Long-term Rainfall Data Research Articles

Debris flows rainfall thresholds in the Apennines of Emilia-Romagna (Italy) derived by the analysis of recent severe rainstorms events and regional meteorological data

Recent severe rainstorms events in October 2014 and September 2015 triggered more than a hundred debris-flows in the western part of the Apennines of Emilia-Romagna (Italy). In this work, we tested a novel method to define debris flows rainfall thresholds for the 2014 and 2015 rainstorms (which have been used as reference events) and to extend these results across the Apennines of Emilia-Romagna, making use of long-term rainfall data of the regional raingauges network. Results are compared, for validation, to rainfall rates recoded during other past rainstorm-debris flows events (which have been used as validation events). At first, the method involves a spatial discriminant analysis between the spatial distribution of debris flows and the high frequency weather-radar rainfall data for the 2014 and 2015 reference events. The analysis defines rainfall cutoff values over rainfall durations from 30′ to 6 h, related to verification indices in the ROC curves, which are used as debris flows rainfall thresholds. Exceedance ratios are calculated between the computed rainfall thresholds and the rainfall rates at 10 years return periods at corresponding rainfall durations computed for raingauges located in the areas affected by the 2014 and 2015 events. The ratios are then used as multipliers of the rainfall rates at 10 years return periods over rainfall durations from 30′ to 6 h calculated for all other raingauges in the regional study area. To spatialize thresholds calculation to the regional scale, the computed thresholds are interpolated across the Apennines of Emilia-Romagna. The research resulted in the assessment of two-levels debris flows rainfall thresholds curves which seem to be adequate to discriminate rainfall rates recorded during past debris flows events used for validation. Discussion evidences advantages and limits of our approach, compares results to existing debris flows thresholds and highlights their possible use in a multi-stage warning procedure at regional scale.

Comparison of fluvial and pluvial flood risk curves in urban cities derived from a large ensemble climate simulation dataset: A case study in Nagoya, Japan

Flood disasters are caused by two types of flooding: pluvial and fluvial flooding. The different hydrodynamic characteristics of these two types have been intensively discussed in a literature. However, their impact and the resulting damage have not yet been examined in a comprehensive manner due to the lack of extreme rainfall data and/or computational costs of flood simulations. Recently, an increasing number of ensemble climate simulation data has become available, providing long-term rainfall data based on numerical climate simulations. This is a case study of a flooding in an urban area set in Nagoya City, Japan. We applied a large ensemble climate simulation database, d4PDF, to a pluvial and fluvial flood model and derived the flood risk curves for each type of flooding. The results indicated that pluvial flooding presents comparable economic risk to fluvial flooding (only 16% and 17% lesser damage at 50- and 100-year return periods, respectively) despite its significantly shallower flood depths (area with flood depth over 45 cm was only 10.5% and 5.4%, respectively). The significant impact of pluvial flooding is attributable to its wider flood area (despite shallower depths), impacting major economic assets sprawled over the city, including areas further away from the river. Furthermore, fluvial flood risk was managed by settling the Nagoya Castle (now the central economic district) on higher altitudes. The results suggest that pluvial flooding could have comparable economic risks to fluvial flooding in urban areas where major economic assets are not concentrated around streams as well as historical countermeasures are installed against fluvial flooding.

Statistical and trend analyses of long-term rainfall data: a case study for Mauritius

This paper focuses on the analysis of monthly rainfall data for the period 1950-2016 for Mauritius using statistical techniques and decomposition method. For the statistical analysis, the data are fitted to commonly used probability distribution function for which parameters are estimated using the method of maximum likelihood estimation (MLE). The Anderson-Darling (A-D), Kolmogorov-Smirnov (K-S) and chi-square (C-S) tests are then employed to determine which PDF best fits the data. The results obtained indicate that the log-normal, GEV and inverse Gaussian PDFs best fit the rainfall data at less than 5% significance level. The ensemble empirical mode decomposition (EEMD) method is employed to study the trends in the data. Results obtained are in terms of intrinsic mode functions (IMFs) and the trendline. The analysis reveals that there is a general linear decrease of 1 mm/year in the amount of rainfall.

