Spatial Variability Of Rainfall Research Articles

Comparing Approaches to Deal With Non‐Gaussianity of Rainfall Data in Kriging‐Based Radar‐Gauge Rainfall Merging

AbstractMerging radar and rain gauge rainfall data is a technique used to improve the quality of spatial rainfall estimates and in particular the use of Kriging with External Drift (KED) is a very effective radar‐rain gauge rainfall merging technique. However, kriging interpolations assume Gaussianity of the process. Rainfall has a strongly skewed, positive, probability distribution, characterized by a discontinuity due to intermittency. In KED rainfall residuals are used, implicitly calculated as the difference between rain gauge data and a linear function of the radar estimates. Rainfall residuals are non‐Gaussian as well. The aim of this work is to evaluate the impact of applying KED to non‐Gaussian rainfall residuals, and to assess the best techniques to improve Gaussianity. We compare Box‐Cox transformations with λ parameters equal to 0.5, 0.25, and 0.1, Box‐Cox with time‐variant optimization of λ, normal score transformation, and a singularity analysis technique. The results suggest that Box‐Cox with λ = 0.1 and the singularity analysis is not suitable for KED. Normal score transformation and Box‐Cox with optimized λ, or λ = 0.25 produce satisfactory results in terms of Gaussianity of the residuals, probability distribution of the merged rainfall products, and rainfall estimate quality, when validated through cross‐validation. However, it is observed that Box‐Cox transformations are strongly dependent on the temporal and spatial variability of rainfall and on the units used for the rainfall intensity. Overall, applying transformations results in a quantitative improvement of the rainfall estimates only if the correct transformations for the specific data set are used.

A method for assessing the influence of rainfall spatial variability on hydrograph modeling. First case study in the Cevennes Region, southern France

Emmanuel et al. (2015) proposed rainfall variability indexes intended to summarize the influence of spatial rainfall organization on hydrograph features at the catchment outlet. The present article shows how the proposed indexes may be used in a real-world case study to analyze the influence of spatial rainfall organization on hydrograph modeling. The selected case study is located in the Cevennes Region of southeastern France. The proposed methodology is as follows: the tested flow events are split into two subsets according to the values of their rainfall variability indexes; then, a comparison is drawn between modeled and measured hydrographs separately for each subset. The results obtained suggest that, on average, modeling results based on high-resolution rainfall data are improved for the subset whose rainfall variability influence is expected to be significant according to index values. Though limited to a relatively small number of hydrographs, this case study can be viewed as a first confirmation that the proposed method, based on the rainfall variability indexes of Emmanuel et al. (2015), is pertinent to investigating the influence of spatial rainfall variability on hydrograph modeling results.

Disparity in rainfall trend and patterns among different regions: analysis of 158 years’ time series of rainfall dataset across India

Rainfall anomaly during crop-growing season can have large impact on the agricultural output of a country, especially like India, where two-thirds of the crop land is rain-fed. In such situation, decreased agricultural production not only challenges food security of the country but directly and immediately hits the livelihood of its farming community. In a vast country like India, rainfall or its anomalies hardly follow a specific pattern, rather it is having high variability in spatial domain. This study focused on the trends of national and regional rainfall anomalies (wetness/dryness) along with their interrelationship using time series data of past 158 years. The significant reducing wetness trend (p < 0.05) over north mountainous India was prominent with an increasing trend over southern peninsular India (p < 0.10). However, long-term annual wetness was increasing over entire peninsular India. The results of change point tests indicate that major abrupt changes occurred between early to mid-twentieth century having regional variations. The regional interrelationship was studied using principal component, hierarchical clustering, and pair-wise difference test, which clearly indicated a significantly different pattern in rainfall anomalies for north east India (p = 0.022), north central India (p = 0.022), and north mountainous India (p = 0.011) from that of the all India. Result of this study affirmed high spatial variability in rainfall anomaly and most importantly established the unalike pattern in trends of regional rainfall vis-a-vis national level, ushering towards paradigm shift in rainfall forecast from country scale to regional scale for pragmatic planning.

