Application of non-linear techniques for daily weather data reconstruction and downscaling coarse climate data for local predictions

Downscaling techniques aim at resolving the scale discrepancy between climate change scenarios and the resolution demanded for impact assessments. Requirements for downscaled climate, to be useful for end users, include reliable representation of precipitation intensities, temporal and spatial variability, and physical parameters consistency. This report summarizes the results of the proof of concept phase in the development and testing of a novel data reconstruction method and a downscaling algorithm based on the multiplicative random cascade disaggregation method using rainfall signals at different spatial and temporal resolutions. The Wavelet Transformed-based Multi-Resolution Analysis (WT-MRA) was used for reconstructing the historical daily rainfall data needed as input for the downscaling methodology, using satellite-derived proxy data. Comparisons with presently used software showed that in all the cases; that is, the reconstructed, generated daily or downscaled daily data, the products developed outperformed the control test by either generating more accurate outcomes or by demanding significantly less parameterizing data

Vegetation dynamics and precipitation sensitivity in three regions of northern Pantanal of Mato Grosso

The wet areas of the Pantanal provide important services such as water and carbon storage, improved water quality, and climate regulation. Analysis and monitoring of vegetated land and precipitation on a regional scale using remote sensing data can provide important information for the preservation of the landscape and biodiversity of the region. Thus, the purpose was to analyze characteristics of the green cycle of the vegetated surface and to what extent the vegetated surface responds to the variability of precipitation in the Pantanal. The areas include the regions of Cáceres (CAC), Poconé (POC), and Barão de Melgaço (BAM) in Mato Grosso. Time series of accumulated precipitation (PPT) and NDVI (Normalized Difference Vegetation Index) were used for the period from 2000 to 2016, obtained on NASA’s Giovanni platform (National Aeronautics and Space Administration). The analysis of the wavelet transform was applied for NDVI data and there was cross-correlation analysis for PPT and NDVI data. The results showed that the highest correlation between PPT and NDVI was positive with a 1-month lag, but was significant with a lag of up to 3 months. The wavelet analyses showed that the largest wavelet powers occurred at the frequency between 0.5 and 1.3 years, i.e., the NDVI series presented the main variances on the approximately annual scale, indicating that these characteristics are important aspects of local phenology variability, such as cumulative green throughout the year and generalized senescence.As áreas úmidas do Pantanal fornecem importantes serviços, como armazenamento de água e carbono, melhoria da qualidade da água e regulação do clima. A análise e o monitoramento da superfície vegetada e da precipitação em escala regional, com uso de dados de sensoriamento remoto, podem oferecer informações importantes para a preservação da paisagem e da biodiversidade da região. Assim, o objetivo deste estudo foi analisar características do ciclo do verde da superfície vegetada e em que medida a superfície vegetada responde pela variabilidade da precipitação no Pantanal. As áreas analisadas compreendem as regiões de Cáceres (CAC), Poconé (POC) e Barão de Melgaço (BAM), em Mato Grosso. Foram usadas séries temporais de precipitação acumulada (PPT) e índice de vegetação Normalized Difference Vegetation Index (NDVI) para o período de 2000 a 2016, obtidos na plataforma Giovanni da National Aeronautics and Space Administration (NASA). Foram aplicadas a análise da transformada wavelet para os dados de NDVI e a análise de correlação cruzada para os dados de PPT e NDVI. Os resultados mostraram que a maior correlação entre a PPT e o NDVI foi positiva com defasagem de um mês, mas foi significativa em até uma defasagem de três meses. As análises wavelet mostraram que as maiores potências ocorreram na periodicidade entre 0,5 e 1,3 anos, isto é, as séries de NDVI apresentaram as principais variâncias na escala aproximadamente anual, indicando que essas características são aspectos importantes da variabilidade da fenologia local, como o verde cumulativo ao longo do ano e a senescência generalizada

Impact of climate change on agricultural and natural ecosystems

This book illustrates the main results deriving from fourteen studies, dealing with the impact of climate change on different agricultural and natural ecosystems, carried out within the Impact of Climate change On agricultural and Natural Ecosystems (ICONE) project funded by the ALFA Programme of the European Commission. During this project, a common methodology on several Global Change-related matters was developed and shared among members of scientific communities coming from Latin America and Europe. In order to facilitate this interdisciplinary approach, specific mobility programmes, addressed to post-graduate, Master and PhD students, have been organized. The research, led by the research groups, was focused on the study of the impact of climate change on various environmental features (i.e. runoff in hydrological basins, soil erosion and moisture, forest canopy, sugarcane crop, land use, drought, precipitation, etc). Integrated and shared methodologies of atmospheric physics, remote sensing, eco-physiology and modelling have been applied

