Drought indices based on monthly precipitation and sometimes temperature are widely used due to their simple calculation with readily available climate data. The portrayal of drought through simple precipitation anomalies or water balances when accounting for temperature may not capture the potentially complex evolution of drought events due to the timing, intensity, and frequency of precipitation events at the daily scale. In this study, we present a new drought index that incorporates a deterministic soil model, HYDRUS-1D, and daily climate data to assess how representative simple drought indices are of soil moisture status in the Southwest. Specifically, we compare our drought index with two widely used drought indices: the Standardized Precipitation Index (SPI) and the Standardized Perception-Evapotranspiration Index (SPEI). Modeled soil moisture output was summed into monthly values for direct comparison between indices. SPI and SPEI proved to be representative of soil moisture status at shallow depths, correlating best at a two-month window. SPI correlated higher with our modeled drought index than SPEI in shallow settings across all study sites. Intense drought events were controlled by the magnitude and frequency of precipitation, with large events creating water surplus and then a slow decay in soil moisture until the next large event. Furthermore, heat map correlations indicate that monitoring drought at depth is dependent upon the previous years monsoon, with the best correlating window growing with distance from monsoon onset. Modeled soil moisture showed volumetric water content increased during monsoon season and remained high through the fall and into the winter months. Higher moisture content increased hydraulic conductivity, priming the soil profile for winter recharge. We believe that the addition of a soil physics based drought index greatly improves drought monitoring conditions for the southwest.

Master of Science / Department of Agronomy / Xiaomao Lin / The high-quality climate data and high-resolution soil property data in Kansas and adjacent states were used to develop drought datasets for the monthly Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and the Standardized Precipitation-Evapotranspiration Index (SPEI) over 1900 to 2014. The statistical analysis of these multiple drought indices were conducted to assess drought occurrence, duration, severity, intensity, and return period. Results indicated that the PDSI exhibited a higher frequency for every category of drought in central and western Kansas than the SPEI by up to 10%. Severe and extreme drought frequency was the highest in southwest Kansas around the Arkansas River lowlands and lowest in the southeast. The mean total drought frequency for eastern, central, and western Kansas was 36%, 39%, and 44%, respectively. The regional mean correlations between the SPI and SPEI were greater than or equal to 0.95 for all regions, but due to statistically significant increases in potential evaporation in western Kansas, the PDSI and SPEI are recommended over the SPI for meteorological and hydrological drought analysis. Drought variability of the last 115 years was analyzed through the Empirical Orthogonal Functions (EOFs) techniques and their Varimax rotations from 1900 to 2014 in Kansas. Large-scale synoptic patterns primarily dominated the Kansas spatial drought structures, especially during long-duration events. The EOFs indicated that the first principal components of drought explained approximately 70% of the drought variability across the state and demonstrated a statistically significant wetting trend over the last 115 years, oscillating at a period of about 14 years for all drought indices. The 99° W meridian line acted as the dominant transitional line demarcating the areas of Kansas’ climate and vegetation relationship as spatial drought presented. The Multivariate El Nino Index (MEI) signal , which modulates global and regional climate variabilities, provided a low-frequency indicator to couple with Kansas drought’s leading modes by varying leads of 3 to 7 months depending on the use of drought index and time steps selected. Large-scale predictors of surface temperature and precipitation are evaluated from the monthly forecasts in Climate Forecast System version 2.0 (CFSv2) from North Dakota down through central Texas (32.6 - 47.7°N and 92.8 - 104.1°W). By using singular value decomposition (SVD), the CFSv2 monthly forecasts of precipitation and 2-m temperature were statistically downscaled using ensemble mean predictions of reforecasts from 1982-2010. Precipitation skill was considerably less than temperature, and the highest skill occurred during the wintertime for 1-month lead time. Only the central and northern plains had statistically significant correlations between observed and modeled precipitation for 1-month lead time. Beyond a 1-month lead time, prediction skill was regionally and seasonally dependent. For the 3-month lead time, only the central plains demonstrated statistically significant mean anomaly correlation. After three-month lead times, the ensemble means of forecasts have shown limited reliable predictions which could make the forecast skill too low to be useful in practice for precipitation. However, temperature forecasts at lead times greater than five months showed some skill in predicting wintertime temperatures.

