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The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

The purpose of this paper is to evaluate the seasonal and spatial variations in the statistical descriptors of the Markov chain model as well as the expected values of the length of dry and wet days and to estimate the probability of dry and rainy sequences in the state of Santa Catarina.

Daily rainfall data from 1970 to 2013 of five rainfall stations in the state of Santa Catarina were used. To model the sequence of dry and wet days, the first order of the Markov chain was used. The statistical descriptors of the Markov model were evaluated, as well as the expected values of the length of dry and wet days and the number of dry and rainy days for each month. Along with geometric distribution, the probability of occurrence of sequences of dry and rainy days was determined. The adherence of the data to geometric distribution was evaluated using the Kolmogorov-Smirnov test.

The results showed that there is a seasonal and spatial variation in Markov model descriptors and also in the duration of the dry and rainy periods. These variations may be related to the mechanisms responsible for the formation and distribution of rainfall in the state, such as the air masses and relief. The Lages station, located in the Plateau of Santa Catarina, had the highest P₀₀ values, reflecting more stable conditions of the atmosphere. In autumn and winter, no marked differences were found between the coastal stations and west of the state. The geometric distribution was adequate for estimating the probability of dry and rainy days.

Although some work has already been carried out on the modeling of the Markov chain in the state of Santa Catarina, this study was found to be more complete with the use of various statistical descriptors of the model and its application in estimating the duration of the cycles of dry and wet periods and the number of rainy days in the period.

To assess the impacts of climate change on stream flow and evaluation of reservoir performances, reliability, resilience and vulnerability (RRV) indices are contemplated. Precipitation, temperature (Tmax, Tmin), relative humidity and solar radiation are the hydrological and meteorological data which have been used extensively. Climate data like RCP2.6, RCP4.5 and RCP8.5 were evaluated for the base period 1976–2005 and future climate scenario for 2021–2050 and 2051–2080 as per the convenience.

The hydrologic engineering center hydrologic modeling system (HEC-HMS) model was used to simulate the current and future inflow volume into the reservoir. The model performance resulted as 0.76 Nash-Sutcliffe efficiency (NSE), 0.78 R2 and −3.17 D and during calibration the results obtained were 0.8 NSE, 0.82 R2 and 2.1 D. The projected climate scenario illustrates an increasing trend for both maximum and minimum temperature though a decreasing trend was documented for precipitation. The average time base reliability of the reservoirs was less than 50% without reservoir condition and greater than 50% for other conditions but volumetric reliability and resilience varies between 50% and 100% for all conditions. The vulnerability result of reservoirs may face shortage of flow ranging from 5.7% to 33.8%.

Evaluating reservoir simulation and hydropower generation for different climate scenarios by HEC-ResSim model, the energy generated for upper dam ranges from 349.4 MWhr to 331.2 MWhr and 4045.82 MWhr and 3946.74 MWhr for short and long-term future scenario, respectively. RCP for Tmax and Tmin goes on increasing whereas precipitation and inflow to reservoir decreases owing to increase in evapotranspiration. Under diverse climatic conditions power production goes on varying simultaneously.

This paper is original and all the references are properly cited.

The purpose of this paper is to analyze the extent to which weather index‐based insurances can contribute to reducing shortfall risks of revenues of a representative average farm that produces corn or wheat in the North China Plain (NCP). The geographical basis risk is quantified to analyze the spatial dependency of weather patterns between established weather stations in the area and locations where the local weather patterns are unknown.

Data are based on the Statistical Yearbook of China and the Chinese Meteorological Administration. Methods of insurance valuation are burn analysis and index value simulation. Risk reduction is measured non‐parametrically and parametrically by the change of the standard deviation and the value at risk of revenues. The geographical basis risk is quantified by setting up a decorrelation function.

Results suggest significant differences in the potential risk reduction between corn and wheat when using insurance based on a precipitation index. The spatial analysis suggests a potential to expand the insurance around a reference weather station up to community level.