Spatial Analysis of High-Resolution Radar Rainfall and Citizen-Reported Flash Flood Data in Ultra-Urban New York City

New York City (NYC) is an ultra-urban region, with over 50% impervious cover and buried stream channels. Traditional flood studies rely on the presence of stream gages to detect flood stage and discharge, but these methods cannot be used in ultra-urban areas. Here we create a high-resolution radar rainfall dataset for NYC and utilize citizen and expert reports of flooding throughout the city to study flash flooding in NYC. Results indicate that interactions between the urban area and land–sea boundary have an important impact on the spatial variability of both heavy rainfall and flooding, sometimes in contrast to results obtained for other cities. Top days of daily and hourly rainfall exhibit a rainfall maximum over the city center and an extended region of higher rainfall downwind of the city. The mechanism for flooding appears to vary across the city, with high groundwater tables influencing more coastal areas and high rain rates or large rain volumes influencing more inland areas. There is also a strong relationship between sewer type and flood frequency, with fewer floods observed in combined sewer areas. Flooding is driven by maximum one-hour to one-day rainfall, which is often substantially less rain than observed for the city-wide daily maximum.

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English

Characterising production environments for maize in eastern and southern Africa using the APSIM Model

Maize is a staple food crop in eastern and southern Africa with significant contribution for food security of this vast region. Efforts to breed superior maize cultivars for the region are challenged by high genotype x environment interactions arising mainly due to variable soil moisture supply caused by high temporal and spatial variability in rainfall. Information on major drought patterns and their frequencies, which can assist in dealing with such interactions in the region, however, is not available. The objectives of this study were therefore to (i) identify major drought patterns and their frequencies, (ii) identify iso-environments based on the similarity of drought patterns and (iii) explore scope for yield improvement through optimising genotype and management in various drought patterns. We used the well validated APSIM model to characterise major drought patterns and their frequencies experienced by maize cropping systems in the target population of environments spread across six countries of the region including Ethiopia, Kenya, Tanzania, Malawi, Mozambique and Zimbabwe. The data-base used for the model simulations consisted of 35 locations, 17–86 years of daily climate records and three cultivars. The dynamic changes in water supply-demand ratio in each season was simulated against the thermal time for each cultivar across the 35 locations and clustering analysis was used to cluster the major drought patterns. The analysis identified four major drought patterns characterised by low-stress, mid-season drought, late terminal drought and early-terminal drought patterns, occurring at 46%, 11%, 22% and 21% of the years, respectively. The frequencies of these patterns varied in relation to locations, genotypes and management. Yield reduction of up to 80% was observed for early terminal drought compared with low-stress drought pattern. There was significant scope for yield improvement through manipulating genotype and management. These results have important implications for germplasm enhancement and deployment over similar environments in the region.

Characteristics of rainfall responsible for debris flows in Wenchuan Earthquake area

The ability to forecast debris flows is important because of their frequent occurrence and potential for large-scale damage in the Wenchuan Earthquake-hit area. Accurate understanding of the characteristics of rainfall responsible for triggering debris flows is necessary for an effective early warning procedure. This study examined the general characteristics and spatiotemporal variation of rainfall events that responsible for debris flows. Analysis was performed on rainfall data extracted during 2008–2013. The results revealed that the rainfall thresholds for debris flows varied annually and showed tendency toward higher conditions required. The coefficient of variation of relevant rainfall events indicated that the characteristics of the rainstorms changed. In addition, the coefficient of deviation revealed the spatial variation of rainfall in subregions, which was attributed to differences of the local climatic conditions that control debris flow occurrence. The primary controlling rainfall indices for triggering debris flows were filtered, and the most suitable models for establishing rainfall thresholds were selected. Ultimately, the rainfall thresholds were obtained for each subregion within the study area.

Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review

Abstract. In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on small-scale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India

Rainfall, a vital component of hydrological cycle, plays a key role in appropriate planning and sustainable management of water resources in water-short regions. However, its uneven distribution over the space and time necessitates getting knowledge about its variability. This study aimed at evaluating efficacy of ordinary and Bayesian kriging techniques in depicting spatial and temporal variability of annual rainfall in arid and semi-arid regions of north-west India. Using 35-year gridded rainfall datasets of India Meteorological Department (IMD at 0.5° resolution, 1971–2005) and Climate Forecast System Reanalysis (CFSR at 0.3° resolution, 1979–2013), geostatistical modelling was performed by employing five kriging techniques with novelty of exploring potential of empirical Bayesian kriging (EBK) technique, for the first time, for rainfall datasets in this study. Performance of the kriging techniques was evaluated by cross-validation based on five criteria. Both exponential ordinary kriging (EOK) and EBK revealed better performance over other kriging techniques. Thus, EOK and EBK were further examined by adopting goodness-of-fit criteria of correlation coefficient (r) and root-mean-square error (RMSE). Very high r values indicated equally excellent performance of both the techniques; however, less RMSE values in case of EBK suggested it as the best-fit technique, which was then used for developing spatially distributed rainfall raster. Spatially distributed 35-year mean annual rainfall (MAR) and coefficient of variation (CV) highlighted very high temporal variability of the annual rainfall (CV > 120%) in the western arid lands of the country with <150 mm MAR values. Thus, under the scenario of scarce availability and high rainfall variability, urgent actions are needed to be taken such as implementation of rainwater-harvesting and groundwater-recharging structures, adoption of less water-requiring crops and micro-irrigation methods in agriculture. Moreover, these findings are useful for the decision makers, planners and resource managers to formulate appropriate strategies for conserving and sustainably managing precious rainwater quantities in arid regions worldwide.

Characterisation of Hydrological Response to Rainfall at Multi Spatio-Temporal Scales in Savannas of Semi-Arid Australia

Rainfall is the main driver of hydrological processes in dryland environments and characterising the rainfall variability and processes of runoff generation are critical for understanding ecosystem function of catchments. Using remote sensing and in situ data sets, we assess the spatial and temporal variability of the rainfall, rainfall–runoff response, and effects on runoff coefficients of antecedent soil moisture and ground cover at different spatial scales. This analysis was undertaken in the Upper Burdekin catchment, northeast Australia, which is a major contributor of sediment and nutrients to the Great Barrier Reef. The high temporal and spatial variability of rainfall are found to exert significant controls on runoff generation processes. Rainfall amount and intensity are the primary runoff controls, and runoff coefficients for wet antecedent conditions were higher than for dry conditions. The majority of runoff occurred via surface runoff generation mechanisms, with subsurface runoff likely contributing little runoff due to the intense nature of rainfall events. MODIS monthly ground cover data showed better results in distinguishing effects of ground cover on runoff that Landsat-derived seasonal ground cover data. We conclude that in the range of moderate to large catchments (193–36,260 km2) runoff generation processes are sensitive to both antecedent soil moisture and ground cover. A higher runoff–ground cover correlation in drier months with sparse ground cover highlighted the critical role of cover at the onset of the wet season (driest period) and how runoff generation is more sensitive to cover in drier months than in wetter months. The monthly water balance analysis indicates that runoff generation in wetter months (January and February) is partially influenced by saturation overland flow, most likely confined to saturated soils in riparian corridors, swales, and areas of shallow soil. By March and continuing through October, the soil “bucket” progressively empties by evapotranspiration, and Hortonian overland flow becomes the dominant, if not exclusive, flow generation process. The results of this study can be used to better understand the rainfall–runoff relationships in dryland environments and subsequent exposure of coral reef ecosystems in Australia and elsewhere to terrestrial runoff.

