Modeling runoff in semiarid rangeland watersheds

Rangeland areas are characterized by sparse and limited hydrological data that make the use of existing mathematical runoff models difficult and impractical. A simple rational mathematical model was developed to predict runoff in three Oregon semiarid rangeland watersheds, using daily precipitation and mean daily temperaturs. The model consists of three main components to estimate snowmelt runoff due to thawing and rainfall, and rainfall runoff. The model calculates daily flows on a continuous basis. Comparison with measured flows for monthly predictions gave coefficients of determination (R²) varying from 0.67 to 0.81 for different basins and different periods of observation, including verification periods. The predicted timing of monthly flows was consistent with observed data. Using the Curve Number method, new approaches are suggested for snowmelt runoff calculations.     

Downscaling CHIRPS precipitation data: an artificial neural network modelling approach

The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) is a high-resolution climatic database of precipitation embracing monthly precipitation climatology, quasi-global geostationary thermal infrared satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 product, atmospheric model rainfall fields from National Oceanic and Atmospheric Administration – Climate Forecast System (NOAA CFS), and precipitation observations from various sources. The key difference with all other existing precipitation databases is the high-resolution of the available data, since the inherent 0.05° resolution is a rather unique threshold. Monthly data for the period from January 1999 to December 2012 were processed in the present research. The main aim of this article is to propose a novel downscaling method in order to attain high resolution (1 km × 1 km) precipitation datasets, by correlating the CHIRPS dataset with altitude information and the normalized difference vegetation index from satellite images at 1 km × 1 km, utilizing artificial neural network models. The final result was validated with precipitation measurements from the rain gauge network of the Cyprus Department of Meteorology.     

Modeling the atmospheric deposition and stormwater washoff of nitrogen compounds

We investigated the suitability of integrating deterministic models to estimate the relative contributions of atmospheric dry and wet deposition onto an urban surface and the subsequent amounts removed by stormwater runoff. The CIT airshed model and the United States Environmental Protection Agency Storm Water Management Model (SWMM) were linked in order to simulate the fate and transport of nitrogen species through the atmosphere and storm drainage system in Los Angeles, California, USA. Coupling CIT and SWMM involved defining and resolving five critical issues: (1) reconciling the different modeling domain sizes, (2) accounting for dry deposition due to plant uptake, (3) estimating the fraction of deposited contaminant available for washoff, (4) defining wet deposition inputs to SWMM, and (5) parameterizing the SWMM washoff algorithm. The CIT–SWMM interface was demonstrated by simulating dry deposition, wet deposition, and stormwater runoff events to represent the time period from November 18, 1987 to December 4, 1987 for a heavily urbanized Los Angeles watershed discharging to Santa Monica Bay. From November 18th to December 3rd the simulated average dry deposition flux of nitrogen was 0.195 kg N/ha-day to the watershed and 0.016 kg N/ha-day to Santa Monica Bay. The simulated rainfall concentrations during the December 4th rainfall event ranged from 3.76 to 8.23 mg/l for nitrate and from 0.067 to 0.220 mg/l for ammonium. The simulated stormwater runoff event mean concentrations from the watershed were 4.86 mg/l and 0.12 mg/l for nitrate and ammonium, respectively. Considering the meteorology during the simulation period, the CIT and SWMM predictions compare well with observations in the Los Angeles area and in other urban areas in the United States.     

青藏高原GPM降水数据空间降尺度研究

高分辨率的降水数据对于复杂地形区的精确水文预报和气候模拟至关重要.利用青藏高原的植被、地形和地理位置特征,建立了与降水的回归模型,将全球降水测量(GPM)IMERG的年降水量从0.1°降尺度至1 km,通过分解年降水获得月降水量数据,并用气象站点的实测数据进行校准.得出以下结论:①GPM IMERG月降水量略大于地面观...     

