Abstract Understanding changes in intraseasonal rainfall variability is critical for improving sub-seasonal prediction. While the amplification of intraseasonal rainfall variability across Australia has been reported, the spatial heterogeneity of this intensification has been overlooked. Here, we observe that the intraseasonal rainfall variability has been intensified, particularly over northeastern Australia during the wet season (November–April) over the past four decades. The intensified intraseasonal rainfall variability is primarily attributed to enhanced intensity of precipitation events, rather than drier spells. Moisture budget analysis indicates that the intensification of intraseasonal abnormal rainfall events over northeastern Australia is primarily due to the strengthened vertical advection of background moisture caused by the intraseasonal vertical motion anomalies. In this process, the strengthening of intraseasonal vertical velocity driven by changes in intraseasonal wind convergence plays a dominant role, accounting for over 90%, whereas the effect of background moisture changes is relatively less significant. Our findings highlight northeastern Australia as a hotspot for amplified intraseasonal rainfall variability, rather than the whole of Australia. This study provides fundamental insights into northern Australia’s changing climate, and highlights the important role of intraseasonal circulation on northeastern Australia rainfall variability.

Since last century, Sikkim has witnessed substantial climatic fluctuations that can affect the local water availability, agricultural productivity, and livelihood security. Despite this growing concern, a limited study incorporated century-long datasets to explore the underlying climatic pattern of the state and often focuses only on trend analysis without applying a diverse set of statistical and spatial techniques. Hence, a comprehensive analysis has been employed to explore the long-term trend and variability of Sikkim’s yearly and seasonal precipitation patterns from 1923 to 2023.Several statistical matrices have been applied viz. Coefficient of Variation (CV), Analysis of Variance (ANOVA) and Tukey’s Honest Significant Difference (HSD) Test to analyze the intra-seasonal and inter-seasonal rainfall variability, Standardized Precipitation Index (SPI) has been used to find out wet and dry years, Precipitation Concentration Index (PCI) shows annual and seasonal rainfall distribution, Simple Moving Average method has been applied to detect long-term trend, Inverse Distance Weighted interpolation technique visualizes the spatial rainfall variation in the state. This research offers new insights into the precipitation behavior and shifting rainfall dynamics such as summer rainfall demonstrates the most significant inter-cluster difference, wet and dry events are significantly increasing in summer season, the highest variability in precipitation distribution can be observed in monsoon rain, the summer and winter rainfall is increasing, where monsoon rainfall is decreasing significantly over the region, monsoon rainfall shows significant increasing trend in Gyalshing and Mangan, where summer and winter rain is increasing at Gyalshing and Mangan. These findings can help in regional adaptation strategies, water resource management, agricultural planning, and minimize climate-related livelihood vulnerabilities, as most of the farmers of this state rely on rainfall for their irrigation purposes.

Abstract This study introduces four univariate regional indices to improve the representation of intraseasonal rainfall variability across South America throughout the year, focusing on Brazil. These indices are constructed using two distinct approaches: the linear Empirical Orthogonal Functions (EOF) method and the unsupervised machine‐learning Self‐Organizing Maps (SOM) technique. Both methods are applied to Outgoing Longwave Radiation (OLR) and precipitation‐filtered anomalies in the 30–90‐day band over the South American domain. Results demonstrate that regional indices provide valuable insights into intraseasonal South American rainfall variability, including phase and strength, compared to global indices of the Madden‐Julian Oscillation (MJO). Despite being computed using only the South American domain, the regional indices capture the tropical‐tropical MJO teleconnection through the zonal wavenumber‐1 structure. The diversity in amplitude and evolution of precipitation, primarily influenced by tropical‐extratropical teleconnections through Rossby wave trains, is more evident when using the non‐linear SOM index. The regional indices also accurately measure the impacts of intraseasonal variability on extreme precipitation events over Brazil. Case studies, such as the 2013/2014 summer drought episode, highlight this ability, when a deficient rainy season severely affected the Southeast Region of Brazil, impacting agricultural production and hydroelectric power generation. During this episode, the regional indices show agreement between drought periods and suppressed precipitation phases, while global indices indicate an inactive MJO phase. These findings underscore the effectiveness of regional indices in capturing intraseasonal variability, offering significant implications for extreme weather prediction and their impacts on South American water resources and socio‐economic activities.

