Spatio-temporal Distribution Of Rainfall Research Articles

Crop Yield Prediction using CNN

Deep learning is a branch of Machine Learning which is completely based on artificial neural networks, as neural networks are going to mimic the human brain so deep learning is also a kind of mimic of the human brain. Farming is the main occupation of India. Crop yield has a direct impact on nation and international economies annually and the yield predicted plays a significant part in the food management and agriculture sector. The task is to build a prediction model for crop production . A prerequisite of intelligent systems has brought artificial neural networks to become a new technology which provides assorted solutions for complex problems in agriculture research. Performance of the agriculture sector mainly hinges on natural forces such as spatio-temporal distribution of rainfall, temperature, climate etc, with the result any deviation of monsoon from the normal pattern brings about numerous fluctuations in area and production. Crop yield has a direct impact on nation and international economies annually and the yield predicted plays a significant part in the food management and agriculture sector. The task is to build a prediction model for crop production. The basic principle of ANN architecture, Data Modeling for Prediction involves four stages namely historical data analysis (Descriptive), Data preprocessing, modeling of Data and Performance Estimation. First classify data based on different attributes. Regression analysis using CNN, it observes the relation between an independent (predictor) and dependent (target) variables. Based on relation training the model will predict crop yield production. application of ANN in predicting crop yield by using various crop performance features as input parameter

Comprehensive evaluation and analysis of water resource characteristics in Lixiahe Plain, China

ABSTRACT Lixiahe Plain's local water resources need to be evaluated effectively and comprehensively. This study is based on the multisource data from 60 natural water samples, 16 rainfall monitoring stations, and 2 evaporation monitoring stations from 1965 to 2020. Synchronous series representativeness analysis, water quality analysis, and water resource availability estimation are conducted to analyze the spatiotemporal distribution characteristics of rainfall, evaporation, and water quality and calculate the availability of various types of water resources. The results indicate that the spatiotemporal distribution of rainfall and evapotranspiration in the study area is uneven, which increases the threat of local floods and droughts. The quality of the main rivers and lakes in the region is good, and the water quality of the drinking water source area and groundwater reaches and even exceeds Class III water standards. In the year 2020, the total water usage in the research area reached 341 million m3. However, the available sum of surface water resources and transit water availability in 2022 was only 201 million m3, so local water supply needs cannot be easily met. Exploring the water resource characterization model of the Lower Rivers Plain can help in local water resource management and protection.

The implication of spatiotemporal rainfall distribution on the estimation of areal rainfall characteristics in Upper Erer Sub-basin, Ethiopia

Abstract The distribution of rainfall is not uniform in various regions of the world. Rainfall study is used to recognize the characteristics, duration, and variability of temporal and spatial rainfall distributions. In Ethiopia, the annual and seasonal rainfall distributions are variable in space and time. This study is focused on the implication of spatiotemporal rainfall distribution on the areal rainfall characteristics evaluation under the Upper Erer Sub-basin, located in Eastern Ethiopia. In the study area, average annual rainfall amount for Dire Dawa, Harar and Haramaya, Girawa, and Gursum stations are found to be 647, 816, 801, 958, and 840 mm with coefficients of variation of 23, 20, 20, 19, and 31%, respectively. However, the rain gauges here are sparsely distributed. The rainfall occurrence and distribution at various gauging stations have been found to vary significantly both temporally and spatially. The rainfall occurrence and distribution at various gauging stations had been found to vary significantly both temporally and spatially. The spatiotemporal rainfall distribution found in the stations was assessed using a joint probability of rain days approach. The result indicated joint probability of rain days estimation approach under monthly time step has better performance than daily, decadal, and seasonal data. The joint probability approach is used along with rainfall amount under monthly rainfall for areal rainfall estimation assessment in rainfall–runoff modeling.

