Spatiotemporal Variability Of Precipitation Research Articles

Spatio-temporal precipitation variability over Western Ghats and Coastal region of Karnataka, envisaged using high resolution observed gridded data

Climatic changes in the recent decades have led to large variations in precipitation over the different geographical regions of the globe. Changes in precipitation pattern over the space and time can severely affect the country like India, which has a large spatio-temporal variability in the precipitation. Any shift in the mean precipitation pattern pose a challenge to economy, agricultural farming and the ecosystem of these regions. In the present study, we analyze the seasonal spatio-temporal variation in trends of long term (1901–2013) observed high resolution (0.25° × 0.25°) gridded daily precipitation data of the Indian Meteorological Department over Western Ghats and coastal region of Karnataka, vulnerable to the risks of climate change. Our analysis shows increasing trend in seasonal ratio of precipitation over the Southern coastal plains and the adjacent Western Ghats region during pre-monsoon (MAM) while the southern coastal plains show decreasing trend in monsoon period (JJAS). Daily intensity index of precipitation during monsoon shows increasing trend in northern plains with decreasing trend in the medium precipitation events. Our study finds that different topographic regions of Karnataka have different responses in the trends of precipitation, particularly the response of plains is quite different to that of the higher elevated Ghat region.

Evaluation of gridded precipitation data in the Hindu Kush–Karakoram–Himalaya mountainous area

ABSTRACTThe accurate measurement of precipitation is crucial for hydrological studies. This is especially true for the Hindu Kush–Karakoram–Himalaya (HKKH) mountain region, which is characterized by high spatiotemporal precipitation variability. The paucity of raingauges makes it difficult to measure precipitation in this region precisely. We conducted evaluation of TMPA 3B42V7 and APHRO 1101 in the HKKH area on a daily basis at a spatial resolution of 0.25° × 0.25°, using 27 raingauges. Statistically, the largest error in the gridded data arose mainly from elevation, followed by volumetric error and Nash–Sutcliffe efficiency. Overall, the TMPA data have a poor correlation with ground observations in the HKKH area, especially for higher altitudes. The western areas are relatively more underestimated and the Karakoram shows higher frequency of bias in the TMPA retrievals. This method could help improve the satellite precipitation estimation algorithm as it considers local physiography and climatic factors.

Rainfall Variability and Trend Analysis of Multiannual Rainfall in Romanian Plain

AbstractThe IPCC climate models predict, for the Central Europe, are for climate changes, being seen variability of temperature, with a growing trend of 1-2,5° C (with 1° C for alpine zone – Carpathians and 2-2,5° C for plains). Current observations in the Romanian plain are not consistent, with an existence of a multiannual variability of temperature and precipitations depending on cyclonal and anti-ciclonal activity. The research is based on calculation of reduced centered index, also the graphical chronological method in information processing (MGCTI) of „Bertin Matrix” type, to show current trends of the spatio-temporal variability of precipitation in the context of global climate change. These are in line with the movement of air masses in Europe in general, and implicitly in Romania, with particular regard to the southern region of the country where the Romanian Plain. The variability of short-term global climate is generally associated with coupling phases of oceanic and atmospheric phenomena including El Niño Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). While El Niño Southern Oscillation (ENSO) affects climate variability in the world, the North Atlantic Oscillation (NAO) is the climate model dominant in the North Atlantic region. The latter cyclic oscillation whose role is still under debate could explain the variability of rainfall in much of the, central Europe area, and support the hypothesis of a return of the rains marking the end of years of drought in Romanian plain. Faced with such great changes that today affect the central Europe region and given the complexity of spatial and temporal dimensions of the climatic signal, a more thorough research of causes and retroactions would allow for a better understanding of the mechanisms behind this new trend.