This study investigates the intraseasonal variability (ISV) of rainfall in Tanzania during the March-April-May (MAM) season, specifically identifying the dominant peaks of ISV in rainfall for that period. The 5-day running mean during the MAM season reveals that Tanzania experienced an irregular pattern of wet and dry days in the year 2022, indicating the presence of ISV that led to fluctuations in weather patterns. Moreover, the study identifies the dominant peak date, where a significant peak was observed in the 10 - 25-day range, showing that ISV exhibits a quasi-biweekly oscillation around 17 days, with composite evolution from day −8 to day +8 after filtering, and day 0 marking peak rainfall. Furthermore, composite atmospheric circulation analysis reveals critical interactions with ISV. Geopotential height wind patterns at 850 hPa indicate that negative/positive geopotential height anomalies over the Western Indian Ocean and Mozambique Channel enhance low-level convergence/divergence of moisture, resulting in wet/dry phase, meanwhile strong positive geopotential height anomalies at 200 hPa are associated with the upper-level divergence that supports peak rainfall (day 0). During Lag −4 to Lag 0, the results revealed dominant negative OLR anomalies (−18 to −20 W/m2) indicating peak dates of ISV of rainfall while the transition to positive OLR anomalies after Lag +2 showed the starting point of a dry phase of ISV. Also, at the initial phase (Lag −8 to Lag −6), weak positive and limited moisture flux anomalies were observed over the region, while in the peak phase (Lag 0), strong positive anomalies dominated, reflecting intense moisture convergence from both the South West Indian Ocean (SWIO) and the Congo Basin, associated with maximum ISV of rainfall activity. After lag 0, transition into the dry phase (Lag +6 to Lag +8), negative anomalies developed as moisture transport diminishes and winds shift, suppressing convergence over Tanzania, leading to the dry phase. The results highlight the significance of integrating ISV patterns into weather forecasting and disaster preparedness to reduce the risks associated with extreme rainfall events like floods and droughts. Additionally, the findings offer valuable insights for managing water resources, planning agriculture, and enhancing climate resilience in areas of Tanzania that depend on rainfall.

Abstract A multichannel singular spectrum analysis of 121 years of daily rainfall over India and 43 years of outgoing longwave radiation and horizontal wind has revealed two dominant intraseasonal oscillations (ISOs) that describe the space–time variability of monsoon rainfall. The two nonlinear oscillations are not perfectly periodic but exhibit broadband spectra centered at 45 and 28 days. These modes have coherent and near-regular spatial structure and temporal variations. This study posits that the two nonlinear oscillations provide a consistent and comprehensive framework to study the variability and predictability of intraseasonal rainfall variations. The active and break cycles of monsoon rainfall over India have been traditionally defined by using an arbitrarily chosen duration of persistence of arbitrarily chosen amount of rainfall averaged over an arbitrarily chosen area. This study shows that the phases of the two objectively derived modes of variability provide an objective method for defining the active and break cycles. The two ISOs are further shown to make negligible contribution to the seasonal mean rainfall and its interannual variability. This study proposes that the investigations of the origins of these nonlinear modes, and modulations of their amplitudes and phases, provide a new paradigm for future research on validation and predictability of intraseasonal variations in climate models.

Role of ocean-atmosphere interaction in intraseasonal variability of summer rainfall over the Indo–Northwest Pacific

Abstract Remote influences on intraseasonal anomalous rainfall over regions that encompass North and South Vietnam are explored using a 38‐year (1979–2016) global dataset over the extended summer (May–October). Wet and Dry composites in filtered daily data with lags of up to 2 weeks are assembled for various rainfall indices over the two subregions, including atmospheric reanalysis products and in‐situ rainfall data. On the regional scale, the moisture flux convergence correlates well with reanalysed rainfall. The large‐scale dynamics associated with these composites are described. Rainfall composites of opposing signs show asymmetrical large‐scale precursors and different pathways of influence. Wet and Dry anomalies in North Vietnam are seen to originate from Europe and propagate at high latitudes. The exact nature of the precursors is sensitive to the definition of the composite index. There is also a pathway of influence along the Asian jet, which impacts South Vietnam, especially for Wet composites which often coincide with Dry conditions in the North. South Vietnam is also influenced by tropical divergent precursors, which are again asymmetric between Wet and Dry composites.

