The Nexus Approach to environmental resources management is increasingly recognized as an important vehicle to achieve sustainability as spelled out in the Sustainable Development Goals (SDGs). In particular, it was argued that the Nexus Approach is key for the sustainable use of environmental resources under conditions of global change and provides a tool to deal with challenges of global change including climate change, urbanization and population growth. Building on conceptual considerations with regard to monitoring and implementation outlined earlier, here, we explore how the Nexus Approach may provide solutions for managing resources in multifunctional land-use systems and resilient cities. In fact, the resources perspective is essential for holistic management of water, soil and waste along the urban–rural axis. Peri-urban areas provide perfect examples of multifunctional systems with manyfold opportunities to closing cycles, improve resource efficiency and mitigate trade-offs. Cases described in this book provide both positive as well as negative examples of what can be achieved by applying nexus thinking and what goes wrong if you don’t. Key messages emerging include: (i) participatory approaches are a central element for successful implementation of a nexus approach, (ii) effective mechanisms of knowledge transfer are a prerequisite of adoption and upscaling of nexus approaches and (iii) the lack of economic incentives and lack of data represent major challenges for the implementation of a nexus approach. Overall, the importance of a nexus mindset of all stakeholders involved in nexus cases and of providing an enabling environment by nexus-oriented governance, including appropriate economic instruments, was confirmed.

Peri-urban areas are characterized by multifunctional land-use patterns forming a mosaic of built-up and agricultural areas. They are critical for providing food and other agricultural products, livelihood opportunities and multiple ecosystem services, which makes them transformative where urban and rural spaces blend. We analyzed land use changes in a peri-urban micro-watershed in Southern India by using Google Earth data to understand the micro-level spatio-temporal dynamics. This study aims at understanding the peri-urban agriculture and landscape changes as related to the change in use of wastewater and groundwater for irrigation. The temporal dynamics of peri-urban system including the changes in built-up, paragrass, paddy rice and vegetable cultivation, groundwater and wastewater irrigated areas in the watershed were evaluated. The detected changes indicate that, as a consequence of urban pressures, agricultural landscapes are being converted into built-up areas and, at the same time, former barren land is converted to agricultural plots. The mapped land use data are used in landscape change modelling for predicting the peri-urban agricultural dynamics and the driving factors in the watershed. Combined with the mapping and modelling approaches for land use change analysis, our results form the basis for integrated resources management in the wastewater influenced peri-urban systems.

Analysis of climate variability and change as a basis for adaptation and mitigation strategies requires long-term observations. However, the limited availability of ground station data constrains studies focusing on detecting variability and changes in climate and drought monitoring, particularly in developing countries of East Africa. Here, we use high-resolution precipitation (1981–2016) and maximum and minimum temperature (T-max and T-min) (1979–2012) datasets from international databases like the Climate Hazard Group (CHG), representing the most accurate data sources for the region. We assessed seasonal, annual, and decadal variability in rainfall, T-max and T-min and drought conditions using the Standardized Precipitation Index (SPI). The impact of changes in Sea Surface Temperature on rainfall variability and droughts is assessed using the Nino3.4 and Indian Ocean Dipole (IOD) indices. The results show maximum variability in rainfall during October–December (OND, short rainy season) followed by March–May (MAM, long rainy season). Rainfall variability during OND showed a significant correlation with IOD in Ethiopia (69%), Kenya (80%), and Tanzania (63%). In Ethiopia, the period June–September (JJAS) showed a significant negative correlation (−56%) with the Nino3.4. Based on the 12-month SPI, the eastern and western parts of the region are getting drier and wetter, respectively with an average of mild, moderate, and severe droughts of more than 37%, 6%, and 2% of the study period, respectively. The observed severe droughts (e.g., 1999/2000) and extreme floods (e.g., 1997/1998) were found to be linked to respective negative and positive anomalies of the Nino3.4. In general, climate data products with high spatial resolution and accuracy help detect changes and variability in climate at local scale where adaptation is required.

Resource productivity is critical for achieving resource-based SDGs, namely food (SDG2), water (SDG 6) and energy (SDG 7) security, and important motivation for adopting a nexus approach. Other aspects of the nexus must, however, not be neglected in order to achieve sustainable development. Based on examples and case studies, we show the importance of considering (also) the social and environmental dimension when adopting a nexus approach. Integrating the social and environmental dimension implies the need for a participatory approach and explicit representation in nexus tools and scenarios. This chapter aims to highlight the necessity of including innovative tools such as ecosystem service and participatory approaches in the assessment of the WEF nexus to adopt resources management to achieve Sustainable development.