Statistical analysis of rainfall trends over Damodar River basin, India

This paper is intended to compute long-term spatiotemporal variability of precipitation on annual and seasonal scale of Damodar River Basin over the period of 113 years (1901–2013). The lag-1 autocorrelation coefficient was applied to check serial dependence in the dataset. After this, non-parametric Mann Kendall and modified Mann Kendall test were used to identify trends and the Theil-Sen’s slope method to estimate magnitude of trend line. Sequential Mann Kendall test was also applied to detect the potential turning point. Results found significant decreasing precipitation trend in annual and monsoon season over the basin except the southeastern part. The maximum decrease was found for annual and monsoon season at the northwestern part while minimum at the northeastern region. The sequential Mann Kendall test identified several non-significant as well as significant turning points for seasonal and annual rainfall at most of the locations.

The spatiotemporal variability of precipitation over the Himalaya: evaluation of one-year WRF model simulation

The Weather Research and Forecasting (WRF) model is used to simulate the spatiotemporal distribution of precipitation over central Asia over the year April 2005 through March 2006. Experiments are performed at 6.7 km horizontal grid spacing, with an emphasis on winter and summer precipitation over the Himalaya. The model and the Tropical Rainfall Measuring Mission show a similar inter-seasonal cycle of precipitation, from extratropical cyclones to monsoon precipitation, with agreement also in the diurnal cycle of monsoon precipitation. In winter months, WRF compares better in timeseries of daily precipitation to stations below than above 3-km elevation, likely due to inferior measurement of snow than rain by the stations, highlighting the need for reliable snowfall measurements at high elevations in winter. In summer months, the nocturnal precipitation cycle in the foothills and valleys of the Himalaya is captured by this 6.7-km WRF simulation, while coarser simulations with convective parameterization show near zero nocturnal precipitation. In winter months, higher resolution is less important, serving only to slightly increase precipitation magnitudes due to steeper slopes. However, even in the 6.7-km simulation, afternoon precipitation is overestimated at high elevations, which can be reduced by even higher-resolution (2.2-km) simulations. These results indicate that WRF provides skillful simulations of precipitation relevant for studies of water resources over the complex terrain in the Himalaya.

Seasonal to Interannual Variability of Satellite-Based Precipitation Estimates in the Pacific Ocean Associated with ENSO from 1998 to 2014

Based on a widely used satellite precipitation product (TRMM Multi-satellite Precipitation Analysis 3B43), we analyzed the spatiotemporal variability of precipitation over the Pacific Ocean for 1998–2014 at seasonal and interannual timescales, separately, using the conventional empirical orthogonal function (EOF) and investigated the seasonal patterns associated with El Niño–Southern Oscillation (ENSO) cycles using season-reliant empirical orthogonal function (SEOF) analysis. Lagged correlation analysis was also applied to derive the lead/lag correlations of the first two SEOF modes for precipitation with Pacific Decadal Oscillation (PDO) and two types of El Niño, i.e., central Pacific (CP) El Niño and eastern Pacific (EP) El Niño. We found that: (1) The first two seasonal EOF modes for precipitation represent the annual cycle of precipitation variations for the Pacific Ocean and the first interannual EOF mode shows the spatiotemporal variability associated with ENSO; (2) The first SEOF mode for precipitation is simultaneously associated with the development of El Niño and most likely coincides with CP El Niño. The second SEOF mode lagged behind ENSO by one year and is associated with post-El Niño years. PDO modulates precipitation variability significantly only when ENSO occurs by strengthening and prolonging the impacts of ENSO; (3) Seasonally evolving patterns of the first two SEOF modes represent the consecutive precipitation patterns associated with the entire development of EP El Niño and the following recovery year. The most significant variation occurs over the tropical Pacific, especially in the Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ); (4) Dry conditions in the western basin of the warm pool and wet conditions along the ITCZ and SPCZ bands during the mature phase of El Niño are associated with warm sea surface temperatures in the central tropical Pacific, and a subtropical anticyclone dominating over the northwestern Pacific. These findings may be useful for prediction of seasonal precipitation anomalies over the Pacific Ocean during El Niño years and recovery years.

VALIDATION OF SATELLITE PRECIPITATION (TRMM 3B43) IN ECUADORIAN COASTAL PLAINS, ANDEAN HIGHLANDS AND AMAZONIAN RAINFOREST

Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 – 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins.