The Response of Tropical Rainfall to Idealized Small‐Scale Thermal and Mechanical Forcing

AbstractPredicting the spatiotemporal distribution of rainfall remains a key challenge in Tropical Meteorology, partly due to an incomplete understanding of the effects of different environmental factors on atmospheric convection. In this work, we use numerical simulations of tropical ocean domains to study how rainfall responds to imposed localized thermal and mechanical forcings to the atmosphere. We use the Normalized Gross Moist Stability—NGMS—to quantify the net precipitation response associated with a given net atmospheric heating. We find that NGMS values differ considerably for different forcings, but show that the relationship between precipitation and column relative humidity collapses along a universal curve across all of them. We also show that the contributions from mean vertical advection of moist and dry static energy only approximate the NGMS well at scales larger than a couple hundred kilometers, indicating that general horizontal mixing processes are not negligible at smaller scales.

Evaluation of Geospatial Interpolation Techniques for Enhancing Spatiotemporal Rainfall Distribution and Filling Data Gaps in Asir Region, Saudi Arabia

Providing an accurate spatiotemporal distribution of rainfall and filling data gaps are pivotal for effective water resource management. This study focuses on the Asir region in the southwest of Saudi Arabia. Given the limited accuracy of satellite data in this arid/mountain-dominated study area, geospatial interpolation has emerged as a viable alternative approach for filling terrestrial records data gaps. Furthermore, the irregularity in rain gauge data and the yearly spatial variation in data gaps hinder the creation of a coherent distribution pattern. To address this, the Centered Root Mean Square Error (CRMSE) is employed as a criterion to select the most appropriate geospatial interpolation technique among 51 evaluated methods for maximum and total yearly precipitation data. This study produced gap-free maps of total and maximum yearly precipitation from 1966 to 2013. Beyond 2013, it is recommended to utilize ordinary Kriging with a J-Bessel semivariogram and simple Kriging with a K-Bessel semivariogram to estimate the spatial distribution of maximum and total yearly rainfall depth, respectively. Additionally, a proposed methodology for allocating additional rain gauges to improve the accuracy of rainfall spatial distribution is introduced based on a cross-validation error (CVE) assessment. Newly proposed gauges in the study area resulted in a significant 21% CVE reduction.

Spatiotemporal Rainfall Variability and Trend Analysis of Shimsha River Basin, India.

Karnataka state has the second highest rainfed agricultural land in India, where agricultural output relies heavily on rainfall. The Shimsha basin, a sub-basin of Cauvery in the state, comes under a semi-arid region and predominantly consists of rainfed agricultural land. Rainfall patterns have changed dramatically with time resulting in frequent floods and droughts. Understanding the spatiotemporal distribution of rainfall and its change patterns in the area would benefit sustainable agriculture planning and water resources management practices. The current study aims to determine the variability and trend in rainfall. The daily rainfall data of the Shimsha basin from 1989 to 2018 is collected, and the annual, seasonal, and monthly rainfall totals and the number of rainy days are derived. All the time series are subjected to statistical methods to examine rainfall variability and trend. The mean, standard deviation, coefficient of variation (CV), and Standardized Anomaly Index are used for the preliminary and variability analysis, while the coefficient of skewness and kurtosis are used to understand the rainfall distribution characteristics. The homogenous and serially independent series are identified by homogeneity and serial correlation tests. The trend in the homogenous and serially independent series is identified by Mann-Kendall and Spearman's rank correlation tests, while the magnitude of the trend is quantified using the Sen's slope technique, and the trend change point is evaluated using the sequential Mann-Kendall test. Based on the study, the average rainfall in the study area is 801.86mm, with CV ranging from 43.3 to 22.27%. The southwest monsoon (SWM) season brings the greatest rain to the basin, followed by the post-monsoon (PM), summer, and winter seasons. In the annual time frame, except one station, all other stations have shown significant or insignificant increasing trends. The seasonal rainfall has shown insignificant rising trends during the summer and winter seasons while insignificant increasing and decreasing trends during the PM season. The SWM season has indicated significant increasing trends, insignificant increasing and decreasing trends. Overall, the study area has noticed an increased annual and seasonal rainfall except for the post-monsoon season, during which the rainfall showed a considerable decline. The findings of the study are helpful in water resource management, agricultural planning, and socioeconomic development in the study area.

Understanding interactive processes: a review of CO2 flux, evapotranspiration, and energy partitioning under stressful conditions in dry forest and agricultural environments.