Local-scale climate change adaptation is receiving more attention to reduce the adverse effects of climate change. The process of developing adaptation measures at local-scale (e.g., river basins) requires high-quality climate information with higher resolution. Climate projections are available at a coarser spatial resolution from Global Climate Models (GCMs) and require spatial downscaling and bias correction to drive hydrological models. We used the hybrid multiple linear regression and stochastic weather generator model (Statistical Down-Scaling Model, SDSM) to develop a location-based climate projection, equivalent to future station data, from GCMs. Meteorological data from 24 ground stations and the most accurate satellite and reanalysis products identified for the region, such as Climate Hazards Group InfraRed Precipitation with Station Data were used. The Soil Water Assessment Tool (SWAT) was used to assess the impacts of the projected climate on hydrology. Both SDSM and SWAT were calibrated and validated using the observed climate and streamflow data, respectively. Climate projection based on SDSM, in one of the large and agricultural intensive basins in Ethiopia (i.e., Awash), show high variability in precipitation but an increase in maximum (Tmax) and minimum (Tmin) temperature, which agrees with global warming. On average, the projection shows an increase in annual precipitation (>10%), Tmax (>0.4 degrees C), Trnin (>0.2 degrees C) and streamflow (>34%) in the 2020s (2011-2040), 2050s (2041-2070), and 2080s (2071-2100) under RCP2.6-RCP8.5. Although no significant trend in precipitation is found, streamflow during March-May and June-September is projected to increase throughout the 21 century by an average of more than 1.1% and 24%, respectively. However, streamflow is projected to decrease during January-February and October-November by more than 6%. Overall, considering the projected warming and changes in seasonal flow, local-scale adaptation measures to limit the impact on agriculture, water and energy sectors are required. (C) 2020 Elsevier B.V. All rights reserved.

In many parts of the world, wastewater irrigation has become a common practice because of freshwater scarcity and to increase resource reuse efficiency. Wastewater irrigation has positive impacts on livelihoods and at the same time, it has adverse impacts related to environmental pollution. Hydrochemical processes and groundwater behaviour need to be analyzed for a thorough understanding of the geochemical evolution in the wastewater irrigated systems. The current study focuses on a micro-watershed in the peri-urban Hyderabad of India, where farmers practice intensive wastewater irrigation. To evaluate the major factors that control groundwater geochemical processes, we analyzed the chemical composition of the wastewater used for irrigation and groundwater samples on a monthly basis for one hydrological year. The groundwater samples were collected in three settings of the watershed: wastewater irrigated area, groundwater irrigated area and upstream peri-urban area. The collected groundwater and wastewater samples were analyzed for major anions, cations and nutrients. We systematically investigated the anthropogenic influences and hydrogeochemical processes such as cation exchange, precipitation and dissolution of minerals using saturated indices, and freshwater-wastewater mixtures at the aquifer interface. Saturation indices of halite, gypsum and fluorite are exhibiting mineral dissolution and calcite and dolomite display mineral precipitation. Overall, the results suggest that the groundwater geochemistry of the watershed is largely controlled by long-term wastewater irrigation, local rainfall patterns and water-rock interactions. The study results can provide the basis for local decision-makers to develop sustainable groundwater management strategies and to control the aquifer pollution influenced by wastewater irrigation.

East Africa is one of the most vulnerable regions of Africa to extreme weather and climate events. Regional and local information on climate extremes is critical for monitoring and managing the impacts and developing sustainable adaptation measures. However, this type of information is not readily available at the necessary spatial resolution. Therefore, here we test trends and variability of temperature (1979–2010) and precipitation (1981–2016) extremes in East Africa, particularly Ethiopia, Kenya, and Tanzania, at a spatial resolution of 0.1 and 0.05°, respectively, using the indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We use gridded data sets with high accuracy and resolution from the Terrestrial Hydrology Research Group, University of Princeton and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Trends of 19 indices are computed by fitting a linear model and using the nonparametric Mann–Kendall test and the magnitude of change is computed using the Sen's slope method. The results show an increasing trend in monthly maximum and minimum values of daily maximum and minimum temperature in large parts of the region. This is accompanied by significant increasing trends in warm nights (TN90p), warm days (TX90p), warm spell duration index (WSDI), and summer days index (SU). In addition, cold days (TX10p) and cold nights (TN10p) showed a significant decreasing trend. In general, the results show an increasing tendency in temperatures extremes, which is in line with rising global mean temperature. In addition, most of the temperature extremes observed after 2000 are warmer than the long‐term mean (1979–2010). Precipitation indices, on the other hand, showed increasing and decreasing trends in Ethiopia, Kenya, and Tanzania, but no general pattern. The outcomes enable identifying hot spot areas and planning of adaptation and mitigation measures at much finer spatial scale than previously possible.