<p>U ovoj disertaciji je definisan i predstavljen pristup monitoringu poljoprivredne su&scaron;e (SPEI-based approach to agricultural drought monitoring - ADM-SPEI) u Vojvodini koji objedinjuje vi&scaron;e poznatih i priznatih metoda i kritičko mi&scaron;ljenje eksperata zasnovano na njihovom znanju i iskustvu i pri tome uzima u obzir lokalne specifičnosti agroklimatskih uslova. Iako je kreiran za područje Vojvodine, predloženi pristup monitoringu poljoprivredne su&scaron;e baziran na SPEI indeksu opisan je generalno kroz tri faze i pripadajuće korake koji su detaljno opisani i obja&scaron;njeni &scaron;to omogućava da se ADM- SPEI pristup modifikuje i primeni u bilo kojim drugim agroklimatskim uslovima. Predstavljeni pristup baziran je na modifikovanom i &scaron;iroko prihvaćenom i kori&scaron;ćenom Standardizovanom Indeksu Padavina i Evapotranspiracije (SPEI). Ovom modifikacijom SPEI je povezan sa&nbsp; specifičnom kulturom &scaron;to omogućava da se su&scaron;a, odnosno uslovi vlažnosti analiziraju individualno za specifičnu ratarsku ili povrtarsku kulturu u agroklimatskim uslovima Vojvodine. Kreiranju pristupa monitoringu poljoprivredne su&scaron;e prethodilo je istraživanje koje se tiče uticaja referentne evapotranspiracije (ET0) na rezultate SPEI indeksa kako bi se potvrdila polazna pretpostavka da različiti metodi za izračunavanje ET0 u okviru SPEI indeksa daju u nekim slučajevima značajno različite vrednosti indeksa na području Vojvodine. Iz navedenih razloga je važno koristiti metod koji najvi&scaron;e odgovara specifičnom području od interesa. Modifikacija SPEI indeksa, odnosno zamena ET0 sa ETc (potencijalnom evapotranspiracijom kulture) izvr&scaron;ena je uključivanjem ekspertskog mi&scaron;ljenja odnosno intervjuisanjem vi&scaron;e stručnjaka različitih ekspertiza iz domena kori&scaron;ćenja i upravljanja vodama u poljoprivredi &scaron;to je omogućilo da se dođe do grupne odluke koja u najvećoj meri reprezentuje lokalne agroklimatske prilike. U tu svrhu upotrebljen je Analitički Hijerarhijski Proces (AHP metod) kao podr&scaron;ka odlučivanju kako bi se dobile individualne odluke stručnjaka i da bi se u narednom koraku dobila jedna grupna odluka o najpogodnijem metodu za izračunavanje ET0, odnosno ETc. U narednoj fazi predloženog pristupa, prateći dalju proceduru originalnog SPEI indeksa izračunati su klimatski vodni bilansi kultura. Analizom su obuhvaćeni podaci sa devet meteorolo&scaron;kih stanica na području Vojvodine, kao i jedanaest ratarskih i povrtarskih kultura. Zatim je statističkim metodama odabrana odgovarajuća teorijska distribucija za koju je potvrđeno najbolje slaganje sa empirijskim vrednostima klimatskog vodnog bilansa kultura i u narednom koraku dobijen modifikovani SPEI povezan sa specifičnom kulturom (agricultural drought SPEI - AD-SPEIcrop). Pristup omogućava i direktnu transformaciju indeksa u vrednosti klimatskog vodnog bilansa kulture, odražavajući potrebe useva za vodom. Sagledavanjem su&scaron;e i sa tog aspekta omogućava analizu mogućnosti sistema za navodnjavanje u borbi protiv su&scaron;e. U sledećoj fazi izvr&scaron;ena je validacija predloženog pristupa sa vi&scaron;e različitih aspekata koji podrazumevaju: ispitivanje povezanosti indeksa sa prinosima kultura, kako na lokalnom nivou op&scaron;tina tako i na nivou cele teritorije Vojvodine; poređenje stepena slaganja sa prinosima kultura u odnosu na originalni indeks SPEI; komparaciju sa op&scaron;te priznatim i prihvaćenim indeksima su&scaron;e (SPI, SPEI i SC-PDSI); i povratnu informaciju od strane eksperata. Bazirajući se na dobijenim rezultatima u fazi validacije može se zaključiti da predloženi pristup monitoringu poljoprivredne su&scaron;e na području Vojvodine baziran na SPEI indeksu može biti uspe&scaron;no primenjen i sa dobrim performansama, odnosno da indeks AD-SPEIcrop, dobijen kao rezultat ovog pristupa, predstavlja adekvatan pokazatelj poljoprivredne su&scaron;e na području Vojvodine.</p> / <p>In the doctoral dissertation, the SPEI-based approach to agricultural drought monitoring (ADM-SPEI) in Vojvodina has been defined and presented. While integrating several well-known and recognized methods and experts&rsquo; critical opinion based on their knowledge and experience, ADM-SPEI takes into account local specificities of agro-climatic conditions. Although it was created for the Vojvodina region, the proposed approach to agricultural drought monitoring based on the SPEI index has been described in three phases alongside the corresponding steps, for which a detailed description and explanation have also been provided. This allows for the ADM-SPEI approach to be modified and applied in any other agro-climatic conditions. The presented approach is based on the modified and widely accepted and used Standardized Precipitation and Evapotranspiration Index (SPEI). The modification enables relating the SPEI to a specific crop, thereby ensuring the possibility for the analysis of drought or moisture conditions separately for specific field or vegetable crops in the agro-climatic conditions of Vojvodina. The creation of the approach to agricultural drought monitoring was preceded by the research of the impact of reference evapotranspiration (ET0) on the results of the SPEI index. The aim of the research was to confirm the initial assumption that different methods for calculating ET0 