Average Annual Rainfall Research Articles

TOMCHILATIB SUG‘ORISH TIZIMIDA TUPROQ NAMLIGINI MASOFADAN VA KLASSIK USULDA ANIQLASH TEXNOLOGIYALARI

This scientific piece of work shows findings from tests of the technology designed for remote determination of the soil moisture in the use of drip irrigation system in Samarkand region. The average annual rainfall of the research area is 370 mm, the area is 240 000 m2. The site where the technology was subject for tests, is located 634 m above the sea level in Payariq district of Samarkand region 40°00’43.97’’ 66°54’51.35. The area was divided into 12 smaller sites, humidity measuring sensors were installed in each of the sites and GSM modules were used for their communication. To ensure efficiency and accuracy of the results, we would check the data collected by each sensor once every 2 days by thermostats, using a classical method. A graph and a table of maximum sensor mutual distances were developed based on the humidity levels measured in the classical way as well as applying the humidity difference in the sensors. The main difference between the conducted scientific work and practical experiments is that monitoring of the moisture levels was carried out at 3 different depths of the soil and in layers of the light-, medium- and heavy loams. As a matter of fact, currently, such technologies are highly demanded as the area of lands in the drip irrigation system covers in average 15-20 hectares, and the soil moisture is important for determining the irrigation intervals and time. However, it is not always possible, time-wise, to determine the soil samples using the classic-, thermostat- or head drying methods. This article will review the methods applied for determining the two types of soil and their accuracy levels.

Rainfall extremes in Northern Vietnam: a comprehensive analysis of patterns and trends

This study examines the characteristics and trends of extreme rainfall in Northern Vietnam from 1961 to 2018, using daily rainfall data collected from 37 meteorological stations. The study reveals that the average annual rainfall varies significantly across stations, ranging from 1140 mm to 4758 mm. The rainy season accounts for 73% to 92% of the total annual rainfall. Most stations show a declining trend in the annual total rainfall during wet days (PRCPTOT) and the number of wet days (WDAY), while rainfall intensity (SDII) has increased in most stations, particularly during the dry season. This can be attributed to an increase in PRCPTOT and a decrease in WDAY in the dry season. The study also finds a general decreasing trend in the annual maximum 1-day precipitation (RX1day) and consecutive 5-day precipitation (RX5day), as well as for the number of moderate (R16mm) and heavy (R50mm) rainfall days. However, most stations in Northern Vietnam demonstrate no trend in the annual maximum number of consecutive dry days (CDD) and the annual maximum number of consecutive wet days (CWD). Furthermore, the frequency of extreme rainfall events in Northern Vietnam exceeding the 5-year and 10-year return values of 1961-2018 has decreased in recent decades at many stations. Overall, the findings of this study provide insights into the changing patterns of extreme rainfall in Northern Vietnam, with significant implications for climate change adaptation and disaster risk reduction efforts in the region.

State of China's climate in 2022

China experienced a warm and dry climate in 2022. The average annual surface air temperature (SAT) was 10.51°C, which was the second highest since 1961. The annual average rainfall was 606.1 mm, which was the lowest since 2012. The seasonal SAT broke the record in spring, summer, and autumn, while the SAT in winter was slightly cooler. More rainfall was observed in winter and spring, but less in summer and autumn. During the flood season from May to September, rainfall was 11.9% less than normal, which was the third lowest since 1961. The spatial distribution of rainfall anomalies exhibited a wet/dry pattern in the north/south of central and eastern China. The onset of the rainy season was generally earlier but with significant differences in rainfall. More rainfall was observed in the pre-flood season in South China and the rainy season in North China and Northeast China. In contrast, less rainfall occurred in the Mei-yu season in the middle and lower reaches of the Yangtze and Huaihe River valleys, the southwestern rainy season, and the autumn rainy season in West China. In 2022, China's drought and flood disasters were stark, and heat waves were strong. Severe droughts occurred along the Yangtze River valley during summer and autumn, while heavy rainfall and flooding struck South China in the pre-flood season and Northeast China in June–July. A historically strongest summer heat wave occurred in central and eastern China, while drastic cooling prevailed in most of China at the end of November. Landfalling typhoons were extremely less frequent.摘要2022 年我国气候暖干特征明显, 年平均气温偏高 0.62℃, 位居1961年以来历史第二高; 年降水量偏少5%, 为2012 年以来最少, 冬春季降水偏多, 夏秋季降水偏少. 汛期(5–9 月)降水量偏少11.9%, 位居1961年以来第3少, 中东部降水距平呈“北多南少”分布. 雨季进程偏早, 雨量空间差异大, 华南前汛期, 华北雨季, 东北雨季降水量偏多, 而长江中下游和江淮梅雨降水偏少. 华南, 东北洪涝灾害重, 南方夏秋连旱严重, 中东部遭遇最强高温过程, 登录台风异常偏少.

