Predicting soil moisture conditions for arable free draining soils in Ireland under spring cereal crop production

peer-reviewedTemporal prediction of soil moisture and evapotranspiration has a crucial role in agricultural and environmental management. A lack of Irish models for predicting evapotranspiration and soil moisture conditions for arable soils still represents a knowledge gap in this particular area of Irish agro-climatic modelling. The soil moisture deficit (SMD) crop model presented in this paper is based on the SMD hybrid model for Irish grassland (Schulte et al., 2005). Crop and site specific components (free-draining soil) have been integrated in the new model, which was calibrated and tested using soil tension measurements from two experimental sites located on a well-drained soil under spring barley cultivation in south-eastern Ireland. Calibration of the model gave an R2 of 0.71 for the relationship between predicted SMD and measured soil tension, while model testing yielded R2 values of 0.67 and 0.65 (two sites). The crop model presented here is designed to predict soil moisture conditions and effective drainage (i.e., leaching events). The model provided reasonable predictions of soil moisture conditions and effective drainage within its boundaries, i.e., free-draining land used for spring cereal production under Irish conditions. In general, the model is simple and practical due to the small number of required input parameters, and due to model outputs that have good practical applicability, such as for computing the cumulative amount of watersoluble nutrients leached from arable land under spring cereals in free-draining soils

Agricultural Drought Monitoring in Kenya Using Evapotranspiration Derived from Remote Sensing and Reanalysis Data

More than half of the people in sub-Saharan Africa live on less than US$ 1.25 per day, and nearly 30% do not receive sufficient nourishment to maintain daily health (UN, 2009a). These figures are expected to rise as a result of the recent global financial crisis that has led to an increase in food prices. Food for Peace (FFP), the program that administers more than 85% of U.S. international food aid, recently reported that the seven largest recipient countries of food aid worldwide are in sub-Saharan Africa (FFP, 2010). In Kenya, the fifth largest recipient of food aid from FFP and a country highly dependent on rainfed agriculture, below-average precipitation in 2009 led to a 20% reduction in maize production and a 100% increase in domestic maize prices (FEWS NET, 2009). Given these sorts of climatic shocks, it is imperative that mitigation strategies be developed for sub-Saharan Africa and other regions of the developing world to improve the international and national response to impending food crises. Crop monitoring is an important tool used by national agricultural offices and other stakeholders to inform food security analyses and agricultural drought mitigation. Remote sensing and surface reanalysis data facilitate efficient and cost-effective approaches to measuring determinants of agricultural drought. In this chapter, we explore how remotely sensed estimates of actual evapotranspiration (ETa) can be integrated with surface reanalysis data to augment agricultural drought monitoring systems. Although water availability is important throughout every stage of crop development, from germination to harvest, crops are most sensitive to moisture deficits during the reproductive stages (Shanahan and Nielsen, 1987). A study that analyzed maize, for example, showed that a 1% decline in seasonal ETa led to an average loss of 1.5% in crop yield, whereas water stress in the same proportion concentrated during the reproductive phases led to a 2.6% decline in crop yield (Stegman, 1982). Agricultural drought can therefore be defined as inadequate soil water availability, particularly during the reproductive phase, caused by low precipitation, insufficient water-holding capacity in the root zone of the soil, and/or high atmospheric water demand (potential evapotranspiration, ETp), which results in a reduction in crop yield. Agricultural droughts differ in timescale and impact from shorter-term meteorological droughts, which are characterized by negative precipitation anomalies on the order of days to weeks, and the longer-term negative runoff and water storage anomalies that characterize hydrological drought (Dracup et al., 1980)

Agricultural Drought Monitoring in Kenya Using Evapotranspiration Derived from Remote Sensing and Reanalysis Data

