Water scarcity, data scarcity and the Budyko curve—An application in the Lower Jordan River Basin

Gunkel, Anne; Lange, Jens

doi:10.1016/j.ejrh.2017.04.004

Cited by 36 publications

(30 citation statements)

References 62 publications

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“…Unfortunately, the Middle East region in general suffers from a paucity of gauge observations in both space and time, which is a prerequisite for any reliable assessment of this high regional climate variability. Besides the political and socioeconomic instability of many countries in this region, the lack of gauge density is associated with poor sitting of instrumentation, the high cost of equipment installation, a record of poor maintenance, and the vast unpopulated areas across characteristic of the region (El Kenawy and McCabe, ; Gunkel and Lange, ). Overall, the countries of the Middle East share common challenges, as gauged data either have a sparse spatial distribution, cover a short time interval, suffer from gaps and discontinuities, or have a coarse temporal reporting resolution (monthly or longer).…”

Section: Introductionmentioning

confidence: 99%

Spatial assessment of the performance of multiple high‐resolution satellite‐based precipitation data sets over the Middle East

Kenawy

McCabe

López‐Moreno

et al. 2019

Intl Journal of Climatology

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This study presents the first comprehensive evaluation of the performance of three globally high‐resolution remotely sensed products in replicating the main characteristics of rainfall over the Middle East, with special emphasis on extreme wet events. Specifically, we employed daily observational data from a network of rain gauges (N = 217), spanning the retrospective period 1998–2013 and covering six countries in the Middle East (i.e., Egypt, Iraq, Jordan, Libya, Saudi Arabia, and Syria), against data derived from three global satellite‐based precipitation products: the Version 7 TRMM (Tropical Rainfall Measuring Mission) Multi‐satellite Precipitation Analysis 3B42 product (TRMM‐3B42), the Climate Prediction Center MORPHing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Alongside a range of conventional statistical error measures (e.g., bias, normalized root‐mean‐square error [nRMSE] and Spearman's rho correlation coefficient), this study also gives priority to evaluate the skill of these products in reproducing characteristics of extreme wet events (e.g., frequency, intensity, duration, onset, anomaly). Results demonstrate that TRMM‐3B42 generally performs well in estimating rainfall totals during the rainy season (ONDJFMA), with a mean bias of 0.05 mm, nRMSE of 0.15 mm, and Spearman's rho of 0.74 for the whole Middle East. In contrast, PERSIANN‐CDR and CMORPH‐BLD underestimate the observed rainfall. Importantly, TRMM‐3B42 outperforms other products in reproducing the frequency and intensity of the most extreme wet events, while PERSIANN‐CDR and CMORPH‐BLD fail to reproduce these key characteristics. Notably, all products perform poorly in reproducing the climatology of the anomalous wet events in the region, indicating that careful scrutiny must be warranted before using these products, particularly for hydrological modelling. Considering the daily resolution of these remotely sensed precipitation products and their reasonable spatial resolution (0.25 × 0.25°) in comparison to available in situ data over the Middle East, results of this work provide a solid scientific reference for national stakeholders and policy makers to decide on the most useful product for their specific applications (e.g., hydrological modelling, streamflow forecasts, water resources management, and hydrometeorological hazard assessment and mitigation).

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Section: Introductionmentioning

confidence: 99%

Spatial assessment of the performance of multiple high‐resolution satellite‐based precipitation data sets over the Middle East

Kenawy

McCabe

López‐Moreno

et al. 2019

Intl Journal of Climatology

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“…In line with this idea, Scheffer et al (2018) recently showed that huge trees only occur in a climate niche with extensive amounts of rainfall. Local constraints on vegetation growth have, with a similar reasoning, been approximated by the Budyko framework (Helman et al, 2017;Xu et al, 2013), which evaluates climate average precipitation, reference evapotranspiration and actual evapotranspiration to separate ecosystems into energy-or water-limited systems (Gunkel and Lange, 2017). Similarly, a recent study by Tao et al (2016) showed a strong relation between tree growth and water yield (P -ET).…”

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confidence: 99%

Global distribution of hydrologic controls on forest growth

Roebroek¹,

Melsen²,

Dijke³

et al. 2020

Preprint

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Abstract. Vegetation provides key ecosystem services and is an important component in the hydrological cycle. Traditionally, the global distribution of vegetation is explained through water availability by precipitation. Locally, however, groundwater can aid growth by providing an extra water source (e.g. oases) or hinder growth by presenting a barrier to root expansion (e.g. swamps). In this study we analysed the global correlation between precipitation, groundwater and forest growth, approximated by the fraction of absorbed photosynthetically active radiation, and linked this to climate and landscape position. The results show that at the continental scale, precipitation is the main driver of forest productivity; wetter climates support higher energy absorption and consequentially more growth. But within all climates, landscape position substantially alters the growth patterns both positively and negatively. The influence of the landscape on vegetation growth varies over climate. The results display the importance of analysing vegetation growth in a climate-landscape continuum.

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“…For long-term soil water storage changes the groundwater recharge influenced by geological features and human interaction water withdrawals are considered to be negligible (Wu et al 2017). However, it can be noted that the storage component of water cannot be neglected at any temporal scale less than annual and the resulting phenomenon and the hydrological responses may vary due to the inclusion of groundwater storages being taken into account (Gunkel & Lange 2017).…”

Section: Distributed Hydrological Model: Pcrastermentioning

confidence: 99%

Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India

2020

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Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression was used to develop the precipitation and temperature projections based on MIROC GCM outputs with the RCP 4.5 scenario. The proposed modelling framework has been demonstrated on a semi-arid river basin of peninsular India, Krishna River Basin (KRB). The basin outlet runoff was predicted to decrease (13.26%) for future scenarios under climate change due to an increase in temperature (0.6 °C) compared to a precipitation increase (13.12%) resulting in an overall reduction in water availability over KRB.

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Water scarcity, data scarcity and the Budyko curve—An application in the Lower Jordan River Basin

Cited by 36 publications

References 62 publications

Spatial assessment of the performance of multiple high‐resolution satellite‐based precipitation data sets over the Middle East

Spatial assessment of the performance of multiple high‐resolution satellite‐based precipitation data sets over the Middle East

Global distribution of hydrologic controls on forest growth

Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India

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