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Water scarcity is a major challenge in the Sahel region of West Africa. Water scarcity in combination with prevalent soil degradation has severely reduced the land productivity in the region. The decrease in resiliency of food security systems of marginalized population has huge societal implications which often leads to mass migrations and conflicts. The U.S. Agency for International Development (USAID) and development organizations have made major investments in the Sahel to improve resilience through land rehabilitation activities in recent years. To help restore degraded lands at the farm level, the World Food Programme (WFP) with assistance from USAID's Bureau for Humanitarian Assistance supported the construction of water and soil retention structures called half-moons. The vegetation growing in the half-moons is vitally important to increase agricultural productivity and feed animals, a critical element of sustainable food security in the region. This paper investigates the effectiveness of interventions at 18 WFP sites in southern Niger using vegetative greenness observations from the Landsat 7 satellite. The pre - and post-intervention analysis shows that vegetation greenness after the half-moon intervention was nearly 50% higher than in the pre-intervention years. The vegetation in the intervened area was more than 25% greener than the nearby control area. Together, the results indicate that the half-moons are effective adaptations to the traditional land management systems to increase agricultural production in arid ecosystems, which is evident through improved vegetation conditions in southern Niger. The analysis shows that the improvement brought by the interventions continue to provide the benefits. Continued application of these adaptation techniques on a larger scale will increase agricultural production and build resilience to drought for subsistence farmers in West Africa. Quantifiable increase in efficacy of local-scale land and water management techniques, and the resulting jump in large-scale investments to scale similar efforts will help farmers enhance their resiliency in a sustainable manner will lead to a reduction in food security shortages. [ABSTRACT FROM AUTHOR]

In this study we analyzed two models commonly used in remote sensing-based root-zone soil moisture (SM) estimations: one utilizing the exponential decaying function and the other derived from the principle of maximum entropy (POME). We used both models to deduce root-zone (0–100 cm) SM conditions at 11 sites located in the southeastern USA for the period 2012–2017 and evaluated the strengths and weaknesses of each approach against ground observations. The results indicate that, temporally, at shallow depths (10 cm), both models performed similarly, with correlation coefficients (r) of 0.89 (POME) and 0.88 (exponential). However, with increasing depths, the models start to deviate: at 50 cm the POME resulted in r of 0.93 while the exponential filter (EF) model had r of 0.58. Similar trends were observed for unbiased root mean square error (ubRMSE) and bias. Vertical profile analysis suggests that, overall, the POME model had nearly 30% less ubRMSE compared to the EF model, indicating that the POME model was relatively better able to distribute the moisture content through the soil column. [ABSTRACT FROM AUTHOR]

Due to large footprints of remotely sensed microwave brightness temperatures, accuracy of microwave observations in areas of large surface heterogeneity has always been a technological challenge. Microwave observations in areas dominated by waterbodies typically exhibit observed brightness temperature several tens of kelvins lower than areas having no surface water. The non-linearity between brightness temperature and other geophysical quantities such as soil moisture makes the accuracy of microwave observations a critical element for accurate estimation of these quantities. In retrieving soil moisture estimates, an error of 1 K in remotely sensed microwave brightness temperatures results in about 0.5–1% error in volumetric soil moisture. Large uncertainties in the observed brightness temperatures make such observations unusable in areas of large brightness temperature contrast. In this article, we discuss a deconvolution method to improve accuracy using the overlap in the adjacent microwave observations. We have shown that the method results in improved accuracy of 40% in brightness temperature estimation in regions of high brightness temperature contrast. [ABSTRACT FROM PUBLISHER]

Among passive microwave frequencies, sensors operating at C- and X-band frequencies have been used with some success to estimate near-surface soil moisture from aircraft and satellite platforms. The objective of this paper is to quantify the sensitivities of soil moisture retrieved via a single-channel single-polarization algorithm to the observed brightness temperature and to retrieval algorithm parameters of surface roughness, vegetation B parameter, and single-scattering albedo. Examination of the regions within the parameter space that produce accurate soil moisture retrievals reveals that reasonably accurate retrievals can be made over a range of conditions using a fixed set of input parameters. Retrievals with horizontally polarized brightness temperature observations are more consistent than with vertically polarized observations. At horizontal polarization, sensitivity to the input parameters is much greater for wet soils than for dry soils, whereas for vertical polarization the moisture dependence is much weaker. At vertical polarization, sensitivities to variations in all parameters are much lower. To ensure that retrieval accuracy specifications are consistently met, high soil moisture conditions should be used in defining parameter accuracy requirements. Given the spatial and temporal variability of vegetation and soil conditions, it seems unlikely that, for regions with substantial rapidly growing vegetation, the accuracy requirements for model parameters in a single-frequency, single-polarization retrieval algorithm can be met with current satellite products. For such conditions, any soil moisture retrieval algorithm using parameterizations similar to those of this study may require multiple frequencies, polarizations, or look angles to produce stable, reliable soil moisture estimates. [ABSTRACT FROM AUTHOR]

