Spatial Variability Of Rainfall Research Articles

A new approach for quantitative precipitation estimation from radar reflectivity using a gated recurrent unit network

Accurate quantitative precipitation estimation (QPE) utilizing radar data plays a vital role in weather monitoring, hydrological modelling and prediction. In previous research, some regression techniques such as the reflectivity (Z) - rainfall (R) relationship and artificial intelligence methods were employed for radar QPE; however, the estimation results remain limited the spatial and temporal variability of rainfall results in large errors in radar QPE. Recently, an emerging deep learning approach that can be applied in hydrological areas for QPE, namely, the gated recurrent unit (GRU) network method, has been used. GRU networks have evolved from long short-term memory (LSTM) networks, with improved training speed and modelling accuracy. In this research, the GRU network is applied to QPE using the three-dimensional structure of reflectivity from the Constant Altitude Plan Position Indicator (3D CAPPI) for an urban catchment in Seoul, South Korea. Simultaneously, a “complete” 3D CAPPI based on a cubic grid of 4 × 4 km2 that is highly suitable for the study area is proposed. Based on the comparison of the results of the GRU model with those of the artificial neural networks (ANNs), recurrent neural networks (RNNs), and LSTM networks, and the Z-R relationship, namely the least-squares model (LSM) for 2 testing strategies including traditional test strategy and leave-one-out cross-validation (LOOCV) strategy, the results indicate that the GRU model is superior to other models in radar QPE, with improvements by 3.3 ∼ 46.2%, 25 ∼ 70% and 1.2 ∼ 44.8% in the root mean square error (RMSE), bias and correlation coefficient (CC). This study presents a valuable method for improving the accuracy of radar QPE that can be actively employed in the future.

A review of video‐based rainfall measurement methods

AbstractAccurate and high spatiotemporal resolution rainfall observations are essential for hydrological forecasting and flood management, especially in urban hydrological applications. However, it is difficult for traditional rainfall gauges, weather radars, and satellites to accurately estimate rainfall while simultaneously capturing the spatial and temporal variability of rainfall well. In this context, video‐based rainfall measurement, a novel method, has the advantages of real‐time performance and low cost and may thus provide a new way to establish rainfall observation networks with high spatial and temporal resolution. In recent years, different algorithms have been developed to recognize raindrops and estimate rainfall from rainfall videos. It has been demonstrated that video‐based rainfall measurement methods can provide comprehensive rainfall information with fine spatial and temporal granularity. However, raindrop visibility and the depth of field effects are difficult to address. The motion blur effect of raindrops may result in substantial errors and uncertainties. A fundamental problem of video‐based rainfall measurements lies in locating raindrops and accurately calculating their actual size. Moreover, the effectiveness of deep learning‐based video rainfall measurement models is greatly influenced by the diversity of the training data. Therefore, enhancing the high robustness and accuracy of video‐based rainfall measurement algorithms and increasing the computational efficiency are paramount to further development, which are prerequisites for their application in practical rainfall monitoring and developing multicamera monitoring networks.This article is categorized under: Science of Water > Methods Science of Water > Hydrological Processes

GIS based identification of water harvesting potential area in the Bale lowland of south eastern Ethiopia

ABSTRACT Assessment of potential surface runoff harvesting sites is an important undertaking in a country like Ethiopia, where high spatial variability in rainfall, recurrent drought, and flash floods are common. In this study, a geographical information system (GIS)-based multi-criteria evaluation was used to select potential rainwater harvesting sites in Oromia, the Bale lowland, Ethiopia. The Analytic Hierarchy Process (AHP) was used for standardization of the criteria and weight overlay analysis of multicriteria for a combination of multi-layers using ArcGIS 10.8. Six criteria layers, including slope, runoff, soil depth, precipitation, soil texture, and land use land cover, were derived to identify the rainwater-harvesting catchment. The analysis, shows that of the total 500 km2 of the catchment area 29.18% of the area has highly suitable 40.87% of the area has suitable 22.49% of the total area has moderately suitable 6.98% of the area has marginally suitable and 0.5% of the area is not suitable for runoff water harvesting at the study area. Providing accurate and precise spatial representation of the physiology and land use for the analysis of runoff generation potential sites within the study area is an important step in developing an integrated strategy for surface rainwater harvesting plans in the study area.