within the SPEI index give in some cases significantly different index values in the Vojvodina region. For these reasons, it is important to use the most appropriate method for the specific area of interest. Carrying out the modification of the SPEI index, i.e., the replacement of the ET0 with the ETc (potential crop evapotranspiration) included the involvement of experts&rsquo; opinions by interviewing experts of various expertise in the domain of water use and water management in agriculture. This led to making a group decision representing the local agro-climatic conditions. For this purpose, the Analytical Hierarchy Process (AHP method) was used as a decision-making support in order to get experts&rsquo; individual decisions and, in the next step, to obtain a group decision on the most suitable method for calculating ET0 and ETc. In the next phase of the proposed approach, following the procedure of the original SPEI index, the cropspecific climate water balances were calculated. The analysis includes data from nine meteorological stations in the Vojvodina region, as well as eleven field and vegetable crops. Then, statistical methods were used to select the appropriate theoretical distribution which proved to best fit to the empirical values of the crop-specific climatic water balance. In the next step, the modified SPEI related to specific crops (agricultural drought SPEI - ADSPEIcrop) was obtained. The approach also enables the direct transformation of the index into the values of the climate water balance of crops reflecting the crop water needs. Examining drought from this perspective as well makes it possible to analyze the capabilities of irrigation systems to cope with drought. In the next phase, the validation of the proposed approach was carried out from several different perspectives including examining the correlation of the index with the crop yields, both at the local county level and at the level of the entire territory of Vojvodina; the comparison between the degree of the agreement of the AD-SPEIcrop and the original SPEI index with the crop yields, respectively; the comparison with the generally acknowledged and accepted drought indices (SPI, SPEI and SC-PDSI); and experts&rsquo; feedback. According to the obtained results in the validation phase, it can be concluded that the proposed approach to agricultural drought monitoring in the Vojvodina region based on the SPEI index can be applied successfully and with good performance, and that the ADSPEIcrop index obtained as a result of this approach is an adequate indicator of agricultural drought in the Vojvodina region.</p>

Panama has a warm, wet, tropical climate. Unlike countries that are farther from the equator, Panama does not experience seasons marked by changes in temperature. Instead, Panama's seasons are divided into Wet and Dry. The Dry Season generally begins around mid-December, but this may vary by as much 3 to 4 weeks. Around this time, strong northeasterly winds known as "trade winds" begin to blow and little or no rain may fall for many weeks in a row. Daytime air temperatures increase slightly to around 30-31 Celsius (86-88 Fahrenheit), but nighttime temperatures remain around 22-23 Celsius (72-73 Fahrenheit). Relative humidity drops throughout the season, reaching average values as low as 70 percent. The Wet Season usually begins around May 1, but again this may vary by 1 or 2 weeks. May is often one of the wettest months, especially in the Panama Canal area, so the transition from the very dry conditions at the end of the Dry Season to the beginning of Wet Season can be very dramatic. With the arrival of the rain, temperatures cool down a little during the day and the trade winds disappear. Relative humidity rises quickly and may hover around 90 to 100% throughout the Wet Season. Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. The presented thesis compares forecast of drought indices based on seven different models of artificial neural networks model. The analyzed drought indices are SPI and SPEI-ANN Drought forecast, and was derived for the period of 1985-2014 on Panama Canal basin; I've selected seven of sixty-one Hydro-meteorological networks, existing in the Panama Canal basin. The rainfall is 1784 mm per year. The meteorological data were obtained from the PANAMA CANAL AUTHORITY, Section of Water Resources, and Panama Canal Authority, Panama. The performance of all the models was compared using ME, MAE, RMSE, NS, and PI. The results of drought indices forecast, explained by the values of seven model performance indices, show, that in Panama Canal has problem with the drought. Even though The Panama is generally seen as a wet country, droughts can cause severe problems. Significant drought conditions are observed in the index based on precipitation and potential evaporation found in this thesis; The Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), were used to quantify drought in the Panama Canal basin, Panama Canal, at multiple time scales within the period 1985-2014. The results indicate that drought indices based on different variables show the same major drought events. Drought indices based on precipitation and potential evaporation are more variable in time while drought indices based on discharge. Spatial distribution of meteorological drought is uniform over Panama Canal.