Abstract During boreal winter (December–February), the South American monsoon system (SAMS) reaches its annual maximum when upper‐tropospheric westerly winds prevail over the equatorial Atlantic. Atmospheric dynamic model simulations suggest that Rossby waves generated over South America can propagate to and potentially influence weather patterns in the Northern Hemisphere (NH). However, observational evidence has been absent previously. Here we focus on southeastern South American (SESA) precipitation anomalies, which can characterize intraseasonal rainfall variability of the SAMS. Since tropical “westerly duct” and convective heating are important factors for cross‐equatorial propagation of Rossby wave (CEPRW), we identify two groups of events based on the two factors. By comparing the events associated with both SESA rainfall and tropical westerlies to the events associated with tropical westerlies only, we find that an anomalous Rossby wave train is triggered by precipitation anomalies over SESA, propagates in the southwest–northeast direction, and subsequently crosses the equator. Over a period of 4 days, near‐surface temperature over northwestern Africa and western Europe becomes warmer, accompanied by increased surface downward longwave radiation and precipitable water. The equatorward propagating Eliassen–Palm flux anomalies originated from SESA support the evidence of CEPRW. Simulations using a time‐dependent linear barotropic model forced by prescribed divergence anomalies over SESA further confirm that SESA rainfall can influence the NH weather patterns through CEPRW. Knowledge of this study will help us better understand and model interhemispheric teleconnections over the American–Atlantic–African/European sector.

West African Sahel is one of the most exposed areas to the adverse effects of climate variability.All the agricultural production systems are affected, exposing local populations to food insecurity and poverty.This study aimed to assess the impacts of intra-seasonal rainfall variability and cropping practices on cereal yields in the North Central region of Burkina Faso.Daily rainfall data covering the 1984-2015 period were collected from eleven stations across the region.The agro-climatic parameters such as the onset and the end of the rainy season, its length, seasonal rainfall amount, rainy days and long dry spells in the rainy months were determined.Annual cereal yields statistics (sorghum, millet and maize) were used.Data on cropping practices were taken into account in this study.The statistical methods for trends and breaks were applied to data series.Simple correlation tests were used to assess the impacts of agro-climatic parameters on cereal yields.The results showed that the North Central region of Burkina Faso experienced extreme rainfall events such as "false starts" of rainy seasons, long dry spells and early rainfall cessation.The onset of the rainy season and the long dry spells in July (duration ≥ 8 days) and August (≥ 6 days) months had negative impacts on cereal yields in the region.The results also highlighted an increase in rainfall since the 1990s and 2000s.Increased rainfall and the positive effects of changes in cropping practices affected cereal yields, which increased significantly (44 to 72%) since that period.Dissemination of climate information, adoption of improved cropping technics and supplemental irrigation are innovating practices that could increase cereal yields in North Central Burkina Faso.

Intra-seasonal rainfall variability and crop yield in the Upper East Region of Ghana

Abstract East Africa is particularly vulnerable to weather extremes, with severe weather linked to thousands of deaths per year. Improved forecasts of convective events in this region are urgently needed, from both nowcasting and numerical weather prediction models. Improving these forecasts requires further knowledge of convection in this region. This study aims to improve understanding of convective events in East Africa, based on a six‐year climatology of convective life cycles and the associated precipitation. Convective systems are identified as contiguous areas of cold cloud in geostationary satellite measurements over East Africa. A tracking algorithm is used to trace the evolution of the properties of these systems through time and space. Matching the systems to surface precipitation obtained from satellite microwave observations provides insight into how the life cycles of these systems relate to precipitation at the surface. Over the region as a whole, 59% of the accumulated precipitation can be attributed to the tracked convective systems. The majority (81%) of heavy precipitation events (10 mmhr−1) are attributable to convective systems, while light rainfall events (1 mmhr−1) are not (1.8%). Most of the tracked convective systems have an area less than 400 km2 and last for less than an hour. However, the less frequent larger longer‐lived systems produce the majority of the regional accumulated precipitation. Composite life cycles of the tracked systems show rapid intensification and the heaviest precipitation initially, followed by a steady increase in area and weakening in intensity before the system decays. Finally, the Madden–Julian oscillation, which plays a key role in intraseasonal rainfall variability in the region, is also linked to the amount of rainfall due to convective systems through changes in the frequency and properties of these systems.