Aerosol indirect effects on orographic clouds and precipitation

The sensitivity of warm orographic cloud development to aerosol indirect effects was investigated through aerosol-aware Weather Research and Forecast model simulations contrasting aerosol-cloud-precipitation interactions using the default (generic) aerosol and regional aerosol measurements from the Integrated Precipitation and Hydrology Experiment in the Southern Appalachian Mountains for three rainfall events: 1) enhanced local convection; 2) a frontal system, and 3) a tropical system. Using the regional aerosol activation spectrum yields higher number of drops than using the default, smaller cloud droplets and delayed rainfall onset under weak synoptic forcing conditions. Evaluation against aircraft measurements in isolated convective clouds reveals that while the model microphysics falls short of reproducing the vertical structure of nonprecipitating clouds, the liquid water content, and the concentration of cloud droplets near cloud base are in keeping with observations. The simulated cloud vertical structure shows the regional signature of orographic enhancement over the mountains vis-a-vis the adjacent plains. In the inner region, valley-ridge circulations organize the spatial patterns of cloudiness under weak synoptic forcing. The formation of early afternoon low-level clouds over the ridges in the summertime reflects the aerosol indirect effect. By contrast, for large-scale systems with strong and sustained moisture convergence at low levels (frontal and tropical systems), mechanically forced rainfall efficiency is enhanced, there is no delay in the onset of precipitation, and the aerosol indirect effect is negligible. This study shows that the impact of aerosol-cloud-precipitation interactions on the spatial variability of orographic rainfall is conditional on weather regime.

Spatiotemporal variability and trends of rainfall and temperature in the tropical moist montane ecosystem: Implications to climate-smart agriculture in Geshy watershed, Southwest Ethiopia

This paper examines the spatio-temporal trends and variability of temperature and rainfall in Geshi Watershed of South Western Ethiopia. The available gridded daily rainfall and temperature data managed by Ethiopian National Meteorological Agency (NMA) for 1986–2020 was used. Spatial variability and temporal trends of temperature and rainfall were analyzed using XLSTAT and ArcGIS 10.8 software. The Mann-Kendall test was used for trend analysis. Variability analysis was also done using the coefficient of variation and precipitation concentration index. The study reveals constant increasing trends in rainfall; and insignificant and high variability but increasing trends of temperature. The variabilities and trends justify the spatio-temporal differences along different seasons (spring locally known as ‘belg’, summer locally known as ‘kiremt’, and annual). The watershed is characterized by high to moderate rainfall coefficient of variation, significant years of high concentration of rainfall, and substantial negative annual rainfall anomalies; that severe variability was found in the summerseason. These variabilities and trends, especially late onset and early cessation of rainfall and increase in temperature created favorable conditions for insect and disease proliferation that eventually reduces agricultural productivity. Household income, which is solely based on agricultural production, declines which in turn affects household resilience. Maladaptation in the form of deforestation, fuelwood and charcoal selling, agricultural land expansion, and practicing input-intensive agriculture that contributes to greenhouse gas (GHG) emissionsare taken as an alternative livelihood strategy. In conclusion, spatiotemporal variabilities and trends of rainfall and temperature affect agricultural productivity, reduced household resilience leading to low adaptive capacity, and increased GHG emissions, which are known to be major pillars of climate-smart agriculture (CSA). In response, area-specific CSA technologies such as small-scale irrigation, improved animal husbandry, efficient inorganic fertilizer use, using manure, crop residue management, using high-yielding improved crop varieties, and using disease-resistant varieties are recommended.

Relevance of climatological information on spatial and temporal variability of Indian Summer monsoon rainfall (ISMR) in recent El Niño years and its impact on four important kharif crops over India

The spatial and temporal variability of Indian Summer Monsoon Rainfall (ISMR) during El Niño years (2002, 2004 & 2009) and its impact on district-wise yield of four important kharif crops viz., rice, maize, pearlmillet and sorghum over India were analysed. The number of districts fall under different rainfall classes are different in normal and El Niño years and found that there is a small shifting of spine curve towards left, which indicate the number of districts under lower rainfall classes increased in El Niño years. As far as National level analysis indicated that the yield of these major kharif crops grown steadily during the study period with higher rate of 43 kg/ha in maize and lower rate of 6.2 kg/ha in sorghum due to technological advancement. Identified high and medium vulnerable districts with respect to above crops in El Niño years based on yield changes > −10 per cent compared to average of previous and succeeding years. 77 and 36 districts identified as highly vulnerable with respect to rice and sorghum, respectively. Whereas 36 and 65 districts in the case of sorghum and maize, respectively. Thus, the results of this study provide an opportunity to policy makers/ researchers/other stakeholders to prepare short and long term contingency plan to cope up with the situation, if there is any forecast of El Niño in future.