An analysis of long-term effects of climate change and socioeconomic activities on grassland productivity of inner Mongolia

In recent years, researchers have recognized the complexity of the interactions between the ecological system and the economic development of human society. However, the complicated relationships overwhelm traditional statistical procedures and require an innovative approach to investigate their dynamics. We proposed this study to provide a unique perspective in analyzing the long-term causal relationships between the grassland productivity, climate change, and socioeconomic development of Inner Mongolia Autonomous Region (IMAR) of China. Our attempt began with acquiring remotely sensed satellite imagery, climatic variations, and aggregated annual reports of the socio-economy of the IMAR in vegetation growing seasons for 15 years. The spatial and temporal dissimilarities of the raw observations prevented us from exploiting the potential of this valuable dataset; thus, we interpolated and extrapolated the data to generate a panel dataset with consistent spatial and temporal resolutions. Then, we took another step to preprocess the panel data by applying a signal filter to isolate the long-term trend of change from the inter- and intra-annual cyclic patterns and used the trends as the input for a panel data model. The results from our statistical analysis indicated that the independent variables explained the variations in the dependent variable extremely well, while the polynomial terms of climatic variables were significant with limited marginal effect and most of the climatic variables showed negative linear impact on the grassland productivity. In the meantime, we found not all socioeconomic variables we attempted to include into the model significantly affected grassland productivity, especially the variables describing the financial status of the IMAR residents

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

EXPLORING SPATIAL AND TEMPORAL VARIABILITY OF SOIL AND CROP PROCESSES FOR IRRIGATION MANAGEMENT

Irrigation needs to be applied to soils in relatively humid regions such as western Kentucky to supply water for crop uptake to optimize and stabilize yields. Characterization of soil and crop variability at the field scale is needed to apply site specific management and to optimize water application. The objective of this work is to propose a characterization and modeling of soil and crop processes to improve irrigation management. Through an analysis of spatial and temporal behavior of soil and crop variables the variability in the field was identified. Integrative analysis of soil, crop, proximal and remote sensing data was utilized. A set of direct and indirect measurements that included soil texture, electrical conductivity (EC), soil chemical properties (pH, organic matter, N, P, K, Ca, Mg and Zn), NDVI, topographic variables, were measured in a silty loam soil near Princeton, Kentucky. Maps of measured properties were developed using kriging, and cokriging. Different approaches and two cluster methods (FANNY and CLARA) with selected variables were applied to identify management zones. Optimal scenarios were achieved with dividing the entire field into 2 or 3 areas. Spatial variability in the field is strongly influenced by topography and clay content. Using Root Zone Water Quality Model 2.0 (RZWQM), soil water tension was modeled and predicted at different zones based on the previous delineated zones. Soil water tension was measured at three depths (20, 40 and 60 cm) during different seasons (20016 and 2017) under wheat and corn. Temporal variations in soil water were driven mainly by precipitation but the behavior is different among management zones. The zone with higher clay content tends to dry out faster between rainfall events and reveals higher fluctuations in water tension even at greater depth. The other zones are more stable at the lower depth and share more similarities in their cyclic patterns. The model predictions were satisfactory in the surface layer but the accuracy decreased in deeper layers. A study of clay mineralogy was performed to explore field spatial differences based on the map classification. kaolinite, vermiculite, HIV and smectite are among the identified minerals. The clayey area presents higher quantity of some of the clay minerals. All these results show the ability to identify and characterize the field spatial variability, combining easily obtainable data under realistic farm conditions. This information can be utilized to manage resources more effectively through site specific application

Spatio-temporal Analysis of Agriculture in the Vietnamese Mekong Delta using MODIS Imagery