Impact of temporal resolution of precipitation forcing data on modelled urban‐atmosphere exchanges and surface conditions

ABSTRACTRepresentative precipitation data sets are very difficult to obtain due to the inherent spatial and temporal variability of rainfall. Gridded rainfall products exist at various scales, but temporal resolution is coarse (daily or, at best, a few hours). This study demonstrates the impact of low temporal resolution precipitation forcing data (PFD) on modelled energy fluxes, runoff and surface conditions, which could have implications for a range of applications including flood forecasting, irrigation scheduling and epidemiology. An evaporation‐interception model originally developed for forests is applied here within the framework of the Surface Urban Energy and Water balance Scheme (SUEWS). The model is forced with rainfall data representative of a range of temporal resolutions (from 5 min to 3 h). Taking the highest resolution case as a reference, differences in model output are found as the temporal resolution of PFD decreases, depending on the timing of rainfall occurrence, intensity and duration. Modelled evaporation, runoff and surface wetness deviate from the reference case, which affect other variables such as the turbulent sensible heat flux. The largest impacts are seen on days with greatest daily total rainfall and, even on days with no rain, differences in antecedent conditions (soil moisture or surface wetness) can cause deviations from the reference case. Errors can be reduced by applying a disaggregation scheme that provides a more realistic distribution of rainfall, importantly, one that allows for intermittent rainfall.

Mapping waterholes and testing for aridity using a remote sensing water index in a southern African semi-arid wildlife area

Waterholes are a key resource that influences wildlife distribution in semi-arid ecosystems. Mapping waterholes can guide intervening decisions for supplementing water resources and managing wildlife distribution patterns. Although remote sensing provides a key to mapping distribution of waterholes, efficiency of existing remotely sensed methods for detecting waterholes have to be evaluated and even new ones developed. In this study, we evaluated performance of the Modified Normalized Difference Water Index (MNDWI) and Superfine Water Index (SWI) at selected optimum thresholds. Kappa results indicated that MNDWI detects waterholes better than SWI. We further validated MNDWI detected waterholes by testing response of waterhole area to temporal rainfall variability and waterhole persistence to spatial rainfall variability. Extent of MNDWI-detected waterholes varied in relation to temporal rainfall variability (p < 0.05). Waterhole persistence was not associated with spatial rainfall variability which could be explained by differences in waterhole types or low spatial rainfall variability.

Assessment of rainfall spatial variability and its influence on runoff modelling: A case study in the Brue catchment, UK

AbstractThis study explores rainfall spatial variability and its influence on runoff modelling. A novel assessment scheme integrated with coefficient of variance and Moran's I is introduced to describe effective rainfall spatial variability. Coefficient of variance is widely accepted to identify rainfall variability through rainfall intensity, whereas Moran's I reflects rainfall spatial autocorrelation. This new assessment framework combines these two indicators to assess the spatial variability derived from both rainfall intensity and distribution, which are crucial in determining the time and magnitude of runoff generation. Four model structures embedded in the Variable Infiltration Capacity model are adopted for hydrological modelling in the Brue catchment of England. The models are assigned with 1, 3, 8, and 27 hydrological response units, respectively, and diverse rainfall spatial information for 236 events are extracted from 1995. This study investigates the model performance of different partitioning based on rainfall spatial variability through peak volume (Qp) and time to peak (Tp), along with the rainfall event process. The results show that models associated with dense spatial partitioning are broadly capable of capturing more spatial information with better performance. It is unnecessary to utilize models with high spatial density for simple rainfall events, though they show distinct advantages on complex events. With additional spatial information, Qp experiences a notable improvement over Tp. Moreover, seasonal patterns signified by the assessment scheme imply the feasibility of seasonal models.

The implications of regional variations in rainfall for reconstructing rainfall patterns using tree rings