Influence of Southern Oscillation Index on Rainfall Variability in Nepal during Large Deficient Monsoon Years

The study was conducted using rainfall time-series data for 42 years from 1977 to 2018. We have identified seven large monsoon deficient years. Among these years, 1992, 2009, and 2015 consisted of El Niño episodes which quantify significant rainfall deficits 19.29, 13.6, and 17.59 % respectively from an average rainfall. With some exceptions, all El Niño years observed deficit rainfall. On El Niño years averaged deficit rainfall was approximately nine percent below than the average monsoon rainfall. The eastern region observed the large deficient monsoon years frequently than the central and western regions of Nepal. The central region recorded large spatial variability of average summer rainfall ranging from less than 200 mm/months in lesser Himalayans to more than 3,000 mm/months in mid-mountainous region. The western region had observed a large deficient summer monsoon anomaly 45 % in the year 1979. Similarly, the central region had 31 % deficient summer monsoon anomalies in 1992, and the eastern region observed 25 % deficient anomalies in 1982. There was a strong correlation between the Nepal Summer Monsoon Rainfall (NSMR) and Southern Oscillation Index (SOI). Generally, large negative/positive magnitude of SOI on the Indian and Pacific Ocean has link to weakening/strengthening NSMR.

Spatial Variations in Tropical Cyclone Rainfall over the Western North Pacific According to ENSO Phase

Abstract This study investigated spatial variations in rainfall accompanying tropical cyclones (TCs) over the western North Pacific (WNP) from June to October over 1998–2019 according to phases of El Niño–Southern Oscillation (ENSO). The rainfall characteristics of TCs include rainfall strength (RS) in the core region, total rainfall area (RA), and total rainfall volume (RV), all of which were calculated from satellite precipitation data. Spatial variation in mean RS presents a west–east dipole pattern (i.e., increases in the west of 125°E and decreases in the east during La Niña, and vice versa during El Niño) similar to that of maximum wind speed (Vmax), indicating a close relationship between them. On the other hand, both of mean RA and RV homogeneously increase during La Niña over the entire basin while they decrease during El Niño. The environmental conditions around TCs, including sea surface temperature, total column water, and divergence in the upper and lower troposphere during La Niña appear generally favorable for precipitation, while those during El Niño are unfavorable. Using multivariable regression models, we quantified the contributions of Vmax and environmental conditions to the variations in TC rainfall. Overall results suggest that the variation in RS according to ENSO are mainly related to Vmax, while those of RA and RV are strongly controlled by environmental conditions.

English

Tracking the changes in the river basin system and identifying areas susceptible to flood disasters is an inclusive step in flood hazard control and management. This research analyzed the spatial variability of rainfall and physiographical attributes of the main tributaries of River Benue in Taraba with the aim of identifying areas susceptible to flooding on the basins. The spatial data subjected to WLC approach for reclassification were annual rainfall, digital elevation model, basin slope, land use, soil types, drainage density, and catchment area. The clincher to flooding in the state favors land use. The composite map of flood vulnerability revealed that vulnerable areas occupied 48.6% while highly vulnerable occupied 0.9% of the entire study area of 44,359.2 km2. In terms of the area extent, the highly vulnerable region covers 410.6 km2; the vulnerable area covers 21,576.6 km2, and the areas that are not vulnerable cover 64.9 km2. The towns that are at risk of a high vulnerability to flooding within the study area are Karim Lamido, Gassol, Ibi, Lau, Donga, Wukari, parts of Takum, Ardo Kola, Kurmi, Bali, and Jalingo. It was recommended among others that as the region’s status is vulnerable to climate change and that this has a strong association with land use and land cover change, development along the floodplain should be discontinued.   Key words: Flood vulnerability; land use change; river catchment area; rank-sum method; river Lamurde, Taraba and Donga.

Isolating climatic, tectonic, and lithologic controls on mountain landscape evolution.