New methodologies using MODIS time‒series imagery were developed for revealing spatio‒temporal changes of agricultural environments and land use patterns in the Vietnamese Mekong Delta. The following methodologies were proposed:a Wavelet based Filter for Crop Phenology (WFCP), a Wavelet‒based fi lter for evaluating the spatial distribution of Cropping Systems (WFCS), and a Wavelet‒based fi lter for detecting spatio‒temporal changes in Flood Inundation(WFFI). The WFCP algorithm involves smoothing the temporal profi le of the Enhanced Vegetation Index (EVI) using the wavelet transform approach. As a result of validation using the agricultural statistical data in Japan, it was shown that the WFCP was able to estimate rice growing stages, including transplanting date, heading date and harvesting date from the smoothed EVI data, with 9‒12 days accuracy(RMSE). The WFCS algorithm was developed for detecting rice‒cropping patterns in the Vietnamese Mekong delta based on WFCP. It was revealed that the spatial distribution of rice cropping seasons was characterized by both annual fl ood inundation around the upper region in the rainy season and salinity intrusion around the coastal region in the dry season. The WFFI algorithm was developed for estimating start and end dates of fl ood inundation by using time‒series Land Surface Water Index and EVI. Annual intensity of Mekong fl oods was evaluated from 2000 to 2004, at a regional scale. Applying a series of wavelet‒based methodologies to the MODIS data acquired from 2000 to 2006, it was confi rmed that the cropping season for the winter‒spring rice in the fl ood‒prone area fl uctuated depending on the annual change of fl ood scale. It was also confi rmed that the triple rice‒cropped area in the An Giang province expanded from 2000 to 2005, because the construction of a ring‒dike system and water‒resource infrastructure made it possible to sustain a third rice cropping season during the fl ood season. The proposed methodologies(WFCP, WFCS, WFFI) based on MODIS time‒series imagery made it clear that while the rice cropping in the Vietnamese Mekong Delta was quantitatively(annual fl ooding) and qualitatively(salinity intrusion) affected by water‒resource changes, there were some regions where the cultivation system was changed from double rice cropping to triple rice cropping because of the implementation of measures against fl ooding.日本の食料自給率 (2005年時の供給熱量ベース) は、40% と先進7カ国の中で最も低い。日本は、その食料海外依存度の高さから、世界的な食料価格の変動の影響を最も受け易い国と言える。近年の経済発展に伴う中国の大豆輸入量の増加や世界的なエネルギー政策の転換 (バイオエタノール政策) は、世界の穀物需給バランスを不安定にさせつつあり、世界的な問題となっている。さらに、地球温暖化による農業生産影響、増加し続ける世界人口、鈍化する穀物生産性を考えれば、世界の食料需給バランスが将来にわたって安定し続けると言うことはできないだろう。他方、食料増産・生産性向上を目的とした集約的農業の展開は、発展途上国の農業環境にさらなる負荷を与えるかもしれない。世界の食料生産と密接な関係にある日本は、自国の食料安全保障を議論する前提として、急速に変わり行く世界の農業生産現場やそれを取り巻く農業環境を客観的に理解し、世界の農業環境情報を独自の手法によって収集・整理する必要がある。そこで、筆者は、衛星リモートセンシング技術を活用することによって、地球規模の視点で、時間的・空間的な広がりを持って変わり行く農業生産活動とそれを取り巻く農業環境情報を把握・理解するための時系列衛星データ解析手法の確立を目指すこととした。本研究では、インドシナ半島南端に位置するベトナム・メコンデルタを調査対象領域とした。ベトナムは、タイに次ぐ世界第2位のコメ輸出国であり、その輸出米の9割近くが、ベトナム・メコンデルタで生産されたものである。筆者は、ベトナム・メコンデルタを世界の食料安全保障を考える上で重要な食料生産地帯の一つであると考え、本地域における農業環境及び土地利用パターンの時空間変化を明らかにするためのMODIS データを用いた新たな時系列解析手法の開発を行った。　本研究において提案する時系列解析手法は、次の三つである。1. Wavelet‒based Filter for Crop Phenology (WFCP) ,2. Wavelet‒based Filter for evaluating the spatial distribution of Cropping System (WFCS) , 3. Wavelet‒based Filterfor detecting spatio‒temporal changes in Flood Inundation (WFFI) . WFCP は、時系列植生指数 (EVI) を平滑化するためにウェーブレット変換手法を利用しており、日本の農業統計データを用いた検証結果から、水稲生育ステージ (田植日、出穂日、収獲日) を約9－12日 (RMSE) の精度で推定可能であることが示された。WFCP を基に改良されたWFCS は、水稲作付パターンの年次把握を可能にし、ベトナムメコンデルタにおける水稲作付時期の空間分布が、上流部において毎年雨期に発生する洪水と沿岸部において乾季に発生する塩水遡上によって特徴づけられていることを明らかにした。WFFI は、時系列水指数 (LSWI) と植生指数 (EVI) から、湛水期間、湛水開始日・湛水終息日を広域把握し、メコン川洪水強度の年次変化を地域スケールで評価することを可能にする。そして、ウェーブレット変換を利用した一連の手法を、2000～2006年までのMODIS 時系列画像に適用することによって、メコンデルタ上流部の洪水常襲地帯において、冬春米の作付時期が、年次変化する洪水規模に依存していることを明らかにした。また、An Giang 省において、堤防建設 (輪中) や水利施設の建設によって、洪水期における水稲三期作が可能になった地域が、2000～2005年にかけて拡大していることを明らかにした。本研究で提案したMODIS 時系列画像を利用した時系列解析手法 (WFCP、WFCS、WFFI) によって、ベトナムメコンデルタにおける水稲生産が水資源の量的 (洪水) ・質的 (塩水遡上) 変動影響を受ける一方、洪水対策の実施によって、栽培体系を二期作から三期作に変更している地域があることを明らかにした

