Average Rainfall Research Articles

Revealing the water vapor transport during the Henan “7.20” heavy rainstorm based on ERA5 and Real-Time GNSS

In July 2021, a heavy rainstorm was sweeping across Henan Province, causing geological disasters such as floods, mudslides, and landslides, which seriously threatened the safety of human life and property. Precipitable water vapor (PWV) is related to the occurrence and scale of rainfall. Here, based on Global Navigation Satellite System (GNSS) observations, in-situ meteorological files (GMET), ephemeris products, ERA5 data, and weather station data, the relationship between PWV and rainstorm from July 1st to 30th was studied. The results show that GMET and ERA5 in July 2021 have high consistency in some stations, with a root mean square error (RMSE) for temperature below 1.6 °C, for pressure below 0.5 hPa, and for relative humidity below 9 %. During the week before the heavy rainstorm, the temperature dropped remarkably and the temperature difference decreased, while the relative humidity increased and the relative humidity difference decreased. Compared with ERA5 PWV, the RMSE of GNSS PWV retrieved using real-time ephemeris is 3.238 mm. Different from the normal rainfall, we found that the PWV variation during the Henan rainstorm experienced a unique “accumulation” period. We also observed a clear correlation between PWV and the rainstorm, both temporally and spatially. In addition, the PWV in the severely damaged area was 20 mm higher than the average value of the past decade. Ten days after the rainstorm, the surface of this area had subsided by 1.5–3 mm. Finally, we found that the topography of Henan, the low vortex, the north-biased subtropical high, and the double typhoons all played a role in the successful transport and deposition of water vapor.

El Niño-Induced Drought Impacts on Reservoir Water Resources in South Africa

The ENSO phenomenon is associated with below average rainfall and influences the climate regime of southern Africa. With the advent of climate change, drought frequencies and magnitudes have worsened in the developing world and this in turn negatively impacts the natural environment and communities’ livelihoods. This study evaluated the relationship between El Niño-induced drought and reservoir water levels over the Albasini Dam Catchment (ADC) areas in Limpopo Province, South Africa. Standardised indices (i.e., SPI and SSI) were used to define drought events over the study area. Mann–Kendall and Sequential Mann–Kendall were used for trends analysis as well as correlation and wavelet coherence to evaluate the relationship between variables of interest. There exists a relationship between El Niño-induced drought event and reservoir water levels. This was shown by the correlation between drought indices and reservoir water levels with the coefficient of determination being stronger at the 12th timescale (i.e., 0.743 and 0.59) compared to the 6th timescale (i.e., 0.07 and 0.44) for both precipitation and streamflow indices, respectively. Wavelet analysis further showed that there existed a phased relationship between the two variables. Although there are other factors that may affect reservoir water resources, these study findings show that El Niño-induced drought also negatively affect water resources. Therefore, this study recommends the development of multidimensional and multiscale management strategies to minimise drought impacts and adaptation in the region.

Genetic diversity and morphological variability of Iranian Silene aucheriana populations inferred from nrDNA ITS sequences and morphological analysis

Abstract We conducted a comprehensive study on the diversity of Silene aucheriana Boiss. populations by analyzing both morphological data and nrDNA ITS sequences. Maximum parsimony and Bayesian methods on representative material from 15 populations throughout Iran demonstrated that they did not form a monophyletic group. Strong positive correlation occurred between epipetalous filaments length to claw (EFLC), basal leaf length (BLL), basal leaf width (BLW), and plant height (PLH) with elevation. While negative correlation achieved between anthophore length (AnL) with the average air temperature and rainfall. Canonical Correspondence Analysis (CCA) showed that most morphological traits were more correlated to elevation than to average rainfall and temperature. Multivariate analysis of morphological traits with ITS analysis displayed a slight divergence between two types of regions based on their elevation.