Statistical and trend analyses of long-term rainfall data: a case study for Mauritius

Statistical and trend analyses of long-term rainfall data: a case study for Mauritius

Spatiotemporal variability and trends of rainfall and temperature in the Northeastern Highlands of Ethiopia

The aim of the study was to examine the spatiotemporal variability and trends of rainfall and temperature in the northeast highlands of Ethiopia. To achieve this objective, long-term historical monthly rainfall and temperature data were recorded and analyzed for more than 100 years (1900–2016). Gridded rainfall and temperature data were gathered from CenTrends Great Horn of Africa v1 and CRU_TS 4.01 with a resolution of 0.1° × 0.1° and 0.50 × 0.5°, respectively. The Mann–Kendall test was used to analyze the trends of rainfall data in different recording stations, and the Sen’s slope estimator was used to determine the magnitude of change. The Inverse Distance Weighted spatial analysis tool was used to illustrate the spatial trends of rainfall and temperature. The results of the coefficient of variation revealed that the decadal, annual and seasonal rainfall distributions were varied though belg rainfall found to be more variable than the others. The MK test result demonstrated that belg and annual rainfall exhibit a decreasing trend as compared to kiremt rainfall. The distribution of kiremt, belg and annual rainfall demonstrated decreasing trends at a rate of 0.432, 0.335 and 0.595 mm/year, respectively. The decadal rainfall also showed a decreasing trend at the rate of 6.537 mm/decade. On the other hand, the averages of annual, maximum and minimum temperature were increased at the rate of 0.0034, 0.0028 and 0.0095 °C/year, respectively. The decadal minimum, maximum and average temperature has shown increasing trend at the rate of 0.098 °C, 0.041 °C and 0.069 °C, respectively. An abrupt declined rainfall and increased temperature were observed since the 1970s. Climate variability strongly affects rain-fed agriculture more than any other activities. Hence, policymakers and stakeholders have to give top priority in the designing and introduction of appropriate area-specific adaptive strategies to reduce the impacts on crop production over livelihood zones. Rainwater harvesting and the development of small-scale irrigation schemes could be taken as viable options where rainfall is scarce and more variable.

SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations

Abstract. Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent “bottom-up” approach that exploits satellite soil moisture observations for estimating rainfall through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data record as a single polar orbiting satellite sensor is used. Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp) satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System programme. The continuity of the scatterometer sensor is ensured until the mid-2040s through the MetOp Second Generation Programme. Therefore, by applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a 12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record is assessed on a regional scale through comparison with high-quality ground networks in Europe, the United States, India, and Australia. Moreover, an assessment on a global scale is provided by using the triple-collocation (TC) technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the gauge-based Global Precipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relatively well at both a regional and global scale, mainly in terms of root mean square error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data record provides performance better than IMERG and GPCC in data-scarce regions of the world, such as Africa and South America. In these areas, we expect larger benefits in using SM2RAIN–ASCAT for hydrological and agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist of the underestimation of peak rainfall events and the presence of spurious rainfall events due to high-frequency soil moisture fluctuations that might be corrected in the future with more advanced bias correction techniques. The SM2RAIN–ASCAT data record is freely available at https://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end of August 2019).

Understanding the Effects of Changing Weather: A Case of Flash Flood in Morogoro on January 11, 2018

