Average Rainfall Research Articles

Canine visceral leishmaniasis: Seroprevalence and georeferencing in the state of Santa Catarina, Brazil

Canine visceral leishmaniasis (CVL) has long been considered an endemic disease in the northern and northeastern regions of Brazil, while the southern region remains non-endemic. However, in recent years, several cases of CVL have been reported in southern states. The objective of this work was to determine the seroprevalence of CVL in dogs in the state of Santa Catarina, Brazil, through immunochromatographic tests (DPP®) and ELISA (Enzyme-Linked Immunosorbent Assay) and its correlation with environmental characteristics through georeferencing. Blood samples from dogs (n = 1227) were collected in six mesoregions of the state and evaluated by the rapid test (DPP®). Positive samples were sent to Lacen (Central Public Health Laboratory) in Santa Catarina to be tested using ELISA. Information obtained from the epidemiological questionnaire was subjected to statistical analysis (Chi-square and Student's t-test; P < 0.05) to verify the correlation between serology and the analyzed variables. The locations (GPS) of the samples were used for georeferencing and creating heatmaps (Kernel Method). Four animals that died from CVL were necropsied and organ samples were collected for molecular analysis (PCR), immunohistochemistry, and histopathology (HE). Of the 1227 dogs analyzed, 22 (1.8%) were reactive in the DPP® and of these, 7 (0.6%) were also positive in the ELISA. A correlation (P < 0.01) was observed between positive serology and region, environment, access to the street, and clinical signs. The positive cases were concentrated in the eastern region of the state, in low-altitude areas with average rainfall and higher average temperatures, and in more populated areas close to forest fragments. PCR, HE, and immunohistochemistry, along with serology, have proven to be efficient for characterizing positive cases.

Comparative Analysis of Univariate SARIMA and Multivariate VAR Models for Time Series Forecasting: A Case Study of Climate Variables in Ninahvah City, Iraq

This study involves a comparison between the application of the univariate SARIMA model and the utilization of VAR methods (vector autoregressive models) for multivariate time series analysis. The analysis is conducted using three-time series variables derived from data representing the monthly average of Humidity (H), Rainfall (R), and Temperature (T) in Ninahvah City, Iraq. Both univariate and multivariate time series approaches are employed to model these series. The paper also outlines the implementation of vector autoregressive, structural vector autoregressive, and structural vector error correction models using the 'vars' package. Additionally, it provides functions for diagnostic testing, estimation of constrained models, prediction, causality analysis, impulse response analysis, and forecast error variance decomposition. Furthermore, it introduces three fundamental functions, VAR, SVAR, and SVEC, for estimating these models. The comparison between the methods is based on evaluating the mean error produced by each approach. The findings of the study indicate that univariate linear stationary methods outperform multivariate models. The analysis of the data was carried out using the R software platform. The primary objective is to assess the performance of univariate and multivariate time series models in handling the given data. The research gap lies in the need for a comparative evaluation of SARIMA and VAR methods for time series analysis in the context of monthly environmental variables. These models were chosen due to their effectiveness in capturing temporal dependencies and interactions among multiple variables in time series data, providing a comprehensive analysis of climatic patterns in Ninahvah City, Iraq. The study aims to address the research gap by comparing these models and justifying their selection based on their capabilities to analyze the specified time series data.

Quality assessment of the GPM IMERG product for lifetime prediction of turbine blades in complex terrain

