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The present study describes the experience of Indonesian teachers coping with Emergency Remote Teaching (ERT) during the COVID-19 pandemic. An understanding of their experiences can help teachers better prepare for ERT in the future. Unlike ERT practices in other countries during the pandemic, there is still a lack of evidence of ERT practices in Indonesia. This descriptive study aims to interpret teachers' experience in managing their ERT. Online questionnaires were distributed to 36 Indonesian university lecturers and 40 Indonesian high school teachers. The instructors shared their experience of ERT practices during the pandemic. This study aims to examine their readiness to cope with ERT. The data show that teachers demonstrated their readiness to host ERT in the way they perceived learning management systems (LMS), developed modules, deployed feedback and comments, and scheduled screen time. Teachers' ERT strategies did not hinder students' successful learning as teachers maintained students' engagement in online learning through their knowledge of basic technology, such as choosing a LMS, providing encouragement through feedback, and producing media such as video lessons. Moreover, teachers could manage their screen time without negatively affecting their own mental and physical health. Finally, further research could seek data from a wider range of participants to obtain a wider picture of teachers' experience in Indonesian ERT.

An autonomous learning attitude is crucial in determining the successful completion of an online program. Such an attitude is not always easy. Students in online programs need to strike a balance between online studies tasks and their other work, maintain motivation, and consistently follow all the stages of the program. It remains to be seen whether these attitudes prevail in some Indonesian MOOC (Massive Open Online Course) students. This paper was a descriptive sketch of learning autonomy among thirty-seven students of an Indonesian MOOC. Only a small percentage of students was genuinely autonomous, while most were not, and experience difficulty in completing the MOOC program. The study found a correlation between autonomy and academic achievement, but did not demonstrate a cause-effect relationship.

An Indonesian MOOC (Massive Online Open Course) aimed to give pre- and in-service English teachers a new online learning experience and equip them to master technological applications for teaching English in class. It has not always been easy for facilitators to assist students to complete MOOC programs. This paper sketches how Indonesian facilitators faced challenges in motivating MOOC students. The experience of facilitators with low and high dropout rates is shown to sharpen and deepen the differences in managing the MOOC program. This study found a relationship between facilitator performance and student retention rates.

The diversity in hydrologic models has historically led to great controversy on the "correct" approach to process-based hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. In this paper, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We illustrate how modeling advances have been made by groups using models of different type and complexity, and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.

Adaptive instruction is a promising solution to the limitations of traditional classroom instruction, which assumes that all students learn in the same way and at the same pace. Adaptive instruction tailors the learning experience to each student’s needs and abilities. Several adaptive instruction tools and platforms exist, including intelligent tutoring systems, learning management systems, mobile apps, AI chatbots, and adaptive machine-learning programs. The Adaptive Instruction of Student Control Theoretical Framework suggests that allowing students to control their use of learning resources leads to better learning outcomes. Implementing adaptive instruction in higher education can be difficult due to faculty buy-in, technical infrastructure, and student motivation. Effective instructional design is crucial for adaptive instruction to support student control and maximize benefits. Overall, instructors must pay attention to student motivation and work to create learning environments that foster motivation, autonomy, and engagement to implement adaptive instruction successfully. [ABSTRACT FROM AUTHOR]

Climatic forcing and landscape properties control catchments' hydrological responses over both seasonal and mean annual timescales. Controls on annual and seasonal water balances are usually studied separately which limits and fragments understanding of catchment behaviour. Establishing process controls on hydrological responses that act across multiple time scales could better unify hydrological theory. Here, we use streamflow and climate data from 56 catchments in the Ozarks and Appalachian regions (US) to test how climate (aridity index) and soil drainage together shape seasonal and mean annual water balances for these humid, snow‐free catchments with little precipitation seasonality. We calibrate a simple conceptual model to observed seasonal streamflow and obtain an effective parameter that summarizes the nonlinearity of drainage of soil moisture to groundwater. Our comparative analysis of catchments in the Ozarks and the Appalachian regions indicates that catchments in the more humid climates have lower streamflow seasonality and higher mean annual flow, irrespective of the nonlinearity of soil drainage. In contrast, in relatively drier climates, more nonlinear soil drainage increases the seasonality of streamflow and reduces mean annual flow. Additional testing across 204 humid catchments with little precipitation seasonality and snowfall in the Southeastern United States further supports our hypothesis that soil drainage nonlinearity significantly modulates seasonal and mean annual water balances. These results reveal how soil drainage nonlinearity provides a process interpretation that connects seasonal and annual water balances and highlights the importance of nonlinearity of soil drainage in hydrological modelling. [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union-EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals.