VALIDATION OF SATELLITE PRECIPITATION (TRMM 3B43) IN ECUADORIAN COASTAL PLAINS, ANDEAN HIGHLANDS AND AMAZONIAN RAINFOREST

Abstract. Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 – 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins.

Understanding Drought Dynamics during Dry Season in Eastern Northeast Brazil

Eastern Northeast Brazil (ENEB) generally experiences a high variability in precipitation in the dry season, with amplitudes that can overcome 500 mm. The understanding of this variability can help in mitigating the socio-economic issues related to the planning and management of water resources this region, which is highly vulnerable to drought. This work aims to assess spatio-temporal variability of precipitation during the dry season and investigate the relationships between climate phenomena and drought events in the ENEB, using univariate (Spearman correlation) and multivariate statistical techniques, such as Principal Component Analysis, Cluster Analysis and Maximum Covariance Analysis. The results indicate that the variability of precipitation in the dry season can be explained mainly (62%) by local physical conditions and climate conditions have a secondary contribution. Further analysis of the larger anomalous events suggests that the state of Atlantic and Pacific oceans can govern the occurrence of those events, and the conditions of Atlantic Ocean can be considered a potential modulator of anomalous phenomena of precipitation in ENEB.

Impacts of uncertainties in European gridded precipitation observations on regional climate analysis.

ABSTRACTGridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio‐temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan‐European data sets and a set that combines eight very high‐resolution station‐based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post‐processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small‐scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate‐mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments.

Trends and Geostatistical Interpolation of Spatio-Temporal Variability of Precipitation in Northern Cameroon

This paper examines the spatial and temporal variability of the mean annual precipitation in the Northern Cameroon on the context of climate change during the time period 1950-2013. The study used homogeneous monthly and annual precipitations database of twenty-five stations located in the Northern Cameroon and Southern Chad Republic. Geostatisticals interpolation methods (Kriging and Inverse Distance Weighting method) associated with Digital Elevation Model were used to establish the spatial distribution of annuals precipitations. The non-parametric Mann- Kendall test and Sen’s slope method were performed to determine respectively trend and magnitude. The result indicates a spatial distribution of precipitation mainly determined by the topography and the geography of the study area. The trend analysis shows a decrease of annual average precipitation across the Northern Cameroon at a rate of ?0.568 mm/year over the time period 1950-2013. The magnitude of decreasing trends ranged between 0.11 and 3.92 mm/year. Statistically significant decreasing trends at the 95% level of confidence were noted at 10% while 5% of stations showed statistically significant increasing. However, 60% of stations indicate a decreasing trend. Monthly analysis of rainfall shows a decreasing trend during June and September while July and August present an increasing trend.

Rainfall and Cloud Dynamics in the Andes: A Southern Ecuador Case Study

Mountain regions worldwide present a pronounced spatiotemporal precipitation variability, which added to scarce monitoring networks limits our understanding of the generation processes involved. To improve our understanding of clouds and precipitation dynamics and cross-scale generation processes in mountain regions, we analyzed spatiotemporal rainfall patterns using satellite cloud products (SCP) in the Paute basin (900–4200 m a.s.l. and 6481 km2) in the Andes of Ecuador. Precipitation models, using SCP and GIS data, reveal the spatial extension of three regimes: a three-modal (TM) regime present across the basin, a bimodal (BM) regime, along sheltered valleys, and a unimodal (UM) regime at windward slopes of the eastern cordillera. Subsequently, the spatiotemporal analysis using synoptic information shows that the dry season of the BM regime during boreal summer is caused by strong subsidence inhibiting convective clouds formation. Meanwhile, in UM regions, low advective shallow cap clouds mainly cause precipitation, influenced by water vapor from the Amazon and enhanced easterlies during boreal summer. TM regions are transition zones from UM to BM and zones on the windward slopes of the western cordillera. These results highlight the suitability of satellite and GIS data-driven statistical models to study spatiotemporal rainfall seasonality and generation processes in complex terrain, as the Andes.