Arid and semiarid environments are characterized by low water availability (e.g., in soil and atmosphere), high air temperature, and irregularity in the spatio-temporal distribution of rainfall. In addition to the economic and environmental consequences, drought also causes physiological damage to crops and compromises their survival in ecosystems. The removal of vegetation is responsible for altering the energy exchange of heat and water in natural ecosystems and agricultural areas. The fluxes of CO2 are also changed, and environments with characteristics of sinks, which can be sources of CO2 after anthropic disturbances. These changes can be measured through methods such as sap flow, eddy covariance, remote sensing, and energy balance. Despite the relevance of each method mentioned above, there are limitations in their applications that must be respected. Thus, this review aims to quantify the processes and changes of energy fluxes, CO2, and their interactions with the surfaces of terrestrial ecosystems in dry environments. Studies report that the use of methods that integrate data from climate monitoring towers and remote sensing products helps to improve the accuracy of the determination of energy fluxes on a global scale, also helping to reduce the dissimilarity of results obtained individually. Through the collection of works in the literature, it is reported that several areas of the Brazilian Caatinga biome, which is a Seasonally Dry Tropical Forest have been suffering from changes in land use and land cover. Similar fluxes of sensible heat in areas with cacti and Caatinga can be observed in studies. On the other hand, one of the variables influenced mainly by air temperature is net radiation. In dry forest areas, woody species can store large amounts of carbon in their biomass above and belowground. The use of cacti can modify the local carbon budget when using tree crops together. Therefore, the study highlights the complexity and severity of land degradation and changes in CO2, water, and energy fluxes in dry environments with areas of forest, grassland, and cacti. Vegetation energy balance is also a critical factor, as these simulations are helpful for use in forecasting weather or climate change. We also highlight the need for more studies that address environmental conservation techniques and cactus in the conservation of degraded areas.

Using CHIRPS Satellite-Based Data for Spatio-Temporal Variability of Rainfall in Dashti Hawler District – Kurdistan Region of Iraq 1982 – 2019

Abstract Rainfall is one of the most essential climatic indicators of climate change, The changes in rainfall in space and time would impact runoff, soil moisture, and groundwater reserves. The study of the climate change effects on water resource planning and management necessitates the analysis of precipitation changes (Khayyat, et. al, p. 35, 2019; M. Nawaz, et. al., 2020, p.2). The availability of long-term and temporally consistent rainfall time series is one of the most important criteria for undertaking a detailed study of regional and temporal variation and trends in rainfall in any location. Agriculture activities, flood disaster risk management and reduction, drought, water harvesting, water supply to communities, and other human activities related to the spatiotemporal distribution of rainfall in any region benefit from the analysis of long-term climate time-series data at a high temporal and spatial resolution. Water resource management, agricultural productivity, and climate change mitigation benefit from understanding the spatial distribution of rainfall and its temporal trends (Morales-Acuña, et al., 2021, p. 1). Rainfall data and measurements from traditional ground weather stations have traditionally been the major sources of such climate data; however, due to limited or nonexistent station networks in many regions of the world, historical records from station observations are insufficient. As a result, satellite-based rainfall data are increasingly being utilized to supplement or replace station observations.

Spatio-temporal and trend analysis of rain days having different intensity from 1901 – 2020 at regional scale in Haryana, India

Our study has investigated spatio-temporal distribution of rainfall and rain day trends during different seasons for all districts of Haryana, India. Gridded rainfall dataset of 120 years (1901 to 2020) from India Meteorological Department (IMD) was analysed using mean rainfall, rainfall deviation, seasonal rainfall ratio (SRR), coefficient of variation (CV), number of rain days, rainfall intensity, trends of rain days, Empirical Orthogonal Functions (EOF) and Principal Component (PC) analysis. Districts lying in eastern Haryana have experienced more rainfall (less variability) than the ones lying in western Haryana during each season. SRR and CV analysis depicted most consistent rainfall during monsoon and maximum variability during post-monsoon season. Highest number of rain days was observed during monsoon season followed by pre-monsoon, winter and post-monsoon season in Haryana. Innovative trend analysis method (ITAM) shows a declining trend in number of rain days during winter and post-monsoon season while an increasing trend was observed during pre-monsoon season. Overall, monsoon season has shown a falling trend in moderate while rising trend was observed in both light and heavy rainfall intensity categories in most of districts. Dominant EOF explained maximum variability during post-monsoon season followed by winter, pre-monsoon, and monsoon season, respectively. PC analysis captured inter-annual variability in rainfall during each season. Our findings highlight qualitative and quantitative aspects of seasonal rainfall dynamics at districts level. This study is beneficial in understanding impact of climate change and climate variability on rainfall dynamics in Haryana which may guide policymakers and beneficiaries in optimizing use of hydrological resources.