Evolutionary analysis of rainstorm momentum and non-stationary variating patterns in response to climatic changes across diverse terrains

This study aims to analyze time-series measurements encompassing rainstorm events with over a century of datasets to identify rainstorm evolution and dimensional transitions in non-stationarity. Rainstorm events are identified using partial duration series (PDS) to extract changes in rainstorm characteristics, namely maximum intensity (MAXI), duration (D), total rainfall (TR), and average rainfall intensity (ARI), in response to climate change. Ensemble empirical mode decomposition is used for trend filtering and non-stationary identification to explore spatiotemporal insight patterns. Trend models for the first–second-order moments of rainstorm characteristics are used to formulate the identified mean–variance trends using combined multi-dimensional linear-parabolic regression. Best-fitting combinations of various distributions (probability density functions) and trend models for multiple characteristic series are identified based on the Akaike information criterion. We analyze the dimensional transition in rainfall non-stationarity based on sensitivity analysis using PDS to determine its natural geophysical causes. The integrated methodology was applied to the data retrieved from nine weather stations in Taiwan. Our findings reveal rainstorm characteristics of “short D but high rainfall intensity” or “lower MAXI but high TR” across multiple stations. The parabolic trend of the first-order moment (i.e., mean) of ARI, D, and TR appears at the endpoint of the mountain ranges. Areas receiving monsoons and those on the windward plain show a rising parabolic trend in the first- and second-order moments (i.e., mean–variance) characterizing MAXI, implying that the occurrence frequency and magnitude of extreme MAXI increases. Non-stationary transitions in MAXI appear for mountain ranges exposed to the monsoon co-movement effect on both windward and leeward sides. Stations in the plains and rift valleys show upgraded and downgraded transitions in the non-stationary dimensions for D, respectively.

Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia

Abstract Normalized difference vegetation index (NDVI) is one of the parameters of vegetation that can be studied by remote sensing of land surface with Sentinel-2 (S-2) satellite image. The NDVI is a nondimensional index that depicts the difference in plant cover reflectivity between visible and near-infrared light and can be used to measure the density of green on a piece of land. On the other hand, the dual-pol radar vegetation index (DpRVI) is one of the indices studied using multispectral synthetic aperture radar (SAR) images. Researchers have identified that SAR images are highly sensitive to identify the buildup of biomass from leaf vegetative growth to the flowering stage. Vegetation biophysical characteristics such as the leaf area index (LAI), vegetation water content, and biomass are frequently used as essential system parameters in remote sensing data assimilation for agricultural production models. In the current study, we have used LAI as a system parameter. The findings of the study revealed that the optical data (NDVI) showed a high correlation (up to 0.712) with LAI and a low root-mean-square error (0.0296) compared to microwave data with 0.4523 root-mean-square error. The NDVI, LAI, and DpRVI mean values all decreased between 2019 and 2020. While the DpRVI continued to decline between 2020 and 2021, the NDVI and LAI saw an increase over the same period, which was likely caused by an increase in the study area’s average annual rainfall and the cautious stance of the Red Global (RSG) project on sustainability.

High-resolution Annual Dynamic dataset of Curve Number from 2008 to 2021 over Conterminous United States

The spatial distribution and data quality of curve number (CN) values determine the performance of hydrological estimations. However, existing CN datasets are constrained by universal-applicability hypothesis, medium resolution, and imbalance between specificity CN tables to generalized land use/land cover (LULC) maps, which hinder their applicability and predictive accuracy. A new annual CN dataset named CUSCN30, featuring an enhanced resolution of 30 meters and accounting for temporal variations in climate and LULC in the continental United States (CONUS) between 2008 and 2021, was developed in this study. CUSCN30 demonstrated good performance in surface runoff estimation using CN method when compared to observed surface runoff for the selected watersheds. Compared with existing CN datasets, CUSCN30 exhibits the highest accuracy in runoff estimation for both normal and extreme rainfall events. In addition, CUSCN30, with its high spatial resolution, better captures the spatial heterogeneity of watersheds. This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales.

Climate Change Vulnerability and Adaptation: The Case of Western Province, Sri Lanka


 
 