Efficiency enhancement of a process-based rainfall–runoff model using a new modified AdaBoost.RT technique

High-efficiency rainfall–runoff forecast is extremely important for flood disaster warning. Single process-based rainfall–runoff model can hardly capture all the runoff characteristics, especially for flood periods and dry periods. In order to address the issue, an effective multi-model ensemble approach is urgently required. The Adaptive Boosting (AdaBoost) algorithm is one of the most robust ensemble learning methods. However, it has never been utilized for the efficiency improvement of process-based rainfall–runoff models.Therefore AdaBoost.RT (Adaptive Boosting for Regression problems and “T” for a threshold demarcating the correct from the incorrect) algorithm, is innovatively proposed to make an aggregation (AdaBoost-XXT) of a process-based rainfall–runoff model called XXT (a hybrid of TOPMODEL and Xinanjing model). To adapt to hydrologic situation, some modifications were made in AdaBoost.RT. Firstly, weights of wrong predicted examples were made increased rather than unchangeable so that those “hard” samples could be highlighted. Then the stationary threshold to demarcate the correct from the incorrect was replaced with dynamic mean value of absolute errors. In addition, other two minor modifications were also made. Then particle swarm optimization (PSO) was employed to determine the model parameters. Finally, the applicability of AdaBoost-XXT was tested in Linyi watershed with large-scale and semi-arid conditions and in Youshuijie catchment with small-scale area and humid climate. The results show that modified AdaBoost.RT algorithm significantly improves the performance of XXT in daily runoff prediction, especially for the large-scale watershed or low runoff periods, in terms of Nash–Sutcliffe efficiency coefficients and coefficients of determination. Furthermore, the AdaBoost-XXT has the more satisfactory generalization ability in processing input data, especially in Linyi watershed. Thus the method of using this modified AdaBoost.RT to enhance model performance is promising and easily extended to other process-based rainfall–runoff models.     

A Reduced Parameter Stream Temperature Model (RPSTM) for basin-wide simulations

Water temperature is a crucial variable that shapes biological communities and controls rates of ecosystem processes in rivers. Fully parameterized heat balance models have been used to provide accurate estimates, but high parameterization costs make them difficult to apply at basin-wide scales. As parts of a collaborative modeling project to address future impacts of climate and land-use management on the Muskegon River, we developed a Reduced Parameter Stream Temperature Model (RPSTM), a mechanistic, spatially explicit but easier to parameterize model. Here we describe and test RPSTM's applicability by conducting a series of daily water temperature simulations (1985–2005). RPSTM performed well along the river network. The predictions were most sensitive to air temperature, depth, and solar radiation, but relatively insensitive to rates of surface runoff. This modeling approach is easily integrated into complex multi-modeling systems to evaluate effects of long-term changes in watershed hydrology, climate, and land management across river networks.     

Snowpack and runoff generation using AMSR-E passive microwave observations in the Upper Helmand Watershed, Afghanistan

Passive microwave estimates of snow water equivalent (SWE) were examined to determine their usefulness for evaluating water resources in the remote Upper Helmand Watershed, central Afghanistan. SWE estimates from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Special Sensor Microwave/Imager (SSM/I) passive microwave data were analyzed for six winter seasons, 2004-2009. A second, independent estimate of SWE was calculated for these same time periods using a hydrologic model of the watershed with a temperature index snow model driven using the Tropical Rainfall Measuring Mission (TRMM) gridded estimates of precipitation. The results demonstrate that passive microwave SWE values from SSM/I and AMSR-E are comparable. The AMSR-E sensor had improved performance in the early winter and late spring, which suggests that AMSR-E is better at detecting shallow snowpacks than SSM/I. The timing and magnitude of SWE values from the snow model and the passive microwave observations were sometimes similar with a correlation of 0.53 and accuracy between 55 and 62%. However, the modeled SWE was much lower than the AMSR-E SWE during two winter seasons in which TRMM data estimated lower than normal precipitation. Modeled runoff and reservoir storage predictions improved significantly when peak AMSR-E SWE values were used to update the snow model state during these periods. Rapid decreases in passive microwave SWE during precipitation events were also well aligned with flood flows that increased base flows by 170 and 940%. This finding supports previous northern latitude studies which indicate that the passive microwave signal's lack of scattering can be used to detect snow melt. The current study's extension to rain on snow events suggests an opportunity for added value for flood forecasting.     