Findings are limited by a weak database in China and, in particular, by the unavailability of individual farm data. Moreover, the low density of weather stations currently limits the examination of the approach in a broader context.

The risk reduction potential of the proposed insurance is encouraging. From a policy point of view, the approach used here can support the adjustment of insurers towards different crops.

This paper is believed to be the first that investigates a weather index‐based insurance designed for an average farm in the NCP and the quantification of geographical basis risk.

This study aims to further understand the factors contributory to fire occurrences in two semi-arid regions in the American Southwest, Clark County in Nevada and Maricopa and Pinal Counties in Arizona.

Statistical and geographic information system analyses were employed to examine the spatial and temporal relationships of various natural and human-caused factors with fire incidences.

Angström fire danger index, average amount of rainfall one month prior, extent of forests and grasslands, and proximities to secondary roads and population centers have significant relationships with fire events.

The importance of the factors contributory to fire occurrence is site-specific even in areas with similar climatic regimes and varies among different geographic regions; as such, researchers will need to conduct specific investigation of each study area.

The findings of this study can be instrumental in facilitating fire managers to derive more informed strategies in fire prevention and management.

While there are many studies on fire, most of them are conducted in wet regions with a lot of vegetative cover; not much work is done on arid areas. This paper considered and compared the spatial and temporal relationships of a wide range of natural and human-caused factors with fire events in two semi-arid areas. The intent was to assess the relative importance of these factors in areas even with similar climatic regimes. As our world is facing unprecedented changes in terms of climate and population growth, it is paramount to have an enhanced understanding of the impacts of these changes on fire regimes. The study areas are hot and dry, and they are located in the wildland–urban interfaces with rapid population growth and urbanization; as such, the research findings may contribute to existing literature.

Small-scale farmers in developing countries are undersupplied with capital. Although microfinance institutions (MFIs) have become well established in developing countries, they have not significantly extended their services to farmers. It is generally believed that this is partly due to the riskiness of lending to farmers. The purpose of this paper is to combine original data from a Madagascan MFI with weather data to estimate the effect of rainfall on the repayment performance of loans granted to farmers.

The basis of the empirical analysis is a unique data set of a commercial MFI in Madagascar and weather data provided by the German Meteorological Service. The repayment performance of loans granted to small-scale farmers is estimated using a two-step estimation approach based on linear probability models (LPMs) and a sequential logit model (SLM).

The results reveal that an excessive amount of rain in the harvest period of rice increases the credit risk of loans granted to small-scale farmers in Madagascar. Furthermore, the results confirm that credit features affect the repayment performance of loans.

Since the returns from weather index-based insurance (at least as a future contract) are perfectly correlated with weather events, the authors can set the effect of weather events on the repayment performance of loans equal to the effect of the returns of weather index-based insurance on the repayment performance of loans. Thus, the results imply that weather index-based insurance might have the potential to mitigate a certain part of the risk in agricultural lending.

The focus and results of the present study are very relevant for MFIs, potential providers of weather index-based insurances as well as for farmers. The results confirm that weather events are a primary reason for the risk perception of lenders in developing countries toward small-scale farmers. Future research should, hence, concentrate on the development of index-based insurances in agricultural lending and consider interventions on different levels, e.g., insurance on the farm and the bank level.

To the knowledge, this is the first study that combines original loan repayment data from a Madagascan MFI with weather data in order to estimate the effect of weather events on the repayment performance of loans granted to farmers. Furthermore, to the knowledge, this is the first study that uses a two-step estimation approach based on LPMs and a SLM to investigate the repayment performance in agricultural lending.

The purpose of this study is to simulate the climate change impacts on winter wheat production and evaluate the possibilities of using various varieties and shifting planting date as two climate change adaptation strategies in Kerman Province, Iran.