AbstractIn Australia, multidecadal periods of floods and droughts have major economic consequences. Due to the short duration of Australian instrumental precipitation records, it is difficult to determine the patterns of these multidecadal periods. Proxy records can be used to create long‐term rainfall reconstructions for regions that are lacking instrumental data. However, the spatial extent over which single‐site proxy records can be applied is poorly understood. Southeast Queensland (SEQ) is an area where tree rings can be used to reconstruct long‐term rainfall patterns, but their regional representation is unknown. In this study, the spatial variability in rainfall across SEQ is investigated from 1908 to 2007 using 140 instrumental rainfall stations. Pearson correlation analysis between stations is used to create groups at the r = 0.80, 0.85, and 0.90 correlation levels, and then annual deviations from the mean are determined. These patterns indicate that rainfall is not uniform across SEQ but can be broken into 2 main spatially consistent groups. Each of these groups is broken down into several subgroups with higher correlation levels. Long‐term streamflow records are found to be correlated to rainfall patterns local to the streamflow stations, indicating that analysis of extreme events should consider spatial precipitation variability. Finally, the only currently available proxy rainfall reconstruction for the region, a 140‐year Toona ciliata tree ring width record from Lamington National Park, is compared to rainfall groups at different correlation levels across all of SEQ. The correlation between the reconstruction and the rainfall station groupings is best for the groups within which the tree‐ring record is spatially located, and this correlation improves as rainfall group correlation increases. Correlation is nearly nonexistent for groupings located at a distance from the tree‐ring site. These results demonstrate the importance of assessing the spatial variability of precipitation so that the spatial applicability of proxy records can be assessed.

Distribution of cloudiness and categorization of rainfall types based on INSAT IR brightness temperatures over Indian subcontinent and adjoining oceanic region during south west monsoon season

To understand the relationship between rain intensity and brightness temperature, Cloud Top Brightness Temperature (CTBT) derived from INSAT three hourly IR radiances having a resolution of 0.25 × 0.25 deg. is compared with corresponding TRMM PR Rain Rate (TPRR) for the Indian Summer Monsoon periods of 2007 and 2008. BT value ranges corresponding to events of various intensities of rain in the four major raining sub regions identified in Indian subcontinent and surrounding ocean are compared. The sub regions identified are (1) Head Bay of Bengal, (2) Central Indian land region, (3) Eastern Arabians Sea and West coast of India and (4) South West Indian Ocean. BT values are grouped into classes of 10°K bin width between 270 and 180°K. Number of occurrence of three classes of rain (light - >4.5 mm, moderate - 4.5–9 mm and heavy 9.0 mm and above cumulative for 3 h) belonging in each BT classes is calculated. It is observed that the three classes of rainfall have distinct characteristic BT ranges. This rain category - BT range relation has geographical (spatial) variability. This could be due to the variation in types of clouds prevalent in the sub regions considered. The present study improves the understanding of the structure and spatial variability of cloudiness and rainfall in and around Indian region during monsoon season.

Landslide susceptibility mapping using precipitation data, Mazandaran Province, north of Iran

Precipitation is a nonlinear and complex phenomenon and varies in time and space. It is also evident that there is a link between precipitation and shallow landslides, and precipitation is always considered as a landslide-triggering factor. This study aims to investigate the relationship between the characteristics of precipitation and the historical shallow landslides in Mazandaran Province, north of Iran. For this purpose, the spatial variability of rainfall was analyzed using monthly rainfall data collected at 15 synoptic stations distributed over the region between 1981 and 2014. Monthly precipitation and other derived parameters were used, and a hybrid model combining principal component analysis and cluster analysis (CA) was applied to all the precipitation parameters to regionalize the region into well-defined clusters in terms of precipitation and prove that there is a link between precipitation and the occurred slides. Then, the rotated PCs were combined and the precipitation characteristics map was produced. Demonstrating the linkage between the precipitation characteristics and the historical slides, the combined map can be considered as landslide susceptibility map. The accuracy of prediction was tested against a random guess and obtained as 77%. It is also noticeable that only 30% of the surface area of the study region in the landslide susceptibility map covers about 80% of the known landslides. The calculated measure suggests that the developed model well predicted the location of the occurred slides using only precipitation data.

