Modelling water budget at a basin scale using JGrass-NewAge system

Water resources availability and its variability is one of the most pressing global problems. Hydrological models are useful to understand the water balance of a basin, providing information for water resource forecast, assessment, and management. The effectiveness of the models in estimating the freshwater space-time availability and variability, however, depends on concurrent and explicitly modelling of all water budget components instead of a single component estimation and optimization. The whole water budget modelling at basin scale requires a combined solution from hydrological and spatial information tools, in-situ and remote sensing data. The present dissertation describes an effort to improve estimation of each water budget component, and water budget closure at various spatial and temporal scales, by combining JGrass-NewAge model system, GIS spatial toolbox, in-situ and remote sensing data. JGrass-NewAge is a system which deploys modern informatics to facilitate models maintainability and reproducible research. It integrates advanced GIS features and the Object Modelling System version 3 infrastructures, which allow for a component-based modelling experience. This means that JGrass-NewAGE is not actually a model, but a set of elements (the components) that can be combined just before runtime to produce various modelling solutions. Topics like calibration of processes, the interpolation forcing and the assessment of forecasting errors can therefore be faced with consistent and solid approaches. In this context also the use of some remote sensing resources can be inserted appropriately and with new techniques. For all the analysis, two significantly different basins, in terms of size and hydrological processes, are considered as case studies. These are Posina river basin in northeast Italy (small size basin) and Upper Blue Nile basin(large size basin) are used as case study. The uDig Spatial Toolbox (uST) GIS infrastructure that is used for generating the hydromorphological parameters is described in the second chapter. A large number of tools are included in uST for terrain analysis, river network delineation, and basin topology characterisation. In addition, the geomorphological settings necessary to run JGrass-NewAGE are shown. The third chapter studies the effect of spatial discretisation and the hillslope size on basin responses. The possible epistemic uncertainty exerted by the use of sub basin spatial discretisation of topographic information in the semi-distributed hydrological modelling has been studied. The use of different spatial representation in hydrological modelling context has been also studied by comparing JGrass-NewAGE with a model configuration called PeakFlow. The latter is an implementation of the geomorphological unit hydrograph based on the width function. The experiment indicates that the Peak-Flow model, with a more accurate spatial representation, reproduce the storm events slightly better than the JGrass-NewAGE model. In the fourth chapter, the thesis set-up JGrass-Newage modelling solution for the estimation of hydrological modelling inputs (rainfall, snow, temperature data) and estimates them, as well as with their errors. Regards to the meteorological forcings (mainly temperature and precipitation), in Posina river basin where there are relatively dense meteorological stations, the effects of different interpolation schemes were evaluated. Since the hydrological processes from rainfall is different from snowfall, a new method of separating rainfall and snowfall was introduced using MODIS imagery data. In the fifth chapter, JGrass-NewAGE was used to estimate the whole set of water balance components. For evapotranspiration (ET) estimation, the Priestley-Taylor component of JGrass-NewAGE is used. In order to calibrate its parameter a new method based on the water budget was implemented. This method uses two different hypothesis on available data (budget stationarity "Budyko hypothesis", and local proportionality of actual evapotranspiration to soil moisture availability). Finally the spatial and temporal dynamics of water budget closure of Posina river basin is presented. The sixth chapter concerns about the inputs data, particularly precipitation, for water balance modelling in a region where ground-based gauge data are scarce. Five high-resolution satellite rainfall estimation (SRE) products were compared and analysed using the available rain gauge. The basin rainfall is investigated systematically, and it was found that, at some locations, the difference in mean annual rainfall estimates between these SREs very high. In addition to the identification of the best performing products, the chapter shows that a simple empirical cumulative distribution (ecdf) mapping bias correction method can provide a means to improve the rainfall estimation of all SREs, and the highest improvement is obtained for CMORPH. In the seventh chapter, using the capability of JGrass-NewAGE components and different remote sensing data, the spatio-temporal water budget of Upper Blue Nile basin is simulated. The water budget components (rainfall, discharge evapotranspiration, and storage) were analysed for about 16 years at daily time step using the modelling solution and remote sensing data set. For the verification of the approaches followed, wide ranges of remote sensing data (MODIS ET product MOD16, GRACE, and EUMETSAT CM SAF cloud fractional cover) are used

Creating climate-smart multi-functional landscapes through integrated soil, land and water management practices and contextualized agroadvisory services at different scales in Ethiopia