Estimation of High-Resolution Evapotranspiration in Heterogeneous Environments Using Drone-Based Remote Sensing

Evapotranspiration (ET) is a key element of hydrological cycle analysis, irrigation demand, and for better allocation of water resources in the ecosystem. For successful water resources management activities, precise estimate of ET is necessary. Although several attempts have been made to achieve that, variation in temporal and spatial scales constitutes a major challenge, particularly in heterogeneous canopy environments such as vineyards, orchards, and natural areas. The advent of remote sensing information from different platforms, particularly the small unmanned aerial systems (sUAS) technology with lightweight sensors allows users to capture high-resolution data faster than traditional methods, described as “flexible in timing”. In this study, the Two Source Energy Balance Model (TSEB) along with high-resolution data from sUAS were used to bridge the gap in ET issues related to spatial and temporal scales. Over homogeneous vegetation surfaces, relatively low spatial resolution information derived from Landsat (e.g., 30 m) might be appropriate for ET estimate, which can capture differences between fields. However, in agricultural landscapes with presence of vegetation rows and interrows, the homogeneity is less likely to be met and the ideal conditions may be difficult to identify. For most agricultural settings, row spacing can vary within a field (vineyards and orchards), making the agricultural landscape less homogenous. This leads to a key question related to how the contextual spatial domain/model grid size could influence the estimation of surface fluxes in canopy environments such as vineyards. Furthermore, temporal upscaling of instantaneous ET at daily or longer time scales is of great practical importance in managing water resources. While remote sensing-based ET models are promising tools to estimate instantaneous ET, additional models are needed to scale up the estimated or modeled instantaneous ET to daily values. Reliable and precise daily ET (ETd) estimation is essential for growers and water resources managers to understand the diurnal and seasonal variation in ET. In response to this issue, different existing extrapolation/upscaling daily ET (ETd) models were assessed using eddy covariance (EC) and sUAS measurements. On the other hand, ET estimation over semi-arid naturally vegetated regions becomes an issue due to high heterogeneity in such environments where vegetation tends to be randomly distributed over the land surface. This reflects the conditions of natural vegetation in river corridors. While significant efforts were made to estimate ET at agricultural landscapes, accurate spatial information of ET over riparian ecosystems is still challenging due to various species associated with variable amounts of bare soil and surface water. To achieve this, the TSEB model with high-resolution remote sensing data from sUAS were used to characterize the spatial heterogeneity and calculate the ET over a natural environment that features arid climate and various vegetation types at the San Rafael River corridor

Improved Understanding of the Linkages and Interactions between Vegetation, Climate, Streamflow and Drought: Case Studies in Germany

