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This paper examines the impact of extreme rainfall shocks on the performance in test scores of students living near at-risk urban areas in Brazil.

To identify the causal effect, we consider the exogenous variation of rainfall at the municipal level conditioned on the distance from the school to risk areas and the rainfall intensity in the school months.

The results suggest that extreme precipitation shocks, defined as a shock of at least three months of high-intensity rainfall, have an adverse impact on both math and language performance. Through a heterogeneous effects analysis, we find that the impact varies by student gender, with girls being more affected. In addition, among students who study near at-risk areas, those with better previous school performance and higher socioeconomic status are more negatively affected.

Our results suggest that extreme weather events can increase the differences in human capital accumulation between the population living near risk areas and those living more distant from these areas.

This chapter describes the climatic setting of the Aral Sea region, investigates how the climate might change during the 21st century, and discusses potential impacts on water resources. Temperature and precipitation fields are analyzed to describe the mean climate for the Aral Sea region. Composite analysis has been employed on the precipitation field from the Global Precipitation Climatology Project (GPCP v2.2) to assess the spatial pattern of changes in precipitation during the last several decades. Furthermore, temperature and precipitation projections available from the 2007 Intergovernmental Panel on Climate Change report are synthesized to examine the nature of climate change during this century.

Cold season precipitation has increased during recent decades, particularly over the mountainous terrain east of the Aral Sea. Climate models also project increases (5−20%) in winter precipitation during the 21st century; however, several models suggest decreases (0 to −15%) in precipitation during summer. Despite the increases in cold season precipitation, the large increases in temperature (4°C) during the 21st century are likely to cause increased evaporation which could exacerbate the regional water budget deficit. This may constrain the water supply in the region, particularly during summer and autumn when water demand is highest. To fully understand the impacts of future climate change on regional water resources, hydrologic models that include anthropogenic management of water will be required.

Three Coupled Model Intercomparison Project Phase 5 models involved in the G4 experiment of the Geoengineering Model Inter-comparison Project (GeoMIP) project were used to investigate the impact of stratospheric aerosol injection (SAI) on the mean surface air temperature and precipitation extremes in Africa.

This impact was examined under G4 and Representative Concentration Pathway (RCP) 4.5 scenarios on the total precipitation, the number of rainy days (RR1) and of days with heavy rainfall (R20 mm), the rainfall intensity (SDII), the maximum length of consecutive wet (CWD) and dry (CDD) days and on the maximum rainfall in five consecutive days (Rx5day) across four regions: Western Africa (WAF), Eastern Africa (EAF), Northern Africa and Southern Africa (SAF).

During the 50 years (2020–2069) of SAI, mean continental warming is −0.40°C lower in G4 than under RCP4.5. During the post-injection period (2070–2090), the temperature continues to increase, but at a lower rate (−0.19°C) than in RCP4.5. During SAI, annual rainfall in G4 is significantly greater than in RCP4.5 over the high latitudes (especially over SAF) and lower over the tropics. The termination of SAI leads to a significant increase of rainfall over Sahel and EAF and a decrease over SAF and Guinea Coast (WAF).

Compared to RCP4.5, SAI will contribute to reducing significantly regional warming but with a significant decrease of rainfall in the tropics where rainfed agriculture account for a large part of the economies. After the SAI period, the risk of drought over the extratropical regions (especially in SAF) will be mitigated, while the risk of floods will be exacerbated in the Central Sahel.

To meet the Paris Agreement, African countries will implement mitigation measures to contribute to keep the surface air temperature below 2°C. Geoengineering with SAI is suggested as an option to meet this challenge, but its implication on the African climate system needs a deep investigation in the aim to understand the impacts on temperature and precipitation extremes. To the best of the authors’ knowledge, this study is the first to investigate the potential impact of SAI using the G4 experiment of GeoMIP on temperature and precipitation extremes of the African continent.

Purpose of the study is to further expand insights into how weather impacts attendance at sporting events. With the NFL having only eight home games a year per team, it is more of an event than other North American sports. We explore this in terms of how sensitive fans are to weather, by not only looking at traditional factors, but also other weather variables available through Accuweather. In addition, the authors explore team success, outcome uncertainty and other factors as determinants of demand.

The method includes Tobit model of attendance in terms of percent of capacity in the National Football League. Model includes factors such as outcome uncertainty, team success, etc. but mainly focuses on weather. Weather factors studied include traditional variables such as temperature and precipitation, and also includes cloud cover, barometric pressure, wind speed and humidity. Different model specifications are included to explore results. Key findings allow for differences between games played outdoors versus indoors.

In terms of control variables, team success, new stadiums and stadium age play a significant role in attendance in terms of percentage of capacity. Outcome uncertainty does not appear to be important, and fans desire the opposite when the home team is an underdog. The main results concern the weather. When only traditional weather variables are included, precipitation plays a key role. With further expansion of the weather variables, it appears that cloud cover offers some additional information beyond precipitation. In addition, barometric pressure plays a minor, but statistically significant role as it relates to attendance in terms of capacity.