Wind turbine blades may suffer leading edge erosion when rain hits the blades extremely fast, resulting in blade damage that will negatively impact power production. Since wind turbines are growing in size, this translates into higher tip speeds when the blades rotate and, therefore, are more prone to erosion. Wind turbines in mountainous terrain may also suffer erosion due to the high winds and precipitation rates. Therefore, it becomes important to estimate blade lifetimes in wind farm sites with terrain complexity. Blade lifetime prediction models utilize a time series of rainfall intensity, wind speeds, and a turbine-specific tip speed curve. In our study, we assess the quality of the Integrated Multi-satellitE Retrievals for GPM (IMERG) final product in a blade lifetime prediction model for a mountainous area during the period 2015-2020. We first compare the IMERG rainfall intensities against in situ observations at 28 stations in Navarra in Northern Spain. We find that the two datasets are closer to agreement when the rainfall intensities are aggregated in monthly rather than 30-minute temporal scales with correlation coefficients between 0.74 - 0.93. We calculate the average annual rainfall in the period, and we find that IMERG over(under)estimates precipitation in 15 (8) stations, in line with previous studies that have pinpointed the limitations of IMERG in complex terrain. We then input the 30-minute IMERG, in situ rainfall intensities, and the 30-minute New European Wind Atlas (NEWA) wind speeds, extracted at each station location and interpolated at 119 m height, into a blade lifetime model. Our results indicate blade lifetimes of 6-17 years in 13 stations, with the in situ data to provide, on average, longer estimates than the IMERG product. Despite the limitations, we conclude that the satellite-based precipitation from IMERG may become a useful dataset for the lifetime estimation of wind turbine blades in complex terrain, with calibration and adjustments of the IMERG data.

English

The present study is to understand how climatic variables such as precipitation and temperature vary over time and how those changes affect stream flow in the Jhelum River basin in Pakistan under different emission scenarios A2 and B2. The simulation results of HadCM3 were employed to create potential climate change scenarios with the Statistically Downscale Model (SDSM). The calibrated model Soil and Water Assessment Tool (SWAT) was used to forecast imminent stream flow to develop a proposed future climate change scenario. Results indicated that cooling patterns were identified in the north portion of the study area whereas warming patterns were detected in the south portion. The projected mean annual maximum temperature (Tmax) of 2020’s 2050’s and 2080’s would be 0.3 oC, 0.8 oC, and 0.99 oC, respectively, under the A2 scenario. The changes in mean annual minimum temperature (Tmin) were also observed as it would be 0.4 oC, 0.7 oC, and 1.4 oC during 2020’s (2021-2040), 2050’s (2041-2070) and 2080’s (2071-2100), respectively. Similarly, it was observed that average annual rainfall would rise by 14%, 10%, and 20% during the 2020s, 2050s, and 2080s, respectively, in the Mangla basin. The results showed an increase in annual stream flows of 100% (1545 m3/sec), with increases in the winter and autumn seasons of up to 409% and 211%, respectively, and a drop in the spring and summer seasons of up to 29% and 25%, respectively, in the 2080’s compared to baseline. Water managers should consider the current trends and variability brought on by climate change to improve water management where water is scarce.

Distribution laws of landslides and theirs influencing factors in the Qiaojia segment of Jinsha River, China

The landslide disaster database is a prerequisite for regional landslide disaster research, and summarizing and analyzing the distribution pattern and influencing factors of landslide disasters is of great significance for carrying out the susceptibility and hazard assessment. The study area is a typical southwest mountainous area, and geological disasters such as landslides are very serious. A total of 3573 landslides were identified after a combination of image interpretation and field investigation in an area of 8.4 ​km2.This paper conducted a spatial analysis to reveal the distribution laws of landslides and analyzed the relationship between landslide and 13 influencing factors such as elevation, slope, slope aspect, topographic relief, soil, land use, lithology, annual average rainfall, ground peak ground acceleration (PGA). It can be concluded that the landslide showed the characteristics of non-uniformity and zonal distribution. A statistics analysis indicates that landslides are significantly correlated with elevation, slope gradient, slope direction, distance from faults, lithology, rivers, highways and so on. Therefore, when constructing engineering in alpine and canyon areas, it is essential to avoid the areas with steep slopes, large height difference, active faults, and the area being distributed by soft or broken hard rock masses to reduce disaster risks.

