Assessing impact of weather variability and changing climate on oil-palm yield in major growing regions of southern Thailand

Oil palm yield is very responsive to weather fluctuations in the growing season. The purpose of this study was to investigate the relationship between yield variation and climate trends in the major oil palm-growing regions, especially in Southern Thailand (Chumphon; CP, Ranong; RN, Krabi; KB, Trang; TR, Satun; ST, Phang-Nga; PN, SuratThani; SR and Nakhon Si Thammarat; NS) where oil palm has been grown in a large plantation. Monthly weather variables from 16 agricultural meteorological stations were analyzed by linear and non-linear regressions over 28 years in each major oil palm-producing region. To evaluate the trends of changes in weather parameters and yield, a statistical model was developed for estimating oil palm yield based on climatic trends during 1994-2017. The results showed that warming trends were observed at all major oil palm-growing regions. There were pieces of evidence of significant correlation in temperature trends which had the strongest values in KB (Tmax, R2=0.534**) and PN (Tmin, R2=0.670**). The highest trends of ET and RH were also markedly increased in SR (R2=0.618**). Whereas precipitation trend had slightly increasing changes in CP (R2=0.220**) and PN (R2=0.233**). In addition, the annual trends in the values of Heliothermal Index, Dryness Index and Cool Night Index were markedly increased in NS, RN and KB, respectively. Comparing climate variables and yield variations over 19 years, the study indicated that the relationships between observed yield and estimated yield had highly significant differences in CP (R2=0.468**), SR (R2=0.735***) and NS (R2=0.579***), but there was lower value in KB (R2=0.098*) than those of the other regions. Therefore, this study indicates that recent climate trends have had an implicit effect on oil palm yield in the major producing regions in Southern Thailand. This study could be a guideline to further planning for oil palm management.

Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA

A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042.

Hydrology without dimensions

By rigorously accounting for dimensional homogeneity in physical laws, the Pi theorem and the related self-similarity hypotheses allow us to achieve a dimensionless reformulation of scientific hypotheses in a lower dimensional context. This paper presents applications of these concepts to the partitioning of water and soil on terrestrial landscapes, for which the process complexity and lack of first principle formulation make dimensional analysis an excellent tool to formulate theories that are amenable to empirical testing and analytical developments. The resulting scaling laws help reveal the dominant environmental controls for these partitionings. In particular, we discuss how the dryness index and the storage index affect the long term rainfall partitioning, the key nonlinear control of the dryness index in global datasets of weathering rates, and the existence of new macroscopic relations among average variables in landscape evolution statistics. The scaling laws for the partitioning of sediments, the elevation profile, and the spectral scaling of self-similar topographies also unveil tantalizing analogies with turbulent fluctuations.

Assessment of the Current and Projected Conditions of Water Availability in the Sevastopol Region for Grape Growing

Viticulture is a sector very sensitive to climate change. Observed and expected changes in temperature and precipitation can change the conditions necessary for viticulture in a particular area or make these conditions totally unsuitable for viticulture. Precipitation (water availability) and air temperature are the key meteorological parameters regulating the quality of grapes and wine. We used an ensemble of model data from the CMIP6 project to evaluate all possible changes in water availability in the area around Sevastopol by the middle and the end of the 21st century for two Shared Socioeconomic Pathway scenarios (SSP2-4.5 and SSP5-8.5). The hydrothermal coefficient and dryness index have been used to evaluate the water availability. The results have shown that, based on the indices values, viticulture in the study region will be possible without irrigation, but, at the same time, the vines may experience a certain level of dryness.

Temperature Vegetation Dryness Index-Based Soil Moisture Retrieval Algorithm Developed for Geo-KOMPSAT-2A

The Korea Meteorological Administration (KMA) has developed many product algorithms including that for soil moisture (SM) retrieval for the geostationary satellite Geo-Kompsat-2A (GK-2A) launched in December 2018. This was developed through a five-year research project owing to the significance of SM information for hydrological and meteorological applications. However, GK-2A’s visible and infrared sensors lack direct SM sensitivity. Therefore, in this study, we developed an SM algorithm based on the conversion relationships between SM and the temperature vegetation dryness index (TVDI) estimated for various land types in the full disk area using two of GK-2A’s level 2 products, land surface temperature (LST) and normalized difference vegetation index (NDVI), and the Global Land Data Assimilation System (GLDAS) SM data for calibration. Methodologically, various coefficients were obtained between TVDI and SM and used to estimate the GK-2A-based SM. The GK-2A SM algorithm was validated with GLDAS SM data during different periods. Our GK-2A SM product showed seasonal and spatial agreement with GLDAS SM data, indicating a dry-wet pattern variation. Quantitatively, the GK-2A SM showed annual validation results with a correlation coefficient (CC) > 0.75, bias < 0.1%, and root mean square error (RMSE) < 4.2–4.7%. The monthly averaged CC values were higher than 0.7 in East Asia and 0.5 in Australia, whereas RMSE and unbiased RMSE values were < 0.5% in East Asia and Australia. Discrepancies between GLDAS and GK-2A TVDI-based SMs often occurred in dry Australian regions during dry seasons due to the high LST sensitivity of GK-2A TVDI. We determined that relationships between TVDI and SM had positive or negative slopes depending on land cover types, which differs from the traditional negative slope observed between TVDI and SM. The KMA is currently operating this GK-2A SM algorithm.