This research explores the relationship between the cognitive variables perceived risks, perceived barriers, perceived self-efficacy, and perceived hazard experience with farmer support for adaptation and the agreement between farmer perceptions with observed climate conditions of drought and excess precipitation. Climate conditions were evaluated using monthly Standardized Precipitation-Evapotranspiration Index (SPEI) values from 1950 to 2014. The remaining variables were measured using a closed ended survey of corn and soybean farmers (N =276) in the Iowa-Cedar Watershed. The relationships were evaluated using Spearman’s Rank Order Correlation (), frequency distributions, and probability analysis. Perceived barriers were found to be a significant predictor of support for adaptation. Transformational adaptations were less supported by farmers than incremental adaptations. Farmers expressed more concern for finances than any other risks or barrier. The majority of farmers reported low to moderate risks to drought and precipitation with high efficacy to cope to future impacts. Lastly, climate conditions indicate that there were more frequent and extreme precipitation events than drought events and that farmer perceptions of climate are consistent with observed climate conditions. However, while climate change projections indicate increased weather extremes in the future, farmers perceive no change in risks. It is unclear whether or not farmers are actually equipped to handle future threats to their crops. Future research should address this problem by conducting a longitudinal study to observe farmers’ perception prior to and after experiencing extreme events.

Published Article / The objective of this study was to characterize meteorological droughts in the Central Region of South Africa, Modder River Basin, C52A quaternary catchment using two popular drought indices: Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI) and to compare the two indices. Drought events were characterized based on their frequency, duration, magnitude and intensity. The indices were computed for the time-scales that are important for planning and management of water resources, i.e. 3-, 6- and 12-month time-scales. The basic meteorological input data used in the computation of these indices were 57 years (1950-2007) of monthly precipitation and monthly temperature data which were recorded at The Cliff weather station in the quaternary catchment. It was found that both SPI and SPEI responded to drought events in similar fashion in all time-scales. During the analysis period, a total of 37, 26 and 17 drought events were identified in the area based on 3-, 6-, and 12-month times-scales, respectively. Considering event magnitude as severity parameter, results from both indices identified the periods 1984-1985, 1992-1993 and 2003-2005 as the severest drought periods in the area. However, when the effects of both drought duration and magnitude are considered (drought intensity), the most severest drought events were identified during the years 1982/83, 1966 and 1973 based on 3-, 6- and 12-month timescales, respectively. It was concluded that although the SPEI generally exhibits veracity over SPI by including, apart from precipitation, additional meteorological parameter, mean temperature, SPI should be adopted as an appropriate drought monitoring tool in an area, like Africa, where meteorological data are scarce.

Despite the lack of robust empirical evidence, a growing number of media reports attempt to link climate change to the ongoing violent conflicts in Syria and other parts of the world, as well as to the migration crisis in Europe. Exploiting bilateral data on asylum seeking applications for 157 countries over the period 2006-2015, we assess the determinants of refugee flows using a gravity model which accounts for endogenous selection in order to examine the causal link between climate, conflict and forced migration. Our results indicate that climatic conditions, by affecting drought severity and the likelihood of armed conflict, played a significant role as an explanatory factor for asylum seeking in the period 2011-2015. The effect of climate on conflict occurrence is particularly relevant for countries in Western Asia in the period 2010-2012 during when many countries were undergoing political transformation. This finding suggests that the impact of climate on conflict and asylum seeking flows is limited to specific time period and contexts.

