Precipitation Time Series Research Articles

Study on NDVI Periodic Change Characteristics of Inner Mongolia Grassland Based on Variational Mode Decomposition Algorithm

Based on the NDVI data of vegetation in Inner Mongolia from 1982 to 2015, the variational mode decomposition (VMD) method, which has been well applied in the field of signal decomposition, is introduced to study the periodicity of vegetation index in Inner Mongolia. The VMD method is used to extract the monthly and annual NDVI and the long time series cycle characteristics of temperature and precipitation in the same period from April 1982 to October 2015 in Inner Mongolia. The results show that temperature and precipitation are important factors affecting the growth of vegetation, and there are 6.99 and 3.49 months of the same oscillation cycle for monthly NDVI and temperature and precipitation time series; when the central frequency is the same, the amplitude of the monthly temperature and precipitation time series increases with the increase of the lag period. The annual scale NDVI has the same period of 16.95, 6.8a, and 4.85a with precipitation, and the same period of 6.8a and 4.85a with temperature. The Residue component shows that the overall NDVI and temperature in Inner Mongolia have shown a significant slow growth trend in the past 30 years. Although the precipitation has shown a significant slow decline trend in the same time period (p = 0.000), the grassland is still in the process of continuous improvement.

Added value of an atmospheric circulation pattern‐based statistical downscaling approach for daily precipitation distributions in complex terrain

AbstractReliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.

A novel framework for spatiotemporal groundwater pumping process estimation based on data-driven approaches

Understanding the spatial and temporal distribution of pumping activities is crucial for effective water resource management. However, obtaining accurate pumping records is often challenging, particularly in areas without efficient groundwater withdrawal permit systems. In this study, we propose a novel approach for identifying pumping activities and estimating their associated pumping rates across space and time. Our data-driven approach integrates empirical orthogonal function (EOF) and Hilbert-Huang transform (HHT) analyses on first-differenced head data to extract high-frequency head variations that are closely related to pumping activities. The identified pumping-associated head variations are used to estimate the on-and-off times of local pumping stations and their associated pumping-rate time series. We test our approach using a hypothetical aquifer with designed pumping and real precipitation time series. Our results show that the EOF analysis is able to distinguish pumping locations where distinct temporal head variabilities are present. HHT analysis is then able to effectively remove noise from the EOF-identified pumping-associated head variations. Compared to the designed pumping data, our results demonstrate that our proposed method is able to produce accurate pumping estimates in terms of both spatial and temporal distribution and total amounts. While our approach does have limitations, such as delayed and boundary effects, our results suggest that it can be an effective tool for pumping estimation in areas with relatively abundant head observations across space and time, which is a common scenario in Taiwan.

Spatial-Temporal Changes of Abarkuh Playa Landform from Sentinel-1 Time Series Data

Playas, as the flattest landforms in semiarid and arid regions, are extremely sensitive to climate changes, such as changes in the hydrologic regime of the landscape. The changes in these landforms due to irrigation, anthropogenic activities, and climate change could be a source of disasters. In this study, we explored the spatial-temporal changes of the Abarkuh Playa in Central Iran using the time series of the Sentinel-1 backscatter dataset in the three scales. Our results showed that the western area of the Abarkuh Playa has been changed to other landforms with different characteristics, which is clear from all backscatter maps. The spatial-temporal analysis of the time series of backscatter data using the independent component analysis and time series of precipitation revealed that the backscatter variations were associated with direct rainfall across the playa and the surface was reacting to changes in the soil moisture content. The results of the power scale showed that the boundary of the playa could successfully be recognized as the oscillating pattern from other landforms in the study area. Moreover, the spatial-temporal analysis of backscatter in the power scale showed that different polarizations could reveal different patterns of surface changes for the playa.

MVI and Forecast Precision Upgrade of Time Series Precipitation Information for Ubiquitous Computing

Missing values are the major problem in a time - series dataset that lead to a very typical phenomenon in a dataset analysis. Better analysis of a dataset in ubiquitous computing can only be achieved after efficiently handling the missing values. This paper envisioned to present the missing values imputation (MVI) in the monthly rainfall dataset in India. Generally, the approximation of the missing values was completed using Non-linear principal component analysis, which is having a scope of enhancement. Kalman filter using Arima model to impute missing values is a better way that can be further improved by using Extended Kalman filtering. Further, a rainfall prediction is carried out using LSTM along with three different optimizers including stochastic gradient descent (SGD), RSM-Prop, and ADAM optimizers. In addition, a comparative prediction of rainfall is depicted which shows the combination of Extended Kalman imputation, LSTM, and ADAM optimizer to outperformance in prediction.