 Sri Lanka, as an island and developing nation, is highly vulnerable to the adverse effects of climate change. Occurrences of natural disasters due to extreme weather conditions such as prolonged droughts, flash floods and landslides deprive lives and livelihoods of people. Building resilience of vulnerable communities and ecosystems over climate change effects within a broader framework of sustainable development remains the country‘s priority. The Western Province is one of the nine provinces of Sri Lanka. It is the most densely populated Province in the country and is home to the legislative capital Sri Jayewardenepura Kotte as well as to Colombo, the nation's administrative and business center. The Province is located in the southwest of the country and has an area of 3,684 square kilometres and 5,821,710 people. It comprises of three administrative districts; Colombo, Gampaha and Kalutara. The Western Province provides the highest contribution to the Gross Domestic Product contributing 41.2% of the Provincial Gross Domestic Product (PGDP), the highest being the service sector (56.5%) followed by Industrial sector (34.6%) and then agriculture (1.7%). With regards to the climate change impacts, the most significant ones for the Province are recurrent flooding as a result of an increase in average rainfall coupled with heavier rainfall events, increase of the minimum temperature and thus the decrease in the difference between maximum and minimum temperatures and also sea level rise as certain parts of the Province borders the coast. The key vulnerable sectors of the Province are food security which comprise of agriculture (including both crops and livestock) and inland fisheries; water resources which includes both drinking water and irrigation, coastal and marine, ecosystem and biodiversity, human settlement and infrastructure, tourism and health. In addition to these sectors there are cross cutting sectors which includes policy, legal, economic and governance, Institutional development and coordination, International cooperation and partnerships and research and development which have a significant stake to the magnitude of impact. Among the adaptation interventions the following are being implemented/proposed for implementation; Climate Smart agriculture/practice and other alternative agricultural systems, increase storage capacity of water, rational management of water distribution and use, collection of data on the endangered/to be extinct/vulnerable fauna and flora due to climate change and protect them, conduct research including citizen research to identify the changes in biodiversity due to climate change, integration of climate change impacts into new and already prepared development plans and construction of salinity barriers. Most of these interventions had been included in the implementation plans of the Nationally Determined Contributions (NDC) by relevant agencies which is in accordance with the Paris Agreement (2015) and submitted to the and the submission of the revised NDC to the UNFCCC with the approval of the Cabinet of Ministers in the country. However, the full implementation of same requires to improve the capacity in institutions including human resources, finances and technological interventions which needs urgent attention.
 Keywords: Climate change, Adaptation, Western Province, Impact, Vulnerability
 
 

Economic losses caused by mastitis and the influence of climate variation on the occurrence of the disease in a dairy cattle farm in southern Brazil.

This study evaluated the economic impacts caused by mastitis in a small dairy farm with similar characteristics and production to most dairy farms in southern Brazil and investigated if climatic variations influenced mastitis occurrence in the region. A farm with, on average, 45 lactating Holstein cattle was monitored from November 2021 to October 2022, and data on mastitis cases, bulk tank milk somatic cell count, animal treatment costs, milk production, animal disposal costs, and production losses were collected. Monthly averages of temperature, relative humidity (RH), and rainfall in the region were obtained. The greatest loss was related to the drop in milk production, resulting in 63.8% of total losses, followed by animal disposal (29.5%), milk disposal (4.6%), and treating animals with mastitis (2.0%), totaling a 10.6% reduction in the annual gross income. There were negative correlations between the clinical mastitis rate and monthly RH and between subclinical mastitis and temperature; the occurrence of subclinical mastitis and average RH were positively correlated. Our findings showed that mastitis negatively impacted the economy and that climate influenced mastitis occurrence.

Seasonal types in homogeneous rainfall regions of the Amazon basin

AbstractDue to its size and geographical features, different average annual rainfall regimes co‐exist in the Amazon basin, with distinct year‐to‐year variability dependent on regions within the basin. In this study, we define and explain the seasonal regional types of annual regimes, that is, years with similar seasonal anomalies. Our work is based on a 205 rain gauge network distributed over five Amazonian countries, spanning a period over 30 years. Using a spectral clustering method, we identified seven sub‐regions within the basin in which annual rainfall regimes are spatially homogenous. For each sub‐domain, we estimated specific parameters that characterize the rainy season (onset and demise dates, sign and duration of rainfall anomalies). Finally, using spectral analysis we identified between two and four ‘seasonal type’ of precipitation in these seven sub‐domains. Most of these seasonal types are in phase with the large‐scale atmospheric circulation, which explains the temporal link with rainfall anomalies. The seasonal types result of the superposition of inter‐annual and intra‐seasonal variability whose factors are then difficult to identify and attribute. Part of the rainfall anomalies characterizing seasonal types is related to the inter‐annual variability of the sea surface temperature in the Atlantic or the Pacific oceans, especially in the northeast and southeast part of the Amazon basin, whereas in other parts, strong intra‐seasonal and local factors have a larger impact. The same sign and duration of anomalies do not concomitantly affect the various regions of the Amazon basin, confirming that one mode of variability does not homogeneously affect precipitation in different parts of the basin.