Experiences show that integrating restoration efforts at landscape level need to be coupled with intensification efforts at farm/plot level to promote synergy and attain multiple benefits. The approaches followed in the creation of multifunctional landscapes are designed to enable achieving this goal. This report presents project activities and outputs related to approaches, implementation modalities and evidence generation exercises from ‘learning’ and upscaling sites. The approaches followed in terms of partnerships and stakeholder engagement are briefly described as these are critical for successful project implementation. Climate-smart agriculture (CSA) and agricultural intensification practices were implemented at landscape and farm level scales. Both farm level interventions related to fertilizer response and irrigation optimizations practices and landscape interventions related to CSA and sustainable land management (SLM) options were implemented at climate-smart villages (CSVs) and scaling sites. Evidence generation related to the impacts of the different practices from project sites and other successful cases in the country was also key component of the activities. These were instrumental to target and scaling technologies. Finally, effort towards integrated conceptual approaches and practical tools to facilitate the targeting and scaling of practices as well as near real-time evidence generation are presented

Indicators of site-specific climate-smart agricultural practices employed in Ethiopia

Indicators of CSA practices/technologies are crucial to measure the performance of CSA practices/technologies and use as a guideline for data collection on the evaluation of CSA practices and technologies. Various indicators of CSA practices under the five categories including crop production, livestock production, integrated soil fertility management, erosion control, water management, and forestry/agroforestry management were identified using experts knowledge and literature review. The result showed that the number of indicators across the three pillars namely productivity and income, adaptation/resilience and mitigation (M,) and with additional indicators for gender equity and social responsiveness. The result also showed that higher numbers of indicators were found in integrated management, agroforestry systems, exclosure management, use of non-timber forest products, forage crop improvement, water harvesting, drip irrigation, river diversion, and promotion of low-carbon emitting animals. The study showed that CSA practices related to forestry and agroforestry management addressed the three pillars of CSA simultaneously. These indicators developed by experts and literature review can be used locally and globally since international system (SI) units are employed in their development. Although this study identified various indicators at practice/technology levels, further assessment is needed to identify result- and policy-level indicators of CSA practices/technologies in Ethiopia

Tailored, climate-informed and location-specific agro-advisory services in the highlands of Ethiopia increased smallholder farmers’ wheat grain yields and profitability

A location-specific, tailored, season-smart agro-advisory decision support tool (DST) in the Highlands of Ethiopia increased wheat yields of smallholder farmers by up to 25% with an average partial profitability of USD 600 per ha per season. The data-driven DST was developed by integrating over 25,000 crop responses to fertilizer application datasets and corresponding biophysical co-variants, using machine learning algorithms. Currently, the DST is being piloted across the highlands of Ethiopia. Documented here, as part of INIT EIA Excellence in Agronomy

Digital solutions to transform agriculture: lessons and experiences in Ethiopia

The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) – hereafter the Alliance and its various partners supported by GIZ-Ethiopia is supporting the transformation plan of Ethiopia through developing structured soils/agronomy database and promoting improved analytical approaches. Through organizing a coalition of experts (soil scientists, agronomists, geospatial analysis experts and data scientists), it was possible to materialize collation/collection of tenths of thousands of soil/agronomy datasets, developing standard data management portal, developing standard data collection guidelines and exploring various machine learning techniques to analyze dataset. With the above accomplishments, the coalition has now moved to the next challenge: mine data, understand patterns and develop solutions that can enable transforming the agricultural system. The team explored different data mining techniques, exchanged experiences and developed frameworks that can facilitate further data analysis endeavors. This included building capacity of national partners in advanced data analytics techniques. Taking advantage of advances in data acquisition, storage, management and analysis, the coalition will soon develop site-specific agroadvisory services. This will involve/integrate climate (onset of rains and planting data), fertilizer recommendation, disease surveillance and early warning complemented with good agronomic practices. The packaged advisories will then be made available for the extension system to communicate to farmers using appropriate means. The coalition will team-up with ‘digital extension’ developers to properly marry ‘content and dissemination mechanisms’. Expected results in the coming few years will involve taking data and data use to the next level, whereby data are “translated” into information and farmer-relevant, gender-specific extension content and disseminated digitally and via analog agricultural advisory services. At the same time, a concerted effort will be made to facilitate the co-creation of an improved Farmer-Data-Research-Extension linkage mechanism for improving the flow and exchange of information between male and female farmers, rural youth, researchers, and extension providers. In the end, enhanced information feedback and linkages will enable extension providers to iteratively and continually improve the quality and efficiency of agricultural advisory services, thereby contributing to transformative agricultural development in Ethiopia

From buffet to best-fits: co-identifying and prioritizing best-bet CSA practices for targeting and scaling and Central Highlands of Ethiopia