Global climate change has significantly impacted the terrestrial ecosystems and water cycles over the past century. This dissertation aims to further improve our knowledge of the linkages and interactions between vegetation, climate, streamflow, and drought. First, the current study investigated long-term variations in vegetation and climatic variables and their scale-dependent relationships by using Rhineland-Palatinate (Southwest Germany) as a case study area. Based upon the monthly normalized difference vegetation index (NDVI), precipitation and temperature data for six different vegetation types in two precipitation regimes (low and high precipitation regimes) of Rhineland-Palatinate, the temporal trends in the original time series of these variables and their relationships were examined. In addition, the further objectives were to evaluate which time-scale is dominantly responsible for the trend production found in the original data and find out the certain time-scales that represent the strongest correlation between NDVI and climatic variables (i.e., precipitation and temperature). A combined approach using the discrete wavelet transform (DWT), Mann-Kendall (MK) trend test and correlation analysis was implemented to achieve these goals. The trend assessment in the original data shows that the monthly NDVI time series for all vegetation types in both precipitation regimes have upward trends, most of which are significant. The precipitation and temperature data for six vegetation types in two precipitation regimes present weak downward trends and significant increasing trends, respectively. The most important time-scales contributing to the trend production in the original NDVI data are the 2-month and 8-month events. For precipitation, the most influential ones are 2-month and 4-month scales. The 4-month periodic mode predominantly affects the trends in the original temperature data. The results indicate temperature is the primary driver influencing the vegetation variability over this study area, while there is a negative correlation between NDVI and precipitation for all vegetation types and precipitation regimes. For the scale-dependent relationships between NDVI and precipitation, the 2-month and 8-month scales generally present the strongest negative correlation. The most significant positive correlation between NDVI and temperature is obtained at the 8- and 16-month scales for most vegetation types. The results might be valuable for water resources management as well as agricultural and ecological development planning in Rhineland-Palatinate, and also offer a helpful reference for other regions with similar climate condition. Then, this study presented a detailed regional investigation of the probabilistic and multi-scale relationships between streamflow and hydroclimatic variables (precipitation, temperature and soil moisture) and the potential links to large-scale atmospheric circulations over Baden-Württemberg, Southwest Germany. First, the joint dependence structure between seasonal streamflow and hydroclimatic variables was established using copulas. On the basis of the joint dependence structure, this study estimated the probability (risk) of hydrological droughts and floods conditioned upon two different scenarios of hydroclimatic variables for different seasons over the study area. Then, it was evaluated how the relationships between hydroclimatic forcings and streamflow vary among different temporal scales using wavelet coherence. The results reveal that the strong positive coupling between streamflow and both precipitation and soil moisture occurs at most temporal scales, particularly at decadal scales, while the multi-scale relationships between temperature and streamflow are significantly weak compared to precipitation and soil moisture. The connections between streamflow variability and large-scale atmospheric circulations were explored by using composite analysis. Although the atmospheric circulation patterns vary in different seasons, it can be found that the high streamflow anomalies for most seasons over Baden-Württemberg are related to strong westerly atmospheric circulations that play an important role in favoring the warm and moist air from the North Atlantic Ocean towards the study area and thus enhancing the precipitation. Moreover, the low streamflow anomalies are generally linked to the northerly circulations that induce the movement of cold air from northern Europe towards this study area and thus result in the reduced precipitation. Finally, a general probabilistic prediction network was developed in this dissertation for hydrological drought examination and environmental flow assessment. This methodology is divided into three major components. First, the joint streamflow drought indicator (JSDI) was proposed to describe the hydrological dryness/wetness conditions based on the monthly streamflow data. The JSDI relies on a high-dimensional (12-d) multivariate probabilistic model to establish a joint distribution model. In the second part, the drought-based environmental flow assessment method was introduced, which provides dynamic risk-based information about how much flow (the environmental flow target) is required for drought recovery and its likelihood under different hydrological drought initial situations. The final part involves estimating the conditional probability of achieving the required environmental flow under different precipitation scenarios according to the joint dependence structure between streamflow and precipitation. Two catchments in Germany were used to examine the usefulness of this network. The results show that the JSDI can provide an overall assessment of hydrological dryness/wetness conditions and does well in identifying both drought onset and persistence. The method also allows quantitative prediction of targeted environmental flow that is required for hydrological drought recovery and evaluates the corresponding risk. In addition, the results confirm that the general network can estimate the conditional probability associated with the required flow under different precipitation scenarios. The presented methodology offers a promising tool for water supply planning and management and for environmental flow assessment. The network has no restrictions that would prevent it from being applied to other basins worldwide