Chronic phalaris toxicity in macropods is widespread and peaks in July in Victoria, Australia.

Phalaris aquatica is pasture species introduced into Australia during early European settlement. Consumption of the plant can cause the neurological condition chronic phalaris toxicity (CPT) in sheep and cattle. In recent years, there has been an increase in reports of CPT in macropods, which has raised concerns regarding its impacts on their welfare. Currently, little is known about the distribution or seasonal patterns of this disease in wildlife, information pivotal in assessing its potential risks. Between 2021 and 2022, we conducted a survey targeting government bodies, veterinary businesses and wildlife organisations to investigate the locations and time of occurrence of CPT in macropods in the state of Victoria, Australia. We received 13 survey responses, 12 verbal reports, a full record of investigated cases from a university veterinary school and cases from a wildlife rescue organisation. Over the period of 11 years, Victoria had 918 cases of CPT recorded in macropods from 36 local government areas, with cases concentrated centrally just north of the state capital of Melbourne and July (midwinter) being the month with the highest case count (n = 220). There was a significant positive correlation between case count and both the abundance of kangaroos (Macropus giganteus and Macropus fuliginosus) (P < 0.01) and the abundance of P. aquatica (P = 0.009), and a significant negative correlation between annual case count and average rainfall of March (P = 0.016) and April (P = 0.02). Understanding these relationships will assist land and wildlife managers in predicting the risk and magnitude of disease outbreaks of CPT each in Victoria.

Rainfall Erosivity Characteristics during 1961–2100 in the Loess Plateau, China

Rainfall erosivity, which signifies the inherent susceptibility of soil erosion induced by precipitation, plays a fundamental role in formulating a comprehensive soil loss equation (RUSLE). It stands as a crucial determinant among the foundational factors considered in a comprehensive soil loss equation’s establishment. Nonetheless, the prediction and quantification of future alterations in rainfall erosivity under the influence of global warming have been relatively limited. In this study, climate change was widely evaluated and 10 preferred global climate models in the Loess Plateau were selected by using the data sets of 27 models simulating climate change and the CN05.1 data set provided by the latest CMIP6. The monthly precipitation forecast data were obtained by using the delta downscaling method. Combined with trend analysis, significance test, and coefficient of variation, the annual rainfall erosivity during 1961–2100 under four SSP scenarios was analyzed and predicted. Among the 27 GCM models used in this paper, the most suitable climate models for simulating monthly precipitation in the Loess Plateau were CMCC-CM2-SR5, CMCC-ESM2, TaiESM1, EC-Earth3, EC-Earth-Veg-LR, INM-CM4-8, CAS-ESM2-0, EC-Earth-Veg, ACCESS-ESM1-5, and IPSL-CM6A-LR. In comparison to the base period (1961–1990), during the historical period (1961–2014), the average annual rainfall erosivity on the Loess Plateau amounted to 1259.64 MJ·mm·hm−2·h−1·a−1, showing an insignificant downward trend. In the northwest of Ningxia, Yulin City and Yanan City showed a significant upward trend. In the future period (2015–2100), the annual rainfall erosivity continues to constantly change and increase. The potential average increase in rainfall erosivity is about 13.48–25.86%. In terms of spatial distribution, most areas showed an increasing trend. Among these regions, the majority of encompassed areas within Shanxi Province, central Shaanxi, and Inner Mongolia increased greatly, which was not conducive to soil and water conservation and ecological environment construction. This study offers a scientific reference for the projected future erosivity characteristics of the Loess Plateau.

Nowcast flood predictions in the Amazon watershed based on the remotely sensed rainfall product PDIRnow and artificial neural networks.

The aim of this study was to develop artificial neural network (ANN) models to predict floods in the Branco River, Amazon basin. The input data for the models included the river levels and the average rainfall within the drainage area of the basin, which was estimated from the remotely sensed rainfall product PDIRnow. The hourly water level data used in the study were recorded by fluviometric telemetric stations belonging to the National Agency of Water. The multilayer perceptron was used as the neural framework of the ANNs, and the number of neurons in each layer of the model was determined via optimization with the SCE-UA algorithm. Most of the fitted ANN models showed Nash-Sutcliffe efficiency index values greater than 0.9. It is possible to conclude that the ANNs are effective for predicting the flood levels of the Branco River, with horizons of 6, 12 and 24h; thus, constituting a viable option for use in river-flood warning systems in the Amazon basin. For the forecast with a 24-h horizon, it is essential to include the average rainfall of the basin that accumulated over the last 48h as input data into the ANNs, along with the levels measured by the streamflow stations. The indirect rainfall estimates provided by PDIRnow are an excellent alternative as input data for ANN models used to predict floods and constitute a viable solution for regions where the density of rain gauge stations is low, as is the case in the Amazon basin.

Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: A case of Wanzhou District, China

Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping (LSM) studies. However, these algorithms possess distinct computational strategies and hyperparameters, making it challenging to propose an ideal LSM model. To investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM, this study constructed a geospatial database comprising 12 conditioning factors, such as elevation, stratum, and annual average rainfall. The XGBoost (XGB), LightGBM (LGBM), and CatBoost (CB) algorithms were employed to construct the LSM model. Furthermore, the Bayesian optimization (BO), particle swarm optimization (PSO), and Hyperband optimization (HO) algorithms were applied to optimizing the LSM model. The boosting algorithms exhibited varying performances, with CB demonstrating the highest precision, followed by LGBM, and XGB showing poorer precision. Additionally, the hyperparameter optimization algorithms displayed different performances, with HO outperforming PSO and BO showing poorer performance. The HO-CB model achieved the highest precision, boasting an accuracy of 0.764, an F1-score of 0.777, an area under the curve (AUC) value of 0.837 for the training set, and an AUC value of 0.863 for the test set. The model was interpreted using SHapley Additive exPlanations (SHAP), revealing that slope, curvature, topographic wetness index (TWI), degree of relief, and elevation significantly influenced landslides in the study area. This study offers a scientific reference for LSM and disaster prevention research. This study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou District. It proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM models. However, limitations exist concerning the generalizability of the model and the data processing, which require further exploration in subsequent studies.

Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam

Rainfall is a triggering factor that causes landslides, especially in the regions where landslides often occur after consecutive days of heavy rainfall. Most previous studies only used a specific rainfall map for landslide susceptibility assessment. However, this approach was unreasonable because rainfall is a time-variant data. This study uses the time series data of 1-day, 3-day, 5-day, and 7-day maximum precipitation from 2016 to 2020 in the mountainous area of Quang Ngai province for landslide susceptibility assessment. These data and other influencing factors were used to develop landslide spatial prediction models using the Extreme Gradient Boosting method. The prediction model's performance was assessed using the statistical index and receiver operating characteristic curve methods. The testing results of 4 cases using consecutive days of maximum rainfall data demonstrated excellent performance. Of these, the model with a 3-day maximum rainfall with ACC = 0.813, kappa = 0.625, SST = 0.872, SPF = 0.754, and AUC = 0.895 had the best performance. In addition, these results were compared to the previous approach that used average annual rainfall. The validation result indicates that the cases using a time series of maximum precipitation (with AUC of approximately 0.9) outperform the cases with average annual rainfall (AUC=0.838). Finally, the model using 3-day maximum rainfall is then used for landslide spatial prediction mapping. These maps provide spatial prediction and assess landslide susceptibility corresponding to rainfall frequencies.

Potential of Irrigated Agricultural Production of Corn (Zea mays L.) in Semi-Arid Region using Geoprocessing

This work aimed to evaluate and map the potential of irrigated agricultural production for corn for the watershed of the region of the middle course of the Paraíba River. Using SPRING, a map of soil irrigation potential and soil retention capacity was prepared and a partial map was generated through matrix crossing. Using average annual rainfall data, the climate condition map was prepared. After using LEGAL, the agricultural production potential map was generated through the matrix crossing between the partial map and the climate scenario. For maize, a Full climatic condition (C1) was identified in 2.02% of the basin's total; the full climatic condition with prolonged rainy season (C2) and the climatic condition moderated by excess water (C3) were not identified; the climatic condition Moderate by water deficiency (C4) in 0.86%; the unfit condition due to severe water deficit (C5) in 97.12% of the basin. The potential of irrigated agricultural production for maize did not show Very High (MA), High (A), Medium (M) and Low (B) potential, only the Very Low (MB) potential in 100% of the basin.