Integrated Drought Evaluation Index: Considering The Feedback of The Soil Ecological Environment on Wheat

With the acceleration of climate instability, drought is causing increasing losses that seriously threaten food security in China. In consideration of the feedback of the ecological environment vulnerability on drought, this study selects the temperature vegetation dryness index to evaluate the boundaries of the regional ecological drought index and integrates many factors, such as precipitation, temperature and human activities, from the four aspects of natural disaster risk management — hazard, vulnerability, exposure and resistance—to establish an integrated drought evaluation index for wheat (IDEIW). The results showed that drought was the main reason for the observed decrease in wheat production of Anyang city, as the most severe water shortages occurred during the physiological water demand period of wheat from March to May. Precipitation scarcities were concentrated throughout the north of the study region, where drought was most frequent and severe. There were highly positive spatial correlations between the IDEIW and the annual yield reduction rate of wheat in dry years, whose bivariate Moran's I values reached 0.39, 0.42, 0.31 and 0.38 in the 2002, 2005, 2011 and 2016, respectively; further, the yield reduction rate increased with drought aggregation. This study clearly demonstrates that, in terms of availability, precision and sensitivity, the IDEIW, which is stronger and stabilizing than the temperature vegetation dryness index and the standardized precipitation index, can be used as an important tool to assess and monitor dynamic variations in agricultural drought and provide a new means for the early warning and forecast management of agricultural drought.

Spatio-Temporal Assessment of Surface Moisture and Evapotranspiration Variability Using Remote Sensing Techniques

The relationship between the physic features of the Earth’s surface and its temperature has been significantly investigated for further soil moisture assessment. In this study, the spatiotemporal impacts of surface properties on land surface temperature (LST) were examined by using Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) and meteorological data. The significant distinctions were observed during a crop growing season through the contrast in the correlation between different multi-spectral satellite indices and LST, in which the highest correlation of −0.65 was found when the Normalized Difference Latent heat Index (NDLI) was used. A new index, named as Temperature-soil Moisture Dryness Index (TMDI), is accordingly proposed to assess surface moisture and evapotranspiration (ET) variability. It is based on a triangle space where NDLI is set as a reference basis for examining surface water availability and the variation of LST is an indicator as a consequence of the cooling effect by ET. TMDI was evaluated against ET derived from the commonly-used model, namely Surface Energy Balance Algorithm for Land (SEBAL), as well as compared to the performance of Temperature Vegetation Dryness Index (TVDI). This study was conducted over five-time points for the 2014 winter crop growing season in southern Taiwan. Results indicated that TMDI exhibits significant sensitivity to surface moisture fluctuation by showing a strong correlation with SEBAL-derived ET with the highest correlation of −0.89 was found on 19 October. Moreover, TMDI revealed its superiority over TVDI in the response to a rapidly changing surface moisture due to water supply before the investigated time. It is suggested that TMDI is a proper and sensitive indicator to characterize the surface moisture and ET rate. Further exploitation of the usefulness of the TMDI in a variety of applications would be interesting.

Effects of climate change and human interactions on water balance dynamics in the River Vistula basin

&lt;div&gt; &lt;p&gt;Water balance modelling is often applied in studies of climate and human impacts on water resources. Annual water balance is usually derived based on precipitation, discharge and temperature observations under an assumption of negligible changes in annual water storage in a catchment. However, that assumption might be violated during very dry or very wet years. In this study we apply groundwater level measurements to improve water balance modelling in nine sub-catchments of the River Vistula basin starting from the river sources downstream. Annual and inter-annual water balance is studied using a Budyko framework to assess actual evapotranspiration and total water supply. We apply the concept of effective precipitation to account for possible losses due to water interception by vegetation. Generalised Likelihood Uncertainty Estimation GLUE is used to account for parameter and structural model uncertainty, together with the application of eight Budyko-type equations. Seasonal water balance models show large errors for winter seasons while summer and annual water balance models follow the Budyko framework. The dryness index is much smaller in winter than in summer for all sub-catchments. The spatial variability of water balance modelling errors indicate an increasing uncertainty of model predictions with an increase in catchment size. The results show that the added information on storage changes in the catchments provided by groundwater level observations largely improves model accuracy. The results also indicate the need to model groundwater level variability depending on external factors such as precipitation and evapotranspiration and human interventions. The modelling tools developed will be used to assess future water balance in the River Vistula basin under different water management scenarios and climate variability.&lt;/p&gt; &lt;/div&gt;