed in situ or transported and treated off-site“ . The evaluation of the status quo for indicator 6.3.1 „proportion of wastewater safely treated“ is based on the Step by Step methodology. The input data required for calculating the percentage of treated wastewater include information on sanitary infrastructure and its condition, possible collection forms and transport (an example calculation of indicator 6.3.1 can be found in WHO & UN-HABITAT, 2018). Tthe local regulation standards for wastewater treatment should also be matched to the definitions of the indicator. Particular attention should be paid to the level of treatment in relation to the end use (UN Water, 2016). In order to obtain the necessary data for the evaluation of the status quo, mainly census data were used, and complemented with infor- mation from administrative sources and regulators, as well as own measurements and qualitative surveys. SSaaffeellyy mmaannaaggeedd Drinking water from an improved water source which is located on premises, available when needed and free from faecal and priority chemical contamination BBaassiicc Drinking water from an improved source, provided collection time is not more than 30 minutes for a roundtrip including queuing LLiimmiitteedd Drinking water from an improved source for which collection time exceeds 30 minutes for a roundtrip including queuing UUnniimmpprroovveedd Drinking water from an unprotected dug well or unprotected spring SSuurrffaaccee wwaatteerr Drinking water directly from a river, dam, lake, pond, stream, canal or irrigation canal Note: Improved drinking water sources are those that have the potential to deliver safe water by nature of their design and construction, and include: piped water, boreholes or tubewells, protected dug wells, protected springs, rainwater, and packaged or delivered water SSaaffeellyy mmaannaaggeedd Use of improved facilities which are not shared with other households and where excreta are saf