Multivariate sensitivity analysis and threshold response of dust to drought indices and their underlying variables.

Multivariate global sensitivity analysis for dynamic crop models

A coupled machine-learning and sensitivity analysis framework to link dust activity in the Tigris-Euphrates basin to climatic and human-induced drivers.

Sensitivity analysis to determine the importance of input variables in groundwater stress

This letter quantitatively analyzes and reduces the uncertainties of soil moisture retrieval using the L-band microwave brightness temperatures and the L-band Microwave Emission of the Biosphere model. Through a global sensitivity analysis using the method of extended Fourier amplitude sensitivity testing, the crucial parameters are identified at different polarizations and incidence angles. The retrieval uncertainties of soil moisture caused by the observation error, parameter uncertainty, and retrieval strategy are then studied based on an ensemble retrieval and Polarimetric L-band Multibeam Radiometer flight data. The results show that soil moisture retrieval accuracy is determined by both the total sensitivity of each model parameter and the coupling effect between soil moisture and other parameters. Consequently, three-parameter retrieval, including soil moisture, optical depth, and roughness, is recommended. During three-parameter retrieval using observations at three angles, H-polarization is doing better than V-polarization due to its higher sensitivity to the optical depth; a good pre-estimation and lower standard deviation of optical depth will improve the soil moisture retrieval results. Increasing the number of brightness temperature observations by using multiangle and dual-polarized radiometer can obviously reduce the uncertainties caused by observation error, parameter uncertainties, and inversion method.

Conversion and interconnection of multiple energies have become the main form of energy development in China. This paper analyzes the evolution of energy internet and proposes its basic framework based on summarized typical features. As we all know, the mathematical model in energy internet plays increasingly key role in arranging and optimizing resources as well as predicting work. In real applications, we find many complicated problems of electrical model, such as high-dimensional, multiple peaks, non-linear, inconsistent, and nonconvex. In order to solve them, this paper combines model simulation with Global Uncertainty and Sensitivity Analysis to analyse and predict the scientific problem from a probabilistic viewpoint. Global Sensitivity Analysis aims to find out how changes of multiple parameters affecting working result of electrical model, and to analyze how interactions among parameters influencing model results. While modeling each type of electrical model, Global Sensitivity Analysis are used less, this paper also lists various sensitivity analysis algorithm, and describes kinds of methods of both local and global sensitivity analysis. The research will provide a complete method data base of sensitivity analysis for each model development in electrical system. Furthermore, the combination of uncertainty and sensitivity analysis at home and abroad is beneficial to figure out the difficulties and focuses of connections among researches on sensitivity analysis and its common features and the spatially explicit landscape modeling.

A High Energy Turnover Improves Appetite Control at Different Levels of Energy Balance

Various drought indices have been used for agricultural drought monitoring, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Drought Response Index (VegDRI), and Scaled Drought Condition Index (SDCI). They incorporate such factors as rainfall, land surface temperature (LST), potential evapotranspiration (PET), soil moisture content (SM), and vegetation index to express the meteorological and agricultural aspects of drought. However, these five factors should be combined more comprehensively and reasonably to explain better the dryness/wetness of land surface and the association with crop yield. This study aims to develop the Integrated Crop Drought Index (ICDI) by combining the weather factors (rainfall and LST), hydrological factors (PET and SM), and a vegetation factor (enhanced vegetation index (EVI)) to better express the wet/dry state of land surface and healthy/unhealthy state of vegetation together. The study area was the State of Illinois, a key region of the U.S. Corn Belt, and the quantification and analysis of the droughts were conducted on a county scale for 2004–2019. The performance of the ICDI was evaluated through the comparisons with SDCI and VegDRI, which are the representative drought index in terms of the composite of the dryness and vegetation elements. The ICDI properly expressed both the dry and wet trend of the land surface and described the state of the agricultural drought accompanied by yield damage. The ICDI had higher positive correlations with the corn yields than SDCI and VegDRI during the crucial growth period from June to August for 2004–2019, which means that the ICDI could reflect the agricultural drought well in terms of the dryness/wetness of land surface and the association with crop yield. Future work should examine the other factors for ICDI, such as locality, crop type, and the anthropogenic impacts, on drought. It is expected that the ICDI can be a viable option for agricultural drought monitoring and yield management.

