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In order to provide a theoretical basis for the rational application of nitrogen fertilizer for tomatoes under aerated drip irrigation, a model of the critical nitrogen dilution curve was established in this study, and the feasibility of the nitrogen nutrition index (NNI) for the real-time diagnosis and evaluation of the nitrogen nutrient status was explored. The tomato variety "FENOUYA" was used as the test crop, and aerated drip irrigation was adopted by setting three levels of aeration rates, namely, A1 (dissolved oxygen concentration of irrigation water is 5 mg L−1), A2 (dissolved oxygen concentration of irrigation water is 15 mg L−1), and A3 (dissolved oxygen concentration of irrigation water is 40 mg L−1), and three levels of nitrogen rates, namely, N1 (120 kg ha−1), N2 (180 kg ha−1) and N3 (240 kg ha−1). The model of the critical nitrogen concentration dilution of tomatoes under different aerated treatments was established. The results showed that (1) the dry matter accumulation of tomatoes increased with the increase in the nitrogen application rate in a certain range and it showed a trend of first increase and then decrease with the increase in aeration rate. (2) As the reproductive period progressed, the nitrogen concentration in tomato plants showed a decreasing trend. (3) There was a power exponential relationship between the critical nitrogen concentration of tomato plant growth and above-ground biomass under different levels of aeration and nitrogen application rate, but the power exponential curves were characterized by A1 (Nc = 15.674DM−0.658), A2 (Nc = 101.116DM−0.455), A3 (Nc = 119.527DM−0.535), N1 (Nc = 33.819DM−0.153), N2 (Nc = 127.759DM−0.555) and N3 (Nc = 209.696DM−0.683). The standardized root mean square error (n-RMSE) values were 0.08%, 3.68%, 3.79% 0.50%, 1.08%, and 0.55%, which were less than 10%, and the model has good stability. (4) The effect of an increased nitrogen application rate on the critical nitrogen concentration dilution curve was more significant than that of the increase in aeration rate. (5) A nitrogen nutrition index model was built based on the critical nitrogen concentration model to evaluate the nitrogen nutritional status of tomatoes, whereby 180 kg ha−1 was the optimal nitrogen application rate, and 15 mg L−1 dissolved oxygen of irrigation water was the optimal aeration rate for tomatoes. [ABSTRACT FROM AUTHOR]

Reliable precipitation data are highly necessary for geoscience research in the Third Pole (TP) region but still lacking, due to the complex terrain and high spatial variability of precipitation here. Accordingly, this study produces a long-term (1979–2020) high-resolution (1/30 ∘ , daily) precipitation dataset (TPHiPr) for the TP by merging the atmospheric simulation-based ERA5_CNN with gauge observations from more than 9000 rain gauges, using the climatologically aided interpolation and random forest methods. Validation shows that TPHiPr is generally unbiased and has a root mean square error of 5.0 mm d -1 , a correlation of 0.76 and a critical success index of 0.61 with respect to 197 independent rain gauges in the TP, demonstrating that this dataset is remarkably better than the widely used datasets, including the latest generation of reanalysis (ERA5-Land), the state-of-the-art satellite-based dataset (IMERG) and the multi-source merging datasets (MSWEP v2 and AERA5-Asia). Moreover, TPHiPr can better detect precipitation extremes compared with these widely used datasets. Overall, this study provides a new precipitation dataset with high accuracy for the TP, which may have broad applications in meteorological, hydrological and ecological studies. The produced dataset can be accessed via https://doi.org/10.11888/Atmos.tpdc.272763 (Yang and Jiang, 2022). [ABSTRACT FROM AUTHOR]