Accurate soil moisture information is vital for flood and drought predictions, crop growth and agricultural water management. Reanalysis soil moisture products with multi-decadal temporal coverage are gradually becoming a good alternative for providing global soil moisture data in various applications compared to in-situ measurements and satellite products. Much effort has been devoted to evaluating the performance of soil moisture products, yet the scale discrepancy between point measurements and grid cell soil moisture products limits the assessment quality. As the land surface and hydrological modelling community evolve towards the next generation of (sub)kilometer resolution models, Cosmic Ray Neutron Sensors (CRNS) that provide estimates of root-zone soil moisture at the field scale (~250 m radius from the sensor and up to 0.7 m deep), may consequently be more suitable for soil moisture product evaluation as they cover a relatively larger footprint, when compared to traditional methods. In this study, we perform a comprehensive evaluation of seven widely-used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40 and GLEAM datasets) against 135 CRNS sites from the UK, Europe, USA and Australia. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products exhibit good temporal correlation with the measurements, with the median of temporal correlation coefficient (R) values spanning from 0.69 to 0.79, though large deviations are found at sites with seasonally varying vegetation cover. Poor performance is observed across products for soil moisture anomalies timeseries, with R values varying from 0.49 to 0.70. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate in arid climates and underestimate in humid regions as well as grassland. Most reanalysis products perform poorly in steep terrain. Relatively low temporal correlation and high Bias are detected in some sites from west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CFSv2, CRA40, GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend ERA5-Land and CFSv2 should be used in humid climates, whereas CRA40 and GLEAM perform better in arid regions. GLEAM is more effective in shrubland regions. Our findings also provide insights on directions for improvement of soil moisture products for product developers. [ABSTRACT FROM AUTHOR]

Ground-based measurement of the spatial distribution of soil moisture can be difficult because sampling is essentially made at a point and the choice of both sample depth and sample spacing affects the interpretation of the measurements. Hydrological interest has generally been in soil moisture of the root zone. Microwave Remote Sensing methods are now available that allow the interpretation of spatial distributions of soil moisture, however, their signals respond to moisture in the upper few centimetres of soil. These instruments are still being developed, but one of the questions surrounding their application is how to interpret the surface moisture in a hydrological context. In this study we compare measurements of soil moisture in 0-30 cm of soil with those in 0-6 cm to examine how representative this surface measure is with regard to the root zone. Detailed spatial measurements of soil moisture were conducted at three pasture sites in the 50 km2 Mahurangi River catchment of northern New Zealand as part of a comprehensive hydrology project; MARVEX (MAhurangi River Variability EXperiment). In three field sites, on each of three occasions, field measurements were made using both 30 and 6 cm dielectric-based instruments. Spatial grids of several hundred moisture measurements were collected over 0-30 cm and compared with those collected simultaneously over 0-6 cm. Results indicate that temporal and spatial issues interfere with correlation of the two sets of series. Rapid wetting of 0-6 cm compared with 0-30 cm is seen following storm activity. Some evidence of the decoupling of moisture content response is also evident when sites are measured on days following a storm. Rapid, but not unrealistic, response to intense rainfall was also observed. Implications are that detailed and accurate knowledge of local soil conditions and a sound model of soil water redistribution are required before surface soil moisture measurements can be used to infer root zone behaviour. Such knowledge was not available in this study, from either published data or field observation. In this study, without suitable a priori knowledge, soil property information was found via calibration. © 2003 Elsevier Science B.V. All rights reserved.

Quantitative determination of the counterions associated with pharmaceutical salts is a mandatory requirement for quality control. Although ion chromatography (1C) is the standard technique in most laboratories, capable of delivering [...]

The derived distribution method is a promising approach for flood estimation in ungauged catchments. In this approach, event runoff coefficient (ERC) is often adopted to behave as the runoff generation component since the behavior of ERC is closely related to flood generation mechanisms and its distribution reflects catchment climatological and landscape characteristics. However, there is a lack of understanding of how to transfer the information on ERC characteristics from gauged to ungauged catchments at catchment scale. Hence, here, we propose a generalized framework to estimate the probability distribution of ERC for ungauged catchments based on knowledge of the spatial and temporal controls on ERC from gauged catchments. Key components of the framework include cluster analysis based on ERC characteristics, linking clusters with catchment attributes, and constructing typical probability distribution of ERC conditioned on its temporal indicators. A total of 290,743 rainfall-runoff events observed in 431 GB catchments during the period 1990–2014 have been employed to verify the framework. Good estimations are obtained with the median value of coefficient of determination (R ²) reaching 0.85 across all test catchments. The results indicate that similar pre-event catchment conditions may cause distinct runoff response in different catchments, thus predicting the correct spatial cluster is crucial to the estimation accuracy. This work sheds light on constructing a stochastic generator model of ERC according to its spatial pattern and temporal dynamic, facilitating a new alternative for flood estimation method in ungauged catchments. [ABSTRACT FROM AUTHOR]