Detecting changes in climate is a prerequisite for a better understanding of the climate and developing adaptation and mitigation measures at a regional and local scale. In this study long-term trends in rainfall and maximum and minimum temperature (T-max and T-min) were analysed on seasonal and annual time scales for East Africa. High resolution gridded rainfall (1981–2016) and temperature (1979–2010) data from international databases like the Climate Hazards Group are used. Long-term seasonal trend analysis shows a non-significant (except for small areas), decreasing (increasing) trend in rainfall in eastern (western) parts of Ethiopia and Kenya and a decreasing trend in large parts of Tanzania during the long rainy season. On the other hand, a non-significant increasing trend in large parts of the region is observed during the short rain season. With regard to annual trends, results largely confirm seasonal analyses: only a few significant trends in rainfall, but significant increasing trends in T-max (up to 1.9 °C) and T-min (up to 1.2 °C) for virtually the whole region. Our results demonstrate the need and added value of analysing climate trends based on data with high spatial resolution allowing sustainable adaptation measures at local scales.

Addressing challenges of water, energy and food security, nexus approaches towards resources management are being developed and starting to be implemented. However, the ecosystem perspective, essential for sustainable resources management, has been identified as a missing element within earlier nexus assessments. With regard to water they have mainly focused on the allocation to different sectors and users, while ecosystem services were rarely explicitly addressed. Existing aquatic ecosystem models are capable of quantifying a wide range of ecosystem services, but have thus far not been comprehensively used in a nexus context. Recent developments in aquatic ecosystem modelling approaches provide opportunities to achieve the sought integration of ecosystem services in the nexus approach. Therefore, we argue for a stronger role of aquatic ecosystem models in nexus assessments.

In East Africa, climate change and variability have shown a strong impact on sectors such as agriculture, energy, and water. To allow mitigation and adaptation of the possible impacts of the projected change in climate, this study applies a Statistical Downscaling Model (SDSM) to generate a high-resolution climate projection, equivalent to future station data, to drive impact assessment models in selected, agricultural intensive, basins of Ethiopia (EthShed), Kenya (KenShed), and Tanzania (TanShed). Observed and large-scale climate variables (predictors) are obtained from the national meteorological agency of Ethiopia and international databases. BROOK90, a physical-based hydrological model, is used to assess the impacts of the projected change in precipitation and maximum and minimum temperature (T-max, and T-min) on the water balance. Based on SDSM, the results show an increase in precipitation, relative to the baseline period (1961-1990), in EthShed (14% - 50%) and KenShed (15% - 86%) and a decrease in TanShed (1.3% - 6.3%) in the 20s (2011-2040), 50s (2041-2070), and 80s (2071-2100) under the three Representative Concentration Pathways (RCPs; RCP2.6, RCP4.5, and RCP8.5). T-max (anomalies up to 3.7 °C) and T-min (anomalies up to 2.76 °C) will be warmer than the baseline period throughout the 21 century in all three basins. In line with the projected change in precipitation and temperature, an increase (decrease) in seasonal and annual streamflow, soil-water, and evaporation in EthShed and KenShed (TanShed) is projected in the 20s, 50s, and 80s. In general, sustainable adaptation measures are required to be developed in a site-specific manner, considering the projected increase in temperature and evaporation in all three basins and a decrease in soil-water and streamflow in TanShed.