A multi-modal machine learning approach to detect extreme rainfall events in Sicily

In 2021 almost 300 mm of rain, nearly half of the average annual rainfall, fell near Catania (Sicily Island, Italy). Such events took place in just a few hours, with dramatic consequences on the environmental, social, economic, and health systems of the region. These phenomena are now very common in various countries all around the world: this is the reason why, detecting local extreme rainfall events is a crucial prerequisite for planning actions, able to reverse possibly intensified dramatic future scenarios. In this paper, the Affinity Propagation algorithm, a clustering algorithm grounded on machine learning, was applied, to the best of our knowledge, for the first time, to detect extreme rainfall areas in Sicily. This was possible by using a high-frequency, large dataset we collected, ranging from 2009 to 2021 which we named RSE (the Rainfall Sicily Extreme dataset). Weather indicators were then been employed to validate the results, thus confirming the presence of recent anomalous rainfall events in eastern Sicily. We believe that easy-to-use and multi-modal data science techniques, such as the one proposed in this study, could give rise to significant improvements in policy-making for successfully contrasting climate change.

Climate and aridity measures relationships with spectral vegetation indices across desert fringe shrublands in the South-Eastern Mediterranean Basin

Mediterranean regions are hot spots of climate change, where the expected decrease in water resources threatens the sustainability of shrublands at their arid margins. Studying spectral vegetation indices' relationships with rainfall and potential evapotranspiration (PET) changes across Mediterranean to arid transition zones is instrumental for developing methods for mapping and monitoring the effects of climate change on desert fringe shrublands. Here we examined relationships between 17 spectral vegetation indices (VIs) and four climate and aridity measures, i.e., rainfall, PET, aridity index (AI), and water deficit (WD), calculated at accumulation lags between 1 and 6months. For this purpose, VIs for 38 sites (100 × 100m each) representing less disturbed areas were extracted from Sentinel 2A images for 3years with high (2016), low (2017), and average (2018) annual rainfall. Most of the VIs had shown the highest correlation with the four climate and aridity measures at 2-month accumulation interval. While NDVI relationships with climate measures gained the widest use, our data suggest that indices combining NIR and SWIR bands better correlate with climate parameters. AI is one of the leading annual measures of dryness worldwide; when calculating it monthly, WD was found to represent better the balance between precipitation and PET across the climate transition zone and to be better correlated with VIs. Relationships between NIR and SWIR VIs and water deficit may thus facilitate improvements in monitoring and mapping desert fringe shrublands' responses to climate change if supported by similar results from other areas.

Error-correction-based data-driven models for multiple-hour-ahead river stage predictions: A case study of the upstream region of the Cho-Shui River, Taiwan

Study RegionThe Cho-Shui River Basin is located in central Taiwan and has a catchment area of approximately 3157 km2 and an annual average rainfall of 2200 mm. It is the longest river in Taiwan, and its mainstream has an average slope of 0.018. However, the prediction of flash floods upstream of the Cho-Shui River has yet to be investigated. Because of the river’s steep slope, typhoons or storms can result in significant flood disasters. Therefore, accurate and reliable river stage prediction is required for flood disaster mitigation. Study FocusA predictor-corrector multiple-hour-ahead methodology was developed and used for constructing seven data-driven models for river stage predictions. The proposed methodology employs both river-stage and residual-error prediction models. The optimal residual-error prediction model was determined using seven data-driven models. An extensive comparison of the seven data-driven models was conducted regarding prediction performance with 1–24-h lead times. New Hydrological Insights for the RegionThe proposed error-correction-based data-driven models exhibited high prediction accuracy, making them suitable for improvements of river stage predictions with the decrease in lead-time-averaged root mean square error (RMSE), up to − 73.7 %. The error-correction-based categorical gradient boosting regression (CGBR) and encoder-decoder long short-term memory (LSTM) models outperformed the other models, yielding the average peak water-level error (PWE) of 0.3 m for the 24-hour-ahead prediction. Thus, these two models could be helpful for early-warning river flood forecasting during typhoons or storms.