Inter- and Intra-annual precipitation variability and associated relationships to ENSO and the IOD in southern Africa

Precipitation variability at inter- and intra-annual scales may influence land-use management decisions in semi-arid savannas worldwide, and in particular, over the Kavango-Zambezi Transfrontier Conservation Area (KAZA) in southern Africa. Over KAZA, spatiotemporal precipitation variability forced by the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) is important to local and regional-scale decisions for planting, livestock grazing, and wildlife migration patterns. We investigate the annual water year (October–September) and early rainy season [October–November–December (OND)] precipitation patterns during 60 years before and after a 1970s shift in the global ocean–atmosphere system for this region in southern Africa. The coincidence of the number of wet (upper tercile) and dry (lower tercile) years and OND seasons during the various phases of ENSO and IOD are compared prior to (1950–1975) and after (1980–2008) the 1970s climate shift over KAZA. Since the 1970s climate shift, KAZA has been significantly drier and observed fewer wet years and OND seasons. ENSO is the dominant forcing of precipitation differences over southern Africa before and after the 1970s climate shift, with IOD playing a secondary role. However, when ENSO and IOD phases are analysed simultaneously, El Niño and positive IOD events are significantly related to dry periods. The forcing of atmospheric circulation over southern Africa before and after the 1970s climate shift during El Niño and La Niña events is significantly different. Prior to the 1970s climate shift, atmospheric circulation during El Niño (La Niña) favoured strong (weak) precipitation increases (decreases). Afterward the 1970s climate shift, atmospheric circulation during La Niña (El Niño) favoured strong (weak) precipitation decreases (increases). The shifting nature of climate modes, especially ENSO, and respective influence on rainfall variability for southern Africa is important to understand to better inform seasonal climate forecasts to improve operational decision-making for land-use and water management decisions in semi-arid savanna regions.

Improving high-resolution quantitative precipitation estimation via fusion of multiple radar-based precipitation products

Summary For monitoring and prediction of water-related hazards in urban areas such as flash flooding, high-resolution hydrologic and hydraulic modeling is necessary. Because of large sensitivity and scale dependence of rainfall–runoff models to errors in quantitative precipitation estimates (QPE), it is very important that the accuracy of QPE be improved in high-resolution hydrologic modeling to the greatest extent possible. With the availability of multiple radar-based precipitation products in many areas, one may now consider fusing them to produce more accurate high-resolution QPE for a wide spectrum of applications. In this work, we formulate and comparatively evaluate four relatively simple procedures for such fusion based on Fisher estimation and its conditional bias-penalized variant: Direct Estimation (DE), Bias Correction (BC), Reduced-Dimension Bias Correction (RBC) and Simple Estimation (SE). They are applied to fuse the Multisensor Precipitation Estimator (MPE) and radar-only Next Generation QPE (Q2) products at the 15-min 1-km resolution (Experiment 1), and the MPE and Collaborative Adaptive Sensing of the Atmosphere (CASA) QPE products at the 15-min 500-m resolution (Experiment 2). The resulting fused estimates are evaluated using the 15-min rain gauge observations from the City of Grand Prairie in the Dallas–Fort Worth Metroplex (DFW) in north Texas. The main criterion used for evaluation is that the fused QPE improves over the ingredient QPEs at their native spatial resolutions, and that, at the higher resolution, the fused QPE improves not only over the ingredient higher-resolution QPE but also over the ingredient lower-resolution QPE trivially disaggregated using the ingredient high-resolution QPE. All four procedures assume that the ingredient QPEs are unbiased, which is not likely to hold true in reality even if real-time bias correction is in operation. To test robustness under more realistic conditions, the fusion procedures were evaluated with and without post hoc bias correction of the ingredient QPEs. The results show that only SE passes the evaluation criterion consistently. The performance of DE and BC are generally comparable; while DE is more attractive for computational economy, BC is more attractive for reducing occurrences of negative estimates. The performance of RBC is poor as it does not account for magnitude-dependent biases in the QPE products. SE assumes that the higher-resolution QPE product is skillful in capturing spatiotemporal variability of precipitation at its native resolution, and that the lower-resolution QPE product provides skill at its native resolution. While the above assumptions may not always be met, the simplicity and robustness observed in this work make SE an extremely attractive choice as a simple post-processor to the QPE process. Also, unlike the other procedures considered in this work, it is extremely easy to update the statistical parameters of SE in real time, similarly to the real-time bias correction currently used in MPE, for improved performance via self-learning.