Precipitation Concentration Index and Rainfall Trend Analysis for South Western Districts of Karnataka India

A statistical analysis of the annual and seasonal (monsoon) rainfall was carried out for different stations located in south western districts of Karnataka state (adjoining to Western Ghats). The statistical methods such precipitation concentration index analysis (PCI), trend analysis using Sen's slope and Man-Kendall were employed for the observed data between 1960-2018. The findings show that the Spatiotemporal distribution of rainfall in these districts has changed during the later part of the data period, i.e., 1990-2018. There is no discernible upward trend in the region's rainfall. The erratic and increasing pattern of rainfall it these district may lead to the flash floods and at times drought situations. The results may of use to the water resources manager and planner of the region to devise an appropriate contingent plan to minimise the loss and for effective management of water resources.

Spatio-Temporal Rainfall Distribution and Markov Chain Analogue Year Stochastic Daily Rainfall Model in Ethiopia

This paper aims at the spatiotemporal distribution of rainfall in Ethiopia and developing stochastic daily rainfall model. Particularly, in this study, we used a Markov Chain Analogue Year (MCAY) model that is, Markov Chain with Analogue year (AY) component is used to model the occurrence process of daily rainfall and the intensity or amount of rainfall on wet days is described using Weibull, Log normal, mixed exponential and Gamma distributions. The MCAY model best describes the occurrence process of daily rainfall, this is due to the AY component included in the MC to model the frequency of daily rainfall. Then, by combining the occurrence process model and amount process model, we developed Markov Chain Analogue Year Weibull model (MCAYWBM), Markov Chain Analogue Year Log normal model (MCAYLNM), Markov Chain Analogue Year mixed exponential model (MCAYMEM) and Markov Chain Analogue Year gamma model (MCAYGM). The performance of the models is assessed by taking daily rainfall data from 21 weather stations (ranging from 1 January 1984–31 December 2018). The data is obtained from Ethiopia National Meteorology Agency (ENMA). The result shows that MCAYWBM, MCAYMEM and MCAYGM performs very well in the simulation of daily rainfall process in Ethiopia and their performances are nearly the same with a slight difference between them compared to MCAYLNM. The mean absolute percentage error (MAPE) in the four models: MCAYGM, MCAYWBM, MAYMEM and MCAYLNM are 2.16%, 2.27%, 2.25% and 11.41% respectively. Hence, MCAYGM, MCAYWBM, MAYMEM models have shown an excellent performance compared to MCAYLNM. In general, the light tailed distributions: Weibull, gamma and mixed exponential distributions are appropriate probability distributions to model the intensity of daily rainfall in Ethiopia especially, when these distributions are combined with MCAYM.

Spatiotemporal change analysis of long time series inland water in Sri Lanka based on remote sensing cloud computing

Sri Lanka is an important hub connecting Asia-Africa-Europe maritime routes. It receives abundant but uneven spatiotemporal distribution of rainfall and has evident seasonal water shortages. Monitoring water area changes in inland lakes and reservoirs plays an important role in guiding the development and utilisation of water resources. In this study, a rapid surface water extraction model based on the Google Earth Engine remote sensing cloud computing platform was constructed. By evaluating the optimal spectral water index method, the spatiotemporal variations of reservoirs and inland lakes in Sri Lanka were analysed. The results showed that Automated Water Extraction Index (AWEIsh) could accurately identify the water boundary with an overall accuracy of 99.14%, which was suitable for surface water extraction in Sri Lanka. The area of the Maduru Oya Reservoir showed an overall increasing trend based on small fluctuations from 1988 to 2018, and the monthly area of the reservoir fluctuated significantly in 2017. Thus, water resource management in the dry zone should focus more on seasonal regulation and control. From 1995 to 2015, the number and area of lakes and reservoirs in Sri Lanka increased to different degrees, mainly concentrated in arid provinces including Northern, North Central, and Western Provinces. Overall, the amount of surface water resources have increased.