Stakeholder-Informed Hydroclimate Scenario Modeling in the Lower Santa Cruz River Basin for Water Resource Management

The Lower Santa Cruz River Basin Study (LSCRB Study) is a collaborative effort of regional and statewide water management stakeholders working with the US Bureau of Reclamation under the auspices of the 2009 SECURE Water Act. The impacts of climate change, land use, and population growth on projected water supply in the LSCRB were evaluated to (1) identify projected water supply and demand imbalances and (2) develop adaptation strategies to proactively respond over the next 40 years. A multi-step hydroclimate modeling and risk assessment process was conducted to assess a range of futures in terms of temperature, precipitation, runoff, soil moisture, and evapotranspiration, with a particular focus on implications for ecosystem health. Key hydroclimate modeling process decisions were informed by ongoing multi-stakeholder engagement. To incorporate the region’s highly variable precipitation pattern, the study used a numerical “weather generator” to develop ensembles of precipitation and temperature time series for input to surface hydrology modeling efforts. Hydroclimate modeling outcomes consistently included increasing temperatures, and generated information related to precipitation responses (season length and timing, precipitation amount) considered useful for evaluating potential ecosystem impacts. A range of risks was identified using the hydroclimate modeling outputs that allowed for development of potential adaptation strategies.

Evaluation of a novel hybrid lion swarm optimization – AdaBoostRegressor model for forecasting monthly precipitation

Precipitation is essential for crop production, water resource management, and other activities. Since precipitation has unpredictable sequential and seasonal characteristics, it is extremely difficult to forecast precipitation. Hence, a better forecasting model will lead to an early warning that minimizes the risk to life and property. Many approaches, such as statistical, Machine Learning (ML), Deep Learning (DL), and hybrid algorithms, are used to build precipitation forecasting systems. Feature selection is especially important for selecting past values (lags) and to eliminate irrelevant and/or redundant features. The optimization algorithm helps identify correct time series lags because it does not make data assumptions. In this work, a hybrid long-term (1-month ahead) precipitation forecast model (ABR-LSO) is proposed by integrating the AdaBoostRegressor (ABR) with the Lion Swarm Optimization (LSO) algorithm. Time series precipitation data for the period 1989–2019 for Idukki, Kerala, India. The ABR model considered 12 antecedent rainfall records as its input features and predicted the rainfall data for the next time step (t + 1), whereas the ABR-LSO hybrid model optimized the input features with LSO. The efficacy of the forecast models is evaluated using statistical indices like mean absolute error (MAE) and root mean square error (RMSE). It has been noted that in models 1, 2, and 3, the LSO-ABR model produced good MAE above ABR model improvements of 5.08 %, 4 %, and 6.12 %, respectively. Precipitation forecasts will help the administrators devise disaster prevention mechanisms against flooding in the downstream areas.

Accuracy of satellite precipitation products in data-scarce Inner Tibetan Plateau comprehensively evaluated using a novel ground observation network

Study regionInner Tibetan Plateau (ITP) is the largest plateau in the world, with only four national basic stations covering an area of 5.76 × 105 km2. Study focusSatellite precipitation products (SPPs) have widely been evaluated on a regional scale. However, a comprehensive quantitative evaluation of SPPs using multiple indices has not been reported. Here, 16 SPPs were quantitatively and comprehensively evaluated for precipitation occurrence and time series using an improved rank score (RS) method. The new observation network, which had more than the number of national stations, was applied for evaluating SPPs to increase the reliability of results. New hydrological insights for the regionIntegrated Multi-satellite Retrievals for the Global Precipitation Measurement (IMERG)-Early and IMERG-Late runs showed the best overall performance, with an RS value of 0.91. The optimal SPPs varied according to the evaluation indices but mostly belonged to IMERG-Early, IMERG-Late, Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Satellite Mapping of Precipitation near-real-time (GSMaP-NRT), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared Rain Rate near-real-time (PDIR-Now). Overall, most SPPs detected precipitation; however, the SPPs poorly measured the precipitation time series but adequately detected precipitation in the range of 10–25 mm in terms of the three indices of precipitation occurrence. The proposed improved RS method can be used to comprehensively evaluate other products and models.