The possible role of fused precipitation data in detection of the spatiotemporal pattern of rainfall erosivity over the Tibetan Plateau, China

As a typical fragile ecological plateau area, the risk of water erosion on the Tibetan Plateau (TP) in China continues to increase with climate change. Rainfall erosivity is one of the most widely used indicators to assess the potential impact of rainfall events on water erosion. However, limited by the scarcity of historical in situ precipitation observations with sufficient spatiotemporal resolution, the estimates of rainfall erosivity over the TP have much larger biases than those of other regions in China. To accurately investigate the spatiotemporal evolution of rainfall erosivity, empirical models were first established to estimate monthly rainfall erosivity using 1-minute in situ precipitation observations from 1711 meteorological stations on the TP. The independent assessment showed that the correlation correction values between the observed and estimated monthly values were greater than 0.81 for all months. The annual rainfall erosivity data were then produced with a 0.1° spatial resolution for the 1979–2018 period based on the China Meteorological Forcing Dataset (CMFD) precipitation data using newly established estimation models. Our results show that the CMFD-based estimates successfully captured the decreasing spatial pattern of the multiyear average annual rainfall erosivity from the southeast to northwest of the TP. In addition, the CMFD-based annual rainfall erosivity had a good linear relationship with the observed values but with a certain overestimation. Therefore, standardized annual rainfall erosivity values were used to detect the changes in rainfall erosivity. For most regions on the TP, annual rainfall erosivity values have exhibited significant increasing trends over the last 40 years. This study provides a theoretical basis and reference for controlling water erosion and climate adaptation on the TP.

The influence of atmospheric precipitation on the formation of the flow of the Zolota Lypa River during 1945-2015

The article describes the main natural conditions and factors of runoff formation in the Zolota Lypa river basin. An analysis of existing studies of river flow was carried out. The experience of measuring the main characteristics of atmospheric precipitation and methods of river flow analysis are analyzed. The trend of air temperature changes during the observation period from 1945 to 2015 was determined and analyzed. The regime of atmospheric precipitation within the basin of the Zolota Lypa River was studied. Long-term changes and intra-annual distribution of atmospheric precipitation, in particular rain, were analyzed, and based on the data of observations of the Berezhany weather station. Periods of atmospheric precipitation fluctuations relative to the standard norm were identified and the influence of atmospheric precipitation on the formation of runoff was determined. The graph of the dependence of the flow of the Zolota Lypa River on atmospheric precipitation showed a fairly clear dependence between the variable values of the flow and atmospheric precipitation. Research of precipitation, water balance of river basins, and the impact of precipitation on river runoff remain relevant in the context of global and regional climate change. Nowadays, many scientific researches all over the world are devoted to the research of the problem of river runoff change under the influence of climatic factors. This kind of research has been developing strongly in Ukraine in recent decades. The basin of the river Zolota Lypa has a temperate-continental climate with mild summers, mild winters and sufficient rainfall. This area is flat, there are no high mountains. That is why during the year it is under the influence of cyclones that form over the Atlantic Ocean and arctic air masses that come in the form of high pressure cores. The average annual runoff is characterized by rather complex patterns of change both in time and space. There are periodic, cyclical and unidirectional changes that occur under the influence of a complex system of factors. Climate is a major factor of the water change. To determine the effect of precipitation on river runoff, the method of comparing difference-integral curves of annual runoff and precipitation, as well as water-balance methods are used. Long-term changes and intra-annual distribution of precipitation, in particular rainfall, according to the observations of the meteorological station Berezhany were analyzed. We constructed a differential integrated curve of modular coefficients of the average annual precipitation. It clearly reflects the cyclical changes in precipitation. Long-term changes and intra-annual distribution of water runoff in the Zolota Lypa River also were analyzed. During the observation period, periods of long-term fluctuations of the average annual precipitation were revealed. The average annual rainfall is 631,8 mm. The annual course is stable, in the greatest amount of precipitation falls in the summer, when there are intense thunderstorms and showers, the least – in winter. For the analysis of the water regime of the Zolota Lypa River, observation data from two hydrological stations (Berezhany and Zadariv) were used. According to the graph of the dependence of the flow of the Zolota Lypa River on precipitation for the studied period, a fairly clear connection between the variable values of the flow and precipitation can be traced. To assess the closeness of the relationship between the amount of precipitation and water consumption, a correlation coefficient of 0.74 was calculated, which is a positive approximation. In this case (with a close connection), it can be asserted that the flow of the main watercourses within the basin of the Zolota Lypa River is directly dependent on the amount of precipitation. Keywords: atmospheric precipitation, river runoff, standard climate norm, difference integral curve, correlation coefficien.

