The added value of high-resolution above coarse-resolution remote sensing images in crop yield forecasting: A case study in the Egyptian Nile Delta

Crop growth models play a major role in sustaining the world-wide food security. These models are used to simulate crop growth during the growing season, and the final crop yield at the end of the growing season, given the farmers’ management practices. At a more strategic level, these crop growth models play an important role to decision makers to take timely decisions regarding food import and/or export strategies. The simulation accuracy of crop growth models relies on the quality of the input data. Since crop yield forecasting applications are often applied over large areas that rely on a spatially distributed crop growth model, the uncertainty in the spatial variation of the input data increases. Remote sensing images are often used in crop growth models because remote sensing images provide spatially distributed input data to these models. These images are available in numerous spatial resolutions, where coarse resolution images are often freely available compared to the more expensive high-resolution images. Therefore, the objective of the current study was to evaluate the added value of high-resolution satellite imagery above coarse-resolution satellite imagery in crop yield forecasting

DEVELOPMENT OF A DECISION-MAKING TOOL FOR PREDICTION OF RAINFALL-INDUCED LANDSLIDES

Landslides are frequently observed in mountainous places following prolonged periods of rain, frequently resulting in substantial topography changes. They pose a significant risk to human lives and the built environment globally, particularly in areas prone to excessive rainfall. While slope failures can occur because of human-caused factors such as slope loading or toe cutting for construction purposes, many failures occur because of rainfall penetrating an otherwise stable slope. A greater understanding of the characteristics and mechanics of landslides is consequently critical for geotechnical research, particularly in evaluating prospective mitigation strategies. The potential of slope failure is a primary consideration when assessing the risk associated with landslide movement. The current research seeks to develop a real-time decision-making tool for rainfall-induced landslides that enables users to compare governing parameters during intense rainfall, comprehend the in-situ stability condition, and therefore assure safety. The first section of the study employs a one-dimensional transient infiltration analytical solution (Yuan and Lu 2005) to evaluate seasonal variations in soil hydrologic behavior. The one-dimensional transient infiltration analytical solution enables better control and flexibility of the soil-water characteristic curve’s transient infiltration equations and fitting parameters. Due to the model\u27s ability to determine fitting parameters, it was possible to calibrate it using in-situ soil hydrologic behavior. The second section of the study will examine how a slope behaves under seasonal rainfall variation utilizing soil hydrologic and mechanical techniques. The case study is based on data collected from a true monitored slope. Two years of monitoring were conducted on the slope. Throughout this time, the place experienced seasonal drying and wetting. Field hydrologic and deformation sensors were installed during the monitoring period. A finite element program was used to generate the monitored slope utilizing in situ slope geometry and initial condition data. Following that, the hydrologic and deformation reactions of the soil were investigated. At two previously reported slope locations, behavioral analysis is conducted. The final section of the study proposes a model for projecting the sub-surface’s volumetric water content using observations of surface rainfall and evapotranspiration. Initially, the prediction model was created using the location of a previously reported site. The prediction model was validated and then tested in six distinct Kentucky locations. The six locations lacked in-situ measurements of soil hydrologic and geotechnical parameters. As a result, Soil Active and Passive Moisture (SMAP) and Web Soil Survey were used to collect soil hydrologic and geotechnical data for the test locations. Combining the data with SMAP\u27s soil hydrology data resulted in the establishment of a safety factor for the test sites. On increasing competitive advantage for member firms. Firm-level outcomes and inter-organizational relationship structures related to network involvement were investigated

Agricultural Drought Monitoring And Prediction Using Soil Moisture Deficit Index

The purposes of this study are: 1) to evaluate the performance of an agricultural drought index, Soil Moisture Deficit Index (SMDI) at continental scale; 2) to develop an agricultural drought prediction method based on precipitation, evapotranspiration and terrestrial water storage. This study applied multiple linear regression (MLR) with the inputs of precipitation from Parameter-elevation Regressions on Independent Slopes Model (PRISM), evapotranspiration from Moderate Resolution Imaging Spectroradiometer (MODIS) MOD 16 and terrestrial water storage (TWS) derived from the Gravity Recovery and Climate Experiment (GRACE) to predict soil moisture and SMDI. The inputs of the MLR model were chosen based on the mass conservation of the hydrological quantities at the near surface soil layer (two meters). In addition, the model also includes seasonal and regional terms for estimation. Comparisons with the US drought monitor （USDM）showed that SMDI can be used as a proxy of agricultural drought. The model exhibited strong predictive skills at both one- and two-month lead times in forecasting agricultural drought (correlation \u3e0.8 and normalized root mean square error \u3c15%)

