Marc F. Müller

Analytical model for flow duration curves in seasonally dry climates

Flow duration curves (FDC) display streamflow values against their relative exceedance time. They provide critical information for watershed management by representing the variation in the availability and reliability of surface water to supply ecosystem services and satisfy anthropogenic needs. FDCs are particularly revealing in seasonally dry climates, where surface water supplies are highly variable. While useful, the empirical computation of FDCs is data intensive and challenging in sparsely gauged regions, meaning that there is a need for robust, predictive models to evaluate FDCs with simple parameterization. Here, we derive a process-based analytical expression for FDCs in seasonally dry climates. During the wet season, streamflow is modeled as a stochastic variable driven by rainfall, following the stochastic analytical model of Botter et al. (2007a). During the dry season, streamflow is modeled as a deterministic recession with a stochastic initial condition that accounts for the carryover of catchment storage across seasons. The resulting FDC model is applied to 38 catchments in Nepal, coastal California, and Western Australia, where FDCs are successfully modeled using five physically meaningful parameters with minimal calibration. A Monte Carlo analysis revealed that the model is robust to deviations from its assumptions of Poissonian rainfall, exponentially distributed response times and constant seasonal timing. The approach successfully models period-of-record FDCs and allows interannual and intra-annual sources of variations in dry season streamflow to be separated. The resulting median annual FDCs and confidence intervals allow the simulation of the consequences of interannual flow variations for infrastructure projects. We present an example using run-of-river hydropower in Nepal as a case study. Key Points Probabilistic derivation of flow distribution in seasonally dry climate Successfully applied in Nepal, California, and Western Australia Disentangles inter- and intra-annual streamflow variations

Impact of the Syrian refugee crisis on land use and transboundary freshwater resources

Significance The notion that sudden impacts on shared international waters can be detected and quantified, even in a war zone, is important to scientists and policy makers, who have been stifled in the past by inaccessibility to such regions and the consequent inability to collect relevant data. Our study uses satellite imagery of war-torn Syria, showing how conflict and migration caused sudden reductions in Syrian agricultural land use and water use. An unexpected effect of the conflict was increased flow in the Yarmouk River to Jordan, which nonetheless remains one of the world’s most water-poor nations. The study illustrates that conflict and human displacement can significantly alter a basin’s water balance with dramatic effects on the transboundary partitioning of water resources. Since 2013, hundreds of thousands of refugees have migrated southward to Jordan to escape the Syrian civil war that began in mid-2011. Evaluating impacts of conflict and migration on land use and transboundary water resources in an active war zone remains a challenge. However, spatial and statistical analyses of satellite imagery for the recent period of Syrian refugee mass migration provide evidence of rapid changes in land use, water use, and water management in the Yarmouk–Jordan river watershed shared by Syria, Jordan, and Israel. Conflict and consequent migration caused ∼50% decreases in both irrigated agriculture in Syria and retention of winter rainfall in Syrian dams, which gave rise to unexpected additional stream flow to downstream Jordan during the refugee migration period. Comparing premigration and postmigration periods, Syrian abandonment of irrigated agriculture accounts for half of the stream flow increase, with the other half attributable to recovery from a severe drought. Despite this increase, the Yarmouk River flow into Jordan is still substantially below the volume that was expected by Jordan under the 1953, 1987, and 2001 bilateral agreements with Syria.

How Jordan and Saudi Arabia are avoiding a tragedy of the commons over shared groundwater

Transboundary aquifers are ubiquitous and strategically important to global food and water security. Yet these shared resources are being depleted at an alarming rate. Focusing on the Disi aquifer, a key non-renewable source of groundwater shared by Jordan and Saudi Arabia, this study develops a two-stage game with incomplete that evaluates optimal transboundary strategies of common-pool resource exploitation under various assumptions. The analysis relies on estimates of agricultural water use from satellite imagery, which were obtained using three independent remote sensing approaches. Drawdown response to pumping is simulated using a 2D regional aquifer model. Jordan and Saudi Arabia developed a buffer-zone strategy with a prescribed minimum distance between each countrys pumping centers. We show that by limiting the marginal impact of pumping decisions on the other countrys pumping costs, this strategy will likely avoid an impeding tragedy of the commons for at least 60 years. Our analysis underscores the role played by distance between wells and disparities in groundwater exploitation costs on common-pool overdraft. In effect, if pumping centers are distant enough, a shared aquifer no longer behaves as a common-pool resource and a tragedy of the commons can be avoided. The 2015 Disi aquifer pumping agreement between Jordan and Saudi Arabia, which in practice relies on a joint technical commission to enforce exclusion zones, is the first agreement of this type between sovereign countries and has a promising potential to avoid conflicts or resolve potential transboundary groundwater disputes over comparable aquifer systems elsewhere.

Monitoring small reservoirs storage from satellite remote sensing in inaccessible areas

In river basins with water storage facilities, the availability of regularly updated information on reservoir level and capacity is of paramount importance for the effective management of those systems. However, for the vast majority of reservoirs around the world, storage levels are either not measured or not readily available due to financial, political, or legal considerations. This paper proposes a novel approach using Landsat imagery and digital elevation models (DEMs) to retrieve information on storage variations in any inaccessible region. Unlike existing approaches, the method does not require any in situ measurement and is appropriate for monitoring small, and often undocumented, irrigation reservoirs. It consists of three recovery steps: (i) a 2-D dynamic classification of Landsat spectral band information to quantify the surface area of water, (ii) a statistical correction of DEM data to characterize the topography of each reservoir, and (iii) a 3-D reconstruction algorithm to correct for clouds and Landsat 7 Scan Line Corrector failure. The method is applied to quantify reservoir storage in the Yarmouk basin in southern Syria, where ground monitoring is impeded by the ongoing civil war. It is validated against available in situ measurements in neighbouring Jordanian reservoirs. Coefficients of determination range from 0.69 to 0.84, and the normalized root-mean-square error from 10 to 16 % for storage estimations on six Jordanian reservoirs with maximal water surface areas ranging from 0.59 to 3.79 km2.

Stochastic modeling of interannual variation of hydrologic variables

Quantifying the inter-annual variability of hydrologic variables (such as annual flow volumes, solute or sediment loads) is a central challenge in hydrologic modeling. Annual or seasonal hydrologic variables are themselves the integral of instantaneous variations, and can be well-approximated as an aggregate sum of the daily variable. Process-based, probabilistic techniques are available to describe the stochastic structure of daily flow, yet estimating inter-annual variations in the corresponding aggregated variable requires consideration of the autocorrelation structure of the flow time series. Here, we present a method based on a probabilistic streamflow description to obtain the inter-annual variability of flow-derived variables. The results provide insight into the mechanistic genesis of inter-annual variability of hydrologic processes. Such clarification can assist in the characterization of ecosystem risk and uncertainty in water resources management. We demonstrate two applications, one quantifying seasonal flow variability and the other quantifying net suspended sediment export.