Monthly Runoff Prediction of Zhengyixia in the Heihe River based on Singular Spectrum Analysis-grey Wolf Optimizer-support Vector Regression Hybrid Model

Hydrologic prediction is an important prerequisite for optimal allocation of water resources, but the traditional forecasting methods generally have the problem of low forecasting accuracy. To improve the accuracy of hydrologic prediction, a hybrid data-driven model is proposed for monthly runoff forecasting, namely, Singular Spectrum Analysis-Grey Wolf Optimizer-Support Vector Regression (SSA-GWO-SVR) model. The proposed model uses SSA to denoise the runoff data to improve the stability and predictability of runoff series, and uses GWO to optimize the parameters of SVR model to enhance the generalization ability of the model. This model is validated by monthly runoff prediction of Zhengyixia in the Heihe River Basin, and the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficient (R) and Nash-Sutcliffe Efficiency Coefficien (NSEC) are used as evaluation criteria. The experimental results show that the prediction accuracy of the proposed model is significantly higher than those of Autoregressive Integrated Moving Average model (ARIMA), Persistent Model (PM), Cross Validation(CV)-SVR and GWO-SVR models, and the can predict the runoff peak well, which indicates that the model is a reliable runoff forecasting model, can capture the intrinsic characteristics of hydrologic runoff more deeply, and provides a new method for hydrologic prediction based on data-driven model.     

基于奇异谱分析-灰狼优化-支持向量回归混合模型的黑河正义峡月径流预测

水文预报是水资源优化配置的重要前提,而传统预报方法普遍存在预测精度低的问题,为提高水文预报的准确性,提出了一种混合数据驱动模型用于月径流预测,即奇异谱分析-灰狼优化-支持向量回归(SSA-GWO-SVR)模型。该模型通过SSA对径流数据进行去噪处理来提高径流序列的平稳性和可预测性,采用GWO对SVR模型的参数进行联合选优,从而增强模型的泛化能力。通过黑河正义峡的月径流预测进行模型验证,以平均绝对误差(MAE)、均方根误差(RMSE)、相关系数(R)和纳什效率系数(NSEC)为模型评价标准。实验结果表明该模型的预测精度明显高于自回归积分滑动平均模型(ARIMA)、持续性模型(PM)、交叉验证-SVR(CV-SVR)和GWOSVR模型,并且它能很好地预测径流峰值,说明该模型是一种可靠的径流预测模型,能够更深入地捕获水文径流的内在特性,为基于数据驱动模型的水文预报提供了一种新方法。     

Comparison of TRMM multisatellite precipitation analysis (TMPA) products and daily-monthly gauge data over Bali

Research has been conducted to compare daily, monthly and seasonal rain rates derived from Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) using rain gauge analysis from 1998 to 2002. Three rain gauges in the Bali islands were employed. Statistical analysis was used to analyse the relationship of the TMPA product with the rain gauge data. Resulting statistical measures consisted of the linear correlation coefficient (r), the mean bias error (MBE), the root mean square error (RMSE) and the mean absolute error (MAE). The results of these analyses indicate that satellite data have lower values than the gauge estimation values. The validation analysis showed a very good relationship with the gauge data on monthly timescales. However, a poor relationship was found between the gauge data and the daily data analysis from the TMPA. The 3B42 and 3B43 products showed the same levels of relationship during the wet season and dry season. The correlation in the dry season was better than during the wet season. Statistical error levels during the wet season were better than in the dry season. The 3B43 showed slight improvement in these values when compared with the 3B42 (both the random error measurement and the scatter of the estimates were reduced). In general, the data from TMPA are potentially usable to replace rain gauge data, especially to replace the monthly data, if inconsistencies and errors are taken into account.     