Floods are the leading cause of hydrometeorological disasters in East Africa. Regardless of where, when, and how the event has happened, floods affect social security as well as environmental damages. Understanding floods dynamics, their impacts, and management is thus critical, especially in climate risk assessment. In the present study, a flash flood (a case of an episodic hydrological event) which happened on January 11, 2018, in Morogoro, Tanzania, is examined and synthesized. Data were courtesy of the National Oceanic and Atmospheric Administration Global Forecasting System (NOAA GFS) (forecast data), Tanzania Meteorological Agency (TMA), and Sokoine University of Agriculture (for the automatic weather data). With the help of ZyGRIB-grib file visualization software (version 8.01, under General Public License (GNU GPL v3)), the forecast data and patterns of the observation from the automatic weather station (temperatures, wind speed and directions, rainfall, humidity, and pressure) and the long-term rainfall data analysis in the study area made it possible. This study contributes to the knowledge of understanding the changing weather for planning and management purposes. Both forecasts and the observations captured the flash flood event. The rain was in the category of heavy rainfall (more than 50 mm per day) as per the regional guidelines. The synergy between the forecasts and the 30-minute weather observation interval captured the fundamental weather patterns that describe the event. For studying the nature and impacts of flash floods in the region, the integration of automatic weather observation into the systems of national meteorological centers is inevitable. Additionally, as part of an integrated disaster risk reduction effort, there is a need for a review on catchment management strategies.

Short-term instead of long-term rainfall time series in rainwater harvesting simulation in houses: An assessment using Bayesian Network

Rainwater harvesting simulations usually take long-term rainfall data as input to size system components. However, long-term time series data are not available in many cities around the world. This article aims to investigate the relation between the rainwater harvesting simulation results and the rainfall characteristics of various time series lengths. For 13 cities, 30-year time series were adopted as long-term time series to investigate the influence of time series length on the simulation outcomes. Short-term time series were extracted from those long-term time series and used as input in several computer simulations applied in a generic residential model. Comparison of short-term and long-term time series results allowed to obtain a representative short-term time series that leads to results similar to those obtained using long-term time series. A statistical analysis associated the simulation results of the representative time series with their rainfall characteristics using three indicators: average annual rainfall, seasonality index, and average number of dry days per year. The average number of dry days per year showed to be the most important short-term time series indicator to be considered. Additionally, a Bayesian Network was proposed to serve as a support-decision making tool for future studies. This network uses rainfall indicators and rainwater demand to predict if a given short-term time series leads to results similar to those obtained using a long-term time series. The validation of the Bayesian Network showed the tool is promising and useful to assess the representativeness of short-term time series used in rainwater harvesting simulations in houses.

Assessment of Drought Vulnerability and Occurrence Zones in North Central Nigeria

Assessment of drought vulnerability and occurrence zones in North Central Nigeria was carried out in this study using standardized Precipitation Index (SPI). The Standardized Precipitation Index (SPI) 12 months’ time scale was computed from long-term rainfall data between 1960 and 2015 obtained from the Nigerian Meteorological Agency (NIMET) Abuja. The SPI results were further subjected to Mann-Kendal and Sen’s slope analysis for trend detection. To assess the vulnerability of drought and occurrence zones, the SPI and other geo-spatial techniques were employed to generate drought vulnerability maps for three epochs: 2000-2005, 2005-2010 and 2010-2015 respectively. The SPI values were interpolated using Inverse Weighted (IDW) techniques in ArcGIS10.5 to generate the vulnerability maps for the selected model years. The outcome of drought trend and vulnerability maps reveals increasing drought trend dominated by near normal and moderate conditions in Abuja, Ilorin and Lokoja while moderate and severe droughts were observed in Minna and Jos. The study therefore recommends holistic drought monitoring and management strategies in order to mitigate its adverse impact on water resources and rainfall-dependent agriculture.

Quantification of potential flood inundation areas in the marsh wetland of Honghe National Natural Reserve, Northeast China

Wetland protection and recovery needs scientific foundations for a better understanding of wetland floods, since the flooding existence and its dynamics are the key aspects in the formation and evolution of wetland ecosystems. The Honghe National Natural Reserve (HNNR) in the Northeast China has been listed in the Ramsar Convention on Wetlands since 2002. The specific dates for determining of an average inundation boundary caused by a regular flooding were firstly fixed from multiple rainfall events by a hydro-meteorological analysis based on the long-term rainfall data. The flood inundation areas in the HNNR marshes were mapped according to the correlation between hydro-geomorphology and regular flood progress. The mapping of the flood inundation areas were supported technically from the application of remote sensing images, combining the hydro-geomorphologic analysis with meteorological statistical data. The final map with the spatial-temporal content of an inundation created by hydro-geomorphological analysis and remote sensing data was validated by the long-term rainfall, soil moisture and surface water level data in the HNNR. The results indicated that the flood inundation areas were mainly distributed in large patches along the two main river courses of Nongjiang River and its branch of Woyalan River in the northern and central HNNR. The flood inundation patches were scattered as seasonal marshes and permanent ponds in the southern and eastern HNNR. The extracted flood inundation area accounted for more than a quarter of the total area of HNNR. The flood inundation area of Nongjiang was much larger than that of Wuyalan.