Routine Climate Monitoring in the State of Hawai‘i: Establishment of State Climate Divisions

Abstract The Hawaiian Islands have some of the most spatially diverse rainfall patterns on Earth, affected by prevailing trade winds, midlatitude disturbances, tropical cyclones, and complex island topography. However, it is the only state in the United States that does not have assigned climate divisions (boundaries defining climatically homogeneous areas), which excludes it from many national climate analyses. This study establishes, for the first time, official climate divisions for the state of Hawai‘i using cluster analysis applied to monthly gridded rainfall data from 1990 to 2019. Twelve climate divisions have been identified: two divisions were found each for the islands of Kaua‘i (Leeward Kaua‘i and Windward Kaua‘i), O‘ahu (Waianae and Ko‘olau), and Maui County (Leeward Maui Nui and Windward Maui Nui), and six divisions were identified for Hawai‘i Island (Leeward Kohala, Windward Kohala, Kona, Hawai‘i Mauka, Ka‘u, and Hilo). The climate divisions were validated using a statewide area-weighted division-average rainfall index which successfully captured the annual cycle and interannual rainfall variations in the statewide average rainfall series. Distinct rainfall seasonality features and interannual/decadal variability are found among the different divisions; Leeward Maui Nui, Leeward Kaua‘i, Kona, and Hawai‘i Mauka displayed the most significant rainfall seasonality. The western Hawai‘i Island divisions show the most significant long-term decreasing trends in annual rainfall during the past 100 years (ranging from −2.5% to −5.0% per decade). With these climate divisions now available, Hawai‘i will have access to numerous operational climate analyses that will greatly improve climatic research, monitoring, education, and outreach, as well as forecasting applications.

Responses of Soil C, N, P and Enzyme Activities to Biological Soil Crusts in China: A Meta-Analysis.

Biological soil crusts (BSCs) are often referred to as the "living skin" of arid regions worldwide. Yet, the combined impact of BSCs on soil carbon (C), nitrogen (N), phosphorus (P), and enzyme activities remains not fully understood. This study identified, screened and reviewed 71 out of 2856 literature sources to assess the responses of soil C, N, P and enzyme activity to BSCs through a meta-analysis. The results indicated that BSC presence significantly increased soil C, N, P and soil enzyme activity, and this increasing effect was significantly influenced by the types of BSCs. Results from the overall effect showed that soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), total phosphorus (TP), and available phosphorus (AP) increased by 107.88%, 84.52%, 45.43%, 27.46%, and 54.71%, respectively, and four soil enzyme activities (Alkaline Phosphatase, Cellulase, Sucrase, and Urease) increased by 93.65-229.27%. The highest increases in SOC, TN and AN content occurred in the soil covered with lichen crusts and moss crusts, and significant increases in Alkaline Phosphatase and Cellulase were observed in the soil covered with moss crusts and mixed crusts, suggesting that moss crusts can synergistically enhance soil C and N pool and enzyme activity. Additionally, variations in soil C, N, P content, and enzyme activity were observed under different environmental settings, with more pronounced improvements seen in coarse and medium-textured soils compared to fine-textured soils, particularly at a depth of 5 cm from the soil surface. BSCs in desert ecosystems showed more significant increases in SOC, TN, AN, and Alkaline Phosphatase compared to forest and grassland ecosystems. Specifically, BSCs at low altitude (≤500 m) with an annual average rainfall of 0-400 mm and an annual average temperature ≤ 10 °C were the most conducive to improving soil C, N, and P levels. Our results highlight the role of BSCs and their type in increasing soil C, N, P and enzyme activities, with these effects significantly impacted by soil texture, ecosystem type, and climatic conditions. The implications of these findings are crucial for soil enhancement, ecosystem revitalization, windbreak, and sand stabilization efforts in the drylands of China.

High-Resolution Precipitation Mapping for Morocco: Integrating Orographic and Geographic Influences

Morocco's mountainous regions play a crucial role in shaping its precipitation patterns, influencing everything from water resources to agricultural potential. However, accurately mapping precipitation in such complex terrain is challenging for traditional methods. This study proposes a model that incorporates both topographic and geographic features and prevailing weather patterns to create more accurate maps of average annual precipitation across Morocco. What sets this model apart is its ability to determine the direction of prevailing weather circulation and incorporate geographic and topographic parameters that influence precipitation patterns. Using data from 1965 to 2010, the model estimates an average annual rainfall of 206.4 mm, equivalent to 146.6 billion cubic meters per year, with a terrain aspect deviation to the dominant moisture flux direction set at 280 degrees. This approach is particularly valuable in regions with limited climate data networks, as it leverages existing information to fill in the gaps. By providing more accurate precipitation maps, this model can be a valuable tool for environmental modeling, water resource management, and agricultural planning in Morocco.