Lake surface water temperature (LSWT) is a key parameter in understanding the variability of lake thermal conditions and evaporation. The MODIS-derived LSWT is widely used as a reference for lake model validations and process studies in data-scarce regions. In this study, the accuracy of the MODIS LSWT was examined on the Tibetan Plateau (TP). In-situ subsurface temperatures were collected at five large lakes. Although the observation period covers from summer to winter, only the observations during the lake turnover period (from October to freeze-up), when the lakes are well mixed, can be used as ground truth. The MODIS LSWT agrees well with the selected in-situ data for the five large lakes, with root mean square error (RMSE) < 1 °C at nighttime and <2 °C in the daytime, indicating a high accuracy of the MODIS LSWT data. Before the turnover period, the water is thermally stratified and the surface water is warmer than the subsurface water, and thus the in-situ subsurface water temperature data and the MODIS LSWT have different representativeness. In this case, if the observations are used as a validation basis, the MODIS errors could be much magnified. This in turn indicates the importance of period selection for the validation. [ABSTRACT FROM AUTHOR]

The high soil organic carbon (SOC) content in alpine meadow can significantly change soil hydrothermal properties and further affect the soil temperature and moisture as well as the surface water and energy budget. Therefore, this study first introduces a parameterization scheme to describe the effect of SOC content on soil hydraulic and thermal parameters in a land surface model (LSM), and then the SOC content is estimated by minimizing the difference between observed and simulated surface-layer soil moisture. The accuracy of the estimated SOC content was evaluated using in situ observation data at a soil moisture and temperature-measuring network in Naqu, central Tibetan Plateau. Sensitivity experiments show that the optimum time window for stabilizing the estimation results cannot be shorter than three years. In the experimental area, the estimated SOC content can generally reflect the spatial distribution of the measurements, with a root mean square error of 0.099 m3 m−3, a mean bias of 0.043 m3 m−3, and a correlation coefficient of 0.695. The estimated SOC content is not sensitive to the temporal frequency of the soil moisture data input. Even if the temporal frequency is as low as that of current soil moisture products derived from passive microwave satellites, the estimation result is still stable. Therefore, by combining a high-quality satellite soil moisture product and a parameter optimization method, it is possible to obtain grid-scale effective parameter values, such as SOC content, for an LSM and improve the simulation ability of the LSM. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) mission provides high-resolution precipitation estimates globally. However, their accuracy needs to be accessed for algorithm enhancement and hydro-meteorological applications. This study applies data from 388 gauges in Nepal to evaluate the spatial-temporal patterns presented in recently-developed GPM-Era satellite-based precipitation (SBP) products, i.e., the Integrated Multi-satellite Retrievals for GPM (IMERG), satellite-only (IMERG-UC), the gauge-calibrated IMERG (IMERG-C), the Global Satellite Mapping of Precipitation (GSMaP), satellite-only (GSMaP-MVK), and the gauge-calibrated GSMaP (GSMaP-Gauge). The main results are as follows: (1) GSMaP-Gauge datasets is more reasonable to represent the observed spatial distribution of precipitation, followed by IMERG-UC, GSMaP-MVK, and IMERG-C. (2) The gauge-calibrated datasets are more consistent (in terms of relative root mean square error (RRMSE) and correlation coefficient (R)) than the satellite-only datasets in representing the seasonal dynamic range of precipitation. However, all four datasets can reproduce the seasonal cycle of precipitation, which is predominately governed by the monsoon system. (3) Although all four SBP products underestimate the monsoonal precipitation, the gauge-calibrated IMERG-C yields smaller mean bias than GSMaP-Gauge, while GSMaP-Gauge shows the smaller RRMSE and higher R-value; indicating IMERG-C is more reliable to estimate precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presents more reasonable spatial distribution than IMERG-C. Only IMERG-C moderately reproduces the evident elevation-dependent pattern of precipitation revealed by gauge observations, i.e., gradually increasing with elevation up to 2000 m and then decreasing; while GSMaP-Gauge performs much better in representing the gauge observed spatial pattern than others. (4) The GSMaP-Gauge calibrated based on the daily gauge analysis is more consistent with detecting gauge observed precipitation events among the four datasets. The high-intensity related precipitation extremes (95th percentile) are more intense in regions with an elevation below 2500 m; all four SBP datasets have low accuracy (<30%) and mostly underestimated (by >40%) the frequency of extreme events at most of the stations across the country. This work represents the quantification of the new-generation SBP products on the southern slopes of the central Himalayas in Nepal. [ABSTRACT FROM AUTHOR]