Spatially distributed rainfall information and its potential for regional landslide early warning systems

Crucial to most landslide early warning system (EWS) is the precise prediction of rainfall in space and time. Researchers are aware of the importance of the spatial variability of rainfall in landslide studies. Commonly, however, it is neglected by implementing simplified approaches (e.g. representative rain gauges for an entire area). With spatially differentiated rainfall information, real-time comparison with rainfall thresholds or the implementation in process-based approaches might form the basis for improved landslide warnings. This study suggests an automated workflow from the hourly, web-based collection of rain gauge data to the generation of spatially differentiated rainfall predictions based on deterministic and geostatistical methods. With kriging usually being a labour-intensive, manual task, a simplified variogram modelling routine was applied for the automated processing of up-to-date point information data. Validation showed quite satisfactory results, yet it also revealed the drawbacks that are associated with univariate geostatistical interpolation techniques which solely rely on rain gauges (e.g. smoothing of data, difficulties in resolving small-scale, highly intermittent rainfall). In the perspective, the potential use of citizen scientific data is highlighted for the improvement of studies on landslide EWS.

Spatial connections in regional climate model rainfall outputs at different temporal scales: Application of network theory

Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961–1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network’s local density) and degree distribution (a measure of the network’s spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are weak, especially when more stringent conditions are imposed (i.e. when T is very high), except at the monthly scale.

Groundwater recharge estimation under semi arid climate: Case of Northern Gafsa watershed, Tunisia

Natural groundwater recharge under semi arid climate, like rainfall, is subjected to large variations in both time and space and is therefore very difficult to predict. Nevertheless, in order to set up any strategy for water resources management in such regions, understanding the groundwater recharge variability is essential. This work is interested in examining the impact of rainfall on the aquifer system recharge in the Northern Gafsa Plain in Tunisia.The study is composed of two main parts. The first is interested in the analysis of rainfall spatial and temporal variability in the study basin while the second is devoted to the simulation of groundwater recharge. Rainfall analysis was performed based on annual precipitation data recorded in 6 rainfall stations over a period of 56 years (1960–2015). Potential evapotranspiration data were also collected from 1960 to 2011 (52 years).The hydrologic distributed model WetSpass was used for the estimation of groundwater recharge. Model calibration was performed based on an assessment of the agreement between the sum of recharge and runoff values estimated by the WetSpass hydrological model and those obtained by the climatic method. This latter is based on the difference calculated between rainfall and potential evapotranspiration recorded at each rainy day. Groundwater recharge estimation, on monthly scale, showed that average annual precipitation (183.3 mm/year) was partitioned to 5, 15.3, 36.8, and 42.8% for interception, runoff, actual evapotranspiration and recharge respectively.

Temporal and spatial changes of rainfall and streamflow in the Upper Tekezē–Atbara river basin, Ethiopia

Abstract. The Upper Tekezē–Atbara river sub-basin, part of the Nile Basin, is characterized by high temporal and spatial variability of rainfall and streamflow. In spite of its importance for sustainable water use and food security, the changing patterns of streamflow and its association with climate change is not well understood. This study aims to improve the understanding of the linkages between rainfall and streamflow trends and identify possible drivers of streamflow variabilities in the basin. Trend analyses and change-point detections of rainfall and streamflow were analysed using Mann–Kendall and Pettitt tests, respectively, using data records for 21 rainfall and 9 streamflow stations. The nature of changes and linkages between rainfall and streamflow were carefully examined for monthly, seasonal and annual flows, as well as indicators of hydrologic alteration (IHA). The trend and change-point analyses found that 19 of the tested 21 rainfall stations did not show statistically significant changes. In contrast, trend analyses on the streamflow showed both significant increasing and decreasing patterns. A decreasing trend in the dry season (October to February), short season (March to May), main rainy season (June to September) and annual totals is dominant in six out of the nine stations. Only one out of nine gauging stations experienced significant increasing flow in the dry and short rainy seasons, attributed to the construction of Tekezē hydropower dam upstream this station in 2009. Overall, streamflow trends and change-point timings were found to be inconsistent among the stations. Changes in streamflow without significant change in rainfall suggests factors other than rainfall drive the change. Most likely the observed changes in streamflow regimes could be due to changes in catchment characteristics of the basin. Further studies are needed to verify and quantify the hydrological changes shown in statistical tests by identifying the physical mechanisms behind those changes. The findings from this study are useful as a prerequisite for studying the effects of catchment management dynamics on the hydrological variabilities in the basin.