Integrated Scenarios of Regional Development in Two Semi-Arid States of North-Eastern Brazil

Scenario analysis of the future is an important tool for supporting sustainability-oriented regional planning. To assist regional planning in two federal states in semi-arid North-eastern Brazil, Ceará and Piauí, we developed integrated qualitative¿quantitative scenarios that show potential developments of the agricultural and water resources situation as well as the internal migration until the year 2025. In these states, regional development is negatively influenced by the high seasonality of rainfall and El-Niño-related drought years. Two reference scenarios, 'Coastal Boom and Cash Crops' and 'Decentralisation - Integrated Rural Development' were developed. First, story lines were created and the development of the driving forces was quantified. Then, an integrated model, which includes modules for simulating water availability, water demand, and agricultural production and income, was applied to compute the temporal development of relevant system indicators in each of the 332 municipalities of Ceará and Piauí. These indicators encompass the fraction of the irrigation water demand than can be satisfied, the volume of water which is stored in the reservoirs at the beginning of the dry season, agricultural productivity and production as well as the internal migration among scenario regions. In addition, the impact of certain policy measures was assessed in the context of both reference scenarios. Reference and intervention scenarios were derived by an interdisciplinary group of scientists and were discussed and refined during policy workshops with planning agencies of Ceará

Potential of using remote sensing techniques for global assessment of water footprint of crops

Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. The WF of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. In this paper evapotranspiration, precipitation, water storage, runoff and land use are identified as key variables to potentially be estimated by remote sensing and used for WF assessment. A mass water balance is proposed to calculate the volume of irrigation applied, and green and blue WF are obtained from the green and blue evapotranspiration components. The source of remote sensing data is described and a simplified example is included, which uses evapotranspiration estimates from the geostationary satellite Meteosat 9 and precipitation estimates obtained with the Climatic Prediction Center Morphing Technique (CMORPH). The combination of data in this approach brings several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which are discussed in detail. This work provides new tools for global WF assessment and represents an innovative approach to global irrigation mapping, enabling the estimation of green and blue water use

Sustainability of small reservoirs and large scale water availability under current conditions and climate change

Semi-arid river basins often rely on reservoirs for water supply. Small reservoirs may impact on large-scale water availability both by enhancing availability in a distributed sense and by subtracting water for large downstream user communities, e.g. served by large reservoirs. Both of these impacts of small reservoirs are subject to climate change. Using a case-study on North-East Brazil, this paper shows that climate change impacts on water availability may be severe, and impacts on distributed water availability from small reservoirs may exceed impacts on centralised water availability from large reservoirs. Next, the paper shows that the effect of small reservoirs on water availability from large reservoirs may be significant, and increase both in relative and absolute sense under unfavourable climate change

Catchment variability and parameter estimation in multi-objective regionalisation of a rainfall–runoff model

This study attempts to examine if catchment variability favours regionalisation by principles of catchment similarity. Our work combines calibration of a simple conceptual model for multiple objectives and multi-regression analyses to establish a regional model between model sensitive parameters and physical catchment characteristics (PCCs). The objective is to test robustness of regionalisation by assessing if generalisation of a wide range of climatic, topographic and physiographic settings in a regional model favours simulation of stream flow at ungauged catchments. Constraints in this work are very stringent performance measures for selection of catchments to establish the regional model and the selection of only PCCs that are available through the database of the National River Flow Archive in the United Kingdom. As such some PCCs have been ignored that have proven to be effective in other studies. For this study 56 well-gauged catchments in England and Wales are used. For model calibration and runoff simulation of ungauged catchments the HBV model is applied. Optimum parameter sets are derived for 48 catchments through Monte Carlo Simulation using an 8-year simulation period. This study aims to adequately simulate all aspects of the hydrograph at the ungauged catchment and therefore four single objective functions are combined in a multi-objective function. After calibration, 17 catchments that are widely spread over England and Wales are selected to establish relationships for seven selected model parameters using 14 PCCs (area, mean elevation, hypsometric integral, catchment shape, standard average annual rainfall, five types of land use and four classes of hydrogeology). Single and multiple regression analysis are applied to identify these relationships. For six model parameters statistically significant relationships could be established three of which are plausible on the basis of hydrologic interpretation. The established relationships are validated at eight gauged catchments that are spread over the UK and cover a large range of values of catchment descriptors. These catchments are assumed ungauged and results revealed that, in general, model parameters determined by the established regional relationships do not perform better as compared to default parameter values. Similar results are obtained for additional validation runs using catchments that are not used in the regionalisation procedure. Since these parameters are based on model performance assessments in a wide range of catchment settings, this suggests that large variability in settings of PCCs does not favour regionalisation. Therefore, for selected catchments the applicability of regionalisation by principles of catchment similarity for HBV model parameters may be questioned

Allocating capital-associated CO2 emissions along the full lifespan of capital investments helps diffuse emission responsibility

Capital assets such as machinery and infrastructure contribute substantially to CO2 emissions over their lifetime. Unique features of capital assets such as their long durability complicate the assignment of capital-associated CO2 emissions to final beneficiaries. Whereas conventional approaches allocate emissions required to produce capital assets to the year of formation, we propose an alternative perspective through allocating required emissions from the production of assets over their entire lifespans. We show that allocating CO2 emissions embodied in capital assets over time relieves emission responsibility for the year of formation, with 25‒46% reductions from conventional emission accounts. This temporal allocation, although virtual, is important for assessing the equity of CO2 emissions across generations due to the inertia of capital assets. To re-allocate emission responsibilities to the future, we design three capital investment scenarios with different investment purposes until 2030. Overall, the existing capital in 2017 will still carry approximately 10% responsibilities of China’s CO2 emissions in 2030, and could reach more than 40% for capital-intensive service sectors