Investigation of Infiltration Loss in North Central Texas by Retrieving Initial Abstraction and Constant Loss from Observed Rainfall and Runoff Events

Accurate modeling of infiltration losses is vital for runoff estimation and thus the development of flood design/protection criteria and water management schemes, etc. In design flood practices, the initial abstraction and constant loss (IACL) method has been widely applied due to its simplicity. However, due to a lack of physical equivalent properties, the IACL method is often subject to issues in parametrization and has large dependency on calibration efforts for storm events. Despite the wide range/variability of IACL values, a single set of IA and CL values is normally adopted for specific flood frequency, which may introduce uncertainty and bias in resulting peak streamflow. In this study, we identified a total of 2,036 rainfall-runoff events for 18 watersheds in North Central Texas to estimate the total losses with their IA and CL components based on time-series of mean areal precipitation (MAP) and streamflow data. Threshold behavior is found for all studied subbasins between the summation of gross rainfall and antecedent soil moisture versus runoff depth: below the threshold, runoff depth is minimal; whereas above it, runoff is largely linearly correlated with the summation of rainfall and antecedent soil moisture. This finding provides a convenient way to estimate/predict total loss or runoff depth given MAP and antecedent soil moisture. In addition, this study shows that the IA and CL values can be approximated by the gamma and Weibull distributions, respectively. The fitted distributions of IA and CL values can be applied in a Monte Carlo simulation framework to stochastically simulate numerous rainfall-runoff events for a flood frequency analysis.

MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies

AbstractThe US Federal Government supports hundreds of watershed monitoring efforts from which solute fluxes can be calculated. Although instrumentation and methods vary between studies, the data collected and their motivating questions are remarkably similar. Nevertheless, little effort toward their compilation has previously been made. The MacroSheds project has developed a future‐friendly system for harmonizing daily time series of streamflow, precipitation, and solute chemistry from 169+ watersheds, and supplementing each with watershed attributes. Here, we describe the breadth of MacroSheds data, and detail the steps involved in rendering each data product. We provide recommendations for usage and discuss when other datasets might be more suitable. The MacroSheds dataset is an unprecedented resource for watershed science, and for hydrology, as a small‐watershed supplement to existing collections of streamflow predictors, like CAMELS and GAGES‐II. The MacroSheds platform includes a web dashboard for visualization and an R package for data access and analysis.

Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka.

Hydrologic models to simulate river flows are computationally costly. In addition to the precipitation and other meteorological time series, catchment characteristics, including soil data, land use, land cover, and roughness, are essential in most hydrologic models. The unavailability of these data series challenged the accuracy of simulations. However, recent advances in soft computing techniques offer better approaches and solutions at less computational complexity. These require a minimum amount of data, while they reach higher accuracies depending on the quality of data sets. The Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference System (ANFIS) are two such systems that can be used in simulating river flows based on the catchment rainfall. In this paper, the computational capabilities of these two systems were tested in simulated river flows by developing the prediction models for Malwathu Oya in Sri Lanka. The simulated flows were then compared with the ground-measured river flows for accuracy. Correlation of coefficient (R), Per cent-Bias (bias), Nash Sutcliffe Model efficiency (NSE), Mean Absolute Relative Error (MARE), Kling-Gupta Efficiency (KGE), and Root mean square error (RMSE) were used as the comparative indices between Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference Systems. Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). The CatBoost algorithm outperformed other algorithms used in this study, with the best correlation score for the testing dataset having 0.9934. The extreme gradient boosting (XGBoost), Light gradient boosting (LightGBM), and Ensemble models scored 0.9283, 0.9253, and 0.9109, respectively. However, more applications should be investigated for sound conclusions.

Data quality control and homogenization of daily precipitation and air temperature (mean, max and min) time series of Ukraine

AbstractIn this paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data.

Combined Forecasting Model of Precipitation Based on the CEEMD-ELM-FFOA Coupling Model

Precipitation prediction is an important technical mean for flood and drought disaster early warning, rational utilization, and the development of water resources. Complementary ensemble empirical mode decomposition (CEEMD) can effectively reduce mode aliasing and white noise interference; extreme learning machines (ELM) can predict non-stationary data quickly and easily; and the fruit fly optimization algorithm (FFOA) has better local optimization ability. According to the multi-scale and non-stationary characteristics of precipitation time series, a new prediction approach based on the combination of complementary ensemble empirical mode decomposition (CEEMD), extreme learning machine (ELM), and the fruit fly optimization algorithm (FFOA) is proposed. The monthly precipitation data measured in Zhengzhou City from 1951 to 2020 was taken as an example to conduct a prediction experiment and compared with three prediction models: ELM, EMD-HHT, and CEEMD-ELM. The research results show that the sum of annual precipitation predicted by the CEEMD-ELM-FFOA model is 577.33 mm, which is higher than the measured value of 572.53 mm with an error of 4.80 mm. The average absolute error is 0.81 and the average relative error is 1.39%. The prediction value of the CEEMD-ELM-FFOA model can closely follow the changing trend of precipitation, which shows a better prediction effect than the other three models and can be used for regional precipitation prediction.