Understanding the dependence of the uncertainty in a satellite precipitation data set on the underlying surface and a correction method based on geographically weighted regression

It is widely acknowledged that the complicated underlying surface is one of the prominent reasons leading to serious uncertainty in satellite precipitation data sets over mountainous regions. However, no analysis has been conducted to quantitatively investigate the correlation between the errors in satellite precipitation data sets and the underlying surface. Using 133 monthly rain gauge observations over the Tibetan Plateau, the Bias and the residuals of ordinary least regression (the latter called ‘?’) in Climate Prediction Center morphing (CMORPH) data were calculated and were fitted with underlying surface factors using the geographically weighted regression (GWR) method, aiming at quantitatively understanding the dependence of the uncertainty in the CMORPH data set on the underlying surface over the Tibetan Plateau. We found that the 39.4% and 50.5% of the variance of the Bias and ?, respectively, could be explained by the digital elevation model, the normalized difference vegetation index and land surface temperature. Furthermore, the explained variance of the Bias and ? could be increased to 53.6% and 75.1%, respectively, by adding the CMORPH to the explanatory variables. Subsequently, the errors in the CMORPH were estimated by the GWR model, which was selected by comparing the explanation strengths of these models, and then the simulated errors were used to correct the precipitation estimated by CMORPH over areas without gauges. Independent validation indicated that the corrected CMORPH showed obviously better performance compared with the uncorrected CMORPH as well as two widely used precipitation estimates – the Universal Kriging interpolation model and the Tropical Rainfall Measuring Mission 3B43. These results reveal the significant influence of the underlying surface on the uncertainty in satellite precipitation data sets over mountainous areas and provide a promising approach to improve the precipitation estimates derived from satellite observations using underlying surface information.     

Modeling and assimilation of root zone soil moisture using remote sensing observations in Walnut Gulch Watershed during SMEX04

Soil moisture status in the root zone is an important component of the water cycle at all spatial scales (e.g., point, field, catchment, watershed, and region). In this study, the spatio-temporal evolution of root zone soil moisture of the Walnut Gulch Experimental Watershed (WGEW) in Arizona was investigated during the Soil Moisture Experiment 2004 (SMEX04). Root zone soil moisture was estimated via assimilation of aircraft-based remotely sensed surface soil moisture into a distributed Soil-Water-Atmosphere-Plant (SWAP) model. An ensemble square root filter (EnSRF) based on a Kalman filtering scheme was used for assimilating the aircraft-based soil moisture observations at a spatial resolution of 800 m × 800 m. The SWAP model inputs were derived from the SSURGO soil database, LAI (Leaf Area Index) data from SMEX04 database, and data from meteorological stations/rain gauges at the WGEW. Model predictions are presented in terms of temporal evolution of soil moisture probability density function at various depths across the WGEW. The assimilation of the remotely sensed surface soil moisture observations had limited influence on the profile soil moisture. More specifically, root zone soil moisture depended mostly on the soil type. Modeled soil moisture profile estimates were compared to field measurements made periodically during the experiment at the ground based soil moisture stations in the watershed. Comparisons showed that the ground-based soil moisture observations at various depths were within ± 1 standard deviation of the modeled profile soil moisture. Density plots of root zone soil moisture at various depths in the WGEW exhibited multi-modal variations due to the uneven distribution of precipitation and the heterogeneity of soil types and soil layers across the watershed.     

Simulating watersheds using loosely integrated model components: Evaluation of computational scaling using OpenMI

Complicated research and management questions regarding watershed systems often require the use of more than one simulation model. Therefore, it is necessary to develop a means to integrate multiple simulation models to predict holistic system response. In this paper we explore the use of a component-based approach for the runtime integration of models, implemented as “plug-and-play” software components. The motivation for this work is to quantify performance overhead costs introduced by adopting a component-based paradigm for loosely integrating hydrologic simulation models. We construct a standard rainfall/runoff watershed model using the Open Modeling Interface (OpenMI) Software Development Kit (SDK) where infiltration, surface runoff, and channel routing processes are each implemented as independent model components. We then analyze the performance of this loosely integrated model to quantify computational scaling, using the Hydrologic Engineering Center’s Hydrologic Modeling System (HMS) for comparison. Our results suggest that the overhead introduced by runtime communication of data is not significant when applied for semi-distributed watershed modeling. Our analysis was limited to semi-distributed watershed modeling, however, and future research is needed to understand performance and accuracy for more data demanding hydrologic models.     