Abstract In semi‐arid environments, rainfall‐driven soil moisture fluctuations exert a strong influence on surface turbulent fluxes. Intraseasonal rainfall variability can therefore impact low‐level atmospheric temperatures and influence regional circulations. Using satellite observations and an atmospheric reanalysis, we investigate whether rainfall variability induced by the Madden–Julian oscillation (MJO) triggers land–atmosphere feedbacks across East Africa.We identify that surface fluxes during the East African wet seasons (March–May and October–December) are sensitive to MJO‐induced precipitation variations across low‐lying regions of South Sudan and highland regions of Uganda and southwest Kenya. For example, during MJO phases 6 to 8, when rainfall is suppressed, surface temperatures and sensible heat fluxes increase, whilst evapotranspiration decreases. Spatial variations in the surface flux response to rainfall variability feed back onto the atmosphere through amplifying MJO‐associated pressure anomalies. During dry MJO events for instance, surface warming across the exit region of the Turkana channel increases the low‐tropospheric along‐channel pressure gradient and intensifies the Turkana jet. We conclude that average surface‐driven temperature fluctuations during a single day are responsible for approximately 12% of MJO‐associated variability of the Turkana jet speed. However, we expect that the accumulation of heat over multiple days to the west of the East African Highlands further amplifies anomalies in the pressure gradient and jet intensity. Modelling experiments are required to quantify the accumulated impact of the surface forcing. Surface‐driven Turkana jet variations influence the East African moisture budget and affect the intensity and inland propagation of coastal convection. Not only is this the first study to investigate the importance of intraseasonal land–atmosphere feedbacks across East Africa, but it is also the first to show that Turkana jet characteristics are partly driven by surface conditions. This work motivates an investigation into whether subseasonal forecast models fully harness the predictability from surface‐induced jet variations.

The Bay of Bengal (BoB) receives a large amount of freshwater from rains and rivers, resulting in large upper-ocean stratification due to the freshening effect. This salinity stratification has been theorized to impact sea-surface temperature (SST) and convection on intra-seasonal time scales by affecting the ocean mixed layer and the barrier layer. This article aims to quantify the impact of salinity stratification on the sub-seasonal variability in SST and convection by using in-situ ocean observations and coupled model experiments. It is shown that monsoon intra-seasonal oscillations (MISOs) exhibit varied levels of intra-seasonal variability in SST and rainfall based on the underlying ocean conditions. The largest intra-seasonal variability in SST does not cause the largest convection variability in the north-western BoB. Instead, moderate variability in SST and rainfall associated with MISOs co-occur with deep mixed layer and thick barrier layer conditions. Realistic representation of river freshwater fluxes in a coupled ocean-atmosphere model leads to improved intra-seasonal SST and rainfall variability. Thick barrier layers in the north-western Bay attenuates the entrainment cooling of the mixed layer, and the high mixed layer heat content provides conducive oceanic conditions for the genesis of monsoon low-pressure systems (LPS), thereby affecting rainfall over India. This study has important implications for operation forecasting using coupled models.

Abstract Persisting wet events (PWEs) and persisting dry events (PDEs) over central India (CI), defined by rain spells lasting for 5 days or more above and below climatology, respectively, represent an important component of the Indian summer monsoon’s intraseasonal variability. However, half of such PREs and PDEs that do not overlap with conventionally defined “active” and “break” spells over CI while contributing about 20% to the seasonal mean remained poorly studied. Here we find that, in contrast to more abundant longer (>5 days) wet and dry spells over the CI, the intraseasonal rainfall variability over northeast India (NEI) is characterized by higher abundance of intense shorter spells (<5 days). Physically, the difference is linked to the fact that monsoon intraseasonal oscillations with a 30–60-day time scale dominate subseasonal variability over the CI, whereas the 10–20-day quasi-biweekly mode dominates subseasonal variability over NEI. While non-overlapping PDEs are associated with large-scale but lower-intensity breaks, non-overlapping PWEs are associated with synoptic events with relatively smaller spatial scales rather than large-scale active events. Here, a percentile-based definition of active and break spells as daily rainfall in excess of the 90th percentile and below the 30th percentile, respectively, persisting for more than 3 days is proposed; this encompasses almost all non-overlapping PWEs and PDEs and is expected to be more useful to the users. Contributions of the subseasonal fluctuations to the seasonal mean and their association with predictable drivers indicate that the seasonal mean rainfall over the NEI is significantly less predictable than that over the CI. Significance Statement Farmers eagerly await the extended range projections of “wet” and “dry” phases of Indian monsoon intraseasonal variability for planting, harvesting, and water resource management. The prevailing definitions for “active” and “break” spells, however, ignored the less intense persisting wet or dry events that contribute roughly equally to the seasonal mean rainfall. To facilitate the process, a new percentile-based definition of the Indian summer monsoon rainfall’s intraseasonal spells is proposed that includes nearly all non-overlapping PWEs and PDEs. The subseasonal fluctuations’ contributions to the seasonal mean and its association with predictable drivers suggest that the seasonal mean rainfall over the NEI is much less predictable than that over the CI, and thus illustrates the contrasting features of spells within these two regions.