In order to overcome limitations of climate projections from Global Climate Models (GCMs), such as coarse spatial resolution and biases, in this study, the Statistical Down-Scaling Model (SDSM) is used to downscale daily precipitation and maximum and minimum temperature (T-max and T-min) required by impact assessment models. We focus on East Africa, a region known to be highly vulnerable to climate change and at the same time facing challenges concerning availability and accessibility of climate data. SDSM is first calibrated and validated using observed daily precipitation, (T-max, and T-min) from 214 stations and predictors derived from the reanalysis data of the National Centers for Environmental Prediction. For projection (2006–2100), the same predictors derived from the second generation Canadian Earth System Model (CanESM2) are used. SDSM projections show an increase in precipitation during the short-rain season (October–December) in large parts of the region in the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100). During the long-rain season (March–May (MAM)) precipitation is expected to increase (up to 680 mm) in Ethiopia, mainly in the western part, and Kenya and decrease (up to −500 mm) in Tanzania in the 2020s, 2050s, and 2080s. However, the western part of Ethiopia will be much drier than the baseline period (1961–1990) during June–September (JJAS) in the 2020s, 2050s, and 2080s, which indicates a shift in precipitation from JJAS to MAM. Annually, precipitation, T-max, and T-min will be higher than during the baseline period throughout the 21 century in large parts of the region. The projection based on SDSM is in line with the direction of CMIP5 GCMs but differs in magnitude, particularly for T-max and T-min. Overall, we conclude that the downscaled data allow for much more fine-scaled adaptation plans and ultimately better management of the impacts of projected climate in basins of Ethiopia, Kenya, and Tanzania.

For many regions of the world, current climate change projections are only available at coarser spatial resolution from Global Climate Models (GCMs) that cannot directly be used in impact assessment and adaptation studies at regional and local scale. Impact assessment studies require high-resolution climate data to drive impact assessment models. To overcome this data challenge, we produced a station based climate projection (precipitation and maximum and minimum temperature) for Ethiopia, Kenya, and Tanzania using observed daily data from 211 stations obtained from the National Meteorological Agency of Ethiopia and international databases. Moreover, 26 large-scale climate variables derived from the National Centers for Environmental Prediction reanalysis data (1961–2005) and second generation Canadian Earth System Model (CanESM2, 1961–2100) are used. Statistical Down-Scaling Model (SDSM) is used to produce the required high-resolution climate projection by developing a statistical relationship between the large- and local-scale climate variables. The predictors are analysed more than 16458 times and we provided 20 ensembles for the current (1961–2005) and future (2006–2100, under RCP2.6, RCP4.5, and RCP8.5) climate.

Savannas are among the most productive biomes of Africa, where they comprise half of its surface. They support wildlife, livestock, rangelands, crops, and livelihoods, playing an important socioeconomic role in rural areas. These water-limited ecosystems with seasonal water availability are highly sensitive to changes in both climate conditions, and in land-use/management practices. Although monitoring programs for African savanna water use have been established in certain areas, most of them are largely restricted to point based measurements or coarse scales, and are not fully capable to provide distributed timely information for planning purposes. In this study we develop a mechanism for monitoring the water used by African savanna from fine scale (meters) to watershed scale, integrating the effects of the water stress. Our hypothesis is that the Ecosystem Stress Index (ESI) is a valuable tool to downscale estimates of actual evapotranspiration at coarse scale, to high resolutions. To monitor savanna water fluxes in a semi-continuous way this study integrates two different ET-estimation approaches: K-C-FAO56 model, integrating reflectance-based "crop" coefficients (SPOT 4 & 5 satellites), is used to derive unstressed savanna evapotranspiration (with high spatial resolution), and the two-source surface energy balance model -TSEB, integrating radiometric surface temperature (AATSR satellites) allows the determination of water stress across savannas (ESI, with low spatial resolution). The difference between estimated and observed surface fluxes derived from TSEB (RMSDLE = 53 Wm(-2), RMSDH = 50 Wm(-2), RMSDRn = 60 Wm(-2), RMSDG = 21 Wm(-2)) were of the same magnitude as the uncertainties derived from the flux measurement system, being sufficiently accurate to be employed in a distributed way and on a more regular basis. The approach of ESI to downscale ET proved to be useful, and errors between estimated and observed daily ET (RMSD 0.6 mmday(-1)) were consistent with the results of other studies in savanna ecosystems. The modelling framework proposed provided an accurate representation of the natural landscape heterogeneity and local conditions, with the potential of providing information suitable from local to broader scales.