• NIRS models were established for flavonoid determination in fresh okra pods. Characteristic spectral regions of flavonoid were found and applied for calibration model. The NIRS calibrations can be effectively used in both breeding programs and food industry. Okra pods have been utilized as a functional food due to their rich active ingredient composition, especially the high content of flavonoid compounds. This study conducted near-infrared spectroscopy (NIRS) modeling optimization and external validation based on the flavonoid components of 219 pod samples. Spectral correlation analyses identified two types of spectral response patterns classified as quercetin-3-O-xylose (1–2) glucoside (QOXG) and total flavonoid content (TFC), consisting of six different spectral regions. Different modeling effects were observed for QOXG and TFC with various spectral region combination analyses, where the lower wave-number region contributed more to both flavonoids calibration models. The combination of standard normal variate / "1, 9, 3" / partial least squares was found to be the most effective for developing calibration models for both flavonoids. The resulting models had small root mean square errors of prediction for external validation and high determination coefficients, indicating their usefulness for rapid prediction of flavonoid composition in okra pods. [ABSTRACT FROM AUTHOR]

Abstract: Soil moisture plays an essential role in the terrestrial water cycle. It is very important to obtain soil moisture for many applications. Soil moisture acquired by remote sensing, land surface modeling, and data assimilation must be evaluated against in-situ measurements before being used, but a procedure should be performed to upscale the point-scale measurements to the grid-scale or footprint-scale. In this study, a new upscaling algorithm is developed by introducing MODIS-derived apparent thermal inertia (ATI). First, a functional relationship between the station-averaged soil moisture and the pixel-averaged ATI is constructed. Second, this relationship is used to calculate the representative soil moisture time series at a certain spatial scale. Last, the Bayesian linear regression is applied to obtain the upscaled area-averaged soil moisture by using in-situ measurements as independent variables. The algorithm is evaluated using a network of in-situ moisture sensors in the central Tibetan Plateau. The results indicate that it can effectively obtain the area-averaged soil moisture, reducing the root mean square error (RMSE) from 0.023m3/m3 before upscaling to 0.013m3/m3 after upscaling. Finally, the algorithm is implemented to the 100km×100km grid box where the network is installed, and the temporal pattern of the upscaled soil moisture agrees with the hydro-meteorological knowledge of this region. [Copyright &y& Elsevier]

Summary: A mesoscale Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN) has been established to study large-scale soil–vegetation–atmosphere interactions and to validate satellite soil moisture products. Soil moisture at four layers (0–5cm, 10cm, 20cm, and 40cm) of 30 sites was monitored since July, 2010. This paper firstly introduces the network and then presents preliminary spatiotemporal analyses based on the in situ soil moisture measurements in SMTMN. Three temporal scales (half-hourly, daily, and 10-days) and three time periods corresponding to typical soil wetness conditions, including frozen soil in winter times, are discussed. Primary findings are: (a) generally 13 randomly distributed sites in the study domain are required (i.e. number of required sites) to estimate areal mean soil moisture with correlation coefficient ⩾0.99 and root mean square difference ⩽0.02m3 m−3. This provides guidance for future soil moisture network establishment in similar regions; (b) both number of required sites and the most representative site are insensitive to temporal scales while conversely sensitive to soil wetness conditions; (c) the combination of a few optimally-selected sites can give more robust estimate of areal mean soil moisture than a single site does because the former contains more information on spatial heterogeneity. These findings can provide not only a practical compromise between maintenance cost and risk on reliability for an existing soil moisture network, but also insights for soil moisture upscaling studies and satellite soil moisture products validations. [ABSTRACT FROM AUTHOR]