Spatial Variations in the Diurnal Pattern of Precipitation over Nepal Himalayas

The Central Himalayan Region (Nepal Himalayas), comprised of two clear sub-parallel mountain ranges, is atypical location for studying the impact of rugged topography on spatio temporal variations of precipitation. The relationship between topography and diurnal cycles of rainfall have been investigated utilizing 13-year (1998–2010) high resolution (0.05° × 0.05°) Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. An investigation of diurnal cycle of precipitation revealed an afternoon maximum during the pre-monsoon season (March–May) and midnight–early morning maximum during the summer monsoon season (June–August)over the southern slopes of the Himalayas. The summer monsoon exhibited a robust spatial variation of diurnal cycle of precipitation, during afternoon-evening time, primary rainfall peak appeared along the Lesser Himalayas (~2,000–2,200 m above mean sea level), while early-morning rain in contrast showed maximum concentration along the southern margin of the Himalayas (~500–700 m above MSL). An afternoon-evening rainfall peak was attributed to higher rain frequency, whereas early-morning rainfall peak was attributed to fewer but rather intense rainfall. It is suggested that, confluence between down slope and moist south easterly monsoon flow triggers convection near the foothills of the Himalayas during early morning period. The results further suggested the morning precipitation moves southward in the mature monsoon season.DOI: http://dx.doi.org/njst.v15i2.12116Nepal Journal of Science and Technology Vol. 15, No.2 (2014), 57-64

How Well Do Gridded Datasets of Observed Daily Precipitation Compare over Australia?

Daily gridded precipitation data are needed for investigating spatiotemporal variability of precipitation, including extremes; however, uncertainties related to daily precipitation products are large. Here, we compare a range of precipitation grids for Australia. These datasets include products derived solely from in situ observations (interpolated datasets) and two products that combine both remote sensed data and in situ observations. We find that all precipitation grids have similar climatologies for annual aggregated precipitation totals and annual maximum precipitation. The temporal correlations of daily precipitation values are higher between the interpolated datasets, but the correlations between the most widely used interpolated product (AWAP) and the two remotely sensed products (TRMM and GPCP) are still reasonable. Our results, however, point to distinct structural uncertainties between those datasets gridding in situ observations and those datasets deriving precipitation estimates primarily from satellite measurements. All datasets analysed agree well for low to moderate daily precipitation amounts up to about 20 mm but diverge at upper quantiles, indicating that substantial uncertainty exists in gridded precipitation extremes over Australia.

Fluctuation of Groundwater Levels and Recharge Patterns in Northern Ghana

Evaluating groundwater levels and recharge patterns is part of sustainably managing the water supply and predicting the possibility of water shortages, especially in light of climate change, extreme events (floods/droughts), increasing population and development. In the northern region of Ghana, groundwater is increasingly relied upon as a source of potable water for rural populations, but seasonal and inter-annual fluctuations of groundwater levels and recharge patterns are not always known. The fluctuation of groundwater levels on a seasonal basis shows that groundwater levels at all sites increase in response to seasonal precipitation. On an annual basis, all sites show an overall net decline of groundwater levels over the study period, which may be associated with below-average departures of precipitation during five of the seven study years. The variability of recharge patterns among five sites is attributed to the spatio-temporal variability of precipitation and hydrogeologic site uniqueness. The overarching potential benefit of this study is to facilitate closing knowledge gaps and contribute to a foundation for a more robust evaluation of groundwater resources in the area, especially as more data become available.