Analysis of Rainwater Availability and Water Requirements in the Amarasi District Area Kupang Regency

The hydrological conditions of the Amarasi District, Kupang Regency have 3-4 wet months and 8-9 dry months according to Oldeman’s classification. Annual rainfall in the last ten years shows the lowest rainfall was 1,461mm and the highest was 2,688mm, the average annual rainfall was 1,859mm. The Amarasi region is included in zones D and E of the Oldeman climate classification. The practice of utilizing limited water resources by planting fodder crops in the form of legumes and grasses as well as food crops in integrated dry land agriculture has been carried out by the Amarasi community. This research examines the availability and demand for domestic, agricultural and livestock water and will produce an availability index as the carrying capacity of regional water availability. The method used to calculate the availability of water originating from rain is runoff analysis based on weighted coefficients for each land use, then analyzed in a monthly series and compared with the level of water demand for each use. Domestic water needs, livestock drinking and irrigation follow SNI 19-6728.1.2002. Then the analysis results are interpreted with tables and time series graphs. The research results show the following time series of water availability: January: 44,626,635.13m3; February: 31,210,646.01m3; March : 20,050,098.53m3; April: 11,726,074.47m3; May : 3,023,180.66m3; June: 1,747,689.52m3; July : 973,284.18m3; August : 168,880.36m3; September: 1,026,614.82m3. October 2,846,522.91m3; November 10,713,903.37m3; and December: 26,871,976.23m3. The amount of water demand in the time series is as follows: January: 1,929,491.70m3; February: 1,391,817.98m3; March: 1,697,543.42m3; April: 781,567.95m3; May : 883,736.22m3; June: 755,911.95m3; July : 284,777.42m3; August : 384,569.42m3; September: 387,871.95m3. October 355,448.02m3; November 3,242,283.15m3; and December: 2,631,159.26m3. Water availability index: January: 4.32 (good), February: 4.46 (good), March: 8.47 (good), April: 6.67 (good), May: 29.23 (slight critical), June: 43.25 (mild critical), July: 29.26 (mild critical), August: 227.27 (bad/severe critical), September: 37.78 (mild critical), October 12.49 (good), November: 30.26 (mild critical) and December 9.79 (good).

Seasonal Incidence of Major Insect Pests of Chilli and Their Correlation with Weather Parameters

Study Design: Randomised Block Design (RBD) with three (3) replications. Place and Duration of Study: Biswanath College of Agriculture, Assam Agricultural University, Assam,India. The experiment was conducted during Rabi 2022-23. Methodology: Suryamukhi variety of chilli from packages of practices for Organic crops of Assam was used for this investigation. Three sucking pests viz, Aphis gossypii, Scirtothrips dorsalis, Amrasca biguttula biguttula and one fruit borer Helicoverpa armigera was found infesting the chilli plants. Weather parameters such as maximum temperature, minimum temperature, average relative humidity, rainfall and bright sunshine hours were correlated with the insect populations. Results: Aphids were positively correlated with sunshine hours (r=0.503*), thrips showed negative correlation with average relative humidity (r= -0.620*), jassids had a negative correlation with minimum temperature (r=-0.701*) and fruit borer showed negative correlation with sunshine hours (r= -0625**). Rainfall had no significant effect on incidence of pest population in chilli. Conclusion: Insect pest populations are highly influenced by weather parameters. Through this research we can develop new pest management strategies according to their appearance and peak activity period.