Multi-tracer approach to constrain groundwater flow and geochemical baseline assessments for CO2 sequestration in deep sedimentary basins

Geological storage of gases will be necessary in the push to net zero and the energy transition to reduce carbon emissions to atmosphere. These include CO2 geological storage in suitable sandstone reservoirs. Understanding groundwater flow, connectivity and hydrogeochemical processes in aquifer and storage systems is vital to prevent risk and protect important water resources, such as the Great Artesian Basin. Here, we provide a ‘tool-box’ of geochemical assessment methods to provide information on flow patterns through the basin's aquifers (changes in chemistry along flow path), stagnant versus flowing conditions (cosmogenic isotopes and noble gases), inter-aquifer connectivity and seal properties (major ions, Sr and stable isotopes), water quality (major ions and metals) and general assessments on residence times of groundwater (cosmogenic isotopes and noble gases). This information can be used with reservoir and groundwater models to inform on possible changes in the above-mentioned processes and serve as input parameters for CO2 injection impact modelling. We demonstrate the use and interpretation on an example of a potential CO2 storage geological sequestration site in the Surat Basin, part of the Great Artesian Basin, and the aquifers that overly the reservoir. The stable water isotopes are depleted compared to average rainfall and most likely indicate greater contributions from monsoonal rain events from the northern monsoonal troughs, where amount and rainout effects lead to the depletion rather than colder recharge climates. This is supported by the modern recharge temperatures from noble gases. Inter-aquifer mixing between the Precipice Sandstone reservoir and the Hutton Sandstone aquifer seems unlikely as the Sr isotope ratios are distinctly different suggesting that the Evergreen Formation is a seal in the locations sampled. Mixing, however, occurs on the edges of the basin, especially in the south-east and east where the Surat Basin transitions into the Clarence-Moreton Basin. Groundwater flow appears to be to the south in the Precipice Sandstone, with a component of flow east to the Clarence-Morton Basin. The cosmogenic isotopes and noble gases strongly indicate very long residence times of groundwater in the central south Precipice Sandstone around a proposed storage site. 14C values below analytical uncertainty, R36Cl ratios at secular equilibrium as well as high He concentrations and high 40Ar/36Ar ratios support the argument that groundwater flow in this area is extremely slow or groundwater is stagnant. The results of this study reflect the geological and hydrogeological complexities of sedimentary basins and that baseline studies, such as this one, are paramount for management strategies.

Average Rainfall and Water Requirements of Bundelkhand Region: Causes, Consequences, and Solutions

Abstract: The Bundelkhand region, receiving an annual rainfall of approximately 1000 mm, with nearly 90% occurring during the short span of July and August, is traversed by major rivers. However, this region grapples with a severe water crisis exacerbated by factors such as burgeoning agriculture, population growth, unpredictable rainfall patterns, the impact of climate change, political complexities, water mismanagement, misuse, and environmental negligence, including the neglect of traditional water sources. This abstract provides an overview of a comprehensive study on the shifting water table dynamics within the Bundelkhand region. It explores the multifaceted factors contributing to the alteration of groundwater levels, examines the far-reaching repercussions of these changes on the environment and livelihoods, and proposes sustainable solutions to mitigate the looming water crisis. Through a synthesis of scientific research and on-ground observations, this study sheds light on the intricate web of natural and anthropogenic drivers responsible for the declining water table. Furthermore, it underscores the urgent need for collective action and policy interventions to safeguard water resources, ensure agricultural resilience, and improve the quality of life for the people of Bundelkhand.

Analisis Neraca Air Lahan untuk Perencanaan Tanaman Padi Gogo dan Jagung pada Sub Das Wolasi

Infomation climate data of a place plays an important role in the development of agriculture in the region. This can be used to find out about the relationship between plants and climate, estimates of planting time, harvest time, drought (water deficit), flooding (water surplus) can be made, and determining the types of plants used. In accordance. This study aims to determine the level of groundwater availability based on land water balance analysis in the Wolasi sub-watershed and to find out how to plan planting patterns of upland rice and corn according to groundwater availability in the Wolasi sub-watershed. This study uses monthly rainfall data for 10 years (2012-2021) and other climate data (air temperature) for 10 years (2012-20121). Calculation of land water balance using the Throntwaite-Mather method. The result of land water balance calculation under conditions of monthly average rainfall distribution of water availability in the Wolasi sub-watershed are classified as very adequate where a surplus occurs from January to July and December. While the water deficit occurs from August to October. At a 75% chance of rainfall, the distribution of water availability is classified as very adequate,where a surplus occurs from January to July and November to December and a water deficit occurs from July to October. In the Wolasi sub-watershed, the montly average rainfall for upland rice can be planted in January and the final harvest in April, while for corn, the initial planting can be done in May and the final harvest in July, upland rice and corn can be planted twice in a row. a year and can also be planted in intercropping. With a 75% chance of rainfall in the Wolasi sub-watershed, the initial planting of upland rice can be done in January and the final harvest in April, while corn plants can be intercropped, planting upland rice and corn can be done once a year.