Recent evidences and developments highlight that climate-smart agriculture (CSA) is best placed to support the growing global populations under the world of land degradation and climate change while sustaining the environment and reducing emissions. It is considered progress and sustainable approach designed to link agricultural production and food security to climate change adaptation and mitigation, in order to guide the management of agriculture and food systems at multiple scales. Implementation and scaling of CSA practices/technologies/options are however resource and knowledge intensive exercises. CSA is also site-specific whereby what can be defined as ‘climate-smart’ in one location may not be smart in other context. It thus requires identifying practices and technologies that fit the landscape conditions under consideration and are profitable to and acceptable by the respective local communities. Climate-smart interventions need also consider local social differences, particularly gender and economic inequalities, to ensure equal benefits for men, women, and marginalized groups and to avoid exacerbating existing discriminations as well as be effective and sustainable. Careful selection among suit of options that satisfy the needs and requirements of nature and society is thus crucial to get meaningful contribution from CSA and promote its adoption. Linkages with key actors and exploring institutional options for targeting and scaling are also of paramount importance to achieve implementation of CSA at landscape scale and maintain sustainability. In this roprt we highlighted major steps and processes that have been followed to identify basket of CSA technologies and identified short-list of best bets that fit the situations of the two CSVs: Godoberet and Doyogena landscapes in two contrasting farming systems of the highlands of Ethiopia. The approach employed a combination of tools such as participatory methods, expert consultations, literature review, and survey data to identify and prioritize key CSA practices that are location- and context-specific. Among a copendum of CSA options, the key five technologies short-listed for the two sites are soil bunds (combined with and Phalaris grass (Phalaris acquatica and tree Lucerne (Chamaecytisus palmensis)), gully stabilization (with on-site and off-site interventions), exclosure, in-situ water harvesting, and agroforestry are the most important ones. Soil bunds of different types integrated with biological options such as grasses and trees (depending on site characteristics) are the most widely used and studied. This is because of the multiple benefits these options can offer: increase income (mainly from grasses and trees), reduce erosion, enhance soil moisture, restore soil health, sequester more carbon in the soil and with trees above ground and serve as livestock feed among others. It is however important to note that the ‘prioritized CSA practices’ highlighted in this report should be considered with caution as multiple and complementary options are ‘profitable’ compared to single ones. In addition, coupling CSA options with other agroadvisories will be more relevant to address complex problems

The design, deployment, and testing of kriging models in GEOframe with SIK-0.9.8

This work presents a software package for the interpolation of climatological variables, such as temperature and precipitation, using kriging techniques. The purposes of the paper are (1) to present a geostatistical software that is easy to use and easy to plug in to a hydrological model; (2) to provide a practical example of an accurately designed software from the perspective of reproducible research; and (3) to demonstrate the goodness of the results of the software and so have a reliable alternative to other, more traditional tools. A total of 11 types of theoretical semivariograms and four types of kriging were implemented and gathered into Object Modeling System-compliant components. The package provides real-time optimization for semivariogram and kriging parameters. The software was tested using a year's worth of hourly temperature readings and a rain storm event (11 h) recorded in 2008 and retrieved from 97 meteorological stations in the Isarco River basin, Italy. For both the variables, good interpolation results were obtained and then compared to the results from the R package gstat

Targeting SLM technologies across landscapes: a framework to facilitate matching SLM technologies with landscape conditions and generate evidences

The aim of this report is to develop a detailed framework that can guide the placement of land restoration options where they can be more effective so that the right ‘places’ are targeted and the appropriate technologies are used. The framework will also form the basis towards developing a decision support tool that can be used to accomplish processes and steps of landscape restoration (Fig. 1). The framework details the steps from diagnosis to identify hotspot areas of intervention, characterize those hotspots to assess potentials, constraints and current status. Once the detailed characterization is done, the next level will be to identify suitable SLM options that can be applied to restore the conditions of the hotspots. In order to make sure that the practices/technologies can serve their purpose there will be a need to characterize them in terms of their potential and requirements. Once the above two are assessed, ex-ante and scenario analysis can be undertaken to evaluate the impacts of the interventions across the landscape catena. This is an essential step to gain an idea of what we will get from implementing the technologies targeting the hotspots. Once this preliminary information is available, we can match the options (LSM technologies/practices) to context (diagnosed hotspots). This is the actual development work on the ground and should be led by the results of the scenario analysis – implement linked/complementary technologies following the landscape continuum. The next step will then be to generate evidences of the interventions using before/after and/or with and without approaches. This is equally important because this is the step where we determine whether the interventions are providing the intended services and functions. Based on lessons, adjustments can be made where necessary. This can be done in near real-time so that incentives can be provided or penalties can be enforced. Tradeoff analysis will also be a key component of this step. Finally, it will be necessary to determine the optimum combinations of land uses and management options to gain optimum benefits in terms of ecosystem services