Measuring and modeling deep drainage, streamflow, and soil moisture in Oklahoma

This dissertation examines multiple components of the Oklahoma water balance in order to answer three independent research questions:i) Can long-term soil moisture monitoring data be used to estimate potential groundwater recharge rates? Daily drainage rates from the root zone were estimated for 78 sites using up to 17 years of soil moisture data from the Oklahoma Mesonet. Mean annual drainage rates ranged from 6 to 266 mm yr-1, with a statewide median of 67 mm yr-1. Drainage estimates were also modeled for four focus sites using HYDRUS1-D. Soil moisture-based drainage rates and HYDRUS1-D drainage rates agreed within 10 mm yr-1 at two drier sites but had discrepancies of >150 mm yr-1 at two sites with >1000 mm yr-1 precipitation.ii) Does incorporating soil moisture information improve seasonal streamflow forecast accuracy? A modified version of the standard Natural Resources Conservation Service (NRCS) principal component analysis and regression (PCR) model was developed to forecast streamflow in four rainfall-dominated watersheds. This model incorporated antecedent precipitation and soil moisture data from long-term monitoring networks into PCR analysis to predict seasonal streamflow volumes at 0-, 1-, 2-, and 3-month lead times. Including soil moisture data improved forecast accuracy by more than 50% over precipitation-based forecasts.iii) Can root zone soil moisture under diverse land cover types be effectively estimated by integrating ground-based meteorological data and remotely-sensed vegetation index data? Estimates of root zone soil moisture were made for four focus locations - a mixed hardwood forest, a loblolly pine plantation, cropland, and tallgrass prairie - by integrating ground-based meteorological data and basal crop coefficient curves derived from remotely-sensed vegetation index data within a soil water balance model. Results show that the model is able to estimate plant available water dynamics moderately well at the four focus locations, but needs further improvements before it can be used operationally

Modelling in ungauged catchments using PyTOPKAPI : a case study of Mhlanga catchment.

Masters Degree. University of KwaZulu-Natal, Durban.Hydrological modeling of rainfall-runoff processes is a powerful tool used in various water resources applications, including the simulation of water yield from ungauged catchments. Many rivers in developing countries are poorly gauged or fully ungauged. This gives rise to a challenge in the calibration and validation of hydrological models. This study investigated the applicability of PyTOPKAPI, a physically based distributed hydrological model, in simulating runoff in ungauged catchments, using the Mhlanga River as a case study. This study is the first application of the PyTOPKAPI model to simulate daily runoff on an ungauged catchment in South Africa. The PyTOPKAPI model was parameterised using globally available digital elevation data (DEM), satellite-derived land cover, soil type data and processed hydro-meteorological data collected from various sources. Historical 30-year (1980-2009) quaternary monthly streamflow (from a well-tested and calibrated model) and daily meteorological variables (rainfall, temperature, humidity and so on) were obtained. The rainfall data were subjected to double mass curve test to check for consistency. The monthly streamflow was transposed to the catchment and disaggregated to daily streamflow time step. The PyTOPKAPI model was calibrated using an average runoff ratio as an alternative to matching streamflow data that is usually used for model calibrations. The simulated results were thereafter compared with the disaggregated monthly quaternary data. The model results show good overall performance when compared with the average runoff ratio, monthly disaggregated streamflow and the expected mean annual runoff in the catchment. In general, PyTOPKAPI can be used to predict runoff response in ungauged catchments, and thus may be adopted for water resources management applications

Integration of Remote Sensing and Proximal Sensing for Improvement of Field Scale Water Management