Flash Floods Prediction Using Precipitable Water Vapor Derived From GPS Tropospheric Path Delays Over the Eastern Mediterranean

A flash flood is a rapid and intense response of a drainage area to heavy rainfall events. In the arid and semi arid parts of the Eastern Mediterranean (EM) region, the spatio-temporal distribution of rainfall is the most important factor for flash flood generation. A possible precursor to heavy rainfall events is the rise in tropospheric water vapor amount, which can be remotely sensed using ground based Global Navigation Satellite Systems (GNSS) stations. Here, we use the Precipitable Water Vapor (PWV) derived from 9 GNSS ground based stations in the arid part of the EM region in order to predict flash floods. Our approach includes using three types of Machine Learning (ML) models in a binary classification task which predicts whether a flash flood will occur given 24 hours of PWV data. We train our models with 107 unique flash flood events and vigorously test them using a nested cross validation technique. The results indicate a good agreement between all three types of models and across various score metrics. In addition, the models are further improved by adding more features such as surface pressure measurements. Finally, a feature importance analysis shows that the most important features are the PWV values from 2 to 6 hours prior a flash flood. These promising results indicate that it is possible to augment the current flash flood warning systems with a near real-time GNSS ground based data driven approach as demonstrated in this work.

Forecasting river basin yield using information of large-scale coupled atmospheric–oceanic circulation and local outgoing longwave radiation

ABSTRACT Global and local climate parameters influence the distribution of precipitation over continents. The spatio-temporal distribution of rainfall and the depth of rainfall over a river basin influence the river basin yield. This study deals with the prediction of the river basin scale yield of the ‘Upper Bhima River basin’ from the Maharashtra State of India. The information on large-scale circulation patterns El Nino-Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO) index, Multivariate ENSO Index (MEI), and local meteorological input viz. Outgoing longwave radiation (OLR) has been used to predict the river basin scale yield. The Artificial Intelligence (AI) tool – Genetic Programming (GP) – is used for developing prediction models. Ten different combinations of input variables are attempted for the development of monthly river basin yield models to arrive at the best input variable combination for the best predictions with varying lead times. Also, three combinations of input variables were tested for the prediction of ‘Seasonal Yield’. The findings of this research work indicate that GP-derived monthly River Basin Scale Yield forecasting models are successful in the prediction of yield with a correlation coefficient of 0.83. The seasonal yields could be predicted with a correlation coefficient of 0.75.

Recurrence quantification analysis of monthly rainfall time series in Pernambuco, Brazil

Precipitation is the main climatic variable that is used for modeling risks indices for natural disasters. We investigated nonlinear dynamics of monthly rainfall temporal series recorded from 1962 to 2012, at three stations in Pernambuco state, Brazil, located in regions with different rainfall regime (Zona da Mata, Agreste and Sertão), provided by the Meteorological Laboratory of the Institute of Technology of Pernambuco (Laboratório de Meteorologia do Instituto de Tecnologia de Pernambuco – LAMEP/ITEP). The objective of this work is to contribute to a better understanding of the spatiotemporal distribution of rainfall in the state of Pernambuco. We use the methodology from nonlinear dynamics theory, Recurrence plot (RP) that allows to distinguish between different types of underlying processes. The results showed that rainfall regime in deep inland semiarid Sertão region is characterized by weaker and less complex deterministic behavior, comparing to Zona da Mata and Agreste, where we identified transitions between chaotic and nonstationary type of dynamics. For transitional Agreste region rainfall dynamics showed stronger memory with longer mean prediction time, while for sub humid Zona da Mata rainfall dynamics is characterized by laminar (slowly changing) states.