In order to overcome limitations of climate projections from Global Climate Models ( GCMs ) , such as coarse spatial resolution and biases, in this study, the Statistical Down-Scaling Model ( SDSM ) is used to downscale daily precipitation and maximum and minimum temperature ( T-max and T-min ) required by impact assessment models. We focus on East Africa, a region known to be highly vulnerable to climate change and at the same time facing challenges concerning availability and accessibility of climate data. SDSM is fi rst calibrated and validated using observed daily precipitation, ( T-max, and T-min ) from 214 stations and predictors derived from the reanalysis data of the National Centers for Environmental Prediction. For projection ( 2006 – 2100 ) , the same predictors derived from the second generation Canadian Earth System Model ( CanESM2 ) are used. SDSM projections show an increase in precipitation during the short-rain season ( October – December ) in large parts of the region in the 2020s ( 2011 – 2040 ) , 2050s ( 2041 – 2070 ) , and 2080s ( 2071 – 2100 ) . During the long-rain season ( March – May ( MAM )) precipitation is expected to increase ( up to 680 mm ) in Ethiopia, mainly in the western part, and Kenya and decrease

Wastewater irrigation is a common livelihood practice in many parts of the developing world. With the continuous irrigation supply, groundwater systems in these regions perceive adverse impacts due to inadequate infrastructure to treat the wastewater. The current study area, Musi River irrigation system, is one such case study located in the peri-urban Hyderabad of South India. The Musi River water, which is used for irrigation, is composed of untreated and secondary treated wastewater from Hyderabad city. Kachiwani Singaram micro-watershed in the peri-urban Hyderabad is practicing wastewater irrigation for the last 40 years. The current quality of (untreated) wastewater used for irrigation is expected to have adverse impacts on the local aquifers, but detailed investigations are lacking. To elucidate the groundwater quality dynamics and seasonality of the wastewater irrigation impacts on the peri-urban agricultural system, we analyzed the groundwater quality on a monthly basis for one hydrological year in the wastewater and groundwater irrigated areas, which exist next to each other. The spatio-temporal variability of groundwater quality in the watershed was analyzed with respect to wastewater irrigation and seasonality using multivariate statistical analysis, multi-way modeling and self-organizing maps. This study indicates the significance of combining various statistical techniques for detailed evaluation of the groundwater processes in a wastewater irrigated agricultural system. The results suggest that concentrations of the major ionic substances increase after the monsoon season, especially in wastewater irrigated areas. Multi-way modeling identified the major polluted groundwaters to come from the wastewater irrigated parts of the watershed. Clusters of chemical variables identified by using self-organizing maps indicate that groundwater pollution is highly impacted by mineral interactions and long-term wastewater irrigation. The study recommends regular monitoring of water resources and development of sustainable management strategies to mitigate the aquifer pollution in wastewater irrigation systems.

Managing environmental resources under conditions of climate change and extreme climate events remains among the most challenging research tasks in the field of sustainable development. A particular challenge in many regions such as East Africa is often the lack of sufficiently long-term and spatially representative observed climate data. To overcome this data challenge we used a combination of accessible data sources based on station data, earth observations by remote sensing, and regional climate models. The accuracy of the Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation (CHIRP), CHIRP with Station data (CHIRPS), Observational-Reanalysis Hybrid (ORH), and regional climate models (RCMs) are evaluated against station data obtained from the respective national weather services and international databases. We did so by performing a comparison in three ways: point to pixel, point to area grid cell average, and stations' average to area grid cell average over 21 regions of East Africa: 17 in Ethiopia, 2 in Kenya, and 2 in Tanzania. We found that the latter method provides better correlation and significantly reduces biases and errors. The correlations were analysed at daily, dekadal (10 days), and monthly resolution for rainfall and maximum and minimum temperature (Tmax and Tmin) covering the period of 1983–2005. At a daily timescale, CHIRPS, followed by ARC2 and CHIRP, is the best performing rainfall product compared to ORH, individual RCMs (I-RCM), and RCMs' mean (RCMs). CHIRPS captures the daily rainfall characteristics well, such as average daily rainfall, amount of wet periods, and total rainfall. Compared to CHIRPS, ARC2 showed higher underestimation of the total (−30 %) and daily (−14 %) rainfall. CHIRP, on the other hand, showed higher underestimation of the average daily rainfall (−53 %) and duration of dry periods (−29 %). Overall, the evaluation revealed that in terms of multiple statistical measures used on daily, dekadal, and monthly timescales, CHIRPS, CHIRP, and ARC2 are the best performing rainfall products, while ORH, I-RCM, and RCMs are the worst performing products.For Tmax and Tmin, ORH was identified as the most suitable product compared to I-RCM and RCMs. Our results indicate that CHIRPS (rainfall) and ORH (Tmax and Tmin), with higher spatial resolution, should be the preferential data sources to be used for climate change and hydrological studies in areas of East Africa where station data are not accessible.