Water is one of the most precious natural resources, and sustainable water resources development ‎‎is a significant challenge facing water managers over the coming decades. Accurate estimation of ‎‎the different components of the hydrologic cycle is key for water managers and planners in order ‎‎to achieve sustainable water resources development. The primary goal of this dissertation was to ‎investigate techniques to combine datasets acquired by remote and proximal sensing and in-situ ‎sensors for the improvement of monitoring near surface water fluxes. This dissertation is ‎separated into three site-specific case studies. First study, investigated the feasibility of using ‎inverse vadose zone modeling for field actual evapotranspiration (ETa) estimation. Results show ‎reasonable estimates of ETa, both daily and annually, from soil water content (SWC) sensors and ‎Cosmic-Ray Neutron Probes (CRNPs). Second study, combined remote and proximal sensing ‎methods to explore the spatial correlation between hydrological state variables and ET flux. ‎Comparison of the datasets reveal that SWC and ETa were linearly correlated but the correlation ‎between depth to the water table and ETa was weak. A simple multivariate linear regression ‎model was used to estimate ETa. The estimated ETa values were then compared to the time ETa ‎integration spline method. The comparison indicates similar seasonal ETa between the two ‎methods in 2015 ‎‎(wet) but a 20% reduction in 2016 (dry). The study highlights the challenge of ‎connecting hydrologic state variables with hydrologic flux estimates. Third study, evaluated the ‎functionality of automatically calibrated Earth Engine Evapotranspiration Flux (EEFlux) to the ‎existing mapping evapotranspiration at high resolution with internalized calibration (METRIC) ‎images in different locations. The comparison results showed that EEFlux is able to calculate ‎Reference evapotranspiration Fraction (ETrF) and ETa in agricultural areas comparable ‎‎(RMSE=0.13) to the ones from trained expert METRIC users. However, the EEFlux algorithm ‎needs to be improved to calculate ETrF and ETa in non-agricultural areas (RMSE=0.21). Given ‎the paucity of in-situ data across much of the globe the field of remote sensing offers an ‎alternative but requires users to be cautious and realistic about associated errors and uncertainty ‎on using such information to help construct a hydrologic budget.‎ Advisor: Trenton E. Franz and Ayse Kili

A climatically-derived global soil moisture data set for use in the GLAS atmospheric circulation model seasonal cycle experiment

Algorithms for point interpolation and contouring on the surface of the sphere and in Cartesian two-space are developed from Shepard's (1968) well-known, local search method. These mapping procedures then are used to investigate the errors which appear on small-scale climate maps as a result of the all-too-common practice of of interpolating, from irregularly spaced data points to the nodes of a regular lattice, and contouring Cartesian two-space. Using mean annual air temperatures field over the western half of the northern hemisphere is estimated both on the sphere, assumed to be correct, and in Cartesian two-space. When the spherically- and Cartesian-approximted air temperature fields are mapped and compared, the magnitudes (as large as 5 C to 10 C) and distribution of the errors associated with the latter approach become apparent

Vulnerability of Ukrainian forests to climate change

Ukraine is a country of the Mid-Latitude ecotone – a transition zone between forest zone and forestless dry lands. Availability of water defines distribution of the country’s forests and decreasing their productivity towards the south. Climate change generates a particular threat for Ukrainian forests and stability of agroforestry landscapes. The paper considers the impacts of expected climate change on vulnerability of Ukrainian forests using ensembles of global and regional climatic models (RCM) based on IPCC Scenarios B1, A2, A1B, and a “dry and warm” scenario A1B+T-P (increasing temperature and decreasing precipitation). The spatially explicit assessment was provided by RCM for the WMO standard period (1961-1990), “recent” (1991-2010) and three future periods – 2011-2030, 2031-2050 and 2081-2100. Forest-climate model by Vorobjov and model of amplitude of flora’s tolerance to climate change by Didukh, as well as a number of specialized climatic indicators, were used in the assessment. Different approaches leads to rather consistent conclusions. Water stress is the major limitation factor of distribution and resilience of flatland Ukrainian forests. Within Scenario A1B, the area with unsuitable growth conditions for major forest forming species will substantially increase by end of the century occupying major part of Ukraine. Scenario A1B+T-P projects even a more dramatic decline of the country’s forests. It is expected that the boundary of conditions that are favorable for forests will shift to north and north-west, and forests of the xeric belt will be the most vulnerable. Consistent policies of adaptation and mitigation might reduce climate-induced risks for Ukrainian forests