Establishing that climate exerts an important general influence on topography in tectonically active settings has proven an elusive goal. Here, we show that climates ranging from arid to humid consistently influence fluvial erosional efficiency and thus topography, and this effect is captured by a simple metric that combines channel steepness and mean annual rainfall, ksnQ. Accounting for spatial rainfall variability additionally increases the sensitivity of channel steepness to lithologic and tectonic controls on topography, enhancing predictions of erosion and rock uplift rates, and supports the common assumption of a reference concavity near 0.5. In contrast, the standard channel steepness metric, ksn, intrinsically assumes that climate is uniform. Consequently, its use where rainfall varies spatially undermines efforts to distinguish climate from tectonic and lithologic effects, can bias reference concavity estimates, and may ultimately lead to false impressions about rock uplift patterns and other environmental influences. Capturing climate is therefore a precondition to understanding mountain landscape evolution.

Drivers of Model Uncertainty for Urban Runoff in a Tropical Climate: The Effect of Rainfall Variability and Subcatchment Parameterization

Urbanization continues to increase in countries with tropical climates and this trend, combined with the likely increasing frequency of extreme rainfall events due to a changing climate, places such development at risk and in need of resiliency assessment. Conceptual models to assess runoff dynamics can be an important component of resiliency assessment, but there are comparatively less data to calibrate these models than are available in the global north. As such, there also is less information with respect to the drivers of model uncertainty and sensitivity. To address this gap in knowledge, we summarize the calibration results of PCSWMM for subcatchment areas in a tropical climate study catchment for which there are substantial rainfall and runoff data. Subsequently, we used the calibrated model to evaluate the impact that rain gauge density may have on runoff estimates. We also investigated the sensitivity of PCSWMM peak flow and total volume estimates to physical subcatchment parameters other than rainfall. With between 38 and 87 events captured for each monitoring station, the NSE, r2, and ISE ratings varied, but generally were in the respective ranges 0.7–0.8, 0.79–0.85, and good–excellent. It can be concluded that PCSWMM performed well in representing the tropical storm events. The rainfall pattern in the study catchment exhibited considerable spatial variability, both annually and seasonally, with annual rainfall increasing from 2063 mm near the coast to 3100 mm less than 17 km further inland. While the model was sensitive to %imperviousness, subcatchment width, impervious Manning’s n, and, to a lesser extent, various surface storage and infiltration parameters, the spatial variability of rainfall had the greatest impact on model uncertainty.

Densidade e classificação da rede pluviométrica e análise espaço-temporal das chuvas na região do alto Rio Paraná, Brasil

ABSTRACT Water management aims to ensure the water availability necessary to meet the current and future demand for water resources. For which it is essential to implement monitoring networks that support the investigation of events that interfere with the rainfall regime of watersheds, such the phases of the ocean-atmospheric phenomenon El Niño – Southern Oscillation (ENSO). The classification of the rainfall network was proposed according to the World Meteorological Organization (WMO) for 14 management watersheds in the Upper Parana River (UPR) region, Brazil. On the other hand, the spatial and temporal variability of annual rainfall was analyzed using geostatistical techniques and confronted with ENSO data. To this purpose, data from 408 stations were collected via the Hidroweb portal, for the period from 1990 to 2020. The low representativeness of data in the region was verified from the observational network. The areas with the lowest and highest rainfall reduction were the north and northwest regions of the UPR, and the areas surrounding the Paraná River, respectively. The years 2019 and 2020 were identified as the most critical period of the last 3 decades with below-average rainfall (-13.21%) in 49.55% of the studied area, indicating a persistence in the drought scenario.

Spatial variability study of rainfal lin Cartagena de Indias, Colombia

Precipitation is a component of the hydrological cycle, knowing its spatial distribution is vital for the management of hydrographic basins, the territory and the development of fundamental activities for society. That is why the present study shows the spatial variability of rainfall in Cartagena de Indias city with a network of rain gauges, made up of nine pieces of equipment, separated from each other by 0.9–27 km. After a year of recording (2019), using historical series of data, it was found that the maximum rainfall occurs in the trimester between September and November, with interpolated maps made by the Ordinary Kriging (OK) method it was found that the maximum rainfall is focused on the north, centre and west of the territory, instead, the maximum intensities are presented in the centre and west, the minimums for both variables are presented to the east and south. The 70 and 90% of the rain events have a duration of less than 30 min and 1 h, respectively. Three-parameter exponential function was fitted to the paired correlation distances, and presented correlations lower than 0.8, 0.5 and 0.2 from distances of 1, 3 and 7 km, respectively, in 30 min rain integration. It was also found that with a pluviometric network conformed by at least six pieces of equipment and separated by a 5 km distance from each other in the urban area, a correlation of 0.5 and compliance with the WMO recommendations would be obtained.