Overtime Modelled Climate Variability Effect in Roysambu Sub-County in Nairobi Kenya: A Retrospective Analysis

The effect of climate variability is unprecedented, affecting an array of ecosystems globally. Intensified effects of rainfall and temperature variations are affecting urbanised ecosystems where urban sprawl has ramified. This study looks at modelled retrospective climate variability changes in the Roysambu area, an urbanised suburb in Nairobi County, Kenya. The study aimed to ascertain the changes in rainfall and temperature in the past 30 years. From the simple regression analysis, the total annual average rainfall has slightly increased in the area (y = 0.5235x + 72.037, R2 = 0.0618). There were changes in temperature where the maximum average temperature had increased by 0.5 °C with a simple regression model (y = 0.012x + 0.6281, R2 = 0.0814). From the findings, climate variability has been experienced in the area through simple regression model retrospective analysis over time

Seroprevalence and associated risk factors of bovine neosporosis in the Khomas region of Namibia.

Neospora caninumis a coccidian parasite that occurs worldwide and is one of the most important causes of abortion, especially in cattle. However, no studies have been performed in Namibia to determine theN. caninumstatus in livestock. Therefore, this study aimed to determine the seroprevalence ofN. caninumin cattle and the associated risk factors in the Khomas region of Namibia. A total of 736 sera were collected from cows in 32 farming establishments. These comprised 698 beef and 38 dairy cattle sera and were tested using a commercial enzyme-linked immunosorbent assay (ELISA) kit. Questionnaires were concurrently administered to determine possible risk factors associated withN. caninumseropositivity. A total of 42 sera were positive (all beef), giving an animal-level seroprevalence rate of 5.7%. Eight of the 32 establishments had at least one positive animal, giving a herd-level seroprevalence of 25%. There was no significant association between seropositivity and the presence of dogs, jackals, history of abortions, farm size, number of cattle or average annual rainfall. The establishments with moderate to high numbers of Feliformia were 9.8 times more likely to be seropositive toN. caninumthan those with none to low levels of the former (p= 0.0245). The authors concluded that the seroprevalence level ofN. caninumin the Khomas region was relatively low compared with other parts of the world and that the role of Feliformia in the epidemiology of bovine neosporosis needed to be further investigated.Contribution:Serological evidence of bovine neosporosis and the associated risk factors are reported in Namibia for the first time. This study contributes to the scientific body of knowledge onN. caninumin Africa, which is currently limited.

The ‘Deluge’ of 25 October 1822 in Genoa, Italy

The ‘Deluge’ of 25 October 1822 in Genoa, Italy

Mapping agro-climatic zone for coffee crop in the Srepok River Basin

The objective of the present study was to map the agro-climatic zone for Robusta coffee crop in the Srepok River Basin. In this study, the trends of temperature and precipitation were identified using by the Mann – Kendall non-parametric test, the water deficit was calculated based on the CROPWAT model, and the agro-climatic zones for Robusta coffee crop were mapped using geographic information system (GIS). Results showed that the average annual temperature and the average annual rainfall had increasing trends. In addition, five agro-climatic zones were identified in the study area, including very appropriate, appropriate, neutral, restricted and inappropriate. Most of the territory was classified as Moderately Suitable for Robusta coffee. Suitable areas for the coffee cultivation were found in the east of the basin. Agro-climatic zoning will help managers and farmers improve crop yields effectively, avoid climate risks and maximize local advantages.

Modelling climate variabilities and global rice production: A panel regression and time series analysis

Climate change threatens agriculture and it remains a present global challenge to food security and Sustainable Development Goals. The potential impact on the supply of crops such as rice is seen as a major food security issue that requires intervention on several fronts. The literature on rice production, climate variations and climate change show several studies outlining various impacts on rice supply as a result of variations in temperature and rainfall. This study intends to further explore the impacts on rice production caused by temperature changes and rainfall variation by analyzing and modelling the production of rice by the top rice-producing countries globally. A time series of the national rice production and yield along with national average annual temperature and rainfall were sourced for 15 major rice-producing countries. The trends of the time series were then compared between the rice productivity variables and temperature and rainfall. A panel regression model was also developed to further assess the relationship between rice production and temperature and rainfall. The time series showed that rice production and yield are increasing for the majority of the countries analyzed. The panel regression model however showed that continued increase in temperature can result in decreased production of rice and that rainfall volume directly impacts rice output and therefore shows rice production is highly susceptible to flooding and drought events caused by climate variabilities.