Landslide occurrences and rainfall trend during the period 1951-2010, Emilia-Romagna Region, Northern Italy

The relation between rainfall trend and landslide occurrences in the Emilia-Romagna region (Northern Italy) is presented. This study is based on the availability of long-term (1931-2017) rainfall data and the Catalogue of Landslide OCcurrences in the Emilia-Romagna region (CLOCkER), which reports the date of more than14,000 landslides from the Middle Ages to the present. In particular, the analyses focus on the role of rainfall as main triggering factor of landslides that occurred in the Emilia-Romagna region during the period between October 1951 and September 2010.Early findings suggest a well-defined correlation, for different intervals of analyses (annual, seasonal, antecedent rainfall period of ninety days), between the landslide occurrences recorded and the rainfall trend. This demonstrates that rainfall can be considered the main triggering factor for landslides occurring in the Emilia-Romagna region.

Drought dynamics and interannual rainfall variability on the Ghaap plateau, South Africa, 1918–2014

With drought expected to increase in frequency and severity as a result of climate change, drought and rainfall variability assessments at interannual time scales using long-term rainfall data are necessary to develop drought mitigation strategies and planning measures, especially in semi-arid and arid environments where drought impact is expected to be adverse. The objective of this study was to determine the occurrence and severity of droughts and interannual rainfall variability trends in the Ghaap plateau, Northern Cape Province, South Africa. This study was based on long-term rainfall data for three meteorological stations (Postmasburg, Douglas and Groblershoop) from 1918 to 2014, sourced from the South African Weather Services (SAWS). Calculation of the Standardized Precipitation Index (SPI) showed that more droughts occurred since the 1990s; these droughts were all moderately dry with SPI values ranging between −1.03 and −1.46, except for the 1992 drought at Groblershoop which was severe. The longest drought duration on record in the study area was 2 years. Fitting of the long-term rainfall data to a non-parametric spline smoother revealed that the total annual rainfall, number of rainfall days and extreme rainfall events were essentially stable. The total annual rainfall, however, followed a secular pattern of fluctuations over the years.

Rainfall thresholds for post-fire runoff and sediment delivery from plot to watershed scales

Wildfire increases the likelihood of runoff, erosion, and downstream sedimentation in many of the watersheds that supply water for Colorado’s Front Range communities. The objectives of this study were to: (1) identify rainfall intensity thresholds for a post-fire runoff or sediment delivery response at plots (≤0.06 ha), hillslopes (0.07–5.2 ha) and watersheds (100–1500 ha) after three Colorado Front Range wildfires for up to four years post-fire; (2) determine how the rainfall thresholds varied by fire location, year post-fire, spatial scale, and mulch treatments; and (3) use long-term rainfall data to map the likely frequency of rainfall events above these intensity thresholds as an indicator of risk for post-fire runoff or sediment delivery from future high severity fires in Colorado. Maximum 60-min rainfall intensity (MI60) thresholds were identified as the values that best separated rain storms that generated responses from those that did not. We found that thresholds did not significantly differ among fires for any year post-fire. Thresholds did significantly vary among spatial scales; for the first two years post-fire, high-confidence thresholds ranged from 4 to 8 mm h−1 across unmulched plots, hillslopes and watersheds. Compared to post-fire year 0, thresholds in year 3 were significantly higher, with high-confidence thresholds up to 22 mm h−1. NOAA Atlas rainfall data were used to compute and map frequencies of threshold exceedance across Colorado. Within the Front Range study area, rain storms with MI60 of 4 mm h−1 have frequencies ranging from 5 to 11 times per summer, while MI60 values of 8 mm h−1 have frequencies of 2–5 times per summer. Maps of threshold exceedance frequency can help identify areas most vulnerable to post-fire runoff and sediment delivery and prioritize post-fire emergency planning.