Impacts of Climate Change on Hydrological Regimes in the Congo River Basin

Surface water resources are essential for a wide range of human activities, such as municipal water supply, fishing, navigation, irrigation, and hydropower. Their regime is also linked to environmental sustainability, water-related risks, human health, and various ecosystem services. Global warming is expected to modify surface water availability, quality, and distribution and therefore affect water use productivity as well as the incidence of water-related risks. Thus, it is important for communities to plan and adapt to the potential impacts of climate change. The Congo River Basin, home to 75 million people, is subject to recurrent flood and drought events, which are expected to worsen as a result of climate change. This study aims to assess future modifications of the hydrological regime of the Congo River and the socio–economic impacts of these projected changes for three future periods: 2011–2041, 2041–2070, and 2071–2100. A Soil and Water Assessment Tool (SWAT) model of the Congo River Basin was developed, calibrated, and validated using daily rainfall observations combined with daily time series of precipitation, temperatures, relative humidity, solar radiation, and wind speed derived from the WFDEI (Watch Forced Era Interim) reanalysis data set. The outputs of ten Regional Climate Models (RCMs) from the Coordinated Downscaling Experiment (CORDEX-AFRICA) were statistically downscaled to obtain future climate time series, considering two Representative Concentration Pathways: RCP8.5 and RCP4.5. The calibrated model was used to assess changes in streamflow in all reaches of the Congo River. Results suggest relative changes ranging from −31.8% to +9.2% under RCP4.5 and from −42.5% to +55.5% under RCP 8.5. Larger relative changes occur in the most upstream reaches of the network. Results also point to an overall decrease in discharge in the center and southern parts of the basin and increases in the northwestern and southeastern parts of the basin under both emission scenarios, with RCP8.5 leading to the most severe changes. River discharge is likely to decrease significantly, with potential consequences for agriculture, hydropower production, and water availability for human and ecological systems.

Occurrence and Distribution of Long-Term Variability in Precipitation Classes in the Source Region of the Yangtze River

Various precipitation-related studies have been conducted on the Yangtze River. However, the topography and atmospheric circulation regime of the Source Region of the Yangtze River (SRYZ) differ from other basin parts. Along with natural uniqueness, precipitation constitutes over 60% of the direct discharge in the SRYZ, which depicts the decisive role of precipitation and a necessary study on the verge of climate change. The study evaluates the event distribution of long-term variability in precipitation classes in the SRYZ. The precipitation was classified into three precipitation classes: light precipitation (0–5 mm, 5–10 mm), moderate precipitation (10–15 mm, 15–20 mm, 20–25 mm), and heavy precipitation (>25 mm). The year 1998 was detected as a changing year using the Pettitt test in the precipitation time series; therefore, the time series was divided into three scenarios: Scenario-R (1961–2016), the pre-change point (Scenario-I; 1961–1998), and the post-change point (Scenario-II; 1999–2016). Observed annual precipitation amounts in the SRYZ during Scenario-R and Scenario-I significantly increased by 13.63 mm/decade and 48.8 mm/decade, respectively. The same increasing trend was evident in seasonal periods. On a daily scale, light precipitation (0–5 mm) covered most of the days during the entire period, with rainy days accounting for 83.50%, 84.5%, and 81.30%. These rainy days received up to 40%, 41%, and 38% of the annual precipitation during Scenario-R, Scenario-I, and Scenario-II, respectively. Consequently, these key findings of the study will be helpful in basin-scale water resources management.

Performance Evaluation of an ANN based Bias Correction algorithm in Monthly and Daily Precipitation Time Series of La Farge Station, USA

Understanding the change of future precipitation over long run is highly necessary in climate change impact studies. Mostly, simulated future precipitation series are found to be biased more with the historically observed precipitation series which need to be corrected before use for any impact studies. Many conventional and data-driven methods are available to correct this bias. In this study, to bias correct the monthly and daily precipitation series, Artificial Neural Network based method is applied and compared with the conventional methods. The normalized root mean squared errors obtained for monthly and daily series are 0.786 and 2.55 respectively. It is found that the performance of ANN-based method is poor in daily series and good only in monthly series. The reason for poor performance in daily series is analysed. In addition, the superiority of ANN based method over conventional method is established in monthly precipitation time series.