Including deeper and richer weather data helps to further explain attendance at sporting events. With the NFL, this may be limited by it being such as event due to the scarcity of games in a season. In addition, the weather variables are not truly independent, although they are not as correlated as may be anticipated on the surface. Use of different types of weather variables in models of attendance may help to deepen our understanding of factors influencing consumer decisions. These factors may play larger roles in sports with wider variance in attendance during the season.

The practical implications are that other weather-related variables besides temperature and precipitation may offer insight into consumer decisions related to attendance at sporting events. Cloud cover gives insights into anticipated poor weather in addition to it directly leading to less of a sunny day to be outdoors at an event. Barometric pressure has been shown to influence headaches and joint pain and may also influence consumer decisions to venture out to sporting events.

As data becomes more widely available in general, it's possible to add additional insights into factors influencing various forms of decision-making. In this study, we show that more information on weather can shed insights into consumer decisions as it relates to attending events such as sports. These decisions likely differ based upon whether the event is held outdoors or indoors. With more entertainment choices as substitutes, it is important to identify key factors which influence consumer decisions to help better structure events in the future.

Weather variables beyond temperature and precipitation are included in a Tobit model for NFL attendance using percentage of capacity as the dependent variable. These weather variables are cloud cover, wind speed, humidity, and barometric pressure. Cloud cover and barometric pressure were found to have some significant effects on percentage of capacity. When included, precipitation itself is no longer found to be significant, but precipitation interacted with games played in domes retains statistical significance as there are key differences between games held outdoors versus indoors.

The purpose of this paper is to analyze vulnerability of food crop production to heavy precipitation in north-eastern Ghana, specifically, the upper east region (UER) and the policy implications for adaptation. Heavy precipitation events are a common part of climatic variability; but little attention is given to its impact on livelihoods as compared to droughts in research and policy domains.

This paper draws on both quantitative and qualitative research methods and data. Rainfall data are analyzed using the Standardized Precipitation Index (SPI). This is compared with quantitative analysis of crop yields and complemented by narratives of farmers from in-depth interviews and focus group discussions.

The results show that heavy precipitation events often lead to low food crop productivity and this suggests that the latter is vulnerable to the former.

Although some adaptation is occurring through a wide range of local measures, these are inadequate for eliminating vulnerability. Thus, additional policy measures are recommended for enhancing farmer adaptation, including: incorporating climate change adaptation policies, including adaptation to heavy precipitation into District Development Planning; building human resource capacity for effective implementation of climate change adaptation policies at district levels; improving market access to seed through improved market infrastructure and rural transportation; establishing Community Seed Banks (CSBs) as back up sources of seed; promoting “nursing and transplant” as an alternative planting method for millet and guinea corn; promoting low costs solar drying technologies for drying food crops; and supporting livelihood diversification through credit and business development services.

The purpose of this paper is to examine the reduction of crop yield uncertainty using rainfall index insurances. The insurance payouts are determined by a transparent rainfall index rather than actual crop yield of any producer, thereby circumventing problems of adverse selection and moral hazard. The authors consider insurances on rainfall indexes of various months and derive an optimal insurance portfolio that minimizes the income variance for a crop producer.

Various regression models are considered to relate crop yield to monthly mean temperature and monthly cumulative precipitation. A bootstrapping method is used to simulate weather indexes and corn yield in a future year with the correlation between precipitation and temperature incorporated. Based on the simulated scenarios, the optimal insurance portfolio that minimizes the income variance for a crop producer is obtained. In addition, the impact of correlation between temperature and precipitation, availability of temperature index insurance and geographical basis risk on the effectiveness of rainfall index insurance is examined.

The authors illustrate the approach with the corn yield in Illinois east crop reporting district and weather data of a city in the same district. The analysis shows that corn yield in this district is negatively influenced by excessive precipitation in May and drought in June–August. Rainfall index insurance portfolio can reduce the income variance by up to 51.84 percent. Failing to incorporate the correlation between temperature and precipitation decreases variance reduction by 11.6 percent. The presence of geographical basis risk decreases variance reduction by a striking 24.11 percent. Allowing for the purchase of both rainfall and temperature index insurances increases variance reduction by 13.67 percent.

By including precipitation shortfall into explanatory variables, the extended crop yield model explains more fluctuation in crop yield than existing models. The authors use a bootstrapping method instead of complex parametric models to simulate weather indexes and crop yield for a future year and assess the effectiveness of rainfall index insurance. The optimal insurance portfolio obtained provides insights on the practical development of rainfall insurance for corn producers, from the selection of triggering index to the demand of the insurance.

The purpose of this paper is to explore the impacts of climate change on crop net revenue by region. Particularly, the authors focus on the impact differences between north and south regions.

The authors applied the Ricardian approach which assumes that each farmer wishes to maximize revenue subject to the exogenous conditions of their farm. The climate data are based on actual measurements in 753 national meteorological stations and the socio-economic data covers 8,405 farms across 28 provinces in China.