The influence of Madden–Julian oscillation (MJO) is examined on intraseasonal rainfall variability during the wet season (November–April) by using the real-time multivariate (RMM) MJO index, ERA5 reanalysis, and daily observed rainfall dataset from 26 stations in Saudi Arabia for the period 1985–2021. The MJO 8 phases are categorized into wet (phases 1, 2, 7 and 8) and dry (phases 3, 4, 5, and 6) based on the Saudi Arabian intraseasonal rainfall characteristics associated with MJO phases. It is observed that 41% (46%) of total (extreme) rainfall events occur during the MJO wet phases, while only 23% (18%) of such events occur during MJO dry phases. The intraseasonal variability signals are isolated from daily dataset by applying a 30- to 90-day period bandpass filter. The analyses are validated by constructing composites of daily filtered precipitation anomalies during MJO 8 phases. The physical mechanism indicates that the significant intraseasonal wetter conditions are linked with enhanced easterly and southeasterly moisture convergence over Saudi Arabia from the Arabian Sea. The atmospheric cyclonic circulation anomalies during the wet phases favor more moisture convergence and vertical moisture advection, which may lead to enhanced convection and rainfall. However, during the dry phases, anticyclonic circulation anomalies enhance moisture divergence and reduce vertical moisture advection and consequently suppress the convection and rainfall activity over Saudi Arabia. The analyses show that the intraseasonal rainfall variability over Saudi Arabia is significantly influenced by the MJO during the wet season. These findings have important implications for sub-seasonal rainfall forecasting in Saudi Arabia.

Intra-seasonal variability of autumn rainfall in the Yangtze River Delta and its related atmospheric circulations

We investigate the impact of resolving air-sea interaction on the simulation of the intraseasonal rainfall variability over the South Pacific using the ECHAM5 atmospheric general circulation model coupled with the Snow-Ice-Thermocline (SIT) ocean model. We compare the fully coupled simulation with two uncoupled ECHAM5 simulations, one forced with sea surface temperature (SST) climatology and one forced with daily SST from the coupled model. The intraseasonal rainfall variability over the South Pacific is reduced by 17% in the uncoupled model forced with SST climatology and increased by 8% in the uncoupled simulation forced with daily SST, suggesting the role of air–sea coupling and SST variability. The coupled model best simulates the key characteristics of the two dominant patterns (modes) of intraseasonal rainfall variability over the South Pacific with reasonable propagation and correct periodicity. The spatial structure of the two rainfall modes in all three simulations is very similar, suggesting the dynamics of the atmosphere primarily generate these modes. The southeastward propagation of rainfall anomalies associated with two leading rainfall modes in the South Pacific depends upon the eastward propagating Madden–Julian Oscillation (MJO) signals from the Indian Ocean and western Pacific. Air-sea interaction improves such propagation as both eastward and southeastward propagations are substantially reduced in the uncoupled model forced with SST climatology. The simulation of both eastward and southeastward propagations considerably improved in the uncoupled model forced with daily SST; however, the periodicity differs from the coupled model. Such discrepancy in the periodicity is attributed to the changes in the SST-rainfall relationship with weaker correlations and the nearly in-phase relationship, attributed to enhanced positive latent heat flux feedbacks.