] Dai [2011] (henceforth D11) reported that the PalmerDrought Severity Index (PDSI) is superior to other statisti-cally based drought indices including the StandardizedPrecipitation Index (SPI) and the Standardized PrecipitationEvapotranspiration Index (SPEI). D11 argued that given thephysical character of the PDSI water balance model, theindex provides robust estimates of drought severity becauseit takes the preceding conditions into account, in contrast toother drought indices that are based purely on past statisticsof particular climate variable(s). However, D11 has over-estimated the ability of the PDSI to realistically simulate thedistributed soil water balance at large spatial scales, andignored the inherent complexity and multiscalar character ofdrought phenomena, which are related to more than themoisture conditions of the soil. In this comment we discussthe complex characteristics of droughts and the limitationsof the PDSI to quantify drought conditions in a variety ofhydrological systems. We describe the advantages of statis-tically based drought indices including the SPI and the SPEI.ThefactthattheSPIandtheSPEIarenot(anddonotintendtobe) physically based indices is more liberating than con-straining, especially when the physical basis of PDSI can beseriously questioned.[

This paper mainly reviews some widely used global sensitivity analysis methods for uncertainty structure, in which the uncertainty is described by probability theory. Global sensitivity analysis can be divided into single output global sensitivity analysis and multivariate global sensitivity analysis based on the number of output response of the structure. The single output global sensitivity analysis has been studied by many researchers and obtains widely development. Due to the different ways of representing uncertainty in probability theory (variance, probability density function, cumulative distribution function, etc.), different global sensitivity analysis methods (variance based method, moment-independent method, etc.) have been proposed. For example, the variance based global sensitivity analysis method can reflect the structure of the model itself and has been widely studied and applied in engineering; while the moment independent global sensitivity analysis method has a more comprehensive description of the uncertainty and reflects more uncertainty information. The multivariate output global sensitivity analysis is developed based on the single output global sensitivity analysis and it mainly focuses on the effects of input variables on the uncertainty of the whole multivariate output. For the models with multivariate outputs, the correlation between different outputs exists and it should be considered when performing the global sensitivity analysis. Comparing to the single output global sensitivity analysis, the multivariate output global sensitivity analysis is much more complicated and computational demanding since there are more uncertainty information for the multivariate outputs. The basic theories of different global sensitivity analysis methods are described in detail in order to have better understanding of these methods. Numerical examples are also used to compare these different methods. Through the comparison, the advantages and shortage of different global sensitivity analysis methods are obtained.

A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation

Abstract. We use the combined Dark Target/Deep Blue aerosol optical depth (AOD) satellite product of the moderate-resolution imaging spectroradiometer (MODIS) collection 6 to study trends over the Middle East between 2000 and 2015. Our analysis corroborates a previously identified positive AOD trend over large parts of the Middle East during the period 2001 to 2012. We relate the annual AOD to precipitation, soil moisture and surface winds to identify regions where these attributes are directly related to the AOD over Saudi Arabia, Iraq and Iran. Regarding precipitation and soil moisture, a relatively small area in and surrounding Iraq turns out to be of prime importance for the AOD over these countries. Regarding surface wind speed, the African Red Sea coastal area is relevant for the Saudi Arabian AOD. Using multiple linear regression we show that AOD trends and interannual variability can be attributed to soil moisture, precipitation and surface winds, being the main factors controlling the dust cycle. Our results confirm the dust driven AOD trends and variability, supported by a decreasing MODIS-derived Ångström exponent and a decreasing AERONET-derived fine mode fraction that accompany the AOD increase over Saudi Arabia. The positive AOD trend relates to a negative soil moisture trend. As a lower soil moisture translates into enhanced dust emissions, it is not needed to assume growing anthropogenic aerosol and aerosol precursor emissions to explain the observations. Instead, our results suggest that increasing temperature and decreasing relative humidity in the last decade have promoted soil drying, leading to increased dust emissions and AOD; consequently an AOD increase is expected due to climate change.

A novel sensitivity analysis of commercial building hybrid energy-structure performance

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought-monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America and to enhance the utility of drought-monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impact information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and its indicators are changing over time as the climate warms and by recommending ways to strengthen the use of indices and indicators in drought decision making.

Combination of multi-sensor remote sensing data for drought monitoring over Southwest China

Preventive control of desert locusts is based on monitoring recession areas to detect outbreaks. Remote sensing has been increasingly used in the preventive control strategy. Soil moisture is a major ecological driver of desert locust populations but is still missing in the current imagery toolkit for preventive management. By means of statistical analyses, combining field observations of locust presence/absence and soil moisture estimates at 1 km resolution from a disaggregation algorithm, we assess the potential of soil moisture to help preventive management of desert locust. We observe that a soil moisture dynamics increase of above 0.09 cm3/cm3 for 20 days followed by a decrease of soil moisture may increase the chance to observe locusts 70 days later. We estimate the gains in early warning timing compared to using imagery from vegetation to be 3 weeks. We demonstrate that forecasting errors may be reduced by the combination of several types of indicators such as soil moisture and vegetation index in a common statistical model forecasting locust presence. Policy implications. Soil moisture estimates at 1 km resolution should be used to plan desert locust surveys in preventive management. When soil moisture increases in a dry area of potential habitat for the desert locust, field surveys should be conducted two months later to evaluate the need of further preventive actions. Remote sensing estimates of soil moisture could also be used for other applications of integrated pest management.