The relative utility of regression and artificial neural networks models for rapidly predicting the capacity of water supply reservoirs

Rapid prediction tools for reservoir over-year and within-year capacities that dispense with the sequential analysis of time-series runoff data are developed using multiple linear regression and multi-layer perceptron, artificial neural networks (MLP-ANNs). Linear regression was used to model the total (i.e. within-year + over-year) capacity using the over-year capacity as one of the inputs, while the ANNs were used to simultaneously model directly the over-year and total capacities. The inputs used for the ANNs were basic runoff and systems variables such as the coefficient of variation (Cv) of annual and monthly runoff, minimum monthly runoff, the demand ratio and reservoir reliability. The results showed that all the models performed well during their development and when they were tested with independent data sets. Both models offer faster prediction tools for reservoir capacity at gauged sites when compared with behaviour simulation. Additionally, when the predictor variables can be evaluated at un-gauged sites using e.g. catchment characteristics, they make capacity estimation at such un-gauged sites a feasible proposition.     

The determination of the three-dimensional distribution of rain from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar

This paper describes a recent development in rainfall estimation using satellite-flown and ground-based radars. The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), its algorithms and data processing are discussed. The ground validation algorithms and processing of the ground-based radar reflectivity data are explained. The estimates of attenuation-corrected radar reflectivity factor and rainfall rate are given at each resolution cell of the PR. The estimated near-surface rainfall rate and average rainfall rate at the altitudes of 2 km are calculated for each beam position. The TRMM PR profiling algorithm and processing of the PR reflectivity for rain distribution are explained. The TRMM rain products and their geophysical parameters are derived from the measurements from the satellite and ground-based radar. The derived geophysical parameters include vertical rain and hydrometeor profile, rain type, radar back-scatter cross-section, raindrop size distribution, rain gauge rain rates and 5-day and monthly average rain rates. For validation purposes the instantaneous and climatological comparison of the rain estimates from both the Precipitation Radar and ground-based radar at Melbourne, Florida, was carried out on the basis of rain type; i.e. convective/stratiform, vertical structure and rain maps. The error sources in rain profile retrieval from space-borne radar; i.e. the PR and ground-based radar with their algorithm limitations are discussed. A second set of data, this time for an area where no simultaneous ground data are available has also been analysed; the data were chosen for the three-dimensional rain distribution over some parts of India. The issues such as discrimination of rain from surface clutter, calibration accuracy and sensitivity of precipitation radar and discrimination of rain echo from noise are discussed.     

Multitemporal comparative analysis of TRMM-3B42 satellite-estimated rainfall with surface gauge data at basin scales: daily,decadal and monthly evaluations

In order to examine the reliability and applicability of Tropical Rainfall Measuring Mission (TRMM) and Other Satellites Precipitation Product (3B42) Version 6 (TRMM-3B42) at basin scales, satellite rainfall estimates were compared with geostatistically interpolated reference data from 12 rain gauge stations for three consecutive years: 2005, 2006 and 2007. Gauge–TRMM-3B42 statistical properties for daily, decadal and monthly multitemporal precipitations were compared using the following cross-validation continuous statistical measures: mean bias error (MBE), root mean square difference (RMSD), mean absolute difference (MAD) and coefficient of determination (r ²) metrics. The averaged spatial–temporal comparisons showed that the TRMM-3B42 rainfall estimates were much closer to the geostatistically interpolated gauge data, with minimal biases of??0.40 mm day^?1,??1.78 mm decad^?1 and??6.72 mm month^?1 being observed in 2006. In the same year, the gauge and TRMM-3B42 rainfall estimates marginally correlated better than in 2005 and 2007, with the daily, decadal and monthly coefficients of determination being 82.2%, 93.9% and 96.5%, respectively. The results showed that the correlations between the gauge-derived precipitation and the TRMM-3B42-derived precipitation increased with increasing temporal intervals for all three considered years. Quantitatively, the TRMM-3B42 observations slightly overestimated the precipitations during the wet seasons and underestimated the observed rainfall during the dry seasons. The results of the study show that the estimates from TRMM-3B42 precipitation retrievals can effectively be applied in the interpolation of missing gauge data, and in the verification of precipitation uncertainties at the basin scales with minor adjustments, depending on the timescales considered.     