The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability of rainfall in Indonesia, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. This study aims to investigate the general impacts of MJO on rainfall at different seasons in Indonesia, particularly during boreal summer. Impacts of the MJO on daily rainfall anomaly during the four climatic seasons: DJF, MAM, JJA, and SON in Indonesia have been evaluated using in-situ data from 86 stations during 1981 - 2012 and remote sensing data using GPM IMERGV06 from 2001 - 2019. The greatest impact of the MJO on rainfall over Indonesia occurs during the DJF and MAM seasons (austral summer), with the magnitude varying across regions. Enhanced rainfall generally occurs over the western parts of Indonesia on phases 2 to 4, central parts of Indonesia on phase 4, and eastern parts of Indonesia on phases 4 to 5. Conversely, suppressed rainfall generally occurs over the western parts of Indonesia on phases 5 to 8, central parts of Indonesia on phases 6 to 8, and eastern parts of Indonesia on phases 1 to 2 and 6 to 8. In addition, the MJO influence during the JJA and SON seasons are slightly less, in terms of intensity, than during the DJF and MAM seasons, which is likely due to the northward shift of ITCZ and, hence, the intraseasonal oscillation convective envelope during boreal summer. Generally, enhanced rainfall occurs over the western and northern parts of Indonesia on phases 2 to 3, and suppressed rainfall on phases 6 to 7. The results indicate that convectively active MJO may increase the possibility of daily extreme rainfall in particular regions in Indonesia at different seasons.

Rwanda is a landlocked country in central-eastern Africa. As a country highly dependent on rain-fed agriculture, Rwanda is vulnerable to rainfall variability. Observational data show that there are two rainy seasons in Rwanda, i.e., the long rainy season and the short rainy season. This study mainly focuses on the dominant intraseasonal rainfall mode during the long rainy season (February–May), and evaluates the forecast skill for the intraseasonal variability (ISV) over Rwanda and its surrounding regions in a state-of-the-art dynamic model. During the long rainy season, observational results reveal that the dominant intraseasonal rainfall mode in Rwanda exhibits a significant variability on the 10–25-day time scale. One-point-correlation analysis further unveils that the 10–25-day intraseasonal rainfall variability in Rwanda co-varies with that in its adjacent areas, indicating that the overall 10–25-day rainfall variability in Rwanda and its adjacent regions (8°S–3°N, 29°–37°E) should be considered collectively when studying the dominant intraseasonal rainfall variability in Rwanda. Composite results show that the development of the 10–25-day rainfall variability is associated with the anomalous westerly wind in Rwanda and its surrounding regions, which may trace back to a pair of westward-propagating equatorial Rossby waves. Based on the observational findings, an ISO_rainfall_index and an ISO_wind_index are proposed for quantitatively evaluating the forecast skill. The ECMWF model has a comparable skill in predicting the wind index and the rainfall index, with both indices showing a skill of 18 days.摘要非洲中东部地区的经济主要依靠自给农业支撑, 该地区农业经济对降水的变化尤为敏感. 本文以卢旺达为例, 观测分析指出卢旺达的次季节降雨主要集中在10–25天; 根据次季节尺度降水变率的单点相关方法, 发现卢旺达的次季节降水变率和周围区域变化一致; 进一步合成结果显示该地区次季节降水变率与异常西风有关, 这可追溯到赤道地区西传的赤道Rossby波. 最后, 本文评估了当前动力模式ECMWF对卢旺达地区(即非洲中东部)次季节降水变率的预报能力, 发现EC模式在对该区域降水和相关风场指数的预报技巧都在18天左右, 且预报技巧表现出一定的年际差异, 这可能与热带太平洋的背景海温信号有关. 该工作增进了当前对非洲中东部地区的次季节降水变率和预测水平的认知, 并且对该地区国家粮食安全和防灾减灾具有启示性意义.

Sand dams, a water-harvesting structure employed by rural communities in drylands have an inconsistent record of effectiveness. While many sand dams are highly functioning, improper siting, siltation, seepage, and high rates of evaporation from shallow sand reservoirs inhibit the water storage capacity of some sand dams. This study examines large-scale drivers of sand dam storage potential through analysis of an integrated surface and subsurface flow model. Multiple simulations were run, and comparative simulation analyses consider the effect of geomorphological factors, intraseasonal rainfall variability, and future climate conditions on sand dam performance criteria. The analyses revealed that a watershed highly cultivated with low water crops actually reduces evapotranspiration below that of natural vegetation and supports higher groundwater recharge. Additionally, intraseasonal variation and volume of rainfall impact sand dam performance less than the prevailing pattern and duration of dry and rainy seasons. Sand dams constructed in watersheds with sandier soils may experience greater connectivity with the stream margins and thus provide additional groundwater recharge. Lastly, climate change may improve some conditions desirable for sand dam performance, such as extending the duration of the rainy season and reducing overall evapotranspiration. However, the interactions between the expected climate change conditions and other geomorphological factors may result in a net decline in sand dam performance. The results of this study may help identify watersheds that are likely to support a sand dam with high potential for capturing and storing water throughout the dry season.