Understanding the relative impacts of natural processes and human activities on the hydrology of the Central Rift Valley lakes,East Africa

Significant changes have been observed in the hydrology of Central Rift Valley (CRV) lakes in Ethiopia, East Africa as a result of both natural processes and human activities during the past three decades. This study applied an integrated approach (remote sensing, hydrologic modelling, and statistical analysis) to understand the relative effects of natural processes and human activities over a sparsely gauged CRV basin. Lake storage estimates were calculated from a hydrologic model constructed without inputs from human impacts such as water abstraction and compared with satellite‐based (observed) lake storage measurements to characterize the magnitude of human‐induced impacts. A non‐parametric Mann–Kendall test was used to detect the presence of climatic trends (e.g. a decreasing or increasing trends in precipitation), while the Standard Precipitation Index (SPI) analysis was used to assess the long‐term, inter‐annual climate variability within the basin. Results indicate human activities (e.g. abstraction) significantly contributed to the changes in the hydrology of the lakes, while no statistically significant climatic trend was seen in the basin, however inter‐annual natural climate variability, extreme dryness, and prolonged drought has negatively affected the lakes. The relative contributions of natural and human‐induced impacts on the lakes were quantified and evaluated by comparing hydrographs of the CRV lakes. Lake Abiyata has lost ~6.5 m in total lake height between 1985 and 2006, 70% (~4.5 m) of the loss has been attributed to human‐induced causes, whereas the remaining 30% is related to natural climate variability. The relative impact analysis utilized in this study could potentially be used to better plan and create effective water‐management practices in the basin and demonstrates the utility of this integrated methodology for similar studies assessing the relative natural and human‐induced impacts on lakes in data sparse areas. Copyright © 2015 John Wiley & Sons, Ltd.     

Scaling of tropical rainfall as observed by TRMM precipitation radar

We used a three-year (1998–2000) dataset of TRMM Precipitation Radar observations to investigate the scaling properties of spatial rainfall fields. This dataset allows consideration of spatial scales ranging from about 4.3 km to 138 km and short temporal scales corresponding to the sensor overpasses. The focus is on the marginal spatial moment scaling, which allows estimation of the scaling parameters from a single scene of data. Here we present a global perspective of the scaling properties of tropical rainfall in terms of its spatial variability, atmospheric forcing, predictability, and applicability. Our results reveal the following: 1) the scaling parameters exhibit strong variability associated with land/ocean contrast and mean precipitation at the synoptic scale; 2) there exists a one-to-one relationship between the scaling parameters and the large-scale spatial average rain rate of a universal functional form; 3) the majority of the scenes are consistent with the hypothesis of scale invariance at the moment orders of 0 and 2; 4) relatively there are more scale-invariant rain scenes over land than over ocean; and 5) for the scenes that are non-scale-invariant, deviation from scale-invariance mainly arises from the increasingly intermittent behavior of rainfall as spatial scale decreases. These results have important implications for the development and calibration of downscaling procedures designed to reproduce rainfall properties at different spatial scales and lead to a better understanding of the nature of tropical rainfall at various spatial resolutions.     

Estimating actual rainfall from satellite rainfall products

The lack of uncertainty measures in operational satellite rainfall (SR) products leads to a situation where users of the SR products know that there are significant errors in the products, but they have no quantitative information about the distribution of these errors. The authors propose a semiparametric model to characterize the conditional distribution of actual rainfall (AR) given measures from SR products. The model consists of two components: a conditional gamma density given each SR, and a smooth functional relationship between the gamma parameters and SR. The model is developed for monthly rainfall, estimated from a satellite with sampling frequency once a day, averaged over an area of 512 × 512 km² in the Mississippi River basin. The conditional distribution results are more informative than deterministic SR products since the whole conditional distribution enables users to take appropriate actions according to their own risk assessments and cost/benefit analyses.     

Modeling distribution of temporal sampling errors in area-time-averaged rainfall estimates

In this paper, the authors examine models of probability distributions for sampling error in rainfall estimates obtained from discrete satellite sampling in time based on 5 years of 15-min radar rainfall data in the central United States. The sampling errors considered include all combinations of 3, 6, 12, or 24 h sampling of rainfall over 32, 64, 128, 256, or 512 km square domains, and 1, 5, or 30 day rainfall accumulations. Results of this study reveal that the sampling error distribution depends strongly on the rain rate; hence the conditional distribution of sampling error is more informative than its marginal distribution. The distribution of sampling error conditional on rain rate is strongly affected by the sampling interval. At sampling intervals of 3 or 6 h, the logistic distribution appears to fit the conditional sampling error quite well, while the shifted-gamma, shifted-weibull, shifted-lognormal, and normal distributions fit poorly. At sampling intervals of 12 or 24 h, the shifted-gamma, shifted-weibull, or shifted-lognormal distribution fit the conditional sampling error better than the logistics or normal distribution. These results are vital to understanding the accuracy of satellite rainfall products, for performing validation assessment of these products, and for analyzing the effects of rainfall-related errors in hydrological models.     