Estimating forest canopy fuel parameters using LIDAR data

Fire researchers and resource managers are dependent upon accurate, spatially-explicit forest structure information to support the application of forest fire behavior models. In particular, reliable estimates of several critical forest canopy structure metrics, including canopy bulk density, canopy height, canopy fuel weight, and canopy base height, are required to accurately map the spatial distribution of canopy fuels and model fire behavior over the landscape. The use of airborne laser scanning (LIDAR), a high-resolution active remote sensing technology, provides for accurate and efficient measurement of three-dimensional forest structure over extensive areas. In this study, regression analysis was used to develop predictive models relating a variety of LIDAR-based metrics to the canopy fuel parameters estimated from inventory data collected at plots established within stands of varying condition within Capitol State Forest, in western Washington State. Strong relationships between LIDAR-derived metrics and field-based fuel estimates were found for all parameters [sqrt(crown fuel weight): R²=0.86; ln(crown bulk density): R²=0.84; canopy base height: R²=0.77; canopy height: R²=0.98]. A cross-validation procedure was used to assess the reliability of these models. LIDAR-based fuel prediction models can be used to develop maps of critical canopy fuel parameters over forest areas in the Pacific Northwest.     

Estimating runoff in ungauged catchments from rainfall,PET and the AWBM model

Multiple linear regressions are used to relate average annual runoff to average annual rainfall and areal potential evapotranspiration (PET) using data from 213 catchments grouped according to location in six of the major Drainage Divisions of Australia. A method is presented for estimating daily runoff from daily rainfall data using the AWBM model, which self-calibrates its surface storage parameters to the estimate of average annual runoff from the regressions, and using default values for its baseflow parameters. Two-thirds of the estimates of average annual runoff were within ±25% of the actual value. The approach can also estimate satisfactorily the monthly and annual runoff series in many catchments, with the simulations being only slightly poorer than those obtained by directly calibrating the AWBM against recorded runoff.     

A maximum entropy approach to satellite quantitative precipitation estimation (QPE)

This paper presents a new algorithm to generate quantitative precipitation estimates from infrared (IR) satellite imagery using passive microwave (PMW) data from Special Sensor Microwave/Imager sensor (SSM/I) satellites as ancillary information. To generate the estimates, we model the probabilistic distribution function (PDF) of the rainfall rates through the maximum entropy method (MEM), applying a cumulative histogram matching (HM) technique to the IR brightness temperatures. This results in a straightforward algorithm that can be formulated as an algebraic expression, providing a simple method to derive rainfall estimates using only IR data. The main application of the method is the direct estimation of rainfall rates and accumulated rainfall from geostationary satellites, providing appropriate temporal and spatial resolutions (up to 15 min/4 km when the Meteosat Second Generation satellite becomes available). The proposed method can be easily applied at GOES or current Meteosat satellite reception stations to generate instantaneous rainfall rates estimates with little computational cost. Here we provide examples of applications using the Global Infrared Database and Meteosat images. Our results have been compared with GOES Precipitation Index (GPI) and validated against Global Precipitation Climatology Centre (GPCC)-land rain gauge measurements, at 5°, monthly accumulations. We have obtained correlations of 0.88 for the algorithm, while the GPI yields correlations of 0.85. Preliminary comparisons with other algorithms over Australia also show how the performances of the algorithm are similar to those of more complex models. Finally, we propose some improvements and fine-tuning procedures that can be applied to the algorithm.