Using Copulas to Evaluate Rationality of Rainfall Spatial Distribution in a Design Storm

In the absence of long-series streamflow records in plain areas, design storm, which serves as the most important input in a hydrologic model, plays an important role in flood control and water resources management. For a large drainage basin, design storm may be estimated for sub-basins separately; thus the spatial distribution of design storm needs to be carefully treated. However, few studies have been carried out to evaluate the rationality of the spatial distribution in a design storm, which means the storm over space should be in accordance with actual needs or its distributing patterns. Taking the Tai Lake Basin (TLB), 3-d Copula-based models combining extreme rainfall of different sub-basins were built using long-term rainfall data, and conditional probabilities of sub-basins encountering certain amounts of rainfall were investigated to evaluate the rationality of the design storm. Results show that the spatial distribution of the design storm based on a typical year is hardly rational, in which rainfall of the northeastern part of the basin is suggested to be weakened while in the southwest to be strengthened; after the rainfall is redistributed based on long-term information, it shows a better rationality of spatial distribution. Such information provides valuable significance in guiding flood control of TLB, and the considered evaluating method can be used for similar basins in plain areas.

SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture

Abstract. Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca et al., 2014). Daily rainfall estimates are generated for an 18-year long period (1998–2015), with a spatial sampling of 0.25° on a global scale, and are based on the integration of the ACTIVE and the PASSIVE ESA CCI SM data sets.The quality of the SM2RAIN-CCI rainfall data set is evaluated by comparing it with two state-of-the-art rainfall satellite products, i.e. the Tropical Measurement Mission Multi-satellite Precipitation Analysis 3B42 real-time product (TMPA 3B42RT) and the Climate Prediction Center Morphing Technique (CMORPH), and one modeled data set (ERA-Interim). A quality check is carried out on a global scale at 1° of spatial sampling and 5 days of temporal sampling by comparing these products with the gauge-based Global Precipitation Climatology Centre Full Data Daily (GPCC-FDD) product. SM2RAIN-CCI shows relatively good results in terms of correlation coefficient (median value > 0.56), root mean square difference (RMSD, median value < 10.34 mm over 5 days) and bias (median value < −14.44 %) during the evaluation period. The validation has been carried out at original resolution (0.25°) over Europe, Australia and five other areas worldwide to test the capabilities of the data set to correctly identify rainfall events under different climate and precipitation regimes.The SM2RAIN-CCI rainfall data set is freely available at https://doi.org/10.5281/zenodo.846259.

Unidirectional trends in daily rainfall extremes of Iraq

The unidirectional trends in rainfall and rainfall-related extremes in Iraq have been assessed using long-term daily rainfall data (1965–2015). The modified version of Man-Kendall (m-MK) test which can discriminate the multi-scale variability from unidirectional trend was used along with Mann-Kendall (MK) test to confirm the trends in the presence of long-term persistence (LTP). The MK test revealed decrease in annual number of rainy days, heavy rainfall days, and consecutive wet days at 60, 53, and 47% stations, respectively, and the same indices during winter at 47, 47, and 33% stations, respectively. Significant decrease in annual and winter total rainfall was also found at a few stations. An increasing tendency in dry spell was observed due to the decrease in total rainfall and the number of rainy days; however, it was still not significant in most of the stations. The Student’s t test and F test revealed significant changes in daily rainfall mean and variability between two 30-year periods, 1965–1994 and 1986–2015. The m-MK test confirmed the results obtained using the MK test in most of the stations. However, the number of stations with significant change was found to decrease when the m-MK test was used. This indicates that some of the significant changes estimated by MK test were due to the presence of LTP. The obtained results confirmed the increase in dry spells and droughts in the region.

