Cover crop performance under a changing climate in continuous corn system over Nebraska.

Fall-planted cover crop (CC) within a continuous corn (Zea mays L.) system offers potential agroecosystem benefits, including mitigating the impacts of increased temperature and variability in precipitation patterns. A long-term simulation using the Decision Support System for Agrotechnology Transfer model was made to assess the effects of cereal rye (Secale cereale L.) on no-till continuous corn yield and soil properties under historical (1991-2020) and projected climate (2041-2070) in eastern Nebraska. Local weather data during the historical period were used, while climate change projections were based on the Canadian Earth System Model 2 dynamically downscaled using the Canadian Centre for Climate Modelling and Analysis Regional Climate Model 4 under two representative concentration pathways (RCP), namely, RCP4.5 and RCP8.5. Simulations results indicated that CC impacts on corn yield were nonsignificant under historical and climate change conditions. Climate change created favorable conditions for CC growth, resulting in an increase in biomass. CC reduced N leaching under climate change scenarios compared to an average reduction of 60% (7 kg ha- 1 ) during the historical period. CC resulted in a 6% (27 mm) reduction in total water in soil profile (140 cm) and 22% (27 mm) reduction in plant available water compared to no cover crop during historical period. CC reduced cumulative seasonal surface runoff/soil evaporation and increased the rate of soil organic carbon buildup. This research provides valuable information on how changes in climate can impact the performance of cereal rye CC in continuous corn production and should be scaled to wider locations and CC species.

Explainable machine learning for the prediction and assessment of complex drought impacts.

Drought is a common and costly natural disaster with broad social, economic, and environmental impacts. Machine learning (ML) has been widely applied in scientific research because of its outstanding performance on predictive tasks. However, for practical applications like disaster monitoring and assessment, the cost of the models failure, especially false negative predictions, might significantly affect society. Stakeholders are not satisfied with or do not "trust" the predictions from a so-called black box. The explainability of ML models becomes progressively crucial in studying drought and its impacts. In this work, we propose an explainable ML pipeline using the XGBoost model and SHAP model based on a comprehensive database of drought impacts in the U.S. The XGBoost models significantly outperformed the baseline models in predicting the occurrence of multi-dimensional drought impacts derived from the text-based Drought Impact Reporter, attaining an average F2 score of 0.883 at the national level and 0.942 at the state level. The interpretation of the models at the state scale indicates that the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) contribute significantly to predicting multi-dimensional drought impacts. The time scalar, importance, and relationships of the SPI and STI vary depending on the types of drought impacts and locations. The patterns between the SPI variables and drought impacts indicated by the SHAP values reveal an expected relationship in which negative SPI values positively contribute to complex drought impacts. The explainability based on the SPI variables improves the trustworthiness of the XGBoost models. Overall, this study reveals promising results in accurately predicting complex drought impacts and rendering the relationships between the impacts and indicators more interpretable. This study also reveals the potential of utilizing explainable ML for the general social good to help stakeholders better understand the multi-dimensional drought impacts at the regional level and motivate appropriate responses.

Quantifying irrigation cooling benefits to maize yield in the US Midwest.

Irrigation is an important adaptation strategy to improve crop resilience to global climate change. Irrigation plays an essential role in sustaining crop production in water-limited regions, as irrigation water not only benefits crops through fulfilling crops' water demand but also creates an evaporative cooling that mitigates crop heat stress. Here we use satellite remote sensing and maize yield data in the state of Nebraska, USA, combined with statistical models, to quantify the contribution of cooling and water supply to the yield benefits due to irrigation. Results show that irrigation leads to a considerable cooling on daytime land surface temperature (-1.63°C in July), an increase in enhanced vegetation index (+0.10 in July), and 81% higher maize yields compared to rainfed maize. These irrigation effects vary along the spatial and temporal gradients of precipitation and temperature, with a greater effect in dry and hot conditions, and decline toward wet and cool conditions. We find that 16% of irrigation yield increase is due to irrigation cooling, while the rest (84%) is due to water supply and other factors. The irrigation cooling effect is also observed on air temperature (-0.38 to -0.53°C) from paired flux sites in Nebraska. This study highlights the non-negligible contribution of irrigation cooling to the yield benefits of irrigation, and such an effect may become more important in the future with continued warming and more frequent droughts.