Urban induced land-use change impact during pre-monsoon thunderstorms over Bhubaneswar-Cuttack urban complex

The effect of urbanization over the Bhubaneswar-Cuttack urban complex, a twin city of eastern India, is investigated in this study. For this purpose, the realistic representation of urban land use (LU) from the ‘Indian Space Research Organization (ISRO)’ and the older ‘United States Geological Survey’ is considered. The sensitivity experiment was performed to choose microphysics that was suitable for the study location. The recent LU simulations improved the amount and spatio-temporal distribution of rainfall over the city. Results suggested that the underlying impervious layer has a remarkable impact on the precipitation, temperature, and other meteorological variables during thunderstorms. Most of the simulated rain is found in the downwind direction of the city core and over the suburban periphery. The variation of the rainfall, showing dual peak characteristics during late evening hour, has excellent correspondence with the associated atmospheric moisture transport and dynamics and thermodynamics. The near-surface temperature and urban heat island found to be well simulated, covering the entire urban core from 12 UTC onward.

Recent changes in the precipitation-driving processes over the southern tropical Andes/western Amazon

Analyzing December–February (DJF) precipitation in the southern tropical Andes—STA ($$12^{\circ }\,\hbox {S}$$–$$20^{\circ }\,\hbox {S}$$; > 3000 m.a.s.l) allows revisiting regional atmospheric circulation features accounting for its interannual variability over the past 35 years (1982–2018). In a region where in-situ rainfall stations are sparse, the CHIRPS precipitation product is used to identify the first mode of interannual DJF precipitation variability (PC1-Andes). A network of 98 rain-gauge stations further allows verifying that PC1-Andes properly represents the spatio-temporal rainfall distribution over the region; in particular a significant increase in DJF precipitation over the period of study is evident in both in-situ data and PC1-Andes. Using the ERA-Interim data set, we found that aside from the well-known relationship between precipitation and upper-level easterlies over the STA, PC1-Andes is also associated with upward motion over the western Amazon (WA), a link that has not been reported before. The ascent over the WA is a component of the meridional circulation between the tropical North Atlantic and western tropical South America—WTSA ($$80^{\circ }\,\hbox {W}$$–$$60^{\circ }\,\hbox {W}$$; $$35^{\circ }\,\hbox {S}$$–$$10^{\circ }\,\hbox {N}$$). Indeed, the precipitation increase over the last 2 decades is concomitant with the strengthening of this meridional circulation. An intensified upward motion over the WA has moistened the mid-troposphere over WTSA, and as a consequence, a decreased atmospheric stability between the mid- and the upper troposphere is observed over this region, including the STA. We further show that, over the last 15 years or so, the year-to-year variability of STA precipitation (periodicity < 8 years) has been significantly associated with upward motion over the WA, while upper-level easterlies are no longer significantly correlated with precipitation. These observations suggests that the STA have experienced a transition from a dry to a wet state in association with a change in the dominant mode of atmospheric circulation. In the former dominant state, zonal advection of momentum and moisture from the central Amazon, associated with upper-level easterlies, is necessary to develop convection over the STA. Since the beginning of the 21st century, DJF precipitation over the STA seems to respond directly and primarily to upward motion over the WA. Beyond improving our understanding of the factors influencing STA precipitation nowadays, these results point to the need of exploring their possible implications for the long-term evolution of precipitation in a context of global warming.

Modeling blue and green water resources availability at the basin and sub-basin level under changing climate in the Weyb River basin in Ethiopia