Rainfall Rate Prediction for Propagation Applications: Model Performance at Regional Level Over Ireland

Three global rainfall rate prediction methods are evaluated in their ability to estimate local precipitation statistics, which are the key to predict the impact of rain on the propagation of electromagnetic waves through the atmosphere. Specifically, the International Telecommunication Union-Radiocommunication Sector (ITU-R) P.837-6, model for rainfall statistics estimation (MORSE), and the ITU-R P.837-7 prediction methods are tested against long-term rainfall data collected in 19 sites in Ireland. The results indicate that the ITU-R P.837-7 prediction method delivers the best performance, and that both the ITU-R P.837-6 prediction method and MORSE exhibit a positive bias, likely due to the overestimation of the yearly rain amount in the maps used as input to such models. The results of the testing activity provide information on the accuracy of rainfall rate prediction methods at regional level, an important factor to consider given the direct link between the magnitude of rain-induced attenuation, and the operational frequency of wireless communication links.

An analysis of the relationship between drought events and mangrove changes along the northern coasts of the Pe rsian Gulf and Oman Sea

Relating the changes of mangrove forests to spatially explicit reductions in rainfall amounts and increases in drought occurrences is a prerequisite for improving the effectiveness and success of mangrove forest conservation programs. To this end, we investigated the relationship between drought events (quantified using the Standardized Precipitation Index [SPI]) and changes in area and canopy cover of mangrove forests on the northern coast of the Persian Gulf and the Oman Sea using satellite imagery and long-term annual rainfall data over a period of 30 years (1986–2016). Statistical analyses revealed 1998 as the year marking the most significant change-point in the mean annual rainfall values in the catchments and mangroves, after which average SPI values consistently remained at lower levels. In the period of 1998–2016, decreases in the mean annual rainfall and increases in the severity of droughts differed spatially and were greater in the catchments and mangroves on the coasts of the Oman Sea than the coasts of the Persian Gulf. These spatially explicit results were closely mirrored by the mangrove response, with differential in reductions in mangrove areas and canopy cover that corresponded closely with the spatial distribution of drought intensities in the different parts of the coasts, with correlation coefficients ≥0.89 for the different coastal regions.

Seasonality and predictability shape temporal species diversity.

Temporal environmental fluctuations, such as seasonality, exert strong controls on biodiversity. While the effects of seasonality are well known, the predictability of fluctuations across years may influence seasonality in ways that are less well understood. The ability of a habitat to support unique, non-nested assemblages of species at different times of the year should depend on both seasonality (occurrence of events at specific periods of the year) and predictability (the reliability of event recurrence) of characteristic ecological conditions. Drawing on tools from wavelet analysis and information theory, we developed a framework for quantifying both seasonality and predictability of habitats, and applied this using global long-term rainfall data. Our analysis predicted that temporal beta diversity should be maximized in highly predictable and highly seasonal climates, and that low degrees of seasonality, predictability, or both would lower diversity in characteristic ways. Using stream invertebrate communities as a case study, we demonstrated that temporal species diversity, as exhibited by community turnover, was determined by a balance between temporal environmental variability (seasonality) and the reliability of this variability (predictability). Communities in highly seasonal mediterranean environments exhibited strong oscillations in community structure, with turnover from one unique community type to another across seasons, whereas communities in aseasonal New Zealand environments fluctuated randomly. Understanding the influence of seasonal and other temporal scales of environmental oscillations on diversity is not complete without a clear understanding of their predictability, and our framework provides tools for examining these trends at a variety of temporal scales, seasonal and beyond. Given the uncertainty of future climates, seasonality and predictability are critical considerations for both basic science and management of ecosystems (e.g., dam operations, bioassessment) spanning gradients of climatic variability.

Long-Term Rainfall Data Analysis for Contingency Crop Planning for Indore Region of Madhya Pradesh

Long-Term Rainfall Data Analysis for Contingency Crop Planning for Indore Region of Madhya Pradesh