It is widely accepted that climate variability is a key driver of vegetation productivity. Yet, there are discrepancies on the ideal timescales of climatic events and vegetation response. The work herein attempts to clarify how those variables interact in the region of California. The Standard Precipitation Evapotranspiration Index (SPEI), a drought index, was used as an indicator of interannual climate variability in the region. Vegetation productivity was accounted with Normalized Difference Vegetation Index (NDVI) or net growth point data. In this study, four parameters were tested: the length of climate events influencing vegetation, the ideal time to be accounted as vegetation response, the start of the growing season, and the lag between climate and vegetation response.  In total, 594 different scenarios were simulated, with 432 considering the correlation between SPEI and NDVI anomalies and the remaining between SPEI and net growth. The findings shows that the Hot Deserts of California have an early start of the drought season, in March or April, with climate events from 6 months prior influencing vegetation greenness for the next 3 months. In those deserts, the direct correlations between SPEI and NDVI have been the highest, of 0.70 (Mojave) and 0.64 (Sonoran), meaning that, in these locations drier periods will decrease vegetation health. Cold Deserts present a later start of the drought season, in May. Vegetation in these regions will have a delayed response to droughts, with scenarios of 1 to 2 months lags between climate events and vegetation response presenting the highest correlations between SPEI and NDVI. Response that is also longer with climatic events influencing the next 9 months of vegetation greenness. When the correlations were significant, Mediterranean California behaved similarly to cold deserts, with a lag between climate and vegetation, and even longer periods of climatic influence on vegetation, of up to 12 months. In colder regions of California, entailing the entire Western Cordillera, Cold Deserts, and some regions of Mediterranean California an inverse relationship between SPEI and NDVI was found. Drier periods early in the season, in March or April will cause vegetation to be greener during the following months. In cold deserts and Mediterranean California, this climate vegetation relationship happened for short climatic events, as only the previous months will have an impact on vegetation for the following three months. The Cordillera was influenced by longer climatic events, of up to three months, and was the location that showed the best inverse correlations between NDVI and SPEI. In these locations, an early snowmelt and higher temperatures, leading to higher evapotranspiration, could explain the increase in greenness of vegetation by drier periods. However, this observation does not hold when considering a larger scale of climatic events. The correlation between SPEI and Net Growth has showed that when longer periods are considered, with climatic events of 12 or 24 months, a decrease in the net growth of plants will happen for the following season. As Californian climate is predicted to become more extreme it is of great importance understanding the possible consequences for vegetation.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23497号 / 工博第4909号 / 新制||工||1767(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 清水 芳久, 教授 米田 稔, 准教授 松田 知成 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Meteorological drought indices are commonly calculated from climatic stations that have long-term historical data and then converted to a regular grid using spatial interpolation methods. The gridded drought indices are mapped to aid decision making by policy makers and the general public. This study analyzes the spatial performance of interpolation methods for meteorological drought indices in the United States based on data from the Co-operative Observer Network (COOP) and United States Historical Climatology Network (USHCN) for different months, climatic regions and years. An error analysis was performed using cross-validation and the results were compared for the 9 climate regions that comprise the United States. Errors are generally higher in regions and months dominated by convective precipitation. Errors are also higher in regions like the western United States that are dominated by mountainous terrain. Higher errors are consistently observed in the southeastern U.S. especially in Florida. Interpolation errors are generally higher in the summer than winter. The accuracy of different drought indices was also compared. The Standardized Precipitation and Evapotranspiration Index (SPEI) tends to have lower errors than Standardized Precipitation Index (SPI) in seasons with significant convective precipitation. This is likely because SPEI uses both precipitation and temperature data in its calculation, whereas SPI is based solely on precipitation. There are also variations in interpolation accuracy based on the network that is used. In general, COOP is more accurate than USHCN because the COOP network has a higher density of stations. USHCN is a subset of the COOP network that is comprised of high quality stations that have a long and complete record. However the difference in accuracy is not as significant as the difference in spatial density between the two networks. For multiscalar SPI, USHCN performs better than COOP because the stations tend to have a longer record. The ordinary kriging method (with optimal function fitting) performed better than Inverse Distance Weighted (IDW) methods (power parameters 2.0 and 2.5) in all cases and therefore it is recommended for interpolating drought indices. However, ordinary kriging only provided a statistically significant improvement in accuracy for the Palmer Drought Severity Index (PDSI) with the COOP network. Therefore it can be concluded that IDW is a reasonable method for interpolating drought indices, but optimal ordinary kriging provides some improvement in accuracy. The most significant factor affecting the spatial accuracy of drought indices is seasonality (precipitation climatology) and this holds true for almost all the regions of U.S. for 1-month SPI and SPEI. The high-quality USHCN network gives better interpolation accuracy with 6-, 9- and 12-month SPI and variation in errors amongst the different SPI time scales is minimal. The difference between networks is also significant for PDSI. Although the absolute magnitude of the differences between interpolation with COOP and USHCN are small, the accuracy of interpolation with COOP is much more spatially variable than with USHCN.