More than half of the people in sub-Saharan Africa live on less than US$ 1.25 per day, and nearly 30% do not receive sufficient nourishment to maintain daily health (UN, 2009a). These figures are expected to rise as a result of the recent global financial crisis that has led to an increase in food prices. Food for Peace (FFP), the program that administers more than 85% of U.S. international food aid, recently reported that the seven largest recipient countries of food aid worldwide are in sub-Saharan Africa (FFP, 2010). In Kenya, the fifth largest recipient of food aid from FFP and a country highly dependent on rainfed agriculture, below-average precipitation in 2009 led to a 20% reduction in maize production and a 100% increase in domestic maize prices (FEWS NET, 2009). Given these sorts of climatic shocks, it is imperative that mitigation strategies be developed for sub-Saharan Africa and other regions of the developing world to improve the international and national response to impending food crises. Crop monitoring is an important tool used by national agricultural offices and other stakeholders to inform food security analyses and agricultural drought mitigation. Remote sensing and surface reanalysis data facilitate efficient and cost-effective approaches to measuring determinants of agricultural drought. In this chapter, we explore how remotely sensed estimates of actual evapotranspiration (ETa) can be integrated with surface reanalysis data to augment agricultural drought monitoring systems. Although water availability is important throughout every stage of crop development, from germination to harvest, crops are most sensitive to moisture deficits during the reproductive stages (Shanahan and Nielsen, 1987). A study that analyzed maize, for example, showed that a 1% decline in seasonal ETa led to an average loss of 1.5% in crop yield, whereas water stress in the same proportion concentrated during the reproductive phases led to a 2.6% decline in crop yield (Stegman, 1982). Agricultural drought can therefore be defined as inadequate soil water availability, particularly during the reproductive phase, caused by low precipitation, insufficient water-holding capacity in the root zone of the soil, and/or high atmospheric water demand (potential evapotranspiration, ETp), which results in a reduction in crop yield. Agricultural droughts differ in timescale and impact from shorter-term meteorological droughts, which are characterized by negative precipitation anomalies on the order of days to weeks, and the longer-term negative runoff and water storage anomalies that characterize hydrological drought (Dracup et al., 1980)

Terrestrial Hydrological Data from NASA's Hydrology Data and Information Services Center (HDISC): Products, Services, and Applications

Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. The North American and Global Land Data Assimilation Systems (NLDAS and GLDAS, respectively) have been generating a series of land surface states (soil moisture, snow, and temperature) and fluxes (evapotranspiration, radiation, and heat flux) variables. These data, hosted at and available from NASA s Hydrology Data and Information Services Center (HDISC), include the NLDAS hourly 1/8 degree products and the GLDAS 3-hourly 0.25 and 1.0 degree products. HDISC provides easy access and visualization and analysis capabilities for these products, thus reducing the time and resources spent by scientists on data management and facilitating hydrological research. Users can perform spatial and parameter subsetting, data format transformation, and data analysis operations without needing to first download the data. HDISC is continually being developed as a data and services portal that supports weather and climate forecasts, and water and energy cycle research

Agriculture, meteorology and water quality in Ireland: a regional evaluation of pressures and pathways of nutrient loss to water

peer-reviewedThe main environmental impact of Irish agriculture on surface and ground water quality is the potential transfer of nutrients to water. Soil water dynamics mediate the transport of nutrients to water, and these dynamics in turn depend on agro-meteorological conditions, which show large variations between regions, seasons and years. In this paper we quantify and map the spatio-temporal variability of agro-meteorological factors that control nutrient pressures and pathways of nutrient loss. Subsequently, we evaluate their impact on the water quality of Irish rivers. For nitrogen, pressure and pathways factors coincide in eastern and southern areas, which is reflected in higher nitrate levels of the rivers in these regions. For phosphorus, pathway factors are most pronounced in north-western parts of the country. In south-eastern parts, high pressure factors result in reduced biological water quality. These regional differences require that farm practices be customised to reflect the local risk of nutrient loss to water. Where pathways for phosphorus loss are present almost year-round—as is the case in most of the north-western part of the country—build-up of pressures should be prevented, or ameliorated where already high. In south-eastern areas, spatio-temporal coincidence of nutrient pressures and pathways should be prevented, which poses challenges to grassland management