Spatial Interpolations of Annual Rainfall in Ethiopia Using Simple and Universal Kriging Techniques

The study of spatial distribution of precipitation is an important aspect of water-related research like, hydrological modeling, disaster prediction and watershed management. But, spatial interpolations of annual average rainfall in Ethiopia using Simple and Universal Kriging type through different models has not yet investigated. This study was focused on to select the best fit semivarogram model for annual rainfall interpolation and to map the spatial distribution of rainfall of Ethiopia. In this study, long term mean annual rainfall which was collected from 931 meteorological stations were interpolated using different mathematical variogram models of Kriging to select the best fit variogram model useful to predict continuous surface values of annual average rainfall in Ethiopia. Spherical model was found to be the best fitted variogram model and most accurate model for interpolation using both Simple and Universal Kriging with the value of ME (0.005), RMSE (1.21) and MSDR (121.32) and ME (0.001), RMSE (0.88) and MSDR (144.11), respectively. Since the kriging results of spherical model indicate good model efficacy for this study, it may be useful to map spatial pattern of annual rainfall in Basin level in Ethiopia.

Climate Change Impact on Groundwater-Based Livelihood in Soan River Basin of Pakistan (South Asia) Based on the Perception of Local Farmers

Based on the perceptions of the local farmers, this study aims to assess the effects of socioeconomic factors and climatic change on the groundwater livelihood system, with a particular focus on in situ Persian wheels/dug wells. Farmers’ perceptions of climate change and how it is affecting their way of life in the Soan River Basin have also been evaluated to determine the most appropriate adaptive interventions. Information and literature about dug wells was unavailable, which stressed the need to carry out this survey. A structured close-ended questionnaire was designed and administered with as much quantitative data as possible. Random sampling opted for a 5 km buffer zone across the Soan River and its tributaries. Union councils having more than 50% of their area lying in the buffer zone were surveyed, and data was collected. Fifty UCs fell within this criterion, and six dug wells from each Union Council were surveyed. The results of our survey collecting local farmer’s perceptions determined that about 70% of respondents agreed about climate change in the Soan Basin of Pakistan, and 62% of farmers reported that climate change severely impacted their livelihood by affecting agricultural productivity and water availability. Ninety-two percent reported summer becoming hot, 72% highlighted that winters are becoming less cold, and 96% reported that average annual rainfall has decreased compared to 10 years before. About 72% of respondents indicated that available water in their dug wells had decreased, and 80% of respondents explained that their crop yield had decreased compared to 10 years before. Sixty percent preferred drip and 35% sprinkler irrigation as efficient water management practices to cope with water shortages. Ninety-five percent of farmers were ready to use solar pumps for irrigation to tame high pumping costs. The study recommends integrating solar pumping with drip and sprinkler irrigation systems to enhance farmers’ cropped area and productivity. These vulnerable farmers can enhance their resilience and profitability by adopting high-value agriculture (tunnel farming, off-season vegetables, etc.) instead of conventional crops.

The improvement of wavelet-based multilinear regression for suspended sediment load modeling by considering the physiographic characteristics of the watershed.

The aim of this study is to model a relationship between the amount of the suspended sediment load by considering the physiographic characteristics of the Lake Urmia watershed. For this purpose, the information from different stations was used to develop the sediment estimation models. Ten physiographic characteristics were used as input parameters in the simulation process. The M5 model tree was used to select the most important features. The results showed that the four factors of annual discharge, average annual rainfall, form factor and the average elevation of the watershed were the most important parameters, and the multilinear regression models were created based on these factors. Furthermore, it was concluded that the annual discharge was the most influential parameter. Then, the stations were divided into two homogeneous classes based on the selected features. To improve the efficiency of the M5 model, the non-stationary rainfall and runoff signals were decomposed into sub-signals by the wavelet transform (WT). By this technique, the available trends of the main raw signals were eliminated. Finally, the models were developed by multilinear regressions. The model using all four factors had the best performance (DC = 0.93, RMSE = 0.03, ME = 0.05 and RE = 0.15).