The Weyb River basin, water from which is utilized for diverse water resources schemes, is one of the important rivers of Ethiopia. However, increase in temperature and change in both magnitude and its spatiotemporal distribution of rainfall under changing climate are adversely affecting the freshwater resources availability. This study aims to model the blue and green water resources availability under changing climate within Weyb River basin in Ethiopia at both basin and sub-basin level using ArcSWAT hydrologic model. All water balance components including blue water flow (sum of the water yield and the deep aquifer recharge), green water flow (actual evapotranspiration), and green water storage (soil water) at both entire basin and sub-basin level were examined. Results reveal that mean annual blue water flow, green water flow, and green water storage exhibit a rise in the entire basin and in all the sub-basins under representative concentration pathway (RCP)8.5/4.5/2.6 scenarios. The mean annual blue and green water flow increases by 28% and 30% under RCP8.5, 20% and 29.89% under RCP4.5, and 17% and 28.65% under RCP2.6 scenarios respectively, while the mean annual green water storage rises by 13.87%, 13.62%, and 13.60% under RCP8.5, RCP4.5, and RCP2.6 scenarios respectively in the entire basin. The sub-basin level analysis reveals that spatial variations of blue and green water resources availability in all the six sub-basins are very high as compared to that of the entire basin analysis under all RCP scenarios. This study provided significant insights into freshwater availability on a sub-basin level under future changing climate and it is paramount important to develop climate change adaptation and mitigation strategies at sub-basin level for optimum planning and management of freshwater availability within the Weyb River basin in upcoming period.

Rainfall over the Himalayan foot-hill region: Present and future

Uttarakhand, one of the Himalayan foot-hill states of India, covers an area of 51,125 km2. This region is enriched with bio-diversity and is one of the highly potential regions in the Central Himalayas for agro-climate, hydro power generation, food-processing, tourism, etc. Present study investigates the spatio-temporal rainfall distribution over the state during Indian summer monsoon period. Observational and modelled (under different Representative Concentration Pathways (RCPs) at radiative forcing 2.6, 4.5 and 8.5 W/m2) rainfall distribution is studied to assess the present and future trends. Study uses standard observational rainfall estimates from APHRODITE, Tropical Rainfall Measuring Mission (TRMM 3B42) and India Meteorological Department (IMD) gridded rainfall datasets and inter-compare these products in order to find out orographic responses during the monsoon months and elevation dependent mean rainfall pattern changes. It is found that rainfall pattern breaks near 3100 m elevation. Comparative analysis reflects that with respect to IMD, TRMM 3B42 rainfall underestimates more than 3 mm/day rainfall whereas, APHRODITE overestimates rainfall below 4.5 mm/day. Future trends in modelled monsoon rainfall are examined and mixed results are found and discussed with possible explanation.

Rainfall and river flow trends for the Western Cape Province, South Africa

Climate change has the potential to alter the spatio-temporal distribution of rainfall, subsequently affecting the supply and demand of water resources. In a water-stressed country such as South Africa, this effect has significant consequences. To this end, we investigated annual and winter rainfall and river flow trends for the Western Cape Province over two periods: 1987–2017 and 1960–2017. Annual rainfall for the most recent 30-year period shows decreasing trends, with the largest magnitude of decrease at the SA Astronomical Observatory rainfall station (-54.38 mm/decade). With the exception of the significant decreasing winter rainfall trend at Langewens (-34.88 mm/decade), the trends vary between stations for the period 1960–2017. For the period 1987–2017, statistically significant decreasing winter trends were found at four of the seven stations, and range from -6.8 mm/decade at Cape Columbine to -34.88 mm/decade at Langewens. Similarly, the magnitudes of decreasing winter river flow at Bree@Ceres and Berg@Franschoek are greater for the more recent 30-year period than for 1960–2017. Correlation coefficients for Vilij@Voeliv rainfall and four river flow stations Berg@Franschoek, Bree@Ceres, Wit River@Drosterkloof and Little Berg@Nieuwkloof) are stronger for shorter periods (i.e. 1987–2017 and 2007–2017) than that for the longer period, 1960–2017. The Intergovernmental Panel on Climate Change emphasises the importance of studies to assist with model prediction uncertainties. To this end, our study expands the understanding of regional hydrological responses to rainfall change in the water stressed region of the Western Cape Province.&#x0D; Significance:&#x0D; &#x0D; Historical trend analyses provide the basis for future rainfall and river flow projections and also improve our understanding of hydrological responses to rainfall change, which is important for water resource planning and management in light of recent rainfall shortages experienced in the Western Cape region.&#x0D; Anthropogenic and natural influences on river systems must be taken into account when assessing the impact of rainfall changes on river flow because these factors ultimately affect water resource management.&#x0D; We identified decreasing winter rainfall trends across the Western Cape Province for the most recent three decades.&#x0D;