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions

A Review of Some Indices Used for Drought Monitoring

Drought is a natural hazard that results from a deficiency of precipitation and water availability from expected or normal amounts, usually extended over a season or longer period. Drought can be hydrological, meteorological, agricultural and socio-economical. It affects the ecology, biodiversity, hydrology and climate and economy and the wellbeing of the societies at local, regional and global levels. Drought causes for significant environmental and economic problems, which in turn affect the balance of food supply and demand leads to poverty. Therefore, drought monitoring and prediction and warning system is a very essential component to minimize vulnerabilities and risks. In this regard, drought indices play a great role. The objective of this review is to show different available drought indices used for monitoring drought events. For investigating drought using a single index is not providing better results, therefore, integrating different indices is recommended because the environmental variable is spatially different and the indices do not use the same model and there are gaps in the model. Thus, by integrating different indices it is possible to achieve better drought results. Keywords: Drought; drought indices; drought monitoring DOI: 10.7176/CER/13-5-01 Publication date:August 31st 2021

Toward impact-based monitoring of drought and its cascading hazards

Growth in satellite observations and modelling capabilities has transformed drought monitoring, offering near-real-time information. However, current monitoring efforts focus on hazards rather than impacts, and are further disconnected from drought-related compound or cascading hazards such as heatwaves, wildfires, floods and debris flows. In this Perspective, we advocate for impact-based drought monitoring and integration with broader drought-related hazards. Impact-based monitoring will go beyond top-down hazard information, linking drought to physical or societal impacts such as crop yield, food availability, energy generation or unemployment. This approach, specifically forecasts of drought event impacts, would accordingly benefit multiple stakeholders involved in drought planning, and risk and response management, with clear benefits for food and water security. Yet adoption and implementation is hindered by the absence of consistent drought impact data, limited information on local factors affecting water availability (including water demand, transfer and withdrawal), and impact assessment models being disconnected from drought monitoring tools. Implementation of impact-based drought monitoring thus requires the use of newly available remote sensors, the availability of large volumes of standardized data across drought-related fields, and the adoption of artificial intelligence to extract and synthesize physical and societal drought impacts.</p

Monitoring land-cover changes in Mediterranean coastal dunes, northwest Tunisia, using remote sensing data

Coastal dune landscapes are subject to morphological and ecological changes. In many parts of the world, coastal dunes are under severe pressure. The present study illustrates an integrated remote sensing and Geographical Information System (GIS) approach, i.e., geospatial techniques for assessing land-cover dynamics in Zouaraa coastal dunes, located in northwest Tunisia. As a main result, the analysis of the situation in the past six decades indicates that the dune area showed a decreasing trend with up to 31% (i.e., 6198 ha) in favour of forest area, which has increased by up to 6485 ha. The geo-spatial analysis revealed that restoration works have positively contributed to stabilize coastal dune systems with a substantial increase in vegetation cover. An increase in drought frequency and intensity was detected during the 1952-2017 period using the SPEI index, which enhanced the vegetation activity and growth in the study area. The SPEI significantly correlated with vegetation greenness on the 12- and 24-months’ time scales. The croplands, water and buildings in the study area have increased respectively by 6% (i.e., 1256 ha), 13% (i.e., 3073 ha) and 3% (i.e., 719 ha). In contrast, land cover like shrub and bare soil has decreased respectively by 13% (i.e., 3073 ha) and 2% (i.e., 1831 ha) during the same period. Furthermore, this study highlights the importance of the revegetation techniques undertaken for conserving coastal dune systems. The findings of this study allow land-use planning decision makers to manage and improve situations in similar coastal regions.This work was supported by the National Research Institute for Rural Engineering, Waters, and Forestry-INRGREF. Laboratory of Management and Valorization of Forest Resources, Tunisia. This research is part of the project: HYDROMED (PID-2019-111332RB-C21)