Performance of bread wheat advanced lines under late sowing

Thirty two advanced wheat lines which included four groups of sister lines and the commercial bread wheat cultivar Borlaug 100 were sown on January 16 and 30, 2019, at the Norman E. Borlaug Experimental Station, in the Yaqui Valley, Sonora, Mexico. Plots consisted of 1 bed 2 m long with two rows and 0.80 m apart without replications, and a seed density of 100 kg ha-1. Average daily temperature (°C), maximum, minimum, relative humidity, rainfall, heat and cold units were recorded from January 1 to May 15, 2019. The variables evaluated were: days to heading, plant height (cm), and grain weight in 20 spikes (g). The average days for heading of lines was 76 for the first sowing date and 73 for the second. The average plant height of the group for the first sowing date was 95 cm and 88 for the second; line W15.92/4/PASTOR//HXL7573/2*BAU/3/WBLL1/5/OAX92.2.5/6/BABAX/LR42//BABAX/3/ ER2000 was the tallest in the first date with 120 cm and with 115 in the second date. The average grain weight of the group in 20 spikes in the first sowing date was 55 g and 40.9 in the second; the line MEX94.15.34/4/PASTOR//HXL7573/2*BAU/3/WBLL1/5/ BABAX/LR42//BABAX/3/ER2000 showed the highest weight in the first and second dates with 71.1 g and 49.5, respectively. In the first date, the line W15.92/4/PASTOR//HXL7573/2*BAU/3/WBLL1/5/IWA8612414/6/BABAX/ LR42//BABAX/3/ER2000 showed the lowest grain weight in 20 spikes with 41.0 g, while in the second date, the sister line PBL94.14.30/4/PASTOR//HXL7573/2*BAU/3/WBLL1/5/BABAX/LR42//BABAX/3/ER2000 (PTSS12SHB 00003T-0TOPB-099Y-099B-7Y-020Y-0B) showed 25.6 g. The average temperature was 18.26 °C with a maximum of 35.2 °C and a minimum of 3.6 °C; the average relative humidity was 67.0 %; there were 13.1 mm of precipitation, and the number of heat and cold units was 146 and 293, respectively.

Effect of water quality parameters and climatic influences on reproductive phenology of Macrognathus pancalus in wetland ecosystem: recommending a long-term management approach and conservation policy.

The study conducted an investigation into the reproductive biology of M. pancalus and assessed the influence of water quality parameters and environmental factors on the spawning pattern within the Gajner Beel ecosystem in Bangladesh. A total of 1218 individuals of M. pancalus (46.39% males and 53.61% females) were collected monthly from the Gajner Beel during January to December 2018 using various fishing gears. The total length (TL) of each individual was measured using digital slide calipers, and the whole body weight (BW) was measured using an electronic balance. Fulton's conditions factor (KF) showed significant differences between males and females. The calculated Lm were 11.11 cm, 11.30 cm, and 11.10 cm based on maximum length, gonadosomatic index (GSI), and the logistic model. The spawning season extended from May through August, with June and July being peak months. The average total fecundity was 1495.52 ± 840.24, with a range of 370 to 4069. During peak spawning season, the average temperature and rainfall were 27°C and 370 mm, respectively. Rainfall, dissolved oxygen, total alkalinity, and pH all had a significant (p < 0.01) positive effect whereas temperature and TDS all had a significant (p > 0.01) negative effect on GSI. Annual air temperature in the study area increased by 0.053 °C/year, with a regression coefficient value (r2 = 0.1695), while annual mean rainfall decreased by 5.97mm/year (r2 = 0.076). This research will contribute to the development of conservation and management approaches of Mastacembelidae fish in relation to current climate variability in sub-tropical waters.