Two types of global circulation model and three scenarios for three periods were used. Daily climatic parameters were generated by LARS-WG (Long Ashton Research Station-Weather Generator). The CERES-wheat model was used to simulate future winter wheat growth, development and production.

The results showed that CO₂ had no effect on the phenology of winter wheat, and the negative impact of temperature on the grain yield was higher than the positive effect of CO₂ enrichment. The length of the reproductive growth period of the winter wheat was significantly shortened as affected by the negative impacts of rise in temperature. The simulated results indicated that the grain yield of common (medium maturing) variety of winter wheat will decline, ranging from −0.27 to −18.71 per cent according to future climate changes. Adaptation strategies showed that the early maturing variety had a higher and more stable grain yield under climate change conditions than medium and delayed maturing varieties. Earlier planting date (20 October) increased wheat grain yield under future climatic conditions than common (November 5) planting date. In reverse, later planting (November 20) would accelerate harmful effects of climate change on wheat grain yield.

The results highlighted the potential of early maturing variety and early planting date as the appropriate agronomical approaches for mitigating harmful impacts of climate change on winter wheat production in arid regions.

The purpose of this paper is to investigate weather risks facing Chinese farmers, and to determine whether farmers would have a preference for weather insurance over other types of agricultural insurance.

The data are based on 1,564 farm households surveyed in Shaanxi, Henan, and Gansu provinces in Central China between October 2007 and 2008.

Results suggest that the greater risk for farmers is drought followed by excessive rain. Heat is less critical as a risk but more significant than cool weather. Results suggest a strong interest in precipitation insurance with 50 and 44 percent of respondents indicating strong interest in the product. Supplementary results indicate that interest is equal between planting, cultivating, and harvesting. Furthermore, results suggest that farmers are willing to adopt new ideas, and where possible action has already been taken to self‐insure through diversification and other means.

This research is based on primary data gathered in China. However, the authors are limited in the access to Chinese weather station data to illustrate how weather insurance operates. Instead, the authors use weather data from the weather station in Ashland, Kansas which has similarities to the wheat growing regions of China. While the example is for illustrative purposes only, the authors cannot claim that it actually represents premiums that might actually be found in China.

The Chinese Government has within the past year authorized an investigation into agricultural insurance. The burst of research and applications of weather insurance in both developed and developing countries suggest that a wide array of applications could be feasible in China. The results are encouraging because they suggest that farmers in China would have an interest in purchasing weather insurance.

The authors believe that this is the first study conducted on weather insurance in China. The survey instrument is designed to specifically determine what weather risks are important to Chinese farmers and the interest that farmers would have in using such a product.

This study aims to prove that land-use change plays a role in the occurrence of hydro-meteorological disasters in Central Java, especially in relation to its upstream and downstream.

The paper presents empirical findings from quantitative research using a spatial analysis and descriptive analysis.

The upstream and downstream area of Central Java is categorized as a rapid development area that results in changes in land use and land cover. The findings showed that there was an increasing number of hydrometeorological disasters such as floods and landslides as the impact of land-use change and rainfall conditions.

Analysis of the relationship between rainfall and disaster events with more technical and specific analysis could be done in the further research.

In this study, more analysis in the context of river basin systems including upstream and downstream in different periods to examine the linkage between them have been considered and incorporated.

Purpose – The UK road freight sector is an example of an economically important sector that can be viewed from two different perspectives with regards to climate change: firstly, as a significant contributor to the problem in terms of greenhouse gas emissions, and secondly as a likely recipient of significant impacts (both positive and negative) of any resulting change in climate, especially any increase in extreme weather. It is this relatively neglected second perspective, freight as a vulnerable sector, which is explored.

Methodology/approach – The way in which the freight sector develops in the future will be explored both in terms of the future type of freight operations and potential changes in vehicle technology.

Findings – There is a need to understand how the overlying socio-economic scenario influences the development of the freight sector.

Originality/value – The examination of the impact of climate change on the freight sector is a particularly original aspect of this chapter.