Nonstationary Changes in Annual Rainfall over Indonesia’s Maritime Continent

The investigation into changing rainfall patterns in the Indonesian Maritime Continent (IMC) involved testing for trends, step changes, and variance nonstationarity using the Mann–Kendall, Pettitt, and White tests, respectively. The analysis covered data from 106 meteorological stations over the period from 1981 to 2021, with all tests conducted at annual time scales to understand the evolving precipitation dynamics in the region. According to the findings of this study, the average annual rainfall in IMC is nonstationary. Rainfall has increased dramatically by 12.72 mm with a significant shift point in 1994. However, this characteristic is likely to vary if the analysis period is extended, reduced, or conducted in different time frames. The spatial analysis indicates that 35.8% of meteorological stations observed a notable increase in rainfall, while 28.3% experienced a significant shift, and 16% displayed considerable variation. Consequently, the study only partially identified the nonstationary nature of rainfall in the IMC. Moreover, the research highlights a substantial rise in rainfall in the central to eastern IMC region, whereas the central to western region predominantly shows a decrease. This nonstationary test helps avoid errors in climatic conditions and analytical methods, presenting recommendations for hydrological projects. Consequently, employing this nonstationary test helps prevent inaccuracies in understanding climatic conditions and analytical techniques, thereby offering valuable suggestions for hydrological projects.

Evaluation of High Yielding Groundnut Varieties for North Transitional Zone of Karnataka State, India

Groundnut (Arachishypogaea L.) is an important oil seed crop grown in the arid andsemi-aridtropics of India under rainfed condition. The productivity is around 800 kg ha-1 in Karnataka as against national productivity of 1500 kg ha-1. The reasons for low productivity are many among them lack of improved variety in groundnut cultivation. Hence, study conducted to evaluate the high yielding groundnut varieties on farmers filed under farmers participatory mode approach. On field 25 FLD’s were planned by AICRP on Groundnut, MARS, UAS, Dharwad, Karnataka on farmers’ participatory mode approach under National Mission on Oilseeds and Oil Palm(NMOOP) programme and were conducted during kharif2021 with 25 farmers from Dharwad district. Each farmers field was considered as replication (25 no.) with 5 varieties as treatments and data was analysed statistically using Randomized complete block design. Statistical significance was tested with F test at 5 per cent level of probability. The study revealed that Dh-256(2850 kg ha-1) and K-1812 (Kadri Lepaxi) (2745 kg ha-1) recorded statistically on far dry pod yield. However, Dh-256 numerically out yielded K-1812 to the extent of 105 kg ha-1. Haulm yield when compared to the other varieties. A significant reduction in stem rot incidence was observed with respect to Dh-256(3.5%), K-1812(3.8%), G2-52(4.5%) and GPBD-4(4.7%) when compared to JL-24(8.1%) which resulted significant reduction plant population of JL-24 at harvest though it was having higher harvest index. The additional cost of cultivation of Rs. 2000 was spent on JL-24 to manage foliar diseases. Significantly higher gross returns were of Rs.148405 ha-1 and Rs.143460 ha-1, respectively by cultivating Dh-256 and K-1812 as against Rs.115740 ha-1 and Rs. 98728 ha-1 of local improved popular variety GPBD-4 and farmers check variety JL-24, respectively. From the results, farmers were very satisfied with Dh-256 and K-1812 newly released varieties of UAS, Dharwad, Karnataka and ARS, Kadiri, Andhra Pradesh. During, kharif2021, groundnut crop growth season was normal (496 mm from South West Monsoon i.e., June to September) for the Dharwad district and it was 4% higher than normal rainfall of 479 mm for the same period in the region, Further, groundnut crop not suffered due to dry spell any point of its growing period.

Contrasting carbon cycle responses to dry (2015 El Niño) and wet (2008 La Niña) extreme events at an Amazon tropical forest