Emergence of heavy tails in streamflow distributions: the role of spatial rainfall variability

Flow events with low frequency often cause severe damage, especially if their magnitudes are higher than suggested by historical observations. The heavier right tail of streamflow distribution indicates the increasing probability of high flows. In this paper, we investigate the role played by spatially variable rainfall in enhancing the tail heaviness of streamflow distributions. We synthetically generated a wide range of spatially variable rainfall inputs and fed them to a continuous probabilistic model of the catchment water transport to simulate streamflow in five German catchments with distinct properties in size and topography. Meanwhile, we used a comparable approach to analyze rainfall and runoff records from 175 German catchments. We identified the effects of spatially variable rainfall on the tails of streamflow distributions from both simulation scenarios and data analyses. Our results show that the tail of streamflow distribution becomes heavier with increasing spatial rainfall variability only beyond a certain threshold. This finding indicates the capability of catchments to buffer growing heterogeneities of rainfall, which we term catchment resilience to increasing spatial rainfall variability. The analyses suggest that the runoff routing through the river network controls this property. In fact, both small and elongated catchments are less resilient to increasing spatial rainfall variability due to their intrinsic runoff routing characteristics. We show the links between spatial rainfall characteristics and catchment geometry and the possible occurrence of high flows. The data analyses we performed on a large set of case studies confirm the simulation results and provide confidence for the transferability of these findings.

Performance Evaluation of Google Earth Engine Based Precipitation Datasets Under Different Climatic Zones over India

Satellite as well as reanalysis-based datasets are widely available and useful in detecting spatial and temporal variability of rainfall at a finer resolution. These products have been widely used in weather forecasting and hydrological and climate studies. However, the accuracy of satellite products varies spatially and across different datasets. In this study, the accuracy of five satellite-based precipitation products with different spatial resolutions, i.e., CHIRPS, ERA5, TRMM, GPM, and TerraClim available on Google Earth Engine (GEE) were compared with India Meteorological Department (IMD) gridded data in six climate zones in India. The statistics such as RMSE, R2, MSE, and PBIAS were computed. It was observed that the performance of each product varies in different climatic zones. The GPM was observed to have high accuracy in arid, semi-arid, and tropical wet zones. TRMM showed a good match in tropical wet and dry, tropical wet, and semi-arid zones. TerraClim and ERA5 showed high accuracy in humid subtropical and montane regions, respectively. It was also observed that CHRIPS was found to be least suitable in all the climate zones across India. The findings from the present studies will serve as a guiding document for the researcher to select appropriate datasets for different applications such as drought monitoring, precipitation anomaly, hydrological models, or other related studies in India.

What determines the success of rural water supply schemes? Insights from an assessment in Maharashtra

Abstract The article discusses a statistical model to explain the varying performance of water supply schemes under different physical environments in the state of Maharashtra in India, characterized by high spatial and temporal variability in rainfall and climate, and heterogeneity in geological and geomorphological conditions. The factors that enhance the performance of the schemes are effective utilizable recharge rates, extent of surface irrigation, and aquifer storage space. The factors that adversely affect the performance are the extent of the population covered by groundwater-based schemes and irrigation water demand per unit area. Subsequently, the districts in Maharashtra where groundwater-based schemes are likely to succeed were identified. The article also explains the practical and policy relevance of the model results for rural water supply planning.

Interpolation of rainfall observations during extreme rainfall events in complex mountainous terrain

AbstractThe representation of rainfall in space is important for hydrological modelling. Accurate estimation of rainfall is particularly challenging in mountainous regions where observations are often sparse relative to the spatial variability of rainfall. In these regions, orographic processes lead to complex patterns of rainfall enhancement and rain shadow depletion. This study testsNatural Neighbour Interpolation (NNI),ordinary kriging (OK)andordinary cokriging (CK), to determine ifCKimproves rainfall interpolation during three extreme rainfall events. Three different elevation indices were considered as secondary variables forCK. Preliminary analysis using long‐term annual average rainfall totals, including additional high elevation rainfall observations, showed thatCKwith an effective elevation index (a directionally smoothed elevation, corrected for degree of ‘orographic processing’ and shifted to account for ‘wind‐drift’ of rainfall) as a secondary variable performed better thanNNIandOKwith an overall improvement of around 40%. Using rainfall totals for long‐term wind direction and wind speed rainfall classes,CKperformance was variable but provided an improvement of approximately 15% for wind direction classeswithoutan easterly wind component. For 15‐min timesteps during extreme rainfall events, there were comparatively small differences (cross‐validation usingRMSE) between interpolation methods, partly attributed to having only relatively low elevation rainfall observations, providing weak constraint. Using cross‐validation andmean biasdid, however, show an improvement for both high and low elevation observation classes. Importantly, cross‐variogram estimation provided differing cross‐validation results when estimated for different rainfall accumulation periods: 15‐min, hourly, daily and long‐term. Variograms and cross variograms estimated at a 15‐min timestep frequency were robust for many timesteps, but were difficult to fit automatically for others. Variograms estimated from longer periods were more reliably estimated, but tended to have lower variance and cross‐variance and longer correlation ranges producing a smoother interpolated rainfall field. Given the weak cross‐validation constraint, care must be taken in identifying the most appropriate method and variogram estimation period.