On average, the rise of annual temperature will hurt farms both in the north or south. The impacts of climate change on both precipitation and temperatures have different seasonal impacts on producers in the north and the south of China. As a consequence, the impact on net farm revenues varies with farms in the north and the south being adversely affected (to different degrees) by a rise in the temperature, but both benefiting from an anticipated increase in rainfall. The results also reveal that irrigation is one key adaption measure to dealing with climate change. Whether in the north or south of China, increasing temperature is beneficial to irrigated farms, while for rainfed farms, higher temperature will result in a reduction in net revenues. The results also reveal that farms in the north are more vulnerable to temperature and precipitation variation than that in the south. Irrigated farms in the south are more vulnerable to precipitation variation than that in the north; but rainfed farms in the north are more vulnerable to precipitation variation than that in the south.

Applying empirical analysis to identify the differences of climate change impacts between north and south regions will help policy makers to design reasonable adaptation policies for various regions.

The purpose of this paper is to empirically examine the financial outcomes from forage production and RI-PRF insurance interval for two locations in Nebraska. Both locations provide historical forage production and precipitation data, allowing the authors to examine the relation between RI-PRF net income and forage production.

The authors focus on evaluating the producer net income and risk (measured as variance of net income) by examining the relation between farm precipitation and production and comparing multiple insurance intervals to no insurance. Each insurance interval will likely have a different relation (basis risk) between observed production and return from insurance and, therefore, a different impact on the variance of net incomes. The impact on variance of net incomes identifies the risk-reducing aspects of RI-PRF insurance intervals. The authors then rank each scenario into four mutually exclusive zones that describe the risk-reducing effectiveness and whether the subsidy is working correctly.

The authors found both risk increasing and decreasing insurance intervals exist at both locations. One insurance scenario (low in BBR) provided the highest net income while increasing risk, suggesting a profit maximizing opportunity. RI-PRF reduces net income risk with intervals insuring during high expected precipitation (growing season); while net income risk increases with intervals insuring low expected precipitation (non-growing season, winter months). The farmer would want to insure during the high expected precipitation months, which coincides with the growing season, since RI-PRF lowers the net income risk. For the government, removing net income risk increasing intervals improves the allocation of government resources.

In this paper, the authors modeled the relation between RI-PRF interval selection using the historical forage production data at two locations in Nebraska. The use of historical forage production data allowed the authors to precisely identify the risk-reducing effectiveness of RI-PRF interval selection.

The purpose of this paper is to identify drought conditions in Jordan in the period 1960‐2006 for two major basins – the Yarmouk and the Zarqa. This study aims to look at the long‐term drought conditions (hydrological droughts) and to investigate the possibility of changes in the monthly precipitation pattern for the period of 1997‐2006, as this period has suffered from the average slightly dry conditions for the two basins.

The Standard Precipitation Index (SPI) is used to assess drought conditions in the Yarmouk and Zarqa basins. The SPI is applied on an annual basis to assess the hydrological long‐term droughts. Average monthly precipitation is used as the input to the SPI procedure.

The SPI is applied on an annual basis (12 months) SPI‐12. Results of the SPI‐12 assessment show that the years 1978, 1995, and 1999 represented extremely dry conditions in both basins. The years 1989 and 1993 represented severely dry conditions in the Zarqa basin and extremely dry conditions in the Yarmouk basin. The statistical procedure for precipitation anomalies identifies precipitation anomalies for the years 2000, 2003, and 2006. However, due to the limited number of anomaly years this study does not conclude that there is a change in the pattern of monthly precipitation of the tested period 1997‐ 2006.

Using the SPI method this work can be extended to compare drought conditions in the remaining major basins in the country.

The paper provides important information about drought conditions in Jordan and ways to assess precipitation anomalies and pattern changes. This can be beneficial to the planning of water resources in Jordan and the associated developments (agricultural, industrial, tourism, and residential).

This paper aims to demonstrate the importance of taking into account precipitation and the vegetation response to it when trying to analyse changes of vegetation cover in drylands with high inter‐annual rainfall variability.

Linear regression models were used to determine trends in NDVI and precipitation and their interrelations for each pixel. Trends in NDVI that were entirely supported by precipitation trends were considered to impose climate‐induced vegetation change. Trends in NDVI that were not explained by trends in precipitation were considered to mark human‐induced vegetation change. Modelling results were validated by test of statistical significance and by comparison with the data from higher resolution satellites and fieldtrips to key test sites.

More than 26 percent of all vegetated area in Central Asia experienced significant changes during 1981‐2000. Rainfall has been proved to enforce most of these changes (21 percent of the entire vegetated area). The trends in vegetation activity driven by anthropogenic factor are much scarcer and occupy about 5.75 percent of the studied area.

Planners, decision makers and other interest groups can use the findings of the study for assessment and monitoring land performance/land degradation over dry regions.

The study demonstrates the importance of taking into account precipitation and the vegetation response to it when trying to analyse changes of vegetation cover in drylands with high inter‐annual rainfall variability.