Spatial and seasonal patterns of precipitation in Greece: the terrain segmentation approach

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 <p>The aim of the study is to investigate the spatial and seasonal variation of precipitation in Greece using multi-temporal data analysis techniques. Mean monthly precipitation grids of 1 km<sup>2</sup> resolution of the period 1950-2000 were used in the analysis. Cross correlation quantified spatio-temporal patterns which are summarized as follows a) the absolute correlations of precipitation versus elevation and longitude are minimized during the winter period, b) the latitude dependency of precipitation presents a seasonal shift where winter precipitation tends to be higher in northern Greece, while summer precipitation tends to be higher in southern Greece. Principal components analysis indicated that the first two components account for the 92.8% of variance in the spatio-temporal variability of precipitation in Greece. Cluster analysis segmented the terrain to 27 regions with distinct seasonal variability of precipitation. The majority of irrigated agricultural land (plains of Macedonia, Thessaly and Thrace) belong to clusters which present the lowest values of annual precipitation (<600 mm year<sup>-1</sup>). The derivation of precipitation signatures for each region of Greece using the proposed terrain segmentation approach can support environmental decision and agricultural planning at a regional (country) scale in relation to water resources management. </p>
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Rainfall-runoff modeling in a flashy tropical watershed using the distributed HL-RDHM model

Summary Many watersheds in Hawai‘i are flash flood prone due to their small contributing areas and frequent intense rainfall. Motivated by the possibility of developing an operational flood forecasting system, this study evaluated the performance of the National Weather Service (NWS) model, the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) in simulating the hydrology of the flood-prone Hanalei watershed in Kaua‘i, Hawai‘i. This rural watershed is very wet and has strong spatial rainfall gradients. Application of HL-RDHM to Hanalei watershed required (i) modifying the Hydrologic Rainfall Analysis Project (HRAP) coordinate system; (ii) generating precipitation grids from rain gauge data, and (iii) generating parameters for Sacramento Soil Moisture Accounting Model (SAC-SMA) and routing parameter grids for the modified HRAP coordinate system. Results were obtained for several spatial resolutions. Hourly basin-average rainfall calculated from one HRAP resolution grid (4 km × 4 km) was too low and inaccurate. More realistic rainfall and more accurate streamflow predictions were obtained with the ½ and ¼ HRAP grids. For a one year period with the best precipitation data, the performance of HL-RDHM was satisfactory even without calibration for basin-averaged and distributed a priori parameter grids. Calibration and validation of HL-RDHM were conducted using four-year data set each. The model reasonably matched the observed peak discharges and time to peak during calibration and validation periods. The performance of model was assessed using the following three statistical measures: Root Mean Square Error (RMSE), Nash–Sutcliffe efficiency (NSE) and Percent bias (PBIAS). Overall, HL-RDHM’s performance was “very good (NSE > 0.75, PBIAS 3 s −1 ) than at the higher ones (140 and 248 m 3 s −1 ) as indicated by lower values of probability of detection and critical success index, and a higher value of the false alarm ratio. These results suggest that HL-RDHM may be suitable for flood forecasting applications in watersheds with steep terrain and strong spatio-temporal variability of precipitation, e.g. US Pacific North-West, and tropical islands.

The large-scale spatio-temporal variability of precipitation over Sweden observed from the weather radar network

Abstract. Using measurements from the national network of 12 weather radar stations for the 11-year period 2000–2010, we investigate the large-scale spatio-temporal variability of precipitation over Sweden. These statistics provide useful information to evaluate regional climate models as well as for hydrology and energy applications. A strict quality control is applied to filter out noise and artifacts from the radar data. We focus on investigating four distinct aspects: the diurnal cycle of precipitation and its seasonality, the dominant timescale (diurnal versus seasonal) of variability, precipitation response to different wind directions, and the correlation of precipitation events with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO). When classified based on their intensity, moderate- to high-intensity events (precipitation > 0.34 mm/3 h) peak distinctly during late afternoon over the majority of radar stations in summer and during late night or early morning in winter. Precipitation variability is highest over the southwestern parts of Sweden. It is shown that the high-intensity events (precipitation > 1.7 mm/3 h) are positively correlated with NAO and AO (esp. over northern Sweden), while the low intensity events are negatively correlated (esp. over southeastern parts). It is further observed that southeasterly winds often lead to intense precipitation events over central and northern Sweden, while southwesterly winds contribute most to the total accumulated precipitation for all radar stations. Apart from its operational applications, the present study demonstrates the potential of the weather radar data set for studying climatic features of precipitation over Sweden.