Zooplankton community structure and ecology of the tropical-highland Lake Hayq, Ethiopia

Lake Hayq, a highland lake in Ethiopia, was stocked with Tilapia fish (Oreochromis niloticus) in late 1970s, offering an opportunity to study the effect of fish predation in a natural lake. Since 1930s, some limnological surveys have been done sporadically documenting a change in zooplankton composition including the disappearance of cladocerans, hypothesizing the stocked planktivorous fish could be a cause. Nevertheless, no detailed research was conducted to identify potential effects of fish stocking predominantly due to its remote location. The article presents data about zooplankton composition, abundance and biomass done between October 2007 and January 2009 on short-time intervals including the underlying limnological variables. The zooplankton community was depauperate comprising two copepods, three cladocerans, and six rotifers taxa, as typical for tropical lakes. Total mean standing biomass of all crustacean zooplankton was 237 mg dry mass m⁻³, which gave Lake Hayq an intermediate position when compared with other tropical lakes. Of copepods, Thermocyclops ethiopiensis was almost an exclusive species, and its temporal variation was influenced by food supply and water temperature. We refute the hypothesis that Tilapia was the cause for the seasonal disappearance of cladocerans, and attribute it to the adverse effect of episodic mixing. Nevertheless, the planktivorous fish probably plays a key role in structuring the cladocerans in particular the large-sized Daphnia magna. In January 2008, we observed a massive planktivorous fish mortality that triggered high algal biomass, which was later grazed by large-sized D. magna demonstrating the trophic cascade hypothesis in a natural ecosystem.     

Parameter Estimation for Groundwater Models under Uncertain Irrigation Data

The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression‐based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least‐squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least‐squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least‐squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.     

Intercomparison of Sensible Heat Flux from Large Aperture Scintillometer and Eddy Covariance Methods: Field Experiment over a Homogeneous Semi-arid Region

Scintillometers are becoming increasingly popular for the validation of satellite remote sensing sensible heat-flux estimates due to the comparable spatial resolutions. However, it is important to gain confidence in the accuracy of the sensible heat-flux measurements obtained by the scintillometer. Large aperture scintillometer (LAS) and eddy-covariance (EC) measurements were collected over a homogeneous, dry and semi-arid region near Las Cruces, New Mexico, USA, where the homogeneity allowed direct comparison of the two instruments despite their differences in footprint sizes. The differences between the sensible heat-flux measured by both LAS and EC systems fall within the differences between two EC systems. We conclude that the large aperture scintillometer is a reliable system for measuring sensible heat flux in a dry semiarid region.     

Incorporating landscape depression heterogeneity into the Soil and Water Assessment Tool (SWAT) using a probability distribution

Modelling the hydrology of North American Prairie watersheds is complicated because of the existence of numerous landscape depressions that vary in storage capacity. The Soil and Water Assessment Tool (SWAT) is a widely applied model for long‐term hydrological simulations in watersheds dominated by agricultural land uses. However, several studies show that the SWAT model has had limited success in handling prairie watersheds. In past works using SWAT, landscape depression storage heterogeneity has largely been neglected or lumped. In this study, a probability distributed model of depression storage is introduced into the SWAT model to better handle landscape storage heterogeneity. The work utilizes a probability density function to describe the spatial heterogeneity of the landscape depression storages that was developed from topographic characteristics. The integrated SWAT–PDLD model is tested using datasets for two prairie depression dominated watersheds in Canada: the Moose Jaw River watershed, Saskatchewan; and the Assiniboine River watershed, Saskatchewan. Simulation results were compared to observed streamflow using graphical and multiple statistical criterions. Representation of landscape depressions within SWAT using a probability distribution (SWAT–PDLD) provides improved estimations of streamflow for large prairie watersheds in comparison to results using a lumped, single storage approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Hydrological modelling of Ethiopian catchments using limited data

The hydrological component of the soil and water assessment tool (SWAT) model is adapted for two Ethiopian catchments based on primary knowledge of the coherence spectrum between rainfall and stream flow data. Spectrum analysis using the available nearby climatic data is made to limit the temporal and spatial scales (inverse rate coefficients) subject to the calibration of compartmentalized runoff models. The exclusion of unwarranted time scales in the calibration implies that the model efficiency (r² values) decrease only moderately between calibration and validation, and the optimization is focused on warranted problems. On the basis of the available data for the two Ethiopian catchments, the implication is that only periods longer than about 50 days can be reliably evaluated in the model. The model structure of SWAT for the surface runoff and groundwater flow response is modified to make the time scales consistent with the results of the spectrum analysis. An optimization algorithm is developed to constrain and combine the model parameters with the spectrum analysis results. Copyright © 2009 John Wiley & Sons, Ltd.     

Suitability of satellite-based hydro-climate variables and machine learning for streamflow modeling at various scale watersheds

ABSTRACT

Streamflow modeling is essential to investigate processes in the hydrologic cycle and important for water resource management application. However, in-situ hydrologic data paucity, because of various factors such as economic, political, instrument malfunctioning, and poor spatial distribution, makes the modeling process challenging. To overcome this limitation, we introduced a satellite remote sensing-based machine learning approach – boosted regression tree (BRT) – that integrates spatial land surface and climate variables that describe the sub-units, and applied it in three variable size watersheds in the Upper Mississippi River Basin (UMRB), USA. The model simulation results were tested using an independent dataset and showed Nash–Sutcliffe efficiency values of 0.80, 0.76, and 0.69 for the UMRB, Illinois River Watershed, and Raccoon River Watershed, respectively. In addition, we compared the performance of the machine learning models with existing process-based modeling results. Overall performance is comparable with the process-based approaches, but with significantly less modeling effort and resources.