Land surface models diverge in their predictions of the Amazon forest's response to climate change-induced droughts, with some showing a catastrophic collapse of forests, while others simulating resilience. Therefore, observations of tropical ecosystem responses to real-world droughts and other extreme events are needed. We report long-term seasonal dynamics of photosynthesis, respiration, net carbon exchange, phenology, and tree demography and characterize the effect of dry and wet events on ecosystem form and function at the Tapajós National Forest, Brazil, using over two decades of eddy covariance observations that include the 2015–2016 El Niño drought and La Niña 2008–2009 wet periods. We found strong forest responses to both ENSO events: La Niña saw forest net carbon loss from reduced photosynthesis (due to lower incoming radiation from increased cloudiness) even as ecosystem respiration (Reco) was maintained at mean seasonal levels. El Niño induced the opposite short-term effect, net carbon gains, despite significant reductions in photosynthesis (from a drought-induced halving of canopy conductance to CO2 and significant losses of leaf area), because drought suppression of Reco losses was even greater. However, long-term responses to the two climate perturbations were very different: transient during La Niña –the forest returned to its “normal” state as soon as the climate did, and long-lasting during El Niño –leaf area loss and associated declines in photosynthetic capacity (Pc) and canopy conductance were exacerbated and extended by feedbacks from higher temperatures and atmospheric evaporative demand and persisted for ∼3+ years after normal rainfall resumed. These findings indicate that these forests are more vulnerable to drought than to excess rain, because drought drives significant changes in forest structure (e.g., leaf-abscission and mortality) and ecosystem function (e.g. reduced stomatal conductance). As future Amazonian climate change increases frequencies of hydrological extremes, these mechanisms will determine the long-term fate of tropical forests.

Mapping Blue Economy Potential Using Spatial Statistical Downscaling Model: Analysis of the Impact of Climate Change on Freshwater Fish Resources

Blue Economy is a sustainable economic concept that focuses on utilizing economic resources in marine, coastal and land ecosystems. Sustainable use of freshwater resources in the blue economy for inland waters supports economic growth with environmental balance. The Statistical Downscaling method is used to understand the impact of climate change on freshwater fish resources. To carry out mapping of the potential of the blue economy, it is carried out by statistical downscaling modeling with satellite variables with Integrated Nested Laplace Approximation parameter estimates. The response variable is satellite variables in the form of average rainfall. The modeling results show that Kalipuro District has the highest blue economy potential, while Kalibaru has the lowest. From the research results, that satellite data on average rainfall is a strong basis for printing statistical downscaling, increasing efficiency with open source digital data sources. Satellite data integration, maximizing analysis and comprehensive blue economy potential efficiency.

Dry Deciduous Forests of the Poona District, Deccan, India

The deciduous vegetation grows in various ghats and hills in the Poona district. The present note describes the type from the Katraj ghats, on the south of Poona at a distance of 6 miles.The area consists of flat plateau at the top, eastern and western slopes and narrow valleys through which small rivulets flow during monsoons. The mountains are formed of trap rock, the strata of which dip towards east. Thus the eastern slopes are gentle with better developed soil but the western escarpments are steep comparatively dry with shallow soil. Seepage water is seen on eastern slope during monsoon. Alluvial soil deposits in the valley. The average rainfall is about 27".The following vegetation types are described:(I) Top of the plateau: They are mainly covered with Boswellia serrata which is regenerating profusely where biotic interference is excluded and the soil is dry shallow and gravelly.(II) The western slopes: These slopes bear a low type of forest. The following trees are common-Boswellia serrata, Lannea coromandelica, Diospyros montana and Largerstroemia parviflora. Shrubs-Acacia sp., Lantana camara, Zizyphus xylopyrus, Rhus mysorensis, Flacourtia, Cipadessa baccifera, Gymnosporia spinosa and Securinega sp.Herbs-Tridax procumbens, Leucas sp., Cocculus sp., Polyzygus tuberosus, Sida sp., Arisaema sp., Desmodium sp., Acanthospermum hispidum, Celosia sp. etc.This vegetation is also affected adversely by the biotic interference since lopping, coppicing and grazing are prevalent.(III) Eastern slope (top portion) ; It bears a community of Boswellia serrata-Diospyros montana-Lannea coromandelica on dry shallow and rocky soil. The commonest shrubs are Acacia sp., Zizyphus xylopyrus, Lantana camara, Cryptolepis buchanani, Woodfordia fruticosa, Cissuspallida and Securinega sp.The common herbaceous vegetation consists ot Locculus sp., Leucas sp., Tridax procumbens, Curculigo sp., Polyzygus tuberosus, etc.(IV) Eastern slope (lower portion): It bears a community of Tectona grandis-Lannea coromandelica-Diospyros-Boswellia serrata.Teak and Lannea coromandelica are predominant on the alluvial deposits towards the valley and higher up. Diospyros sp. and Boswellia become prominent in dry trap soil. Diospyros on alluvial soils is heavily infected by Viscum. But incidence of the parasite decreases in trees which are growing on dry trap rocks elsewhere in the area. Commonest shrubs in the community are Acacia sp., Clerodendron serratum, Zizyphus xylopyrus, Cissus pallida and Lantana camara. Commonest herbs are Leucas sp., Tridax procumbens, Hemidesmus indicus, Curcuma montana, Cocculus sp., Polyzygus tuberosus, Trichodesma sp., Crotalaria sp., Indigofera sp., Celosia sp., Aerva javanica, etc.Various stages of succession are seen in the area and regeneration of teak, Boswellta, Lagerstroemia. Lannea is common on different types of soils.