Influence of the Temporal and Spatial Variability of Rainfall on the Sizing of Tanks for Rainwater Harvesting in Brazil: A Multivariate Analysis

Influence of the Temporal and Spatial Variability of Rainfall on the Sizing of Tanks for Rainwater Harvesting in Brazil: A Multivariate Analysis

Spatial concentration and variability of rainfall in the Iguaçu River Basin, State of Paraná−Brazil

The analysis of rainfall as an environmental factor that influences landscape dynamics is an important and ongoing topic of discussion. This discussion can be centred on the discovery of impacts caused by the increase or decrease in rainfall frequency and intensity. From this perspective, this study sought to analyse the rainfall variability in the Iguaçu River basin, located in the State of Paraná, southern Brazil. The main objective was focused on the temporal‐spatial rainfall distribution in the study area and its interaction with landscape dynamics (different land uses). Precipitation data for the period 1988−2018 were obtained from the ‘Águas Paraná’ Institute. To calculate the Concentration Index−CI values of the study area, data analysis was performed using the ‘R’ software with Climatol and Precindicon subroutines of the software, and analysis of Landsat 8 satellite images through ArcGIS. The results indicated that there was a well‐defined spatial variability in the study area, as the CIs defined higher rainfall concentrations upstream from the river basin and lower rainfall concentrations downstream—implying that these processes may be strongly associated with land use.

Drought Severity and Frequency Analysis Aided by Spectral and Meteorological Indices in the Kurdistan Region of Iraq

In the past two decades, severe drought has been a recurrent problem in Iraq due in part to climate change. Additionally, the catastrophic drop in the discharge of the Tigris and Euphrates rivers and their tributaries has aggravated the drought situation in Iraq, which was formerly one of the most water-rich nations in the Middle East. The Kurdistan Region of Iraq (KRI) also has catastrophic drought conditions. This study analyzed a Landsat time-series dataset from 1998 to 2021 to determine the drought severity status in the KRI. The Modified Soil-Adjusted Vegetation Index (MSAVI2) and Normalized Difference Water Index (NDWI) were used as spectral-based drought indices to evaluate the severity of the drought and study the changes in vegetative cover, water bodies, and precipitation. The Standardized Precipitation Index (SPI) and the Spatial Coefficient of Variation (CV) were used as meteorologically based drought indices. According to this study, the study area had precipitation deficits and severe droughts in 2000, 2008, 2012, and 2021. The MSAVI2 results indicated that the vegetative cover decreased by 36.4%, 39.8%, and 46.3% in 2000, 2008, and 2012, respectively. The SPI’s results indicated that the KRI experienced droughts in 1999, 2000, 2008, 2009, 2012, and 2021, while the southeastern part of the KRI was most affected by drought in 2008. In 2012, the KRI’s western and southern parts were also considerably affected by drought. Furthermore, Lake Dukan (LD), which lost 63.9% of its surface area in 1999, experienced the most remarkable shrinkage among water bodies. Analysis of the geographic distribution of the CV of annual precipitation indicated that the northeastern parts, which get much more precipitation, had less spatial rainfall variability and more uniform distribution throughout the year than other areas. Moreover, the southwest parts exhibited a higher fluctuation in annual spatial variation. There was a statistically significant positive correlation between MSAVI2, SPI, NDWI, and agricultural yield-based vegetation cover. The results also revealed that low precipitation rates are always associated with declining crop yields and LD shrinkage. These findings may be concluded to provide policymakers in the KRI with a scientific foundation for agricultural preservation and drought mitigation.