We developed a dynamic economic model to optimize irrigation water allocations during water deficit periods for three major crops grown in the humid southeastern United States. Analysis involved the use of crop simulation models to capture (a) the yield water relationship and (b) soil moisture dynamics from one week to another week. A hydrological model was used to find the water supply; combinations of hydrological and simulation models were used to find the optimal water allocation during each week in corn, cotton and peanuts. Results indicated that farmers should irrigate the most valuable crop first (peanuts) before applying water to other crops (corn and cotton). Results also showed that, because of restriction on total water supply, an increase in crop acreage did not increase the net revenue of the farm in a proportionate amount. Results should provide guidelines to water managers, engineers, policy makers, and farmers regarding an optimal amount of water allocation that will maximize net returns when water shortage is a serious concern. [ABSTRACT FROM AUTHOR]

Over the past two decades, successful estimation of soil moisture has been accomplished using L-band microwave radiometer data. However, remaining uncertainties related to surface roughness and the absorption, scattering, and emission by vegetation must be resolved before soil moisture retrieval algorithms can be applied with known and acceptable accuracy using satellite observations. Surface characteristics are highly variable in space and time, and there has been little effort made to determine the parameter estimation accuracies required to meet a given soil moisture retrieval accuracy specification. This study quantifies the sensitivities of soil moisture retrieved using an L-band single-polarization algorithm to three land surface parameters for corn and soybean sites in Iowa, United States. Model sensitivity to the input parameters was found to be much greater when soil moisture is high. For even moderately wet soils, extremely high sensitivity of retrieved soil moisture to some model parameters for corn and soybeans caused the retrievals to be unstable. Parameter accuracies required for consistent estimation of soil moisture in mixed agricultural areas within retrieval algorithm specifications are estimated. Given the spatial and temporal variability of vegetation and soil conditions for agricultural regions It seems unlikely that, for the single-frequency, single-polarization retrieval algorithm used in this analysis, the parameter accuracy requirements can be met with current satellite-based land surface products. We conclude that for regions with substantial vegetation, particularly where the vegetation is changing rapidly, any soil moisture retrieval algorithm that is based on the physics and parameterizations used in this study will require multiple frequencies, polarizations, or look angles to produce stable, reliable soil moisture estimates. [ABSTRACT FROM AUTHOR]

BSTRACT: A macroscale hydrologic model is developed for regional climate assessment studies under way in the southeastern United States. The hydrologic modeling strategy is developed to optimize spatial representation of basin characteristics while maximizing computational efficiency. The model employs the 'grouped response unit' methodology, which follows the natural drainage pattern of the area. First order streams are delineated and their surface characteristics are tested so that areas with statistically similar characteristics can be combined into larger computational zones for modeling purposes. Hydrologic response units (HRU) are identified within the modeling units and a simple three-layer water balance model, Soil and Water Assessment Tool (SWAT), is executed for each HRU. The runoff values are then convoluted using a triangular unit hydrograph and routed by Muskingum-Cunge method. The methodology is shown to produce accurate results relative to other studies, when compared to observations. The model is used to evaluate the potential error in hydrologic assessments when using GCM predictions as climatic input in a rainfall-runoff dominated environment. In such areas, the results from this study, although limited in temporal and spatial scope, appear to imply that use of GCM climate predictions in short term quantitative analyses studies in rainfall-runoff dominated environments should proceed with caution. [ABSTRACT FROM AUTHOR]

BSTRACT: An assessment of current and future water quality conditions in the southeastern United States has been conducted using the EPA BASINS GIS/database system. The analysis has been conducted for dissolved oxygen, total nitrate nitrogen and pH. Future streamflow conditions have been predicted for the region based on the United Kingdom Hadley Center climate model. Thus far, the analyses have been conducted at a fairly coarse spatial scale due to time and resource limitations. Two hydrologic modeling techniques have been employed in future streamflow prediction: a regional stochastic approach and the application of a physically based soil moisture model. The regional model has been applied to the entire area while the physically based model is being used at selected locations to enhance and support the stochastic model. The results of the study reveal that few basins in the southeast exhibit dissolved oxygen problems, but that several watersheds exhibit high nitrogen levels. These basins are located in regions of intense agricultural activity or in proximity to the gulf coast. In many of these areas, streamflow is projected to decline over the next 30-50 years, thus exacerbating these water quality problems. [ABSTRACT FROM AUTHOR]

The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities. We focus on the recent DL upsurge in East Africa from October 2019 though June 2020, utilizing known locust observations from the United Nations Food and Agriculture Organization (FAO). We compare this information to results from the current literature and combine the two datasets to create "optimal thresholds" of breeding conditions. When considering the most optimal conditions (all thresholds met), the soil texture combined with modeled soil moisture content predicted the estimated DL egg-laying period 62.5% of the time. Accounting for the data errors and uncertainties, a 3 × 3 pixel buffer increased this to 85.2%. By including soil moisture, the areas of optimal egg laying conditions decreased from 33% to less than 20% on average. [ABSTRACT FROM AUTHOR]