Landslide Detection Using Analysed UAV Imagery

Landslide is one of the disasters that often occur in Malaysia. It causes thousands of injuries and death records. Plus, a lot of money has been spent on search and rescue purposes and upgrading utilities to prevent landslide occurrence. Malaysia’s climate, categorized as equatorial, is hot and humid throughout the year. Malaysia has been exposed to an average rainfall of 250cm in a year as rainfall has been the main contributor to landslide occurrence. Thus, an early warning system can be convenient to reduce the hazard risk. This research used Unmanned Aerial Vehicles (UAVs) or drones embedded with RGB cameras to capture data. UAVs or drone is a part of technology revolutionary. The UAV has been widely used in different industries including agriculture, mining, land surveyor, and search and rescue. With the UAV capability, in this research, the UAV is used to measure the slope of the earth by capturing orthomosaic data and will be required to process using Geographical Information System (GIS) software to gain access to other data such as imaging data and digital surface model (DSM). With the capability of Pix4D software, the imaging data such as thermal and spectral imaging can be generated. The data will be compared with rainfall data from Jabatan Meteorologi Malaysia to compare the effect of rainfall on slope conditions. The data for the result will be a great contribution to the early warning system as it contributes towards the accuracy of landslide prediction based on the volume of rainfall.

Water use and yield response of rainfed safflower (Carthamus tinctorius L.) in Vertisols with varying soil depths

AbstractSafflower (Carthamus tinctorius L.) is an edible oilseed crop mainly cultivated in marginal lands. This study evaluates safflower crop water requirements to understand its feasibility to cultivate under rainfed ecosystem through a field experiment undertaken at the International Crops Research Institute for the Semiarid Tropics research farm, India. Eight improved and stress‐tolerant safflower cultivars (five spiny and three non‐spiny) were evaluated in Vertisols at three soil depths, that is, shallow: &lt;0.60 m, medium: 0.60–1.20 m, and deep:1.20–1.80 m, over 3 years (2009–2012). Wet, normal, and deficit rainfall years were experienced during 2009/2010, 2010/2011, and 2011/2012, respectively. Soil moisture, crop yield, and growth parameters were measured, and field‐scale hydrology was captured through a calibrated one‐dimensional water balance model. Safflower responded to available residual soil moisture which varied with soil depth and rainfall received in different years. Total crop water use was 300–320 mm during the postrainy season, of which about 70% was extracted in deep Vertisols and 55% in medium Vertisols through residual soil moisture. In addition, 30% of water requirement was met through postrainy season rainfall. Safflower grown in shallow Vertisols could only meet 40% of crop water requirement. Spiny cultivar NARI‐H‐15 grown in deep soil recorded a maximum yield of 1890 kg ha−1 in the wet year. Seed yield from spiny cultivars grown in deep and medium soils was nearly similar (1500–1600 kg ha−1) during wet and normal years; a significant reduction in yield (&gt;50%) occurred in shallow soils and also during a rainfall deficit year. Spiny cultivars produced 10%–50% higher seed yield compared to non‐spiny cultivars. Growing safflower in medium and deep Vertisols provides opportunities for crop intensification.