Economic aspects of urban greenness along a dryland rainfall gradient: A time-series analysis

Studies at the global scale show that urban greenness is not equally distributed across and within cities. Yet, quantification of urban greening trends in drylands is still lacking. We have modeled urban greenness dynamics and its determinants, using Landsat-based time-series analyses of NDVI and census data, of 23 localities along a dryland climatic gradient in southern Israel, between 1997 and 2019. NDVI trends and their associated temporal changes in local average wages, age of locality, and average annual rainfall were analyzed by exploiting a panel structure and model parameters estimation with fixed effects to control for unobserved differences between localities, and to strengthen causal interpretation. Results show positive NDVI trends in all localities—indicating that urban construction increases greenness in drylands. However, the localities varied greatly in the slopes of their respective linear NDVI trends (0.05 <α < 0.161). Specifically, we found that, ceteris paribus, the differences in average wages between localities is the most important factor in explaining spatio-temporal differences between the respective NDVI of localities. Given the well-known high correlation between individuals’ wages and other characteristic such as education and income (all important determinants of the Socio-Economic classification of local authorities), this leads us to conclude that residents’ economic characteristics are an important predictor of the level of greenness. Harlin’s Granger causality test for wages and NDVI panel data indicate that wage is a Granger cause of urban greenness (p < 0.001)—however, greenness was not found to be the driving force of wage.

Insights into spatial differential characteristics of landslide susceptibility from sub-region to whole-region cased by northeast Chongqing, China

Landslides have differential characteristics in different regions. This study explores landslide susceptibility mapping (LSM) based on different evaluation units and proposes a strategy for landslides’ differential characteristics in different sub-regions. Based on data of lithology, elevation, and historical landslides, terrain units (TUs) and slope units (SUs) were obtained. LSM was developed using the Random Forest (RF) model and Light Gradient Boosting Machine (LGBM) model. The LGBM-TUs showed the highest performance and were therefore, selected to obtain LSM. The study area was divided into four sub-regions using the geographically weighted regression (GWR) model, along with spatial differential characteristics of topography conditions. The distribution and characteristics of landslides within each sub-region were assessed using GeoDetector. The results illustrated the reliability of the LGBM-TUs model. Lithology, elevation, and average annual rainfall were the dominant factors, while the influence of other factors on the occurrence of landslides was strengthened only when these factors interacted. This study proposed a new method for LSM research to insight the spatial differential characteristics of landslides in various sub-regions. Our results provide novel insights into landslide mitigation.

Flood Susceptibility Assessment of Lagos State, Nigeria using Geographical Information System (GIS)-based Frequency Ratio Model

Flood is a common disaster affecting the lives and properties of humans. It has a history of causing great damage to infrastructure; disrupt transportation, also, a greater degree of flooding can lead to caving in of the earth causing landslides. Oftentimes, Lagos state, the economic capital of Nigeria has been subjected to flooding owing to heavy rainfall coupled with other causative factors. This study aims to prepare a flood susceptibility map of Lagos state using the frequency ratio model and Geographic Information System (GIS). In this paper, we have considered ten salient contributing factors to flooding, they are; slope, curvature, drainage proximity, drainage density, soil type, average annual rainfall, topographic wetness index, land use &amp;amp; land cover, normalized difference vegetation index, and elevation to delineate the area susceptible to flooding. The flood inventory map was prepared from 100 flood points identified from news reports, and Google Earth Imagery and was further divided into 70 for training and 30 for testing the model. The result shows that 12.54% and 11.62% of the total area of Lagos state have very high and very low levels of flood susceptibility, respectively. The Area Under the Curve has been used to validate the model and was found to perform satisfactorily with a success rate of 64% and a prediction rate of 61%. This work is a necessary input for mitigating flood hazards in the state and will serve a good purpose in making decisions for city planners and the government.

Spatial and Temporal Pattern of Crop Concentration and Diversification and its Determinants in Madhya Pradesh

This study assessed the spatial and temporal pattern of crop diversification and concentration at the district level in Madhya Pradesh using Simpson's index of diversification and Bhatia's location coefficient. The results indicated that wheat, chickpea, and soyabean emerged as the dominant crops, and the area under other crops reduced over the 50 years from 1968 to 2017. Crop diversification reduced till 2007 and slightly improved in 2008-17. Balaghat remained the least diversified throughout the study period. Regression results indicated crop diversification was significantly affected by cropping intensity, fertilizer usage, annual average rainfall, and irrigation with surface water. The study advocated for adequate policy support for coarse cereals to increase crop diversification and ensure nutritional security. It also argued for adequate policy support to districts poorly performing in agriculture for a balanced development of the sector in the state.