Rain process models and convergence to point processes

Abstract. A variety of stochastic models have been used to describe time series of precipitation or rainfall. Since many of these stochastic models are simplistic, it is desirable to develop connections between the stochastic models and the underlying physics of rain. Here, convergence results are presented for such a connection between two stochastic models: (i) a stochastic moisture process as a physics-based description of atmospheric moisture evolution and (ii) a point process for rainfall time series as spike trains. The moisture process has dynamics that switch after the moisture hits a threshold, which represents the onset of rainfall and thereby gives rise to an associated rainfall process. This rainfall process is characterized by its random holding times for dry and wet periods. On average, the holding times for the wet periods are much shorter than the dry ones, and, in the limit of short wet periods, the rainfall process converges to a point process that is a spike train. Also, in the limit, the underlying moisture process becomes a threshold model with a teleporting boundary condition. To establish these limits and connections, formal asymptotic convergence is shown using the Fokker–Planck equation, which provides some intuitive understanding. Also, rigorous convergence is proved in mean square with respect to continuous functions of the moisture process and convergence in mean square with respect to generalized functions of the rain process.

Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

In this paper, we describe and analyze two datasets entitled “Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021” and “Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”. These datasets provide the homogenised monthly and daily mean (TG), minimum (TN), and maximum (TX) temperature and precipitation (RR) records since 1960 at 366 stations in China and 56 stations in Greece. The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720. For China, the regional mean annual TG, TX, TN, and RR series during 1960–2021 showed significant warming or increasing trends of 0.27°C (10 yr)−1, 0.22°C (10 yr)−1, 0.35°C (10 yr)−1, and 6.81 mm (10 yr)−1, respectively. Most of the seasonal series revealed trends significant at the 0.05 level, except for the spring, summer, and autumn RR series. For Greece, there were increasing trends of 0.09°C (10 yr)−1, 0.08°C (10 yr)−1, and 0.11°C (10 yr)−1 for the annual TG, TX, and TN series, respectively, while a decreasing trend of −23.35 mm (10 yr)−1 was present for RR. The seasonal trends showed a significant warming rate for summer, but no significant changes were noted for spring (except for TN), autumn, and winter. For RR, only the winter time series displayed a statistically significant and robust trend [−15.82 mm (10 yr)−1]. The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

Potential of GIS and remote sensing in mapping land degradation: catchment of the Manyame River, Zimbabwe

Abstract The Manyame River Catchment area in Zimbabwe, is experiencing severe land degradation mainly due to legal and illegal land husbandry practices. These practices are negatively impacting on the sustainability of the existing ecosystems. The conditions of land can be inferred using its vegetative cover e.g., Normalised Difference Vegetation Index (NDVI). Quantitative data relating to land degradation based on notable physical features such as gullies, for Manyame River Catchment at landscape scales is poor. This study focused on the distribution and magnitude of land degradation in the Manyame River Catchment area. The study mapped out the contours of human induced land degradation using a residual trend analysis (RESTREND) method. In particular, the study used remote sensed data (NDVI and precipitation time series) to analyse the shifts over period 2000 to 2017. The analysis used R statistical software packages (RESTREND and Kendal) and Geographic Information System (GIS) techniques to quantify the degradation trends. The results indicated extracts of those areas which experienced significant human induced land degradation during the study period. RESTREND effectiveness was assessed using Mann Kendal Test. The results of this study can be used by natural resources practitioners in monitoring, assessing and managing environmental changes using GIS tools.

Interactions between large‐scale and mesoscale processes define long‐term rainfall variability and availability of water resources in Western Bahia, Brazil

AbstractWestern Bahia, a region in Northeast Brazil highly dependent on water availability due to the large concentration of irrigated lands, has been suffering from decreasing precipitation in the last decades. The mean climate of Northeast Brazil and its variability have been linked to teleconnections with the Atlantic and Pacific sea surface temperatures, including the Atlantic Multidecadal Oscillation (AMO). This study investigates how large‐scale (AMO) and mesoscale (orographic) interactions are related to precipitation patterns in the region. Time series of precipitation and naturalized flows (corresponding to the flow that would occur in the river section if there were no anthropogenic actions) for the period 1981–2020 are analysed, indicating an average reduction of 11.5% in rainfall since the 1980s. The analyses show a significant (α = 0.05) rainfall reduction in the wettest months, the December and January bimester (DJ). A strong and significant negative correlation between AMO and precipitation (R = −0.62, α = 0.01) indicates a decrease in DJ rainfall when the AMO index is positive. The five rainiest and five driest DJ bimesters between 1981 and 2020 were selected to investigate the changes in atmospheric circulation. In the rainiest years, large‐scale mechanisms interact with the local topography to promote a major convection centre over Western Bahia. The driest years show suppressed convection over the topographic gradient and a change in the convection centre several hundred kilometres to the west. The continued increase in temperatures in the North Atlantic may perpetuate the decline in Western Bahia precipitation, putting pressure on water availability and impacting economic activities in the region.