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Several factors contribute to the increase in irrigation demand: population growth, demand for higher value-added products, and the impacts of climate change, among others. High-quality water is essential for irrigation, so knowledge of water quality is critical. Additionally, water use in agriculture has been increasing in the last decades. Lack of water quality can cause drip clog, a lack of application uniformity, cross-contamination, and direct and indirect impacts on plants and soil. Currently, there is a need for more automated methods for evaluating and monitoring water quality for irrigation purposes, considering different aspects, from impacts on soil to impacts on irrigation systems. This work proposes a data-driven method to address this gap and implemented it in a case study in the PCJ river basin in Brazil. The methodology contains nine components and considers the main steps of the data lifecycle and the traditional machine learning workflow, allowing for automated knowledge extraction and providing important information for improving decision making. The case study illustrates the use of the methodology, highlighting its main advantages and challenges. Clustering different scenarios in three hydrological years (high, average, and lower streamflows) and considering different inputs (soil-related metrics, irrigation system-related metrics, and all metrics) helped generate new insights into the area that would not be easily obtained using traditional methods. [ABSTRACT FROM AUTHOR]

This paper describes the process of creating a global survey of experts to evaluate drought resilience indicators. The lessons learned include five main points: (1) the heterogeneity in the conceptual background should be minimized before the construction of the survey; (2) large numbers of indicators decrease the engagement of respondents through the survey, and ways to apportion indicators whilst maintaining reliability should be considered; (3) it is necessary to design the survey to balance response rate and accuracy; (4) the survey questions should have clear statements with a logical and flowing structure; and (5) reaching experts with different domain experience and representing different regions is difficult but crucial to minimize biased results. [ABSTRACT FROM AUTHOR]

Understanding how physical climate-related hazards affect food production requires transforming climate data into relevant information for regional risk assessment. Data-driven methods can bridge this gap; however, more development must be done to create interpretable models, emphasizing regions lacking data availability. The main objective of this article was to evaluate the impact of climate risks on food security. We adopted the climatic impact-driver (CID) approach proposed by Working Group I (WGI) in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). In this work, we used the CID framework to select the most relevant indices that drive crop yield losses and identify important thresholds for the indices. When these thresholds are exceeded, the impact probability increases. We then examine the impact of two CID types (heat and cold, and wet and dry) represented by indices of climate extremes considering the impact on different crop yield datasets, focusing on maize and soybeans in the central agro-producing municipalities in Brazil. We used the random forest model in a bootstrapping experiment to select the most relevant climate indices. Then, we applied the Shapley Additive Explanations (SHAP) with the XGBoost model explanatory analysis to identify the indices thresholds that caused impacts. We found that the mean precipitation is a highly relevant CID. However, there is a window in which crops are more vulnerable to precipitation deficit. For soybeans, in many regions of Brazil, precipitation below 80 mm/month in December, January, and February represents an increasing risk of crop yield losses. This is the end of the growing season for those regions. In the case of maize, there is a similar pattern with precipitation below 100 mm/month in April and May. Indices of extremes are relevant to represent crop yield variability. Nevertheless, including climate means remains highly relevant and recommended for studying the impact of climate risk on agriculture. Our findings contribute to a growing body of knowledge critical for informed decision-making, policy development, and adaptive strategies in response to climate change and its impact on agriculture. [ABSTRACT FROM AUTHOR]

Drought resilience indexes are essential tools for an evidence-based decision-making process. They can be constructed by enquiring experts about the most relevant indicators. This communication presents the challenges found a priori of creating a global survey with experts in drought. The lessons learned include: (1) the heterogeneity of the conceptual background should be minimized prior the construction of the survey; (2) large number of indicators decreases the engagement of respondents; (3) the format should balance response rate and accuracy, (4) raising diverse experts by knowledge areas, experience and regional representations is difficult, but crucial to minimize biased results. [ABSTRACT FROM AUTHOR]

Ensuring food security against climate risks has been a growing challenge recently. Weather index insurance has been pointed out as a tool for increasing the financial resilience of food production. However, the multi-hazard insurance design needs to be better understood. This paper aims to review weather index insurance design for food security resilience, including the methodology for calculating natural hazards' indices, vulnerability assessment, and risk pricing. We searched for relevant research papers in the Scopus database using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol. Initially, 364 peer-reviewed papers from 1 January 2010 to 19 February 2022 were screened for bibliometric analysis. Then, the 26 most relevant papers from the last 5 years were systematically analyzed. Our results demonstrate that despite a significant research effort on index insurance, most papers focused on food production. However, research considering other aspects of food security, such as transportation, storage, and distribution, is lacking. Most research focuses on droughts. Other hazards, such as extreme temperature variation, excessive rainfall, and wildfires, were poorly covered. Most studies considered only single-hazard risk, and the multi-hazard risk studies assumed independence between hazards, neglecting the synergy hypothesis between hazards. Lastly, we proposed a conceptual framework that illustrates design paths for a generalized weather index insurance design and evaluation. Solutions for addressing multi-hazard problems are considered. An illustrative example demonstrates the importance of testing the multi-hazard risk hypothesis for weather-based index insurance design for soybean production in Brazil. [ABSTRACT FROM AUTHOR]

The climate-related variables, river discharge, and water temperature, are the main factors controlling the quality of the bank filtrate by affecting infiltration rates, travel times, and redox conditions. The impact of temperature and discharge on manganese release from a riverbed were assessed by water quality data from a monitoring transect at a riverbank filtration site in Dresden-Tolkewitz. Column experiments with riverbed material were used to assess the Mn release for four temperature and three discharge conditions, represented by varying infiltration rates. The observed Mn release was modeled as kinetic reactions via Monod-type rate formulations in PHREEQC. The temperature had a bigger impact than the infiltration rates on the Mn release. Infiltration rates of <0.3 m3/(m2·d) required temperatures >20 °C to trigger the Mn release. With increasing temperatures, the infiltration rates became less important. The modeled consumption rates of dissolved oxygen are in agreement with results from other bank filtration sites and are potentially suited for the further application of the given conditions. The determined Mn reduction rate constants were appropriate to simulate Mn release from the riverbed sediments but seemed not to be suited for simulations in which Mn reduction is likely to occur within the aquifer. Sequential extractions revealed a decrease of easily reducible Mn up to 25%, which was found to reflect the natural stratification within the riverbed, rather than a depletion of the Mn reservoir. [ABSTRACT FROM AUTHOR]