Urban drought challenge to 2030 sustainable development goals.

In the first two decades of the 21st century, 79 global big cities have suffered extensively from drought disaster. Meanwhile, climate change has magnified urban drought in both frequency and severity, putting tremendous pressure on a city's water supply. Therefore, tackling the challenges of urban drought is an integral part of achieving the targets set in at least 5 different Sustainable Development Goals (SDGs). Yet, the current literatures on drought have not placed sufficient emphasis on urban drought challenge in achieving the United Nations' 2030 Agenda for Sustainable Development. This review is intended to fill this knowledge gap by identifying the key concepts behind urban drought, including the definition, occurrence, characteristics, formation, and impacts. Then, four sub-categories of urban drought are proposed, including precipitation-induced, runoff-induced, pollution-induced, and demand-induced urban droughts. These sub-categories can support city stakeholders in taking drought mitigation actions and advancing the following SDGs: SDG 6 "Clean water and sanitation", SDG 11 "Sustainable cities and communities", SDG 12 "Responsible production and consumption", SDG 13 "Climate actions", and SDG 15 "Life on land". To further support cities in taking concrete actions in reaching the listed SDGs, this perspective proposes five actions that city stakeholders can undertake in enhancing drought resilience and preparedness:1) Raising public awareness on water right and water saving; 2) Fostering flexible reliable, and integrated urban water supply; 3) Improving efficiency of urban water management; 4) Investing in sustainability science research for urban drought; and 5) Strengthening resilience efforts via international cooperation. In short, this review contains a wealth of insights on urban drought and highlights the intrinsic connections between drought resilience and the 2030 SDGs. It also proposes five action steps for policymakers and city stakeholders that would support them in taking the first step to combat and mitigate the impacts of urban droughts.

Developing a remotely sensed drought monitoring indicator for Morocco.

Drought is one of the most serious climatic and natural disasters inflicting serious impacts on the socio-economy of Morocco, which is characterized both by low-average annual rainfall and high irregularity in the spatial distribution and timing of precipitation across the country. This work aims to develop a comprehensive and integrated method for drought monitoring based on remote sensing techniques. The main input parameters are derived monthly from satellite data at the national scale and are then combined to generate a composite drought index presenting different severity classes of drought. The input parameters are: Standardized Precipitation Index calculated from satellite based precipitation data since 1981 (CHIRPS), anomalies in the day-night difference of Land Surface Temperature as a proxy for soil moisture, Normalized Difference Vegetation Index anomalies from MODIS data and Evapotranspiration anomalies from surface energy balance modeling. All of these satellite-based indices are being used to monitor vegetation condition, rainfall and land surface temperature. The weighted combination of these input parameters into one composite indicator takes into account the importance of the rainfall based parameter (SPI). The composite drought index maps were generated during the growing seasons going back to 2003. These maps have been compared to both the historical, in situ precipitation data across Morocco and with the historical yield data across different provinces with information being available since 2000. The maps are disseminated monthly to several main stakeholders groups including the Ministry of Agriculture and Department of Water in Morocco.

Remotely sensed high resolution irrigated area mapping in India for 2000 to 2015.

India is among the countries that uses a significant fraction of available water for irrigation. Irrigated area in India has increased substantially after the Green revolution and both surface and groundwater have been extensively used. Under warming climate projections, irrigation frequency may increase leading to increased irrigation water demands. Water resources planning and management in agriculture need spatially-explicit irrigated area information for different crops and different crop growing seasons. However, annual, high-resolution irrigated area maps for India for an extended historical record that can be used for water resources planning and management are unavailable. Using 250 m normalized difference vegetation index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) and 56 m land use/land cover data, high-resolution irrigated area maps are developed for all the agroecological zones in India for the period of 2000-2015. The irrigated area maps were evaluated using the agricultural statistics data from ground surveys and were compared with the previously developed irrigation maps. High resolution (250 m) irrigated area maps showed satisfactory accuracy (R2=0.95) and can be used to understand interannual variability in irrigated area at various spatial scales.