Catchment Storage Command Relationship for Sustainable Rainfed Agriculture in the Semi-Arid Regions of Rajasthan, India

This study conducted to evaluate catchment storage and command relationship and water use strategies under supplemental irrigation for sustainable rainfed agriculture in the semi-arid regions of Rajasthan, India. In southern Rajasthan, a small category of farmers is above 78%, the potential evapotranspiration is greater than the average rainfall with prevailing arid conditions, and rainfed agriculture is a challenging task. An agricultural micro watershed of 2.0 ha evaluated to establish a catchment storage command area (CSC) relationship and micro irrigation system as an effective water use strategy. The significant results indicate that a farm pond with a storage capacity of 560 m3 with permanent lining (cement + brick) is sufficient to harvest runoff water from a 2.0 ha catchment under the rainfall conditions of below normal (up to 50% deficit), long-term average, and wet years. Harvested rainwater can be used to irrigate a command area of even up to 1.0 ha, with supplemental irrigation of 5 cm in both the seasons of kharif as well as rabi. The two crops, maize (Zea mays) in the kharif season and coriander (Coriandrum sativum) in the rabi season, were significantly profitable with supplemental irrigation by adopting a drip irrigation system.

Mapping long-term Transformation of Wetlands and Annual Rainfall Variability in Madhubani District (1975-2022).

In addition to supporting a rich biodiversity, wetlands serve as ecosystems that connect aquatic and terrestrial settings. They also play a critical ecological role by providing essential supplies. According to the National Wetland Atlas (2010), Bihar has a total of 21,988 wetlands with a total area of 4,03,209 hectares. These wetlands exhibit great range of floral and faunal diversity with large number of endemic species. The present paper endeavors to map the natural wetlands of Madhubani district of North Bihar and assess their evolving hydro-physiographic characteristics. The study area is endowed with numerous small wetlands as it receives high average annual rainfall in Bihar. This study primarily emphasizes on identification of wetlands and their spatial-temporal assessment during the period 1975-2022 by utilizing datasets from various sources like topographical maps, satellite imageries, and other ancillary data. It uses geospatial tool like Arc Hydro and indices like Topographic Wetness Index (TWI) and Normalized Difference Wetness Index (NDWI) for identification and spatial-temporal assessment of wetlands during pre-monsoon and post-monsoon season. The study finds that despite being one of the rainiest districts of Bihar, the annual rainfall trends in Madhubani is decreasing. This has impacted upon the water quality, productivity, biodiversity and annual recharge of wetlands of Madhubani. Its entire geographic region has thus been significantly reduced as a result. Other important factors like accelerated anthropogenic intervention, land use changes and process of urbanization have contributed towards rapid transformation of floodplains of Madhubani District. These changes may affect future water potential of the region and thus threaten sustainability in the region.

Flood Disaster Risk Assessment in Wuhan City Based on GIS Analysis and Indicator Ranking Using Random Forest

In recent years, with the acceleration of urbanization and the frequent occurrence of extreme weather globally, the risk of urban flood disasters has gradually increased, and its potential consequences are immeasurable. Therefore, conducting risk assessment of urban flood disasters is of great significance, as it is one of the foundations and decision-making means for Disaster Prevention and Mitigation, and has become a hot topic and trend in current research. This paper starts by exploring the concept and formation mechanism of urban flood disasters, taking Hazard Factors, Disaster-prone Environment sensitivity, Vulnerability of Exposed Bodies, and Disaster Prevention and Mitigation Capabilities as primary indicators. Based on this, a risk assessment index system is established with 14 secondary indicators, such as annual average rainfall, distance to water systems, elevation, and terrain undulation. The spatialization of each indicator data point is processed through ArcGIS10.7, and the importance of hazard and sensitivity indicators is ranked using the Random Forest algorithm. The indicators are then weighted using a combination of the Analytic Hierarchy Process (AHP) and the entropy method, and the combined weights of each assessment indicator are calculated. Taking Wuhan City as the research area, the weights of each indicator are input into the established risk assessment model. ArcGIS spatial analysis techniques and raster calculation functions are utilized to solve the fuzzy comprehensive evaluation of the assessment model, obtaining zoning maps of risk levels for hazard, sensitivity, vulnerability, disaster prevention, and mitigation capabilities, as well as the distribution of comprehensive risk levels. The validity and rationality of the model results are verified by actual disaster data, providing important reference for urban flood disaster prevention in the future.